Innovative Systems Approach for Designing Smarter World [1st ed.] 9789811566509, 9789811566516

Table of contents :
Front Matter ....Pages i-xii
New Trends in Systems Approaches to Realize Smarter World (Toshiya Kaihara)....Pages 1-15
Conceptual Framework for Designing, Planning, and Operating Smart Platforms as Societal Foundations: A System-Optimization Standpoint and a Spiral-up Systems Approach (Eitaro Aiyoshi, Junichi Murata)....Pages 17-36
Viewing Systems from Boundary and Evolution—Toward Developing New Systems Approach (Yasuaki Kuroe)....Pages 37-53
Key Factors for Promising Systems Approaches to Society 5.0 (Motohisa Funabashi)....Pages 55-71
Growing Systems in Smarter World (Hiroshi Kawakami)....Pages 73-82
System and Information. A Viewpoint Toward a Novel Systems Approach (Hajime Kita)....Pages 83-93
Interpenetration of System Borders Mediated by Human Activities: Weaving Trees with Rhizome (Katsunori Shimohara)....Pages 95-108
Toward Modeling Learning Behavior from a Micro–Macro Link Perspective (Shingo Takahashi)....Pages 109-121
Understanding Disruptive Innovation Through Evolutionary Computation Principles (Takao Terano)....Pages 123-132
Smartification of Social Infrastructure for Efficient Power and Energy Use (Keiichiro Yasuda, Ken-ichi Tokoro)....Pages 133-145
The State of Art and Future Direction on Smart Home Systems (Takashi Nishiyama)....Pages 147-156
System of Systems Approach to Multiple Energy Systems (Kazuyuki Mori, Toshiyuki Miyamoto, Shoichi Kitamura, Yoshio Izui)....Pages 157-168

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Toshiya Kaihara Hajime Kita Shingo Takahashi   Editors

Innovative Systems Approach for Designing Smarter World

Innovative Systems Approach for Designing Smarter World

Toshiya Kaihara Hajime Kita Shingo Takahashi •

•

Editors

Innovative Systems Approach for Designing Smarter World

123

Editors Toshiya Kaihara Graduate School of System Informatics Kobe University Kobe, Hyogo, Japan

Hajime Kita Institute for Liberal Arts and Sciences Kyoto University Kyoto, Japan

Shingo Takahashi Department of Industrial and Management Systems Engineering Waseda University Tokyo, Japan

ISBN 978-981-15-6650-9 ISBN 978-981-15-6651-6 https://doi.org/10.1007/978-981-15-6651-6

(eBook)

© Springer Nature Singapore Pte Ltd. 2021 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

“Society 5.0” has been proposed in the 5th Science and Technology Basic Plan as a future society that Japan should aspire to. The concept of Society 5.0 is “A human-centered society that balances economic advancement with the resolution of social problems by a system that highly integrates cyberspace and physical space.” For the realization of Society 5.0, it is necessary to integrate several social systems smartly so as to emerge sustainable social value, and innovative systems approach is expected to play an important role as a key methodology for the realization. This book presents an innovative systems approach toward a new concept of a “Smarter World,” i.e., a society with advanced and sustainable social infrastructures for autonomous and dynamic value network creation. These infrastructures encompass a wide range of facilities and services, such as transportation, energy, communication, water, sewerage, logistics, education, disaster prevention, medical care and welfare. Modifying these social infrastructures requires a great deal of planning, design and management, as they can have a huge impact on the health and well-being of residents, as well as environmental sustainability. Moreover, the explosive growth of the Internet of Things (IoT) technologies has opened tremendous and innovative opportunities, yet they are accompanied by new risks to society. Advanced social infrastructures are composed of multiple interdependent systems, which are integrated to a form of a System of Systems (SoS). Therefore, there are emerging needs to develop a systems approach so as to establish the best preconditions for achieving a smarter world. This book consists of 12 chapters, and they are classified into 2 types, such as academic article (Chapters “New Trends in Systems Approaches to Realize Smarter World”–“Understanding Disruptive Innovation Through Evolutionary Computation Principles”) and practical introduction (Chapters “Smartification of Social Infrastructure for Efficient Power and Energy Use”–“System of Systems Approach to Multiple Energy Systems”) related to innovative systems approach. Chapter “New Trends in Systems Approaches to Realize Smarter World” presents an outline and overview for a new systems approach to integrate the various specialized areas and introduces the research activity of the Research Committee on v

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Innovative Systems Approach for Realizing Smarter World in the Society of Instrument and Control Engineers (SICE) in Japan. Furthermore, an application example of new systems approach into new manufacturing system in IoT era is described. Chapter “Conceptual Framework for Designing, Planning, and Operating Smart Platforms as Societal Foundations: A System-Optimization Standpoint and a Spiral-up Systems Approach” provides an explanation of platform and the systems approach to designing, planning and operation of the “smart platforms” as social infrastructures are addressed especially from the viewpoints of system optimization. Moreover, sustainable evolution is focused as one of the smart properties of platforms and outlines the “spiral-up systems approach” to designing, planning and operation of the platforms. Chapter “Viewing Systems from Boundary and Evolution—Towards Developing New Systems Approach” overviews the future perspective of a systems approach and discusses about how to build it, based on the discussions in the activities of the Research Committee on Innovative Systems Approach for Realizing Smarter World. The key is “Viewing Systems from Boundary and Evolution.” Chapter “Key Factors for Promising Systems Approaches to Society 5.0” gives a comprehensive view of what kind of works have been done as social systems sciences, in particular systems approaches, in terms of envisioning and developing socio-technical systems, and future research directions is proposed. Chapter “Growing Systems in Smarter World” discusses adaptation of artifacts from a viewpoint of human–machine systems. The change of phases of adaptive systems is pointed out from substitution of function performed by human to mutual adaptation with human. Obtaining an insight from biological phenomena called mutual growth, mutually growing systems and human are discussed in a spiral structure. Chapter “System and Information. A Viewpoint Toward a Novel Systems Approach” discusses system and information for development of a novel systems approach. First, an overview of knowledge of system science and engineering and that of informatics are given, and the necessity of bridging these two disciplines is pointed out. Also, several viewpoints toward a novel systems approach are listed, such as importance of models, treatment of information and system structure, especially that called platforms. Chapter “Interpenetration of System Borders Mediated by Human Activities: Weaving Trees with Rhizome” discusses interpenetration of systems through human activities of understanding and design of System of Systems. An insight is obtained from human activities that nomadically interact with various independent systems, and a concept of “Rhizome” that connects independent systems having “tree-” like hierarchical structures is proposed. With this concept, this chapter proposes a bottom-up approach to achieve interpenetration systems. Chapter “Toward Modeling Learning Behavior from a Micro–Macro Link Perspective” comprehensively describes the adaptive process critical in the SoS, especially learning behavior from a micro-macro link perspective in cybernetics.

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A micro-macro loop is considered as the interaction between individual-level and systems-level learning processes. Chapter “Understanding Disruptive Innovation Through Evolutionary Computation Principles” discusses innovation, one of the key issues of Smarter World. After a brief overview on innovation in management science field, ideas of innovation with evolutionary computation are surveyed. Then, the system requirements for open or free innovation are discussed. Chapter “Smartification of Social Infrastructure for Efficient Power and Energy Use” presents an overview of the history of power systems and describes the features of power systems, including the latest trends in the research and development. A smart grids and smart communities as social infrastructure for efficient power and energy use both within Japan and abroad are also discussed Chapter “The State of Art and Future Direction on Smart Home Systems” provides the current status of smart home technologies, such as security and monitoring the elderly, and HEMS and conversation terminals. Then, research trends related to smart home in Japan and overseas are introduced. Finally, a vision toward smarter home is discussed. Chapter “System of Systems Approach to Multiple Energy Systems” presents two energy trading methods for efficient energy use, saving of energy cost and reduction of CO2 emissions. Then, it is shown that many participants in markets of energy resources can create the value of effective utilization of energy resources and reduction of energy cost by carrying out multiple energy trading. This book introduces state-of-the-art concepts of and methodologies for systems approaches toward SoS as well as their practical applications by gathering contributions from leading system scientists and technology creators. As such, it offers a valuable resource for systems engineers, system integrators and researchers in related engineering fields, as well as government policymakers. Kobe, Japan Kyoto, Japan Tokyo, Japan March 2020

Toshiya Kaihara Hajime Kita Shingo Takahashi

Contents

New Trends in Systems Approaches to Realize Smarter World . . . . . . . Toshiya Kaihara Conceptual Framework for Designing, Planning, and Operating Smart Platforms as Societal Foundations: A System-Optimization Standpoint and a Spiral-up Systems Approach . . . . . . . . . . . . . . . . . . . Eitaro Aiyoshi and Junichi Murata Viewing Systems from Boundary and Evolution—Toward Developing New Systems Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yasuaki Kuroe

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Key Factors for Promising Systems Approaches to Society 5.0 . . . . . . . Motohisa Funabashi

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Growing Systems in Smarter World . . . . . . . . . . . . . . . . . . . . . . . . . . . Hiroshi Kawakami

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System and Information. A Viewpoint Toward a Novel Systems Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hajime Kita

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Interpenetration of System Borders Mediated by Human Activities: Weaving Trees with Rhizome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Katsunori Shimohara

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Toward Modeling Learning Behavior from a Micro–Macro Link Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Shingo Takahashi Understanding Disruptive Innovation Through Evolutionary Computation Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Takao Terano

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Smartification of Social Infrastructure for Efficient Power and Energy Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Keiichiro Yasuda and Ken-ichi Tokoro The State of Art and Future Direction on Smart Home Systems . . . . . . 147 Takashi Nishiyama System of Systems Approach to Multiple Energy Systems . . . . . . . . . . . 157 Kazuyuki Mori, Toshiyuki Miyamoto, Shoichi Kitamura, and Yoshio Izui

About the Editors

Toshiya Kaihara is a Professor and Councilor at the Division of Systems Science, Graduate School of System Informatics, Kobe University in Japan. He received his Ph.D. degrees from University of London, UK and Diploma of Imperial College (DIC) from Imperial College, London, UK. His primary research interests are Systems Science and Systems Optimization, and their implementation into social systems. He has authored over 250 scientific journals and articles. He is a Fellow of the Japan Society of Mechanical Engineers (JSME) and Collége International pour la Recherche en Productique (CIRP). He also serves as the Director of the Manufacturing System Division of the Japan Society of Mechanical Engineers, President of the Scheduling Society of Japan, President of the Institute of Systems, Control and Information Engineers in Japan, among others. He has received several awards from academic societies, such as Outstanding Paper Award from CIRP, Best Article Award in Communications of Japan Industrial Management Association, and many others. Hajime Kita is a Professor at Institute for Liberal Arts and Sciences, Kyoto University in Japan. He also serves as Director General of Institute for Information Management and Communication, a central IT organization of Kyoto University. He receives his Ph.D. degree from Kyoto University. His research interests are evolutionary computation, social simulation and education of informatics in university. He is author and editor of several books: Agent-Based Simulation: From Modelling Methodologies to Real-World Applications, Control of Traffic Systems in Buildings, Agent-Based Approaches in Economic and Social Complex Systems V, and Realistic Simulation of Financial Markets. Shingo Takahashi is Professor in the Department of Industrial and Management Systems Engineering and Director of Institute for Social Simulation at Waseda University. He holds M.S. and Ph.D. in Systems Science from Tokyo Institute of Technology. His current research interests include modeling and simulation of social systems as complex adaptive systems, especially focusing on agent-based social simulation and soft system thinking as practice, aimed at applying to soft, i.e. xi

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ill-structured, problem situations involving various people with plural world views. He is a board member for several societies, such as The Japan Association for Social and Economic Systems Studies, Japan Society for Research Policy and Innovation Management, The Japan Society for Management Information, and member of editorial boards of journals, such as International Journal of General Systems and Japan Industrial Management Association.

New Trends in Systems Approaches to Realize Smarter World Toshiya Kaihara

Abstract This paper presents an outline mainly of our investigation to create a new systems approach to integrate the various specialized areas of the Smarter World Research Group in the Society of Instrument and Control Engineers (SICE) in Japan as a new trend of a systems approach to realize Smarter World, an advanced social infrastructure. Our activity has a close relation with the construction of “Super Smart Society (Society 5.0)” proposed by the Fifth Science and Technology Basic Plan in Japan and its central part “Super Smart Society service platform”. Furthermore, this report presents an application example of a new systems approach proposed here while picking up “new manufacturing system” as an example among the 11 foregoing systems. Keywords Circulating and spiraled-up evolution · Smarter world · Society 5.0 · Systems approach · System of systems (SoS)

1 Introduction Social infrastructure roughly comprises components of the bases of public, such as traffic, energy, communication, water, and sewage, the bases of business, such as manufacturing, distribution, information processing, sightseeing, and services, and the bases of life, such as education, medical treatment, care, and disaster prevention. Furthermore, as social infrastructure increases to an ever-larger scale, with sophisticated information, conjugation, and networking, it becomes increasingly necessary to realize a new society able to create new values and services continuously for all the stakeholders composing the society. This paper is adapted from Toshiya Kaihara, “New Trends in Systems Approaches towards Smarter World”, Journal of the Society of Instrument and Control Engineers, Vol. 55, No. 8, pp. 641–649 (2016). Partly translated by permission of The Society of Instrument and Control Engineers. T. Kaihara (B) Graduate School of System Informatics, Kobe University, 1-1 Rokkodai, Nada, Kobe, Hyogo, Japan e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 T. Kaihara et al. (eds.), Innovative Systems Approach for Designing Smarter World, https://doi.org/10.1007/978-981-15-6651-6_1

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Therefore, the ideal mode of the future new social systems in the internet of things (IoT) age [1] has been investigated from the standpoint of systems approaches in the Research Group of New Systems Approach for Realization of a Safe, Secure, and Comfortable Society (The Smarter World Research Group) started in January 2014 under the systems and information division of the Society of Instrument and Control Engineers (SICE) [2]. In this sense, we have proposed the concept of Smarter World, which requires safety, security, and comfort while maintaining sustainability as a more sophisticated goal with autonomous and dynamic value network creation for social infrastructure. Moreover, we have advanced in consideration about systems approaches contributing to realizing Smarter World, while defining an innovative society in which autonomous formation of value networks for various subjects composing the society is proceeding quickly and continuously. As addressed in the special issue of Journal of the Society of Instrument and Control Engineers [3], the Fifth Science and Technology Basic Plan [4], with comprehensive planning for science and technology of Japan for five years from 2016 fiscal years was presented, and Super Smart Society (Society 5.0) concept was proposed. Moreover, “Super Smart Society service platform” is shown in this as a common basic technology to integrate the 11 systems specified in the earlier “Comprehensive Strategy on Science and Technology Innovation 2015”. Super Smart Society and the common basic service platform proposed here are only concept-level proposals. The establishment of scientific theory and methodology that can realize them will be an important task for the future five years. Given the background described above, the activity aimed at creating a new systems approach is introduced in this paper as carefully examined by the Smarter World Research Group [5]. Furthermore, future prospects for the systems approach will be overviewed.

2 Activities to Create a Systems Approach Herein are introduced mainly the activities of the Smarter World Research Group under the system/information department of the present society aimed at creating a new systems approach.

2.1 Basic Approach As described in the preceding chapter, to create continuous values and services based on cooperation of systems for various social infrastructure that is advancing in scale, with sophisticated information, networking, and conjugation, one must adopt the concept of System of Systems (SoS) [6]. The SoS concept deals comprehensively with various systems included not only in engineering areas but also in areas of

New Trends in Systems Approaches to Realize … Fig. 1 Concept of interdisciplinary fusion based on system of systems (SoS)

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Smarter world Realization of a safe, secure, and comfortable society Settlement of the social problems such as energy and the environment Creation of next-generation technologies contributing to future industry and society

nature, life, society, and humanity. Furthermore, one should aim for value creation, circulation, and reproduction of the whole SoS (Fig. 1). Furthermore, reconsideration of the systems approach itself is necessary to realize such Smarter World using a systems approach to ascertain and approach a subject as a system. Methodologies developed by traditional science and engineering systems include developed methodologies such as distributed systems, hierarchical systems to catch large-scale systems, and human–machine systems that include soft system approach to be more conscious of humans. Based on these results, we aim at considering a new systems approach to integrate specialized areas of various branches. For instance, investigation has been made of circulating and spiraled-up approaches to consider system development by repeating the three acts of analysis, abduction, and synthesis (Fig. 2). More detailed explanation is described in Chapter “Interpenetration of System Borders Mediated by Human Activities: Weaving Trees with Rhizome”, Sect. 4. The system and information division of SICE possesses many sections for the investigation of different fields of view of artifacts, nature, life, society, and human systems of mutually different time–space scales. Many researchers and practitioners

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Fig. 2 Circulating and spiraled-up systems approach

have profound knowledge of one or more of these fields. It is fundamentally important to act across these sections and the research groups as well as the subject fields (systems) to achieve a new circulated and spiraling systems approach. Therefore, in our research group, to share the ascertained systems as one at an abstract level, a committee was formed of members with experience as chief investigators in each section who can oversee the respective research activities of the sections. Furthermore, an investigation has been proceeding into a new systems approach to integrate the specialized areas of various branches.

2.2 Concrete Activities To propose such a new systems approach, one must start from a concept that presents different modes from those of conventional sections and research groups to aim at some degree of deep digging into scientific principles in firmly established areas. Thereby, one attempt to extract and sort themes by eliciting opinions through questionnaires and through discussion by repeating divergence and convergence of ideas in a workshop style while adopting ideas such as design thinking. As the first step of these activities, we conducted the extraction of keywords through brainstorming to consider a new systems approach and a sort of relation using the KJ method. Several important results have been obtained. One is represented as an example here and in Fig. 3. This figure depicts a systematization that has been made of the tasks and targets held by the systems approach to realize Smarter World in the future, together with relations among many elemental technologies to support the current systems approach. As the figure shows, a fusion of humans to compose the society and systems, flexibility in the system modeling, and so on are important tasks, as is extrication from traditional hard systems of science. This is common to the task to realize Super Smart Society service platform for which “It is possible to correspond carefully to the various needs of greater society by providing just the good or service needed, only when needed, and only in the quantity needed.” That task methodology

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Fig. 3 Example of extraction and organization of keywords in the systems approach

is explained in the Fifth Science and Technology Basic Plan. Furthermore, this important research principle will become increasingly necessary to solve problems from now on. Furthermore, in the figure are shown concrete keywords necessary to carry forward a fusion of human and systems inside the framework of “Innovative fusion of humans and systems”. In addition, the concept of new systems including the contents is summarized inside the framework of “Fusion of new systems and concepts”. During the investigation, what are noticed as important keywords are “supersystem” and a “circulating and spiraled-up systems approach”. Hereinafter, an outline of the investigation stated with respect to each realization of the present research groups will be introduced in order along with a summary of the tasks. More detailed explanation is described in Chapter “Viewing Systems From Boundary and Evolution—Towards Developing New Systems Approach”, Sect. 2.

2.3 Summary of Tasks The following are extracted as tasks that a systems approach should address to realize Smarter World. • Correspondence to real complexity • New concept of system modeling • Concrete action toward fusion of different areas

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• Modeling and insight of behavioral principle of human composing the society • Necessity of social presence. When social systems are targeted, a systems approach must follow systems that are constantly changing and developing and which are affected by unexpected disturbances. It will be important to realize a systems approach for indivisible systems and those with changing relations among people, objects, and materials.

2.4 System of Systems (SoS) System of Systems (SoS) is a comprehensive concept for management of the life cycle and design, production, and operation of individual elements while targeting systems composed of many artifacts. According to Maiser, SoS is an aggregation of elements, each of which can be regarded as a system: each elemental system composing the SoS should be justified with special features of operational independence and managerial independence [6, 7]. Here we are attempting a more concrete definition for SoS, which is regarded to possess such a general feature to realize Smarter World from several points of view. (1) Point of view of a boundary between the elemental systems SoS is a system which cannot decide the boundary between elemental systems, or a system for which boundaries change. In addition, the system expands by the sensing of boundaries and by particularly addressing tasks occurring at the boundary: it can be characterized as such aggregation which causes change in the boundary between systems, with mutual penetration and functional change. The directivity to inquire into the potential ability and possibility of SoS is the boundary, which is autonomously changeable. (2) Description related with the hierarchy It is a system where systems from lower-order to higher-order levels interact because the hierarchy cannot be defined uniquely. Furthermore, the transition proceeds stepby-step as top-down integration, bottom-up combination, and coexistence of new systems. The scope of diversity of possible resolutions is included completely. Functional deployment and contraction between descriptions of different resolutions can be integrated. (3) Description related with value creation It is a system in which a purpose itself creates a purpose, and also a methodology to create a new value by combining traditional systems. Furthermore, several elemental systems having purposes and functions with a certain degree of perfection work well through mutual influence. In some cases, those elemental systems possess a framework by which to view reality. In addition, there exist purposes and restrictions of

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individual elemental systems, or some common elements, structures, and mechanisms to catch the systems. Furthermore, it will be possible to integrate them into one system from the perspective of the elements, structures, and mechanisms. These features are a fundamentally important condition to construct systems with changeable relations of humans, objects, and values, and can be a framework to recognize the situations of social problems in Smarter World. Furthermore, these features play an important role in realizing “Super Smart Society service platform” as proposed in the Fifth Science and Technology Basic Plan. (4) Description related with the structure The system structure can be understood as having a fractal structure or a semilattice structure [8]. Conversion from exclusion to connotation is done. Peripheral participation is also possible. Furthermore, interprotocols connect the individual elemental systems and express the total functions while defining the parts sharing the protocol as individual systems. The elemental systems, which exist autonomously or dispersedly, can express functions while cooperating, interlocking, or integrating. One might add that discussion was also made showing that the SoS concept can be captured as “global science”, integrated as a systems approach with all sciences as subsystems to resolve social problems. More detailed explanation about SoS is described in Chapter “Interpenetration of System Borders Mediated by Human Activities: Weaving Trees with Rhizome”, Sect. 2.

2.5 Circulating and Spiraled-up Systems Approach With regard to the circulating and spiraled-up systems approach, the driving force as a source of circulation and spiral-up and the meaning of circulation and spiral-up itself can be summarized as follows, while organizing the definition of each stage forming the circulation and spiral and their relation (action). Actually, this summary is partially overlapped with the organization of SoS in the previous section. (1) Structures It has a micro–mezo–micro structure and is connected with circulation as Problem resolution using extrinsic criteria ⇒ Integration process of internal process ⇒ Creation of new values as a whole system ⇒ Complement of micro and macro. In addition, as described previously for global science, there can be circulation among sciences such as Natural science Life science ⇔ Social science ⇔ Human science. Furthermore, one can consider the circulating and spiraled-up type structure between Cyber ⇔ Physical, People ⇔ System, and elements of fractal structure. (2) Actions Regarding the circulating and spiraled-up systems approach, it seems possible to have a flow of actions such as Analysis ⇔ Synthesis ⇔ Abduction as presented in

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Fig. 2 and to co-evolute the construction process of real systems by the action flow, while each has an alert evolutionary process. Moreover, it will be possible to proceed by spirally absorbing, integrating, and fusing the related expanding and developing areas while particularly addressing the systems approach. (3) Driving forces As in the case of Smarter World as a target, prediction is difficult. Moreover, divergence of the prediction and uncertainty of the approach come to constitute a driving force. The circulation is repeated. Consequently, the approach will be improved in a spiral sense. Furthermore, for efficient evolution of society, the goal is reset at any time based on analysis of the current state, while setting a goal to cancel the gap appropriately to maximize the driving force for evolution. However, at this time, if the gap is cancelled at once, single-track evolution becomes inefficient: The turnpike of the turnpike theory [9] becomes spiral-shaped and is driven. (4) Meanings Some meanings of the circulating and spiraled-up systems approach arranged in this way are selected as follows: to understand the mechanism for driving along the direction out of hierarchy; to construct an interface between people and the society; to establish a conceptual philosophy from mathematical aspects to realize growing systems approaches; and so on. However, the arrangement described above has even now progressed only halfway. The investigation will be followed continuously from now on.

3 Towards Creation of a New Systems Approach Discussion from various points of view is now in progress to produce a new systems approach to realize Smarter World, also with tasks introduced in the preceding section and organization and systematization of new keywords. In this chapter are introduced several concepts described in the investigation processes undertaken to date. Furthermore, to show concrete social implementation image of our activities, we introduce the concept of “Super Smart Society service platform” captured from the perspective of SoS and the circulating and spiraled-up systems approach we propose while selecting a “new manufacturing system” as a target among the 11 foregoing systems specified in the “Comprehensive Strategy on Science and Technology Innovation 2015” introduced in the first chapter.

3.1 New Concept of Systems Approach Here we introduce our activities based on arrangement of the circulating and spiraledup systems approach introduced in Sect. 2.5.

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(1) Structures First, to address micro–mezo–microstructures, work on new system modeling is expected to be important. Particularly when Smarter World explicitly including people is targeted, it will be necessary to consider modeling technologies incorporating various systems of different spatiotemporal levels as targets. In Fig. 4 herein is shown the multi-scale system modeling which is now being considered [10]. This figure shows system modeling technology to support policy, strategies, and intention and control from the top while dividing decision variables in Smarter World into three spatiotemporal levels for convenience. In the present state, various modeling methods are adopted individually and independently at each level. No circulating and spiraled-up structure exists at any level. Furthermore, as one might expect, because the spatiotemporal levels to be addressed differ from one another, it is necessary to adopt an appropriate SoS structure for modeling. As described in the preceding chapter, one important keyword related to Smarter World is value creation. There exist in each hierarchy values such as personal value created from a micro level where the spatiotemporal granularity is fine. By contrast, values of the whole society are treated at the macro level, and that of the group and organization are required by a mezo-level intermediate between them. When viewing the multi-scale modeling from the point of the value creation, it will be important to

Fig. 4 Multi-scale system modeling

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Fig. 5 Double helix system to grow with humans

construct system modeling technologies to connect these seamlessly while modeling each value. Next, in Smarter World that includes people explicitly, the relation between people and systems exhibits structures that are different from the traditional systems approach. This relation is presented in Fig. 5. Here the keyword is “Growing system”. The growing systems considered here are not only adaptive control with changeable parameters and updated versions of operating systems provided with purposes. Not only should convenience and functionality be pursued; it is necessary that the system itself should have functions to evolve through human interaction and to practice self-restraint and apoptosis based on ecological perspectives of goods. More detailed explanation is described in Chapter “Growing Systems in Smarter World”, Sect. 4. This idea is positioned mainly for innovative human and system integration in Fig. 3. (2) Actions As described in the preceding chapter, when Smarter World is studied as a target, it is difficult to approach it using a traditional deductive or inductive approach. Furthermore, it is important for the new systems approach to elucidate the development of systems in the form of evolutional repetition among three actions of analysis, abduction, and synthesis. The relation of this action flow and real systems is depicted in Fig. 6. As this figure illustrates, induction analysis, abduction, deduction, synthesis, and implementation into a target system in the spiraled-up structure are not, respectively, independent. Further included here are necessary relations such as consideration of the influences on abduction and results in the stage of induction analysis, to influence deduction synthesis and results in the stage of abduction, and to do implementation into a target system in the stage of deduction synthesis. As described, the spiral chain structure presumes a convoluted structure of continuous functions. A more detailed explanation is described in Chapter “Conceptual Framework for Designing, Planning, and Operating Smart Platforms as Societal Foundations: A System-Optimization Standpoint and a Spiral-up Systems Approach”, Sect. 7.

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Fig. 6 Evolutionary circulating and spiraled-up system with convolution structure

Fig. 7 Circulating and spiraled-up system and its driving force

(3) Driving forces In the circulating and spiraled-up systems approach, what drives the spiral quickly, suitably, and evolutionally depending on the social situation is the driving force. A strong driving force can bring about high-speed spiraling upward using a circulating and spiraled-up systems approach. We continue to try to make such driving forces evident.

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First, in Fig. 7 is shown the site where the driving force occurs in circulating and spiraled-up systems. The driving force itself is implemented and included naturally and independently by the circulating and spiraled-up systems approach and in real society, respectively. In addition, there is a driving force created by the interaction between the two. The society, which changes little by little, makes a request to present a solution for a systems approach. In contrast, a systems approach presents candidates for a solution to the society along with requests to evaluate it. Through such interaction, the driving force is created. The circulating and spiraled-up approach proceeds continuously. Furthermore, when the smarter society is targeted, one can consider a prevailing situation in which the system cannot be divided into individual subsystems, or excised while possessing SoS characteristics. In such cases, it will be necessary to investigate a new systems approach to target systems that cannot be divided or excised. In general, a boundary is formed reflecting the relations between elements and the system purpose in a system of plural elements: when the purpose and relation of the elements are fixed, the boundary is decided. Then, based on this, modeling, further analysis, synthesis, and abduction are conducted. As a result, the purpose and the relation change. That change drives the boundary like osmotic pressure. This flow can be considered as a circulating and spiraled-up systems approach. We designate this as a boundary systems approach in our activities. Numerous twists and spirals exist in this boundary systems approach. Then they proceed to evolve while retaining their relations (Fig. 8). More detailed explanation about the boundary systems approach is described in Chapter “Conceptual Framework for Designing, Planning, and Operating Smart Platforms as Societal Foundations: A System-Optimization Standpoint and a Spiral-up Systems Approach, Sect. 4, and Chapter “Interpenetration of System Borders Mediated by Human Activities: Weaving Trees with Rhizome”, Sect. 4. Fig. 8 Boundary systems approach

New Trends in Systems Approaches to Realize …

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3.2 Platform in a New Manufacturing System To illustrate a concrete social implementation image of our activity, we introduce the concept of “Super Smart Society service platform” captured from the perspective of SoS and the circulating and spiraled-up systems approach we propose, while specifically addressing a “new manufacturing system” as a target among the 11 systems presented above. At the top of Fig. 9 are shown business processes, including recycling, exhibiting the product lifecycle exemplifying traditional manufacturing. The lower half shows the service platform. There, humans, knowledge, technology, funds, and social capital are resourced and modularized. The most suitable resource matching and combinations are conducted instantly with a cloud system under the IoT environment. As a result, the functions and services are provided which individual users truly need and which can satisfy the utility value with rapid prototyping [11]. Furthermore, not only the utility value of a user but also the experiential value of a product and service providers to produce advances [12]. Therefore, it is possible to realize value co-creation using the Super Smart Society service platform.

Fig. 9 Example of platform in manufacturing

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4 Conclusions This paper has presented an outline mainly of our investigation to create a new systems approach to integrate the various specialized areas of the Smarter World Research Group in SICE as a new trend of a systems approach to realize Smarter World, an advanced social infrastructure. Our activity has a close relation with the construction of “Super Smart Society (Society 5.0)” proposed by the Fifth Science and Technology Basic Plan and its central part “Super Smart Society service platform”. Furthermore, this report presents an application example of new systems approach proposed here while picking up “new manufacturing system” as an example among the 11 foregoing systems. Aside from this point, investigations of application examples for “energy value chain” and “intelligent transportation system” are currently proceeding. Because large-scale and complicated systems are developing rapidly because of the wider spread of IoT through society, the role played by systems approaches is expected to become larger and larger. The importance of systems approaches is increasing in future science technology: one can only expect rapid future development. Acknowledgements This paper is based on the research activity at “Research Group on innovative systems approach for realizing Smarter World” in the Society of Instrument and Control Engineers. The author would like to thank all the research group members for our fruitful discussions and comments both from a theoretical perspective and from a practical viewpoint in terms of the systems approach.

References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

11.

K. Ashton, That ‘Internet of Things’ thing. RFID J. (2009) https://www.sice.jp/english/english_links_jsnfs.html Systems Technology for Society 5.0. J. Soc. Instrum. Control Eng. 55(4) (2016) (in Japanese) Outline of the Fifth Science and Technology Basic Plan. https://www8.cao.go.jp/cstp/english/ basic/5thbasicplan_outline.pdf T. Kaihara, H. Kita, New trends in systems approaches, in Proceedings of the 58th Joint Conference on Automatic Control (CD-ROM) (2015) (in Japanese) M.W. Maier, Architecting principles for system of systems. Syst. Eng. 1(4), 267–284 (1998) T. Kaihara, K. Shimohara, System of systems concept and supoer-smart society. J. Soc. Instrum. Control Eng. 55(4), 288–290 (2016). (in Japanese) S. Sawaizumi, O. Katai, The quest for serendipity, Kadokawa (2007) (in Japanese) T. Bewley, An integration of equiliblium theory and turnpike theory. J. Math. Econ. 10(2-3), 233–267 (1982) N. Nikhanbayev, T. Kaihara, N. Fujii, D. Kokuryo, A study on multiscale modeling and simulation approach for social systems, in Proceedings of International Symposium on Flexible Automation 2018 (USB) (2018) S.L. Vargo, R.F. Lusch, Service-dominant logic: continuing the evolution. J. Acad. Mark. Sci. 36(1), 1–10 (2008)

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12. T. Kaihara, N. Nishino, K. Ueda, M. Tseng, J. Váncza, P. Schönsleben, R. Teti, T. Takenaka, Value creation in production: Reconsideration from interdisciplinary approaches. CIRP Annals 67(2), 791–813 (2018)

Toshiya Kaihara is a Professor of Graduate School of System Informatics, and Director of Value Creation Smart Production Research Center at Kobe University, Kobe, Japan. He received his B.E. and M.E. degrees in Precision Engineering from Kyoto University, Kyoto, Japan, and his Ph.D. degree in Mechanical Engineering from Imperial College London, UK. His current research interests are systems theory, systems optimization, systems modelling/simulation, and their application into production, logistics, and social systems. He is author of more than 200 publications. He is a member of IFIP, IEEE, CIRP, ASME, and many others. He currently serves chairman at academic societies in Japan, such as Scheduling Society of Japan (SSJ), and the Institute of Systems, Control and Information Engineers (ISCIE). He is a fellow of the international academy for production engineering (CIRP: College International pour la Recherche en Productique) and the Japan Society of Mechanical Engineers (JSME).

Conceptual Framework for Designing, Planning, and Operating Smart Platforms as Societal Foundations: A System-Optimization Standpoint and a Spiral-up Systems Approach Eitaro Aiyoshi and Junichi Murata Abstract The term “platform” is recently used to refer to not only information systems but also the social infrastructures in general such as electric power systems. We define a platform as a system that creates values by inviting suppliers and consumers of things, energy, and services, providing good matches between them or groups of them, and transporting the things, energy or services from the suppliers to the consumers, and we call this particular type of platforms “infrastructural platforms”. As in “smart society” and “smart energy”, the word “smart” is frequently used to characterize the new properties of social systems. Therefore, we address the systems approach to designing, planning, and operation of the “smart platforms” as social infrastructures especially from the viewpoints of system optimization. In the approach, four conceptual frameworks of “smartness” related to designing, planning, and operation of the platforms are discussed from the viewpoint of system optimization and identified as follows: (1) Introduction of “market mechanism” to relax and satisfy the supply and demand balance, and thereby to enable the autonomous and distributed decision making; (2) Addition of more flexibilities in designing, planning and operation by the introduction of “flexibility variables” under the decentralized decision-making; (3) Introduction of policy enforcing mechanism by “policy variables” to compensate for the shortage of values caused by incompleteness of the market mechanism; (4) Increase of temporal and/or spatial degree of freedom by use of “complementary elements” introduced by stakeholders under the decentralized decision-making. (5) Moreover, we focus on “sustainable evolution” as one of the smart properties of platforms, and outline the “spiral-up systems approach” to designing, planning, and operation of the platforms.

E. Aiyoshi (B) The Institute of Statistical Mathematics, 10-3 Midori-Cho, Tachikawa, Tokyo 190-8562, Japan e-mail: [email protected] J. Murata Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 T. Kaihara et al. (eds.), Innovative Systems Approach for Designing Smarter World, https://doi.org/10.1007/978-981-15-6651-6_2

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Keywords Smart platform · Societal foundation · System optimization · Network model · Market mechanism · Decentralization · Hierarchical structure · Flexibility variable · Policy variable · Complementary element · Temporal flexibility · Spatial flexibility · Spiral-up systems approach · Sustainable evolution

1 Introduction The term “platform” [1, 2] has recently been used to refer to not only information systems but also social infrastructures in general such as electric power systems, water, and sewage networks, communication networks, and transportation networks. Along the same lines, we define, in this paper, a platform as a system that creates values by inviting suppliers and consumers of things, energy, and services, providing good matches between them or groups of them, and transporting the things, energy, or services from the suppliers to the consumers, and we call this particular type of platforms “societal platforms”. As in “smart society” and “smart energy”, the word “smart” [3] is frequently used to characterize the new properties of social systems. Its meaning and usage are, however, not well established yet, and the study of its implications in terms of systems science and engineering has just started. Therefore, in this paper, we consider the systems approach to designing, planning, and operating “smart platforms” as social infrastructures from the viewpoint of system optimization. In the proposed approach, the platforms are modeled as networks that involve many stakeholders. Four conceptual frameworks of “smartness” related to designing, planning, and operating the platforms are discussed from the viewpoint of system optimization, as follows: (1) Introduction of “market mechanisms” to relax and satisfy the supply and demand balancing condition, and thereby enable autonomous and decentralized decision making; (2) Addition of more flexibility of design, planning, and operation by the introduction of “flexibility variables” under decentralized decision-making; (3) Introduction of a policymaking mechanism by “policy variables” to compensate for the lack of certain kinds of values caused by incompleteness of the market mechanisms; (4) Increase of the temporal and/or spatial degrees of freedom by the use of “complementary elements” introduced by stakeholders under decentralized decision-making. Moreover, we focus on “sustainable evolution” as one of the smart properties of platforms, and outline the “spiral-up systems approach” [4] for designing, planning, and operating the platforms.

Conceptual Framework for Designing, Planning, and Operating …

19

2 Modeling Platforms as Networks We consider frameworks for designing, planning, and operating societal platforms— such as electrical power systems, water-supply and sewage systems, communication networks, and transportation networks—and model this framework from the perspective of system optimization. Such societal platform systems may be described by a network [5] of nodes corresponding to spatial locations and arcs expressing relationships between nodes. Denoting by (N , A) the pattern of nodes and arcs produced by modeling a given platform as a network, the supply, transport, and consumption of goods and energy within the network may be described by a model equation of the form h(N ,A) (u, v, X ) = 0,

(1)

where N , A denote the sets of nodes and arcs. To simplify the discussion, we assume that only a single type of good or energy is transferred from suppliers to consumers and that supplies from multiple supply points in the platform correspond to inflow from mutually distinct nodes in the network. Similarly, consumption at the platform’s multiple demand points corresponds to outflow from mutually distinct nodes in the network. We denote by P the set of inflow nodes in the network, corresponding to supply points in the platform, and by Q the set of outflow nodes, corresponding to demand points. For simplicity, we assume P ∩ Q = ∅ (the empty set). We denote by u the vector whose components are the elements of the set {u n |n ∈ P}, the supply volumes from multiple supply points, in increasing order of the node index. Similarly, v is the vector whose components are {vn |n ∈ Q}, the demand volumes from multiple demand points. Thus, if the platform is an electric power network, then u and v respectively correspond to generated power and consumed power in loads. We describe the utilization state of a platform with respect to these supply and demand volumes by a set X = {xmn |(m, n) ∈ A}, whose elements xmn are directed flows on the arcs (m, n) of the network model. Using these definitions, Eq. (1) may be described explicitly by an equality constraint expressing the condition of balance of inflow and outflow at each node:

 (l,n)∈A

xln −

 (n,m)∈A

xnm

⎧ ⎨ −u n , n ∈ P = 0, n ∈ P¯ ∩ Q¯ ⎩ n∈Q vn ,

∀n ∈ N .

(2)

If the platform has no storage elements, then Eq. (1) or Eq. (2) holds separately for each time instant. In the following chapters, we will write the components of the supply and demand vectors in increasing order of the natural numbers that index their components, i.e., u = (u 1 , . . . , u P )T , v = (v1 , . . . , v Q )T where P and Q here denote the numbers of supply and demand points. It will be clear from the context whether P and Q denote sets of nodes or numbers of nodes.

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3 Setting Boundary Conditions via the Autonomous Mechanism of the Market If the network has no storage elements, then its inflow and outflow volumes (that is, the platform’s supply and demand volumes) must agree at each time instant, imposing the constraint P 

up =

p=1

Q 

vq .

(3)

q=1

However, the first fundamental principle of “smartness” is that this condition of supply-demand equality is not to be imposed on the platform explicitly, but rather to be achieved autonomously by the introduction of market mechanisms. Because the decentralization of decision-making resulting from the introduction of the market brings the roles of decision-makers to suppliers and consumers, here we assume that the parties appearing as consumers in the model are not the individual consumers themselves, but rather retailers or similar agents. To summarize, the reasons for introducing market mechanisms into a platform of this sort are the following: (a) to relax the condition of supply-demand equality by introducing a mediating variable referred to as “price”; (b) to decentralize decision-making authority oversupply and demand volumes and distribute this authority to suppliers and consumers; (c) to ensure that the supply-demand equality condition is satisfied by an autonomously adjusting mechanism involving a feedback system in which price serves as a mediating variable. The utilities associated with the production of supply volume u p and consumption of demand volume vq are converted in their entirety to currency values and denoted by f p (u p ) and gq (vq ), respectively. Also, we write the utility derived by suppliers from the supply of quantities u = (u 1 , . . . , u P )T to the market and the utility derived by consumers from procurement of quantities v = (v1 , . . . , v Q )T from the market, with all transactions conducted at price ϕ, as follows: L p (u p , ϕ) = f p (u p ) + ϕu p ,

p = 1, . . . , P,

(4a)

Mq (vq , ϕ) = gq (vq ) − ϕvq , q = 1, . . . , Q.

(4b)

Then the supply and demand volumes to maximize these utilities are chosen by solving following decision problems for suppliers and consumers, respectively: max L p (u p , ϕ) up

subj. to u p ∈ U p

,

p = 1, . . . , P,

(5a)

Conceptual Framework for Designing, Planning, and Operating …

max Mq (vq , ϕ) vq

subj. to vq ∈ Vq

, q = 1, . . . , Q.

21

(5b)

Here U p , p = 1, . . . , P, Vq , q = 1, . . . , Q are sets expressing the ranges of supply and demand volumes. The role of the market mechanism is to determine the transaction price ϕ¯ at which the optimal supply and the optimal demand volumes u op (ϕ), p = 1, . . . , P and vqo (ϕ), q = 1, . . . , Q of problems (5a) and (5b) satisfy Eq. (3), i.e., P 

u op (ϕ) ¯ =

p=1

Q 

vqo (ϕ). ¯

(6)

q=1

Then the optimal supply volumes u op (ϕ), p = 1, . . . , P and optimal demand volumes vqo (ϕ), q = 1, . . . , Q at transaction price ϕ¯ are given as network boundary conditions by the inflow and outflow volumes of Eq. (1) [or Eq. (2)]. Taking these as given, the operator of the platform, whom we refer to as the platformer, determines the platform utilization state X (= {xmn }) to maximize the utility F(X ), which includes items such as income from utilization fees from suppliers and consumers determined by the platform utilization state X . This problem is formulated as follows: max F(X ) X

¯ vo (ϕ), ¯ X) = 0 subj. to h(N ,A) (uo (ϕ), xmn ∈ Cmn , (m, n) ∈ A.

(7)

The total utility defined by adding the utility associated with the optimal platform utilization state X o (ϕ) ¯ obtained as the solution of problem (7) to the sum of the utilities derived by suppliers and consumers at the optimal supply and demand volumes ¯ p = 1, . . . , P and vqo (ϕ), ¯ q = 1, . . . , Q for transaction price ϕ¯ becomes u op (ϕ), L(uo (ϕ), ¯ vo (ϕ), ¯ X o (ϕ), ¯ ϕ) ¯ =

P 

L p (u op (ϕ), ¯ ϕ) ¯ +

p=1

=

P  p=1

Q 

Mq (vqo (ϕ), ¯ ϕ) ¯ + F(X o (ϕ)) ¯

q=1

f p (u op (ϕ)) ¯ +

Q 

gq (vqo (ϕ)) ¯ + F(X o (ϕ)), ¯

(8)

q=1

by cancellations between utility contributions due to the establishment of the supply-demand equality condition (6). Moreover, the solution   Q {u op (ϕ)} ¯ Pp=1 , {vqo (ϕ)} ¯ q=1 , X o (ϕ) ¯ is the optimal solution of the following optimization problem subject to the explicit constraint of supply-demand equality condition

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(3): max

(u,v,X )

P 

f p (u p ) +

p=1

subj. to

Q 

gq (vq ) + F(X ),

(9a)

q=1

P 

up =

Q 

p=1

vq ,

(9b)

q=1

h(N ,A) (u, v, X ) = 0,

(9c)

u p ∈ U p , p = 1, . . . , P, vq ∈ Vq , q = 1, . . . , Q,

(9d)

xmn ∈ Cmn , (m, n) ∈ A.

(9e)

In the converse case that we relax problem (9) by introducing a Lagrange multiplier ϕ for the supply-demand equality condition (9b), we obtain the problem of maximizing the function L(u, v, X, ϕ) given by max L(u, v, X, ϕ)

(u,v,X )

=

P  p=1

max L p (u p , ϕ) + up

Q  q=1

max Mq (vq , ϕ) + max F(X ), vq

X

(10)

where the max operation on the left-hand side, representing overall optimization, may be distributed to separate max operations for each supplier, consumer and a platformer, and further decomposed into problems (5) and (7) [6]. Note that the determination of supply and demand volumes under the decentralization of decisionmaking authority due to the market mechanism is positioned at upper level, and the platformers’ problem (7) in a corresponding form with boundary conditions chosen by the upper level is placed at the lower level in a hierarchical structure. As a textbook conceptual model of the “invisible hand” guiding the market toward the transaction price ϕ¯ at which Eq. (6) is satisfied, we consider a feedback mechanism with dynamics described by a differential equation : ⎧ ⎫ Q P ⎨ ⎬  dϕ(t) =a vqo (ϕ(t)) − u op (ϕ(t)) , ⎩ ⎭ dt q=1 p=1 or alternatively, we may consider the equivalent integral equation

(11a)

Conceptual Framework for Designing, Planning, and Operating …

ϕ(t) = ϕ(0) + a

⎧  t ⎨ Q ⎩ 0

q=1

vqo (ϕ(τ )) −

P  p=1

⎫ ⎬ u op (ϕ(τ )) dτ, ⎭

23

(11b)

where a > 0. Thus, the market mechanism plays the role of an integral controller, involving the convolution of supply and demand volumes from past order histories, until the transaction price ϕ¯ is determined. The transaction price is then realized automatically from the solution trajectory {ϕ(t)|t ∈ (0, ∞)} as the stable equilibrium limt→∞ ϕ(t) satisfying limt→∞ (dϕ(t)/dt) = 0. A sufficient condition for this equilibrium to be stable is for the supply function u op (ϕ) to be strictly increasing and the demand function vqo (ϕ) to be strictly decreasing. In the structure that we have described, a market modeled by Eq. (11) is positioned as a cybersystem above the platform representing the physical system with supply and demand volumes determined as its boundary conditions. Such a hierarchical structure, in which the platform’s operating mechanisms by obeying these market mechanisms, might be one of cyber-physical systems (CPS) [7]. It is not possible for the decisions of the platformer to regulate the market, and the market’s determination of supply and demand volumes does not take into account the operational state of the platform. The reason for this is that if decisions regarding supply and demand volumes were made in consideration of the platform’s operational state, then they would interfere with each other at the platform’s operation, impeding the decentralization of decision-making authority to suppliers and consumers. We consider the hierarchical structure by which the platform operates amidst the decentralization of decision-making authority due to the introduction of these market mechanisms as the foundation of the first embodiment of “smartness”. Figure 1 shows a graphical depiction of this hierarchical structure, together with a dynamic model symbolizing the market mechanism. We pause here to note that, even if the platform is operated optimally with the satisfaction of the supply-demand equality condition and the decentralization of decision-making authority brought about by market mechanisms, the optimal solution obtained in this way—despite being a solution to a problem (9), maximizing the sum of the utilities derived by all suppliers, consumers, and a platformer— does not necessarily give an arbitrary Pareto optimal solution of the multi-objective optimization problem of maximizing each individual utility. For example, the fair Pareto-optimal solution, in which the maximum values of all utilities coincide, is not necessarily given by maximizing the weighted sum of the various utilities [8]. In other words, the introduction of market mechanisms by itself cannot achieve fairness among the suppliers and consumers as the platform’s user.

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Fig. 1 Schematic diagram of a platform

4 Increasing Degree of Freedom by Introducing Flexibility Variables The relaxation of the supply-demand equality condition brought about by the introduction of market mechanisms and the decentralization of decision-making authority to include suppliers, consumers, and platformers may allow the creation of new degrees of freedom that increase the utility derived by individual decision-makers. The introduction of these new variables allows decisions to be made at the discretion of individual decision-makers, without passing through the market, and the resulting enlargement of the space of decision variables creates value. We take this to be the characteristic of the second embodiment of “smartness”. More specifically, we introduce new variables, which might be termed flexibility variables that create possibilities for suppliers to supply goods outside the marketplace of the platform in question, and for consumers to procure goods from outside that marketplace. The flexibility variables introduced for the pth supplier and the qth consumer are denoted by y p and z q , respectively. Denoting the utility associated with production by f p (y p , u p ) and the utility associated with consumption by gq (z q , vq ), the functions corresponding to Eq. (4) with market transactions taken into account are L p (y p , u p , ϕ) = f p (y p , u p ) + ϕu p ,

p = 1, . . . , P,

Mq (z q , vq , ϕ) = gq (z q , vq ) − ϕvq , q = 1, . . . , Q.

(12a) (12b)

Conceptual Framework for Designing, Planning, and Operating …

25

For the case of an electric power system,y p corresponds to the volume of electric power generation delivered to another company’s electric power system, i.e., outside the network in question, and z q corresponds to the power generated by consumers using their own renewable energy sources. These variables represent linkages with other platforms and interaction with the exterior environment. The introduction of these new variables allows the platform to be designed as an open system. In addition, by designing the linkages to other platforms through these flexibility variables, it is possible to construct a larger system known as a system of systems (SoS) [9, 10]. In this case, which signify planar expansions of the network model, it is possible to obtain still more degrees of freedom, and the new value that they create, by determining the flexibility variables themselves which govern the strength of linkages, or by determining the operating variables for conversion elements inserted into linkage arcs, which indirectly determine the strength of linkages. Moreover, the flexibility variables introduced to increase degrees of decidable freedom may similarly be introduced into the decision-making problem (7) for the platformer. For example, one could imagine flexibilities deriving from energy conversions between platforms with different types of energy. A case like this could be thought of as a form of SoS, indicating a layered structure of different types of networks; however, we are forced by lack of space to omit discussion of linkage and layering of platforms of the type described above. In cases where storage elements, such as batteries in electrical power systems, are added to a platform as new complements, one can regard quantities such as the rate of inflow, the rate of outflow and the stored volume (reflecting the state of the storage element) as flexibility variables, discussed above, which serve to increase the degrees of freedom. However, the introduction of new variables due to storage elements creates new mechanisms, namely the redistribution of resources on the time axis, which is considered in the later section.

5 Introducing Policymaking Mechanisms and Associated Policy Variables Even under the decentralization of decision-making authority due to the introduction of the market, the solution that merges the results of decisions made by suppliers, consumers, and a platformer are equivalent in principle to solutions obtained by an overall optimization problem with the sum of the all utilities derived by suppliers, consumers, and a platformer. We referred in Sect. 3 that, regardless of how one assigns weights to their various utilities, there exists a Pareto-optimal solution that cannot be obtained by optimizing the sum type of utility functions. Thus, as a third element of “smartness”, we introduce policymaking mechanisms to ensure that platform users (the participants in the market) have a benefit by using the criterion such as fairness that cannot be established by market mechanisms alone.

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From the standpoint of the platformer who is an operator of the platform and the suppliers and consumers who use those platforms, policy variables introduced to compensate fairness are predetermined parameters; one example to be considered might be a platform usage tax. We denote these policy variables by c and the objective function that determines them, based on a fairness criterion, as . One example of such an objective function is the max-min criterion. More specifically, the maxima of the functions (12), giving the utilities derived by consumers and suppliers by participating in the market, are ¯ u op (ϕ), ¯ ϕ), ¯ p = 1, . . . , P, Mq ((z qo (ϕ), ¯ vqo (ϕ)), ¯ ϕ), ¯ q = 1, . . . , Q, and L p (y op (ϕ), these may be regarded as functions of a policy variable c to evaluate these quantities. Writing the utilities derived by suppliers and consumers as f p (y p , u p ; c), gq (z q , vq ; c) to emphasize their dependence on the policy variable c, Eq. (12) becomes L p (y p , u p , ϕ; c) = f p (y p , u p ; c) + ϕu p ,

(13a)

Mq (z q , vq , ϕ; c) = gq (z q , vq ; c) − ϕvq .

(13b)

As the maximal solutions of these functions also depend on c, we denote them respectively by (y op (ϕ; c), u op (ϕ; c)) and (z qo (ϕ; c), vqo (ϕ; c)). Similarly, the transaction price that satisfies a supply-demand equality condition of the similar form of Eq. (6) with these inputs also depends on c and is denoted ϕ(c). ¯ Then, the maximum utilities derived by suppliers and consumers from participating in the market may be expressed in terms of functions of c as

¯ c), u op (ϕ(c); ¯ c), ϕ(c); ¯ c, λ p (c) = L p y op (ϕ(c);

p = 1, . . . , P,

(14a)

μq (c) = Mq z qo (ϕ(c); ¯ c), vqo (ϕ(c); ¯ c), ϕ(c); ¯ c , q = 1, . . . , Q.

(14b)

If policymakers use an objective function  (perhaps a max-min criterion) to compensate fairness, then we can consider the problem of optimizing the policy variable c to maximize the minimum utility with weight, i.e., the utility derived by the “loser of the market competition”: (λ(c), μ(c))   = min {w1 p λ p (c)| p = 1, . . . , P}, {w2q μq (c)|q = 1, . . . , Q} ,

(15)

among the maximum utilities λ p (c), p = 1, . . . , P, μq (c), q = 1, . . . , Q obtained by all suppliers and consumers in the market. Thus, the optimization problem is expressed as max (λ(c), μ(c)), c

(16)

Conceptual Framework for Designing, Planning, and Operating …

27

where λ(c) = (λ1 (c), . . . , λ P (c))T and μ(c) = (μ1 (c), . . . , μ Q (c))T , and w1 p , p = 1, . . . , P and w2q , q = 1, . . . , Q are positive weighting coefficients depending on activities and other factors of suppliers and consumers. This policy determination does not directly affect the decisions of suppliers and consumers, but rather acts indirectly on the results of those decisions through market mechanisms. If we were to include the policymakers in the above in Fig. 1, they would be located above the others—above the platform (and the associated network model) of the first layer, above the platformer of the second layer, above the market and its suppliers and consumers on the third layer—on the fourth and highest layer. In contrast, the pattern (N , A) of network arcs and nodes can be regarded as policy variables for the policymaker, who may be in charge of the task of designing platforms themselves through the determination of these variables. Because these variables affect the platform’s optimal utilization state X o , we can regard this state as a function of (N , A); that is, we can write X o ((N , A)). Similarly, the maximum utility derived by the platformer corresponding to this optimal utilization state can be written as F(X o ((N , A))) to emphasize its dependence on the variables (N , A), and the policymaker evaluates F(X o ((N , A))) with a function  in order to determine the structural variables (N , A). This problem may be formulated as

max Ψ F(X o (N , A))

(N ,A)

where F(X o (N , A)) = max F(X ) X

¯ vo (ϕ), ¯ X) = 0 subj. to h(N ,A) (uo (ϕ), xmn ∈ Cmn , (m, n) ∈ A.

(17)

Again, in this type of problem for the policymaker, the structure of the platform is not evaluated directly; instead, the policymaker designs the platform indirectly through consideration of optimal operation by the platformer.

6 Creating Value by Introducing Complementary Elements 6.1 Creation of Value from Temporal Flexibility Platforms create value for users by acting in concert with complements that act in mutually complementary ways to create utility. For example, thinking of an electrical power network as a societal platform, the power generators owned by electric utility industries and the electrical power itself that they generate are complements, as are electrical appliances owned by the consumers who consume that power. We consider the possibility of creating additional user-demanded value by adding new complements to a platform, thus complementing and improving the functionality of existing platforms, to be the fourth framework of “smartness” in a platform. In

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this paper, we refer to such complements as complementary elements and consider designing, planning, and operating platforms that incorporate storage elements as complementary elements. In systems without storage elements for goods or energy, design, planning, and operation may be conducted independently at different time instant; in contrast, the presence of storage elements requires that design, planning, and operation consider the influence of the past on the future, creating a type of value that we might call temporal flexibility by establishing links between separate time instant. Examples include the introduction of pumped storage generation and batteries in electric power systems. In what follows, we consider cases in which suppliers or consumers introduce storage elements, denoting the set of nodes at which such elements are positioned by S. The decision problem by suppliers or consumers who introduce storage elements is then the planning problem for a time interval [0, K ] stretching from the present toward the future. Assuming that the inflow and outflow for a storage element take place at a single portal and indicating the inflow/outflow volume for the storage element at node s ∈ S with a new decision variable ys for the associated supplier, the problem for the supplier in possession of the storage element is formulated as max

(ys (·),u s (·))

K 

L s (ys (k), u s (k), ϕ(k); c),

(18a)

k=1

subj. to ys (k) ∈ Ys , u s (k) ∈ Us ,

(18b)

bs (k) = bs (k − 1) − ys (k), k = 1, . . . , K ,

(18c)

bs (0): given,

(18d)

where bs (k) is the stored volume, ys (k) is the procurement volume from the storage element, and u s (k) is the network inflow volume supplied to the market at time k. If ys (k) is positive, then production decreases by this amount, whereas if ys (k) is negative, a volume equal to its absolute value is stored in the storage element and the production volume increases by that amount over the volume supplied to the market. Equation (18c) describes increases and decreases in the stored volume of the storage element, and ys (·), u s (·) are time series on the time interval [0, K ], i.e., ys (·) = {ys (k)|k = 1, . . . , K }, u s (·) = {u s (k)|k = 1, . . . , K }. Similarly, the problem for consumers with storage elements is max

(z s (·),vs (·))

K 

Ms (z s (k), vs (k), ϕ(k); c),

(19a)

k=1

subj. to z s (k) ∈ Z s , vs (k) ∈ Vs ,

(19b)

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29

bs (k) = bs (k − 1) − z s (k), k = 1, . . . , K ,

(19c)

bs (0): given,

(19d)

where vs (k) is the volume procured from the market at time k. If the procurement volume z s (k) from the storage element is positive, then this volume is added to the volume vs (k) procured from the market and consumed, whereas if z s (k) is negative, a volume equal to its absolute value is stored in the storage element, and this volume is subtracted from vs (k). We note that the determination of time-series variables for supply and demand volumes in the presence of storage elements requires for the time series ϕ(·) = {ϕ(k)|k = 1, . . . , K } of the transaction price throughout the time interval to be given in advance as predicted values, and that the solutions of the decision problems (18) and (19) for suppliers and consumers can be thought of as functions of the time series the time-series solutions of (18) and

denoting ϕ(·) of the price. Thus, (19) by yso [ · ; ϕ(·)], u os [ · ; ϕ(·)] and z so [ · ; ϕ(·)], vso [ · ; ϕ(·)] , a supply-demand equality condition equivalent to Eq. (6) must be satisfied by all optimal supply and demand volumes, including the optimal time series of volumes supplied to the market ¯ and volumes procured from the market vso [ · ; ϕ(·)] ¯ given the transactionu os [ · ; ϕ(·)] price time series ϕ(·). ¯ Moreover, considering the dynamics of the market mechanisms that achieve the transaction-price time series ϕ(·), ¯ a differential equation or integral equation similar to (11) is in effect for the double-indexed price variable ϕ(k; t) at each update at time k, and thus the transaction price ϕ(k) ¯ must be rapidly obtained as the stable equilibrium solution within the interval of time k. Here, it is important to keep in mind that, whereas the volumes supplied to the market and procured from the market at each time k must satisfy the supply-demand equality condition, this ¯ −yso [ · ; ϕ(·)] ¯ and condition is not imposed on the production volumes u os [ · ; ϕ(·)] o o ¯ + z s [ · ; ϕ(·)] ¯ determined by the time-series solutions to sales volumes vs [ · ; ϕ(·)] problems (18) and (19) in response to the transaction-price time series ϕ(·), ¯ yielding an additional degree of freedom. For example, Fig. 2 shows the platform with storage elements at the supplier’s node s=1 and the consumer’s node s=9.

Fig. 2 Platform including storage elements as complementary elements

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Once the time series of supply and demand volumes for the transaction-price time series have been determined, platformers can use them to determine the optimal time series of platform utilization states during the time interval in question. If there are no storage elements at nodes other than inflow/outflow nodes of the network (nodes involving suppliers or consumers), then the operating problem for platformers ¯ of ¯ vo ( · ; ϕ(·)) corresponding to problem (7) under given time series uo ( · ; ϕ(·)), supply and demand volumes may be formulated as a set of K separate problems for separate time instant, even in the presence of storage elements for suppliers and consumers, of the form max F(X (k)),

(20a)

o subj. to h(N ,A) uo (k; ϕ(·) ¯ , v (k; ϕ(·)), ¯ X (k)) = 0,

(20b)

X (k)

xmn (k) ∈ Cmn , (m, n) ∈ A, k = 1, . . . , K .

(20c)

In the case that storage elements are located at nodes managed by platformers other than inflow/outflow nodes involving suppliers or consumers, then denoting that set of nodes by S and the time series of stored volumes for a storage element at node s by bs (·) = (bs (1), . . . , bs (K ))T and setting b(·) = {bs (·)|s ∈ S}, this time series becomes a decision variable in conjunction with the time series of utilization states X (·) = {X (k)|k = 1, . . . , K }, and the problem faced by platformers can be formulated as max

K 

X (·),b(·)

F(X (k), b(k))

(21a)

k=1



subj. to h(N ,A) uo ( · ; ϕ(·)), ¯ vo ( · ; ϕ(·)), ¯ X ( · ), b(·) = 0 xmn (k) ∈ Cmn , (m, n) ∈ A, k = 1, . . . , K .

(21b) (21c)

In contrast to Eq. (20b), the network-model Eq. (21b) is a dynamic model involving transitions among utilization states X (·) with interference between time instant, given by

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Here, we assume that the initial values bn (0), n ∈ S ∩ P¯ ∩ Q¯ of all stored volumes are given. As Eq. (22b) shows, the equality constraints imposing the balance of inflow and outflow are relaxed at nodes with storage elements, yielding new degrees of freedom to compensate for the imbalance portions by variations in stored volumes. This enables the existence of a variety of admissible platform utilization states X . From the above discussion, we see that the structures determining the degrees of freedom in operating problems for platforms incorporating storage elements as complementary elements differ depending on whether storage elements are introduced by suppliers and consumers alone, or are additionally introduced by platformers.

6.2 Creation of Value from Spatial Flexibility In this section, we consider the creation of value using what might be called spatial flexibility: by shifting the positions within a platform of complementary elements added to that platform, we can exploit the mobility of a limited number of complementary elements to improve efficiency and productivity. Repositioning of these complementary elements may be modeled directly as the movement of complementary elements within the network. If we suppose the platform to be an electrical power network and the complementary elements to be batteries, then an example of spatial flexibility would be the mobility of battery-equipped electric vehicles within the power system. Here, to simplify the discussion, we consider movement between nodes other than inflow/outflow nodes managed by platformers and assume that storage elements owned by suppliers or consumers are non-mobile, with positions fixed at inflow/outflow nodes. To describe the mobility of storage elements such as those described above, we assume that B storage elements are present and denote the stored volume of element β at time k by bβ (k) and the node number of the element’s position by sβ (k). The set of node positions of all elements at time k is S(k) = {s1 (k), . . . , sβ (k), . . . , s B (k)}. We assume that no two elements of the set have the same node number and that no more than one storage element is assigned to the same node at the same time. Then the model corresponding to Eq. (22a–d) can be expressed as a network model with variable structure:

(23)

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In this model, the time series of the set of nodes at which storage elements are placed, S(·) = {S(k)|k = 1, . . . , K }, are also decision variables, and the pattern of movement of storage elements within the network S(·), and the time series of the associated accumulation volumes, b(·) = (b1 (·), . . . , b B (·))T , are determined by platformers. This type of operational problem may be formulated as max

X (·),S(·),b(·)

K 

F(X (k), S(k), b(k))

k=1

¯ vo (·; ϕ(·)), ¯ X (·), S(·), b(·)) = 0 subj. to h(N ,A) (uo (·; ϕ(·)), xmn (k) ∈ Cmn , (m, n) ∈ A, k = 1, . . . , K .

(24)

7 Sustainable Evolution Through Spiral-up Systems Approach Once a social system has been built, major changes in the external environment may require revisiting designing, planning, and operating the system, but “scrap and build”—that is, starting over at zero and rebuilding from the ground up—is not realistic. Instead, it is desirable to incorporate the capacity to update and improve, that is, to evolve as necessary in order to adapt to new external environments under a certain type of continuity into the system itself. We refer to this as the sustainable evolution property of a social system, and consider it to be a component of the smartness of the system. In this section, we consider this sustainable evolution in the platform by relating to the spiral-up systems approach proposed in Ref. 4 as the conceptual framework for social problem-solving. The spiral-up systems approach is a unified systems approach that is characterized by the repetition of a multi-stage process: implementation/operation → induction → abduction → deduction → implementation/operation. In the induction stage, modeling, learning, and identification with new data are executed to update and improve the current state of a system. In the abduction stage, the awareness of problems in the stakeholders regarding the system is symbolized and formulated. In the deduction stage, the rational solution to the symbolized and formulated problems is searched and the obtained solution is assessed through simulations or experiments. The results of this assessment are then applied to the actual system in the implementation and operation stage, and the process sequence repeats persistently. One additional feature of this spiral-up systems approach is that, by involving multiple stakeholders in the repetition of the implementation/operation → deduction → abduction → induction → implementation/operation sequence, the repetition itself can be made to exhibit diversity, and information exchange between stakeholders can yield a variety of trials and refinements of their results toward implementation and operation. Each cycle of such a repeated process is known as

Conceptual Framework for Designing, Planning, and Operating …

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“generation”, and the process of various trials and refinements based on the results is called “selection”; the resulting conceptual approach ensures that sustainable evolution is exhibited not only by the target system but also by the systems approach used for the design, planning, and operation themselves. Figure 3 shows the above concepts of the spiral-up systems approach. More specifically, in considering the application of the spiral-up systems approach to designing, planning, and operating a platform, first suppose an existing platform has been implemented and operated on the basis of a previous-generation systems approach. At any given time, the platform is subject to changes and variations due to its so-called externalities, including: 1. Structural deformation of the platform due to unexpected influence from the external environment; 2. Changes in the properties required of the platform or in the corresponding utility derived by stakeholders; 3. Changes in the platform’s boundary conditions due to changes in the population of market participants or changes in economic status; 4. Changes in utility due to productivity improvements or relaxed technical constraints on the platform due to technological innovation; 5. Acquisition of new data from the platform, or disposal of obsolete data. To adapt to these changing external conditions, the induction → abduction → deduction process is executed for a new generation, and a platform for this new generation is implemented and put into operation. We use the subscript (k) to indicate modeling of the k th generation, whereas the superscript (i) indicates modeling of the ith stakeholder associated with design, planning, and operation; thus, the model function for the kth generation of the platform is (i) . Similarly, problem h(N ,A)(i) , and the platformer’s utility function is updated as F(k) (k)

(i) (i) (7) formulated in the abduction stage is described as P(k) . In general, the problem P(k) differs for each stakeholder; even if the problem were the same for all stakeholders, the solutions would differ depending on the solution method used by the ith stakeholder. (i) . Therefore, letting I be the number of stakeThus, we denote the solution by X¯ (k) (i) , i = 1, . . . , I holders in generation k, we formulate I optimization problems P(k) (i) ¯ and obtain I optimal solutions X (k) , i = 1, . . . , I as candidate proposals for implementation and operation. Then, when evaluating proposed solutions via simulation or experiments, to eliminate conflicts of interest among stakeholders and find points of compromise, we use methods for social choice and/or consensus-building (see, e.g., Refs. [11–14]). This approach refines the set selected for implementation and operao , which is used for implementation and operation as tion to a single candidate X¯ (k+1) the platform of the (k + 1)th generation. We consider the environmental adaptability of a platform as an additional aspect of the platform’s “smartness” with the repeated application of this type of systems-approach process to ensure sustainable evolution.

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Fig. 3 Spiral-up systems approach

8 Conclusions In this paper, we considered conceptual frameworks for designing, planning, and operating smart platforms as societal foundations from the perspective of system optimization. In particular, we discussed several manifestations of “smartness” in such platforms, including (1) the introduction of market mechanisms to relax and satisfy supply-demand equality conditions; (2) the decentralization of decisionmaking authority made possible by (1) and the resulting possibility of introducing flexibility variables to increase the number of degrees of freedom; (3) the introduction of policymaking mechanisms and policy variables to guarantee value criteria that cannot be achieved by market mechanisms; and (4) the introduction of complementary elements, again made possible by the decentralization of decision-making authority, and the increase in temporal and spatial degrees of freedom made possible by their operation. We also discussed the relationship between this smartness and the spiral-up systems approach as a methodology for designing, planning, and operating platforms with the capacity for sustainable evolution. The objective of this paper was to provide conceptual frameworks and guidelines for designing, planning, and operating platforms. It was not our intention to present detailed models of specific problems arising in actually designing, planning, or operating such systems. Indeed, detailed models of specific problems require insight from the operating and managerial stakeholders associated with them, as well as from the suppliers and consumers who use the platform.

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Herein, the determination of transaction prices to satisfy supply-demand equality conditions was entrusted to the textbook type of market mechanisms, whereas the matching of sellers and buyers in markets was carried out using information platforms, and the settling of accounts after deals takes place on those same platforms. Thus, the markets supporting these activities are distributed throughout such platforms. Consequently, it is ultimately necessary to model the platforms that transact the data needed to match buyers with sellers and settle accounts, to view the resulting a dual-type of network models with a complement to the platform for goods and energy considered in this paper, and to consider both models as interconnected components of a larger multilayered structure. We hope to address challenges to the dual type model in future work.

References 1. L.C. Reillier, B.R. Reillier, Platform Strategy, How to Unlock the Power of Communities and Networks to Grow Your Business (Routledge, London, 2017) 2. H. Kita, System and information, a viewpoint toward a novel systems approaches. J. Soc. Instrum. Control Eng. 55(8), 675–679 (2016) (in Japanese) 3. ISO/TS37151: Smart Community Infrastructure—Principles and Requirements for Performance Metrics 4. E. Aiyoshi: Proposal of a spiral-up systems approach—towards solutions to large-scale and complex social problems. J. Soc. Instrum. Control Eng. 55(8), 675–679 (2016) (in Japanese) 5. L.R. Ford Jr., D.R. Fulkerson, Flows in Network (Princeton University Press, 1962) 6. C. Lemaréchal, Lagrange relaxation, in Computational Combinatorial Optimization: Optimal or Provably Near-optimal Solutions (Lecture notes in computer science 2241), ed. by M. Jünger, Naddef (Springer, Berlin, 2001) 7. S.K. Khaitan, J. Mccalley, Design techniques and applications of cyberphysical systems: a survey. IEEE Syst. J. 9(2), 1–16 (2014) 8. K. Miettinen, Introduction to noninteractive approaches, in Multiobjective Optimization, ed. by J. Branke, K. Deb, K. Miettinen, R. Słowi´nski (Springer, Berlin, 2008) 9. M.W. Maier, Architecting principles for systems-of-systems. Syst. Eng. 1(4), 267–284 (1998) 10. D. DeLaurentis, R.K. Callaway, A system-of-systems perspective for public policy decisions. Rev. Public Policy Res. 21(6), 829–837 (2004) 11. A. Feldman, Welfare Economics and Social Choice Theory (Martinus Nijhoff, Boston, 1980) 12. A. Arbel, The Analytic Hierarchy Process with Interval Judgments Multiple Critera Decision Making (Spriger, Berlin, 1992) 13. N. Bhushan, R. Kanwal, Strategic Decision Making, Applying the Analytic Hierarchy Process (Springer, London, 2004) 14. Y. Dong, J. Xu, Consensus Building in Group Decision Making, Searching the Consensus Path with Minimum Adjustments (Springer, Singapore, 2016)

Eitaro Aiyoshi received the Doctor of Engineering degree from Keio University and joined the Faculty of Science and Technology, Keio University in 1980. From 1996 to 2016, he was a professor at the same university. Since 2016, he is an emeritus professor at Keio University and a visiting professor of the Institute of Statistical Mathematics. His research interests are system optimization and computational optimization.

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Junichi Murata received the Doctor of Engineering degree from Kyushu University in 1986. He then became a research associate at Kyushu University. From 2010, he has been and is a professor in the Faculty of Information Science and Electrical Engineering, Kyushu University. His research interests include optimization, machine learning and their applications to energy management systems.

Viewing Systems from Boundary and Evolution—Toward Developing New Systems Approach Yasuaki Kuroe

Abstract In recent years, systems in the real world have become larger and more complex, and the purposes of the systems have become more demanding and increasingly diverse. It is, therefore, urgent to build systems approaches that create new systems engineering and science. In this chapter, the author overviews the future perspective of a systems approach and discusses how to build it, based on the discussions in the activities of the Research Committee on Innovative Systems Approach for Realizing Smarter World established in the Society of Instrument and Control Engineers (SICE). The key is “Viewing Systems from Boundary and Evolution”. Keywords Systems approach · Evolution · Boundary · Computational intelligence

1 Introduction In recent years, systems in the real world have become larger and more complex, and the purposes of the systems have become more demanding and increasingly diverse. It is, therefore, urgent to build systems approaches that create new systems engineering and science. On the other hand, systems engineering and science have made steady progress and have contributed significantly to various real problems so far. However, unfortunately it seems that their perception in society is not so high, and it is not easy to understand what and how they have been utilized. With this in mind, the Future Initiative Committee in Systems Information Division, in the Society of Instrument and Control Engineers (SICE) , discussed ways to overcome these issues and made some proposals. In particular, since around 2012, Adapted from Y. Kuroe “Viewing Systems From Boundary and Evolution—Toward Developing New Systems Approach– (written in Japanese),” Journal of The Society of Instrument and Control Engineers, Vol. 55, No. 8, pp. 657–664 (2016). Partly translated by permission of The Society of Instrument and Control Engineers. Y. Kuroe (B) Kansai University, 3-3-35 Yamate-cho, Suita-shi, Osaka 564-8680, Japan e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 T. Kaihara et al. (eds.), Innovative Systems Approach for Designing Smarter World, https://doi.org/10.1007/978-981-15-6651-6_3

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discussions have focused on issues such as building a new systems approach, systematizing systems science and technology, and outreach activities in the area and educational activities. At the same time, we decided to establish a new research committee as a forum to concretely discuss on creation and building of a new system approach. In order to establish it appropriately, it is necessary to define specific targets. We gave it Smarter World and named it “Research Committee on Innovative Systems Approach for Realizing Smarter World” (abbreviated as ‘Research Committee on Smarter World’). The research committee started its activities in 2014. The definition of Smarter World is given in other articles in this book. The Cabinet Office in Japan adopted the 5th Science and Technology Basic Plan in January 2016 and set as a priority issue “The World’s First Construction of a Super-smart Society (Society5.0)”. The Smarter World in the research committee unintentionally accords with Super-smart Society in Society5.0, and it is indispensable to build a new systems approach in order to realize both of them. In this chapter, based on the discussions in the activities of the research committee thus established, the author overviews the future perspective of a systems approach and discusses how to build it. The key is “Viewing Systems from Boundary and Evolution” in the title of the chapter.

2 Activating Discussions First of all, look at Fig. 1. This figure is an example of summary of the activities of the Research Committee on Smarter World and was created as follows. The members of the committee first extracted keywords that are considered important in investigating and studying the future innovative systems approach. Next they categorized the keywords and performed grouping, and then extracted concepts from them, associated between groups and gave them interpretations. These procedures were performed by the KJ like method and three figures were created; one of them is Fig. 1. Since the figure thus created would be a rough sketch, it could mislead readers, however, it is conceivable that something interesting for readers can be extracted. This is the reason why such a rough sketch is shown in the beginning of this chapter. The figure can be viewed and understood as follows. The group at the top left is a list of keywords such as mathematical science, control theory, data science, and optimization. It is rather a mathematical system science and engineering and is the bottom line of the systems approach. It is named “Hard system science”. A list of keywords of the block under the block “Hard system science” (the bottom right of the figure) is related to system modeling and analysis, and they are divided into two subgroups. The upper one which contains keywords such as data mining, big data, and so on is categorized as “Systems approach based directly on data”. The lower one which contains keywords such as computational modeling, algorithm models, and so on is categorized as “New perspective on models and modeling”. It is pointed out that those subgroups and the group “Hard system science” are insufficient in terms of treating humans when dealing with the Smarter World and are also insufficient respect

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Fig. 1 Categorizing keywords and linking among them to explore new systems approaches

to the macro–meso approach. For this reason, it is urgently necessary to integrate humans and systems innovatively. In order to make this necessity clear, the lines with arrows are drawn from the keyword groups “Hard system science” and “Systems approach based directly on data” to “Innovative humans/System integration”. On the other hand, to the assertion that a new system concept is needed in order to realize the fusion, the line with arrow is drawn from this “Innovative human-system fusion” to the keyword group named “New system concept/Fusion’,’ and those two groups are thus related. Furthermore, the group “New system concept/Fusion,” its right-hand group “Inspirational systems approach”, and “Human modeling” are related by drawing the line with bidirectional arrow, which means that a completely new systems approach can be created only by interacting new system concepts and their fusion with human characteristics. In particular, the emphasis is on “Human characteristics are inspiration and consensus building” and keywords related to systems approaches to realize them are listed in the group named “Inspirational systems approach.” To the right of the group “Innovative humans/System integration,” there is a group named “(Existing) approach that fuses people and systems” and their interrelationships can evolve them into better approaches. This evolutions further evolve the “Inspiring systems approach” and “Modeling of human,” which are represented by the line with the upward arrow. Furthermore, “New system concept/Fusion” and

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“Inspiring systems approach” lead to “Hard system science” which is the starting point of the explanation of this figure, which means that they influence and develop “Hard system science.” Therefore, a loop is formed by following the lines with arrows indicating the relationship among the blocks of each keyword group, and this figure shows as a whole that by circulating this loop, a new systems approach is created and it evolves. Finally, a line with an arrow is drawn from the blocks in this loop to the keyword group named “Contribution to society” on the far right of the figure, which means that the result of creating and evolving a new systems approach contributes to the society. The systems approach of spirally circulating the analysis, synthesis, and abduction, which is being discussed in the Research Committee on Smarter World, is described in the other chapters of this book, but the approach explained by using Fig. 1 is also of a circulated and spiral-up systems approach. Inspired by the above discussion, the author overviews the future perspective of a systems approach and discusses how to build it, which will be given in the following sections.

3 Brief History of Systems Concept and Systems Approach 3.1 What Is Systems Approach? Before getting into the main subject, that is, discussion on developing new systems approach, we take a look at the brief history of systems concept and systems approach. For this purpose, we should make it clear what is “systems approach” and give it a definition. It can be said that the systems approach is to view the objects and things to be analyzed and synthesized, or the problems to be solved, as systems, and to think about them as systems. This definition may seem to be a bit rough, but it is quite essential and the systems approach ultimately is ways of viewing and thinking. The term systems approach encompasses a very wide range of concepts, such as system thinking [1], and has a history of research. The systems approach referred to in this chapter could include them, however, the one that includes the concept of design in the sense of engineering in the approach is referred to as the systems approach in this chapter.

3.2 Concepts and Their Brief History It has been recognized that the discussions on the systems approach began in the late 1940s, typical representatives of which are “General System Theory” proposed by von Bertalanffy [2] “Cybernetics” proposed by Norbert Wiener’s [3]. In particular, Norbert Winner proposed the concept that would be the cornerstone of today’s infor-

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mation and communication society and anticipation of its arrival. Summarizing up boldly without being afraid to be misunderstood, it can be said Winner asserts two things in his book [3]. The first one is that, viewing a system from the viewpoint of information, it can be treated in one unified theoretical framework whether it is a machine, a creature, or a society. Second one is that, feedback of information plays basic and important roles in the function of systems. These are definitely the essence of systems science and engineering as interdisciplinary disciplines. A real system and its associated problem are modeled based on information, the problem solving is performed through the obtained model, and then the results are returned and applied to them, which is the essence of the systems approach. Thinking about a system as a model, we can introduce mathematical science into it and develop general theories and methods for analysis and design independent of individual systems. A lot of theories and methods have been developed, including system theory, control theory, optimization theory, and so on, and they have made significant contributions not only to engineering but also to fields such as economy, society, medicine, and so on. The importance of feedback of information is also highly suggestive. Feedback is to return results to their causes and to make them desired ones. In order to make a system yield a desired result, it is necessary to create a mechanism that reverses causes and their results, that is, to create an inverse system, which is impossible. However, feedback makes it possible to create an inverse system approximately. Furthermore, the results that have occurred cannot be returned, however information can be returned, which also suggests the importance of viewing a system from the viewpoint of information. Winner discussed the importance of feedback not only for feedback in closed-loop control, but also for learning and self-reproducing machines and self-organizing systems, and also discussed their feasibility. In this sense, it can be said that his perspective of systems is very proactive. Since “General System Theory” or “Cybernetics” was proposed, several new system concepts and systems approaches have been proposed. It is difficult to survey them systematically, but the followings can be listed. For example, Tada’s “Super system” [4], which is a new system concept from the field of immune and biological systems, “Autopoiesis” proposed by Maturana and Valera [5, 6], “Synergetics” by Haken in the physics field [7], and a “Complex systems” proposed by the Santa Fe Research Institute group, which caused a boom and a social phenomenon [8]. These concepts have been proposed from the area of life system, physical systems, and social systems, and it can be said that they do not explicitly include the concept of “design” in engineering ,which is a key element in the systems approach considered in this chapter. On the other hand, there are some systems concepts that include “design”, which have been proposed mainly in the field of engineering, typical examples of which are as follows. Those are “large-scale system” [9], which deals with the analysis and design of large-scale systems consisting of many subsystems, “distributed cooperative system”, which provides systems with functions of decentralization and cooperation [10], and “autonomous decentralized system” [11] in which there is no central operating or governing mechanism in the system. Instead each subsystem in the system

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autonomously manages its own functionality and its coordination with other subsystems, which forms an overall function of the whole system [11]. The other ones are “emergent system” [12] and “hybrid system” [13]. The former is the system which adds to “autonomous decentralized system” design methodologies in order to create and emerge new functions and structures from the interaction between micro and macro [12]. The latter is the system in which a time-driven system and a discrete event system are coupled together [13]. While being proposed in the way, recently, the concepts of “cyber-physical system” (CPS) [14] and “system of systems” (SoS) [15] have recently been proposed and attracted much attention. The reasons that the concepts of systems and systems approach have changed in this way are considered as follows. First is that the range of systems to be handled has expanded due to the change of the times. Second is that what can be achieved has remarkably improved both qualitatively and quantitatively due to the development of base technologies and the remarkable development of information and computer technology. The other is that the integration of system concepts proposed in the fields of life, physical, or social systems into the concept of design in the sense of engineering has progressed. In that sense, CPS can be considered as a product of the concepts of “hybrid systems” and “emergent systems.” SoS can be considered as a product of “large-scale systems,” “distributed cooperative systems,” “autonomous distributed systems,” and in addition “emergent systems.”

3.3 Changes in Development of Methodologies and Tools Theories, methodologies, and tools that implement the systems approach have also being changing and evolving. What have been played central roles are mathematical systems science and engineering methods, which are called “Hard system science” in Fig. 1, and they have steadily progressed and need to be developed further in the future. One of the important methodologies and tools for considering future systems approach, the author would like to raise, is research and achievement in the field called “Computational Intelligence (CI).” Although there are some definitions and ways of understanding CI, the Technical Committee on Computational Intelligence set up in the Systems and Information Division, SICE decides its activity policy as follows. Its strategies and methodologies are “pursuit not only brain but also biologically and ecologically motivated computational paradigms and intelligent information processing, and integrate as hybrid intelligent systems,” and its research area is “Neural Networks, Connectionist Systems, Genetic Algorithms, Evolutional Computation, Fuzzy Systems, Soft Computing, Chaos, Complex Systems, Reinforcement Learning” [16]. There are two major problems to be solved in CI: “how to build intelligent systems” and “how to build systems intelligently or how to solve intelligently various problems related to systems” [17]. The latter is especially important for building a new systems approach, which will be discussed in the later subsection.

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4 Toward Building a New Systems Approach 4.1 Systems and Problems that Should Be Treated and Solved In the real world, the systems should be dealt with are becoming larger and more complex and extend not only to the area of engineering but also to life, energy, environment, economy, and society. In addition, it is necessary to handle multiple systems simultaneously, not individually. There arise several problems in dealing with such systems. One is that there coexist system elements and subsystems with big discrepancy in temporal and spatial scales, that is, problems associated with micro, meso and macro. Another problem is that they are interconnected in layers, a hierarchical structure of them cannot be defined, and, furthermore, the essential problem is that they interact and penetrate among them. These problems cannot be solved by the conventional systems approach, and it is necessary to develope a way of understanding and approaching to, and seamlessly connecting these problems. In the conventional systems approach, first thing to do is to determine the area, that is, the boundary, for a target system or a problem, and then determine their input and output. However, there are many systems where their boundaries cannot be determined in advance and the boundaries could change. It is also necessary to consider not only the boundaries of the system or problem, but also those of elements and subsystems that make up the system, and also to consider that their relationships and structures could change. The problems mentioned above are found everywhere not only in engineering systems but also social systems, life systems, environmental systems, and so on. For example, consider the case of treating diseases such as the heart of a human biological system with the systems approach. Biological systems are multilayered from the cell level to the organ level and human level, and the musculoskeletal system, cerebral nervous system, blood circulatory system, immune system, and others are intricately intertwined. For example, consider the problem of modeling drug administration and its outcome, and it is very difficult to model it with first determining its boundary. There is also the following example in physical systems. The international fusion experimental reactor is being constructed with the aim of realizing magnetic fusion plasma, and the modeling of plasma phenomena and development of simulation methods are urgently required in order to predict their performance, establish control methods, and optimize operating scenarios. However, it is extremely difficult to model plasma phenomena, because they include various physical phenomena with very wide time scale and spatial scale [18]. Furthermore the boundary of plasma cannot be measured directly and the it is changing, and it becomes an important problem to determine plasma boundary [19]. Another example is the Internet of Things (IoT), in which objects, people, software, and hardware are connected via the Internet, and is exploding rapidly. The IoT also has to be treated as a system, and the problems described above appear. Furthermore, as shown in Fig. 1 in Sect. 2, in developing a new systems approach, how to deal with humans becomes quite important. There are three viewpoints in

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dealing with humans: the first one is dealing with humans as components of the system, the second one is to dealing with humans who are outside the system and view the system, and the third one is to deal with humans so that they take both of the viewpoints and they alternate and change. Another important concept in dealing with humans is the treatment of the values of humans. In the conventional systems approach, it is usual to set objectives and goals for a system and design it to achieve them, and the values are incorporated into the objectives and goals. However, when the system boundary changes, the objectives and goals themselves change, and in many cases, the values cannot be determined in advance. It is, therefore, necessary to consider a mechanism that can create and emerge new values.

4.2 Systems Approach Based on Computational Intelligence We now consider how we build a systems approach that solves the problems and issues discussed in the previous section. The author firmly believes that the introduction of CI described in Sect. 3.3 is promising. As mentioned earlier, the issues of CI with respect to systems science and engineering are “how to build intelligent systems” and “how to build systems intelligently or how to solve intelligently various problems related to systems.” In particular, the latter one of these two would lead to building a new systems approach. In order to create a new systems approach, a mechanism that evolves the approach itself is necessary. One approach to achieve this is to use the framework of the swarm reinforcement learning method [20] proposed by the author and his colleague, which will be discussed below. First, the frameworks of reinforcement learning and swarm reinforcement learning are explained.

4.2.1

Framework of Swarm Reinforcement Learning Method

Reinforcement learning [21] is a learning method in which an agent having a task to be fulfilled acquires the optimal policy by trial and error based on rewards obtained through interaction with the environment, and the general framework of which is shown in Fig. 2. This learning method has attracted a great deal of attention, and many studies have been done because it enables an agent to solve complicated problems with unknown and complex environments only by a simple update procedure which tries to maximize the profits that would be obtained from now to the future. Also, the main task of the system designer does not need to model the problems and only his/her task is to design the reward, which enables the agent to learn autonomously. The author and his colleagues have proposed the swarm reinforcement learning method that introduces the concept of swarm intelligence to the reinforcement learning method [20]. In this method, we consider a pair of agent and environment in reinforcement learning, which we call a learning world. In the swarm reinforcement learning method, multiple learning worlds are prepared as shown in Fig. 3, and learning is performed in the following two ways. One is that in each learning world,

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Fig. 2 General framework for reinforcement learning.

each agent learns by using usual reinforcement learning method, simultaneously and in parallel. The other is that each learning world also learns through exchanging some information on the learning results of each learning world. In this information exchange, the quality of the learning outcomes of each learning world is evaluated by some method, and the multi-point search optimization method and the evolutionary computation method, such as, PSO, ant colony optimization, and genetic algorithm are used. Therefore, it is possible to evolve the learning world by passing the information of the excellent learning world to each learning world. Thus, in the swarm reinforcement learning method, two learning strategies are performed: individual learning in each agent in each learning world and learning based on information exchanges among learning worlds. The authors have already proposed methods to apply the swarm reinforcement learning method to some control problems, such as the formation problem of swam robots and some dilemma problems, and have confirmed its effectiveness [20]. Furthermore, in the real world there are a lot of learning problems into which one wants to introduce prior knowledge and heuristics that humans possess. We have also proposed a swarm reinforcement learning method that can introduce them [22]. In this method, in the framework of swarm reinforcement learning method shown in Fig. 3, one prepares and builds the same number of learning worlds as that of prior knowledge and heuristics to be introduced and also prepares usual learning worlds. And these two types of learning worlds perform two learning strategies: individual learning in each agent in each learning world and learning based on information exchanges among learning worlds. Note that, in the method, the following points have to be considered. First is how to identify whether the introduced prior knowledge and heuristics really work. Second is that since some of the introduced heuristics may be effective in the early stages of learning and some may be effective in the middle or final stage of the learning, the problem is when and how to select and eliminate them during the learning. In the proposed swam reinforcement learning method, information exchange among the learning worlds solves these problems. The reference [22] applied the proposed method to the learning of Othello games and demonstrated that the selections and eliminations are performed appropriately.

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Fig. 3 General framework for swarm reinforcement learning

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Possible Systems Approach

We now consider how to build a new systems approach using the framework of the swarm reinforcement learning method described in Sect. 4.2.1. It is considered the following approach is able to solve the problems discussed in the Sect. 4.1, that is, one in terms of big discrepancy in temporal and spatial scales, one in terms of macro–meso–micro structures, and one in terms of system boundaries; they cannot be determined or they may change. It is difficult to construct a mathematical model for a system having such problems, and then it is not possible to apply conventional systems approach based on mathematical models. On the other hand, with the recent development of computer technology and computational science, the simulation technology of systems using computers has remarkably progressed, and simulators for various systems have been developed. It is conceivable to develop analysis and design methods directly using such simulators as models of the target systems. Furthermore, recent developments in sensor technology and information processing technology have made it possible to collect, store, and process large amounts of data, that is big data. It is also conceivable to analyze and design the system by directly using such data. Integrating the mathematical model, simulator, and data, which we call an integrated model, the following systems approach can be considered. Consider the one who is responsible to analyze and design the system or one who is responsible to solve problems related to the systems as agents in reinforcement learning method, which we call it an analysis and design agent. Consider the framework as shown in Fig. 4 where the environment in reinforcement learning shown in Fig. 2 is replaced by the integrated model. In this framework, the analysis and design agent acquires the analysis and design method by learning through the interaction with the integrated model. In other words, the agent applies the analysis and design method currently available to the integrated model as an action, and the agent receives the rewards and evaluations as a result, and the agent tries to improve the analysis and design method based on the received rewards. The agent could obtain the optimal analysis design method by repeating this procedure, similar to the agent in the usual reinforcement learning. As mentioned earlier, the target model can be an integrated model that integrates mathematical models, simulators, data, and so

Fig. 4 Systems approach based on computational intelligence

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on and is able to handle models that are far more complex than the mathematical models used in conventional analysis and design method. In recent years, simulation technology has progressed remarkably; for example, the development of a simulator that connects hierarchical structures from micro to macro has been begun [18, 23]. And the research called data assimilation [24, 25], that improves the models used in the simulator by using a variety of data obtained from the system, is also in progress. In this sense, it can be said that the approach shown in Fig. 4 is very promising. Furthermore, it is considered that the approach can handle the systems where the boundaries cannot be determined or they change, by using a large-scale simulator that includes the entire range of the systems and their problems. Note that the approach shown in Fig. 4 does not explicitly illustrate the interaction with the real world or real system for the sake of simplicity; however, a diagram that takes these into account can be drawn, an example of which is Fig. 5. Note that, in this figure, in addition to the analysis and design agent, modeling agent and theory developing agent are also represented as agents, and they are responsible to model and to develop theories. For example, if one wants to introduce humans’ prior knowledge or metaheuristics, it can be realizing by setting up an agent that is responsible to perform it, as in the reference [22]. All the agents learn by interacting the integrated model and the real system or real world while exchanging information among them to acquire optimal policies.

4.2.3

Systems Approach Coevolving with Real System

The approach shown in Fig. 4 can be extended to the framework of the swarm reinforcement learning shown in Fig. 3, the result of which is shown in Fig. 6. In this figure, multiple agents are set as 1, 2, …, n. For example, agent 1 is a modeling agent, 2 is a simulator development/ execution agent, 3 is a boundary viewing/ identifying agent, 4 is a heuristic or human agent, 5 is a hard system science agent, 6 is a soft system science agent, and so on. Here, the term hard system science refers to an agent responsible to develop mathematical systems science and engineering technologies represented by the keyword group in the upper left group in Fig. 1. The soft system science agent also means an agent that is responsible for other methods such as CI. Each agent learns by interacting with the real world and also learns through information exchanges among the agents. Incorporating an evolution mechanism into these information exchanges by using a method evolved from the swarm reinforcement learning, it could become possible to develop a systems approach that enables the real system, and each agent to evolve together every time the learning loop through information exchanges is circulated. In the explanation of Fig. 1 in Sect. 2, the statements “Hard system science has insufficient viewpoint in dealing with humans”, “Human characteristics are inspiration and consensus building” , and “Inspired systems approach” are listed, and they should be solved and realized. It is considered that these can be realized by the abovementioned heuristic agent. Considering system modeling, for example, the difficulty comes from the fact that modeling possesses both mathematical aspect and

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Fig. 5 Computaional Intelligence-based systems approach interacting with real systems

artistic aspect [26]. It is considered that this difficulty can be overcome as follows. In the framework shown in Fig. 6, the modeling agent and the heuristics agent or the human agent exchange information among them, which enables to develop a modeling method which can combine mathematical aspect and artistic aspect, and furthermore, can incorporate values of humans.

4.3 Boundary Systems Approach Concept At the end of this chapter, the author would like to further consider the problem related to system boundaries and raise some issues. When developing a new systems approach that is able to deal with a system whose boundaries cannot be determined or whose boundaries change, especially when developing its design theory, the following problems arise: how to treat the driving force at the boundaries and how to control it. For example, biological cells have a boundary called cell membrane, and the boundaries are driven by osmotic pressure. Living organisms have not only spa-

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Fig. 6 Systems approach coevolving with real systems (for example, agent 1 is a modeling agent, 2 is a simulator development/ execution agent, 3 is an agent that views and identifies boundaries, 4 is a heuristic or human agent, 5 is a hard system science agent, 6 is a soft system science agent, and so on and these agents are coevolving with real systems.)

tial boundaries, but also boundaries due to various functions and their relationships, and it can be seen that the boundaries are determined and changed depending on how to extract them, and it seems that these changes can be driving forces that sustain life. Furthermore, let us consider the economic system. The changes in the consumption tax rate and official discount rate, for example, can be viewed as osmotic effects on various functions and relationships of the economy, which in turn determine or change the boundaries. It can be seen that those determination and changes act as the driving force and bring the economy to a certain state. It is necessary to consider a systems approach that can solve these problems concerning system boundaries. An example of the problems of determining the driving force, that is, input at the boundaries is one designing the boundary value input of a distributed parameter system. However, it presumes that the boundaries can be determined in advance, and its solution cannot be applied directly for the problems related to system boundaries described above, and it is necessary to develop a systems approach that can handle them. For this purpose, it can be considered that the system concepts which are proposed from the field of life science can be cues, the typical examples of which are Tada’s “Super system” and Maturana and Valera’s “Autopoiesis” mentioned in Sect. 3.2. Although we cannot elaborate on these in detail, in “Super system” Tada developed a system theory on how to define the self and non-self in the immune system and how the boundaries between them are determined. “Autopoiesis” is a system that produces its own components and determines its own boundaries. Its features are

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as follows: It is autonomous and individual, it determines its own boundaries, and it has no input and output. These systems concepts can be cues, and by extending them how can we develop a new systems approach that can acquire solutions of the problems concerning system boundaries described above? It can be considered that one promising approach is to utilize the framework of Fig. 6 explained in the previous section, however, there are many issues to be resolved. For example, in order to introduce the driving force at such boundaries, it becomes a problem how to model the boundaries and the driving force, and how to introduce an agent that handles them. Also, the systems approach in this chapter requires the perspective of designing a system in the sense of engineering as stated in Sect. 3.1, and the viewpoint of viewing system is necessarily outside of the system. On the other hand , in “Autopoiesis” the viewpoint is inside of the system, and in order to resolve the issues by using the concept of “Autopoiesis” the problem is how to realize the in and out movement of the viewpoint of the agent in Fig. 6. Kawamoto, who introduced “Autopoiesis” to Japan and advances his own research on it, pointed out the followings [6, 27]. General systems theory dealing with dynamic equilibrium (homeostatic) systems can be considered as the first-generation system theory, the system theory dealing with open and dynamic non-equilibrium systems by Pregogine and Haken et. al. can be considered as the second-generation systems theory , and “Autopoiesis” as the third-generation systems theory. The Research Committee on New Systems Approach for Realizing Smarter World defines the “boundary systems approach” as the fourth systems theory, has started to discuss on how to develop a new system approach that can solve the problems concerning system boundaries such as how to provide driving force to system boundary like osmotic pressure, which includes modeling methodology such as ‘weak modeling’ in which only boundary conditions are given.

5 Conclusion In this chapter, based on the discussions at the Research Committee, the author discussed the current status and future prospect on the new systems approaches. The key is viewing systems from boundaries and evolution. From the viewpoint of evolution, it is found that the circulated and spiral-up approach, which evolves real systems and systems approaches/methodologies together with the progress of time, is a powerful approach. In addition, there are various boundaries, such as not only spatiotemporal boundaries among elements and subsystem but also boundaries among various functions and their relationships, those among various system theories and methodologies and so on. It is important to view from all these boundaries in order to build a new systems approach. Systems science and engineering are essentially interdisciplinary and multidisciplinary and crossing various academic fields, and it is necessary to view systems from the boundaries of various fields, which also implies the importance of viewing from boundaries.

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The title of this article was given inspired by the Japanese-translation edition of the book “Seeing the World from the Boundary” [28], with “Evolution” being added to it by the author. This book discusses political geographical boundaries, such as national borders and the territoriality of human activities, and is not directly related to the subject of this chapter. In order to build a new systems approach, it is also necessary to exchange borderlessly with various academic fields. Although the systems approach based on CI and boundary systems approach described in this chapter are only at the stage of presenting frameworks or concepts, the author anticipates that this framework and concept will evolve into systems design theories by participations and initiatives of many researchers. Acknowledgements References [18, 19] were suggested by Professor Sadao Masamune, with whom the author has been conducting the joint research at the Plasma Control Science Research Center in Kyoto Institute of Technology. The author expresses his thanks to Prof. Masamune.

References 1. P.B. Checkland, System Thinking (Wiley, System Practice, 1984) 2. L. von Bertalanffy, General System Theory (George Braziller, New York, 1968) 3. N. Wiener, Cybernetics: Or Control and Communication in the Animal and the Machine, 2nd edn. (The MIT Press, Cambridge, 1961) 4. T. Tada, The immune system as a supersystem. Ann. Rev. Immunol. 15, 1–13 (1997) 5. H.R. Maturana, F.J. Varera, Autopoiesis and Cognision (D. Reidel Pub., 1980) 6. H.Kawamoto, Autopoiesis The Third Generation System (Seido-sha Publishers, 1995) (in Japanese) 7. H. Haken, Information and Self-Organization—A Macroscopic Approach to Complex Systems (Springer, Berlin, 1988) 8. M. Mitchell, Complexity: A Guided Tour (Oxford University Press, Oxford, 2009) 9. For example, Special Issue on Large Scale Systems, J. Soc. Instrum. Control Eng. 25(3) (1986) 10. For example, Special Issue on Decebtralization and Coordination. J. Soc. Instrum. Control Eng. 26(1) (1987) 11. For example, Special Issue on Decentralized and Autonomous Systems. J. Soc. Instrum. Control Eng. 29(10) (1990) 12. For example, Special Issue Emergent Systetms—Toward a New Paradigm for Artificial Systems. J. Soc. Instrum. Control Eng. 35(7) (1996) 13. For example, P. J. Antsaklis, ed., Specail issue on Hybrid Systems: theory and applications. Proc. IEEE 87(7) (2000) 14. E.A. Lee, Cyber-physical systems—are computing foundations adequate?, in NSF Workshop on Cyber-Physical Systems: Research Motivation, Techniques and Roadmap (2006), pp. 16–17 15. For example, M. Jamshidi, ed., System of Systems Engineering—Innovation for the 21st Century (Wiley, 2009) 16. Special Issue Past and Future of Computational Intelligence, What Will It be Like in SICE. J. Soc. Instrum. Control Eng. 54(8) (2015) 17. Y. Kuroe, Computaional intelligence—present status and prospect. J. Soc. Instrum. Control Eng. 54(8), 553–560 (2015) 18. F. Atsushi, Survey of multi-scale integrated simulation in magnetic fusion plasmas. J. Plasma Fusion Res. 85(9), 597–601 (2009) 19. A. Beghi, A. Cenedese, Avances in real-time plasma boundary reconstruction. IEEE Control Syst. Mag. 25(5), 44–64 (2005)

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20. Y. Kuroe, H. Iima, Swarm reinforcement method. J. Soc. Instrum. Control Eng. 52(6), 540–547 (2013) 21. R.S. Sutton, A.G. Barto, Reinforcement Learning (MIT Press, Cambridge, 1998) 22. K. Kumashiro, Y. Kuroe, H. Iima, Swarm reinforcement learning method introducing multiple heuristic policies, in Proceedings of the 60th Annual Conference of the Institute of Systems, Control and Information Engineers (ISCIE), Kyoto (2016), pp. 343–345 23. H. Sakaguchi, K. Kusano, D. Suetsugu Editors: Multi-Structure Dynamics Universal Law in the Life, Space, and Earth Science (University of Tokyo Press, Tokyo, 2008) 24. M. Sharan, et al., Data Assimilation and Its Applications (Springer, Basel, 2012) 25. M. Asch et al., Data Assimilation: Methods, Algorithms, and Applications (Society for Industrial and Applied Mathematics, Philadelphia, 2016) 26. H. Kimura, Nonuniqueness, Uncertainty, and Complexity in Modeling, Applied and Computational Control, Signals, and Circuits (1999), pp. 121-150 27. T. Iba, An autopoietic systems theory for creativity. Procedia Soc. Behav. Sci. 2, 6610–6625 (2010) 28. C. Alexander, Diener, J. Hagen, Borders: A Very Short Introduction (Oxford University Press, 2012) [Viewing From Borders, Introduction to Borders Studies (Iwanami Shoten, Publishers, Japanese Edition Translated by F. Kawakubo and A. Iwashita, 2015)]

Author Biography Yasuaki Kuroe received the Ph.D. degree from Kobe University, Kobe, Japan, in 1982, he joined the Department of Electrical Engineering, Kobe University, as an Assistant Professor. In 1991, he moved to Kyoto Institute of Technology, Kyoto, Japan, and became a Professor. Since 2016, he has been a Professor Emeritus with Kyoto Institute of Technology. He is now a Visiting Professor with Kansai University, Osaka, Japan and a Research Fellow with Doshisha University, Kyoto, Japan. His current research interests include computational intelligence, control and system theory and its applications, and computer aided analysis and design.

Key Factors for Promising Systems Approaches to Society 5.0 Motohisa Funabashi

Abstract Advances in science and technology dramatically shake the framework of society so far and provide opportunities for revolutionizing society. For the system sciences community, it is an urgent task to develop a new social vision and to embody it as socio-technical systems. Social systems sciences are expected to provide central guidance for this effort. In this paper, a comprehensive view would be given of what kind of works have been done as social systems sciences, in particular, systems approach in terms of envisioning and developing socio-technical systems, and future research directions would be proposed. Specifically, while surveying the global trends in this field, the efforts will be presented for the 5th Science and Technology Basic Plan, which aims to a systemic society (Society 5.0) as Japan’s science and technology policy for fiscal 2016–2020. Although these are inclined to be extremely subjective arguments, and it is customary to present results that have been widely discussed, it is hoped that this paper will serve as the starting point for further study. Keywords SoS engineering · Socio-technical systems · Knowledge circulation place · Super smart society

1 Introduction Half a century ago, Systems Science and technology was in the spotlight. Miura, Executive Vice President of Hitachi, who worked on the commercialization of Systems Science and technology from an early stage, gave the following foreword of a book on systems technologies [1] at that time, “The Apollo project in the 1960s finally succeeded, but behind it there was starting of the electronics age. Relying on the power of systems engineering that integrates many technologies to achieve a Adapted from Motohisa Funabashi “Societal Challenges Requiring Systems Approaches”, Journal of The Society of Instrument and Control Engineers, Vol. 55, No. 8, pp. 665–670 (2016). Partly reprinted by permission of The Society of Instrument and Control Engineers. M. Funabashi (B) The Transdisciplinary Federation of Science and Technology, Tokyo, Japan e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 T. Kaihara et al. (eds.), Innovative Systems Approach for Designing Smarter World, https://doi.org/10.1007/978-981-15-6651-6_4

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single goal for landing on the moon, the hope is growing that this technology will bring about a rosy future society. The name, systems engineering, has come to be used for everyday conversation, but the world has changed dramatically, shifting from high growth to low growth, and requiring efforts to preserve the environment as well as to save resources and energy, and realizing a welfare society. Scarce resource countries like Japan are required to shift to high-value-added industries, but one of the promising directions to solve this problem is systems development into an information society.” As typical approaches to systems development at that time, many online systems were realized in various fields such as finance, manufacturing, transportation, and distribution in the industry. In addition, as example of efforts in the public sector, systems engineering was practiced in space and aircraft development named “Taikei-kogaku” which means systematic engineering in Japanese [2]. The Planning & Programming Budgeting System (PPBS), which formulates national business budgets under cost-benefit evaluations was introduced to the government [3] and utilization of system dynamics was promoted for local government management in a couple of districts [4]. It is well known that the computerization of society has progressed dramatically due to the advent of personal computers, the spread of the Internet, and the penetration of mobile devices. Regarding the efforts in system sciences and technologies community particularly as observed in the Society of Instrument and Control Engineers (SICE), a leading academic society for systems and control in Japan, Ichikawa [5] and Tanaka [6] outlines the following progress by time. In the early 1960s, modeling of dynamic properties and optimization of large-scale systems were discussed as a technique for realizing the system. After that, fuzzy concepts, multi-objective system evaluation techniques, and Petri Net for modeling discrete event systems got popularity and new system concepts such as autonomous decentralized systems and emergence systems were produced. SICE played an important role in the incubation of new fields, covering systems engineering as well as information engineering. It goes without saying that these efforts have contributed to the real world in various ways, but they may have been somewhat modest in view of the dramatic dissemination of information technology. For systems development by information technology, soft systems approach such as the Soft Systems Methodology (SSM) were practiced, and the keyword of strategic information systems was also raised, but until recently, systems engineering with a high level of modeling abstraction, such as the Unified Modeling Language (UML), has not been discussed much in SICE. Now half a century has passed since the system attracted public interest. Interest in social systems has increased in the SICE system sciences and technologies community [7]. It is expected that efforts for various problems in society are starting to create a big trend. Under such circumstances, in this paper, a proposal of future research directions will be made, taking a bird’s-eye view of the systems approaches in terms of planning and development of the systems. It will be proposed what kind of challenges in the socio-technical systems development should be conducted. Specifically, the domestic and international trends in this field will be reviewed, and the systems approach will be studied to the 5th Science and Technology Basic Plan,

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which is aimed at realizing socio-technical systems as Japan’s government policy. The present paper has been extremely subjective and has not yet been fully debated. However, it is hoped that this will provide an important starting point for further discussions [8].

2 Social Demands and Expectations to Systems Approaches In order to examine the systems approaches for social issues, the trends of the world around 2030 as well as the policy issues of Japan will be reviewed, and the research trends related to them will be described.

2.1 Systems Approach to Social Demands It is almost impossible to describe the social demands of various people. Here, as a source of information that is considered to have a great influence globally, let’s take up reports from the US National Intelligence Service [9, 10], and the Economist [11]. Although these discussions are wide-ranging, Table 1 shows the results of picking up common items from four fields: politics, economy, society, and technology. This table describes the four fields in relation to today’s policy topics in Japan, as well as the topics in the overview report on systems approaches compiled by the Japan Science and Technology Agency [12]. In addition, some examples of contributions from the systems approach expected in the future are added. • Political field: It is daily discussions on how to build a national security system against the world power shift and how to acquire international negotiating power against the relatively losing economic superiority. As a systems approach in this field, advanced approaches such as analysis of international order change using a multi-agent model have been made [13]. The importance of building a model like this is natural, but what is expected in parallel is the construction of a surveillance system for the social situation that serves as an input for the model. The social measurement relying on the remarkably advanced ICT environment is enjoying a new big opportunity, and this approach is indispensable also in the social field described separately. • Economic field: Japan’s serious challenges include reduction of fiscal deficits, innovation in productivity as well as the creation of new industrial sectors, preparation for disasters, and secure energy. The systems approach has contributed greatly to these situations. The Dynamic Stochastic General Equilibrium (DSGE) model [14] is one of the significant achievements, which was developed in the economics community, but whose structure conforms to optimal control theory and has been used in central banks in advanced countries. In Europe, the agent-based economic model EURACE [15] was developed because the DSGE model cannot handle

• Surveillance technology base for model analysis and strategic planning in international affairs

• Aging population in developed countries and China • Rising urban population ratio • Expansion of personal power • Increasing social fragmentation

(continued)

• Measurement technology for social events • Deepening of modeling technology • Technology for searching and forming policy proposals

• Response to an aging society • Modeling of human behavior with a low birth rate (securing (consumption behavior, social welfare costs, information diffusion, establishing a medical and opinion/formation, epidemic nursing care system, responding phenomena, overconcentration) • Real world simulation with to young people) • Regional revitalization (town, millions of agents person, work)

• International order change analysis

Examples of expectations for systems approaches

Society

• Plan and build a security system • Acquire international influence

Trends in systems approaches

• Response to government budget • DSGE model, EURACE, • Deepening modeling deficits artificial market (finance, labor) technology • Innovation of labor productivity • Environmental policy planning • Technology for exploring and and creation of new industries model, energy system planning forming complex policy and operation proposals • Strengthen public infrastructure • Consensus planning and • Energy security and stable building supply

• Emergence and hegemony of new powers (China, India) and transition to state zero • Terrorism, proliferation threat increase • Spread of nationalism

Politics

Japan interest

Economics • Globalization and formation of the Asian century • A frequent economic crisis • Increasing climate change

World interest

Field

Table 1 Trends of systems approaches with societal interests

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World interest

Japan interest • GSS, ESD&SYS (EDSE)

Trends in systems approaches • Building an vision of Super Smart Society

Examples of expectations for systems approaches

DSGE Dynamic Stochastic General Equilibrium, GSS Global Systems Science, ESD&SYS (EDSE) Engineering Systems Design & Systems Science (Engineering Design & Systems Engineering)

Technology • ICT, machinery and production • Creation of Social Innovation technology, resource by Science and Technology management technology, health (Super Smart Society) management technology, biotechnology, space development, and utilization technology

Field

Table 1 (continued)

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the events caused by complex information structures. In SICE, efforts on artificial market modeling and experimentation are remarkable. Also, socio-technical efforts such as policy analysis in the energy, economy, environment, and safety facets and development of distributed cooperative energy management systems are underway. In this way, very remarkable activities have been made based on systems approaches, but some of them are limited to qualitative discussions. These will require further progress, and in addition, it will be necessary to approach how to formulate specific evaluation measures. With the liberalization of electricity, interests in the institutional design are growing. From a system approach perspective, the following are expected: The full exploitation of the enormous computing power is to bring about a better and more complex institutional system that humans can hardly handle. • Social field: For Japan, the declining birthrate and aging population are social issues ahead of the world. Appropriate system planning for social welfare and elderly care is what everyone wants, and how to survive and regenerate a region in danger of extinction. Various social phenomena have been modeled, including multi-agent simulation, but more powerful approaches are needed to meet actual social demands. Let’s look at an example of regional revitalization. The national government has requested prefectures and municipalities throughout the country to formulate a regional population vision and regional version strategy for regional revitalization. As a result, 99.8% of all prefectures and municipalities completed the formulation at the end of 2015. It can be taken that a great effort was made. For strategic planning, the national government has developed and provided a regional economic, tourism and population database called the Regional Economic and Society Analyzing System [16]. It is very important to effectively utilize such a nationwide database to guide the appropriate direction of regional development. • Technical field: The 5th Science and Technology Basic Plan, which sets the direction of science and technology policy for five years from fiscal 2016, is underway. Here, it is said that the nation should develop a new mechanism that will lead to a “Super Smart Society” in which new values and services are created one after another. Specifically, service and business systemization, system sophistication, and collaborative operation between multiple systems are listed, but the concrete picture of the “Super Smart Society” has not been clarified. For those involved in system science and technological research and development, drawing this picture is very important. On the other hand, global trends are focusing on the evolution of system science and technology itself. The next section describes these typical approaches.

2.2 Examples of Current Challenges in the Systems Approaches With expectations for effective systems approaches based on social demands, Table 1 points out the measurement of social systems, design of social system configuration,

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and other facets in addition to modeling of social systems. Let’s examine exemplified projects which are in progress. (1) Global Systems Science [17] The European Union research program Horizon 2020 (2014–2020) has a framework called FET (Future Emerging Technologies). Global Systems Science (GSS) was taken up in 2014 as a research project that was positioned as an exploratory and incubation stage. GSS is aimed at climate change, global finance, urban growth (urbanization and migration), and so on, clarifying the inherent characteristics and questions, and inferring appropriate consequences of decision-making by reflecting the experiences of practitioners combined with advanced computational technologies. The budget of 10 million euros was allotted over two years. According to the GSS website, 17 topics (infectious diseases, conflicts, financial and economic crises, social innovation, urban transport, policy modeling, ecosystems, urban development, non-equilibrium social models, etc.) have been working domains. This research has been handed over to the theme of New Science for a Globalized World since 2016 [18]. A research project called FuturICT [19] was planned ahead of GSS, with D. Helbing at the Swiss Federal Institute of Technology Zurich as the leader. The aim was to acquire a project of 1 billion euros in 10 years, called FET-Flagships of Horizon 2020. The proposal included a global social simulator that integrated the economy, society, and environment, a crisis monitoring system for socio-economic activities, and a decision-making platform that visualized the consequences of policy options and supports discussion between policymakers and citizens. This proposal was not adopted as a Flagship, but some of them were inherited by GSS. (2) NSF Engineering Systems Design [20] & Systems Science [21] The National Science Foundation (NSF) has been supporting researches in system sciences and technologies as the Engineering Systems Design (ESD) and the Systems Science (SYS) Programs since 2015. The standard support amount is 500,000 dollars per research. In 2015, 29 researches were adopted by ESD and 16 researches were adopted by SYS. ESD is supposed to support new engineering and system design methods and basic research that lead to practice in a global context (fields such as energy and cyberphysical systems, cross-sections of economy, social policy, environment, etc.). The following are examples of research. • Design method for X: Here, X assumes a specific field such as energy or system characteristics such as resilience. – Information and communication technologies that should be incorporated into systems engineering: visualization, human–computer interaction, social networking and network collaboration. – Formalism and algorithms related to modeling: Formalism for expressing and manipulating shapes, functions and behavior, algorithms for analysis, simulation, optimization and inference.

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– Integrated framework for design and systems engineering evolution: A framework that integrates individual models, model formalism, algorithms, etc. On the other hand, SYS supports basic research that provides a theoretical basis for design and systems engineering. In particular, SYS seeks to develop exploratory models for design and systems engineering with the background of probabilistic theory, decision theory, game theory, organizational sociology, behavioral economics, cognitive psychology, and so on. The following are shown as research examples. • Process (search strategy, guidance, and control): Clarify the strategy, guidance, and control for efficient design and systems engineering which consists of a huge number of analysis and synthesis processes. • Organization (division, communication, and motivation): Resolve issues in organizational sociology such as problem division, inter-organization communication, and organizational motivation in a form suitable for the organization. • Modeling (model generation/use/evaluation): What modeling formalism should be used, what is the cognitive structure of modeling, and how to reuse and share models. • Research methodology: Evaluation measures and measurement methods for expressing good design and systems engineering. Paredis [22], the program director, summarizes the activities of ESD and SYS in Fig. 1. The background is the concept of value-driven design (VDD) [23]. In VDD,

Fig. 1 Systems engineering model in NSF [22]

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design and systems engineering pursue the creation of artifacts that maximize value opportunities determined in a global context, rather than simply satisfying customer demands. For example, as a global context, soaring fuel (economic), climate change (environmental), high-performance motors, and batteries (technology) will enable electric vehicles as new value opportunities. The global context has always been reborn, and researchers must respond to this. As a result, systems engineering and design methods evolve according to the theoretical and ICT technology infrastructure. Practitioners must adopt the accomplishments of ESD and SYS in their business. Figure 1 depicts this evolving dynamics. Since 2017, EDS and SYS are merged into the Engineering Design and System Engineering (EDSE) Program with the Design of Engineering Material Systems (DEMS) Program [24]. Currently, EDSE supports 61 researches. (3) SE Vision 2025 The International Council on Systems Engineering (INCOSE) is an international organization fostering systems engineering. In 2014, they announced “Systems Engineering Vision 2025” [25], which looks to the next 10 years. Here, the achievements of systems engineering so far are described, and future challenges for 2025 are listed. • Recognition of the current situation: Systems engineering has continuously responded to the increasing complexity of systems for many years. Although it has gained a certain level of evaluation through industry, academia, and government, its practices differ depending on the industry, organization, and system type. These practices are still based on experience, but a theoretical basis is being developed. Mutual development between industries through the practice of systems engineering is progressing slowly and reliably. However, global demand goes far beyond advances in systems engineering. The integration of fields, development phases, and projects is the key to system engineering development. • Challenges: The keywords for application, innovation, and foundation are as follows. – Application: Ensuring system engineering practices and infrastructure robustness through application across industrial fields, supporting decision-makers by applying systems engineering to policy fields – Innovation: Understanding complexity (definition of measures), strengthening using ITC, collaborative engineering, design theory in the System of Systems (SoS), multi-participant architecture, resilient architecture, cybersecurity, decision support, virtual engineering – Foundation: Creation of theoretical foundation (including not only science but also humanities and social systems), knowledge system development, crossdisciplinary theory construction.

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3 Systems Approach to “Super Smart Society” The 5th Science and Technology Basic Plan, which would be the basic policy of science and technology development for the next five years starting from April 2016, was approved by the Cabinet in January 2016. Recognizing that science and technology are changing the society, this plan has four goals: sustainable national growth and autonomous development of local communities, and rich and high-quality life satisfying safe and security, appropriate response to global issues and contribution to global development, continuous creation of knowledge assets. With the recognition that the revolutionary age by advanced ICT is approaching, it is said that the plan aims to realize the “Super Smart Society” ahead of the world. The “Super Smart Society” means that everyone can enjoy a high-quality and comfortable life by receiving delicate and attentive services based on the advanced integration of cyberspace and physical spaces, systemization of services and businesses, advancement of systems, and integration of multiple systems. The series of initiatives geared toward realizing this ideal society are now being intensively promoted as “Society 5.0” which comes after hunter–gatherer society, agricultural society, industrial society, and information society. During the drafting of the Basic Plan, it was observed that there was a strong interest in the “systems” in the Cabinet. For this reason, in September 2015, the “Super Smart Society Co-creation WG (Chairperson: Dr. A. Maeda)” was set up in September 2015 as a place to consider what contribution SICE can make to the Basic Plan. Issues such as the importance of reference models and their architectures for integrating heterogeneous systems and the need for a technical infrastructure that tightly links physical systems in real space and virtual systems in cyberspace were extracted. This result was published as a public comment on the Basic Plan draft [26]. In the following, based on these activities, it will be described how to create and embody the “Super Smart Society”. (1) Development Plan of Social Systematization in the Basic Plan In the Basic Plan, the systemization of society is assumed to be realized with the development of 11 leading systems (optimization of the energy value chain, intelligent transportation systems, new manufacturing systems, etc.) specified in the previous year’s policy, the Science and Technology Innovation Comprehensive Strategy 2015. In addition, it is going to build a common basic platform (the Super Smart Social Service Platform) that enables cooperation and coordination of these multiple systems. Assuming these 11 leading systems are based on cyber-physical systems, the entire system including the service platform is drawn as shown in Fig. 2 [27]. (2) Development Plan of the Reference Model As envisioned in the Basic Plan, a bottom-up approach to building a service platform from concrete systems is essential. On the other hand, a top-down approach seems to be very important to gain the support of the world as well as the domestic market.

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Service platform for Super Smart Society Service functioning & integration Cloud System C

System A

Data storing / retrieval, application People

Organization Component Component Component Component

Internet

S

A P

S

A P

P : Physical system S : Sensor

C

C

C

C

Field system

S

A

S

P

A P

C : Cyber system A : Actuator

Fig. 2 Conceptual diagram of the Super Smart Social Service Platform proposed by the 5th Science and Technology Basic Plan [27]

Drawing the “Super Smart Society” from the top-down requires the convergence of wisdom and knowledge from various directions, and it is appropriate to take a systems approach. Figure 3 shows the outline of the approach that references the Cyber-Physical Systems Framework by NIST [28]. On the left-hand side are the 11 leading systems that form the Super Smart Society envisioned in the Basic Plan. The sum of the concerns of the participants involved can be said to be the requirements for the Super Smart Society. First, it is necessary to extract these concerns. The figure illustrates the requirements as a social system, the feasibility as a business, functions such as measurement, control, and learning, security, and life cycle. With these requirements, in mind and referring to the generalized model for service systems [29], it will be drawn a picture of a system in the Super Smart Society. In this paper, it is assumed that by considering the use, construction/operation, and quality assurance, integration of these results would derive a basic reference model for systems and platforms in the Super Smart Society. (3) Experimental Study and Derived Key Factors To draw the Super Smart Society, it is necessary not only to call for system scientists but also to involve the field of social science and those who are working in the

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Hospitality system Smart food chain system

Function (measurement, control, learning) Safety

Life cycle (persistence / evolution)

Realization

Assurance

Quality target / quality measurement / reliability estimation / ...

Interest categories

Business

Requirement

Use case / function requirement / Non-function requirement / ...

Society / cocreation (cooperation)

Design / manufacturing / operation / disposal /

11 leading systems Optimization of energy value chain Global Environmental Information Platform Infrastructure maintenance / update Natural disaster resilient society Intelligent transportation system New manufacturing system Integrated material development system Regional comprehensive care system

Super smart Super smart Super smart society society society service service service system system system (quality (construction (Use) / management) assurance)

Smart production system Super smart social service system (platform) reference model

Fig. 3 Framework for development of the Super Smart Social Service Platform

development of the 11 leading systems. Prior to such multi-disciplinary efforts, it is important to simulate in advance what kind of content can be obtained. The results of a preliminary experiment will be shown. Considering all the 11 leading systems that make up the Super Smart Society requires a lot of labor, so that representative systems including optimization of the energy value chain, intelligent transportation systems, and new manufacturing systems were taken up and examined. The results are shown in Fig. 4. The main stakeholders in each system were identified, and it was examined why society needed each system and what its fundamental mission was. Each system has its own unique requirements, but, in general, the following are common as key factors for promising systems approaches: “Provide a participatory knowledge circulation place” and “Strengthen systematization capabilities in the IoT era”. It was concluded that these two were platform candidates for the Super Smart Social Service Systems. • Provide a Participatory Knowledge Circulation Place: Every system requires the construction of hypotheses and experimental efforts at various levels of stakeholders. In order to discover sustainable consumption and new promising business opportunities, people conduct simulations in various ways. It is important to provide an information environment, a participatory knowledge circulation place,

Key Factors for Promising Systems Approaches …

Society / co-creation (Cooperation)

Energy Value chain optimization

Intelligent transportation system

New manufacturing system

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Business stakeholders

- Environmental, stable and low cost energy under uncertainties in foreign prices and technology development

- Consumer - Supply and distributor - Equipment manufacturer

- Superiority as an industrial base - Care for traffic vulnerable - Efforts to address environmental resource issues

- Mobility consumers - Mobile service providers - Mobility equipment manufacturer - Energy distributor

- Continuous value creation (network, advanced science adoption) - Responding to emerging markets

- Supply chain consumer - Supply chain supplier - Demand and supply information broker

Platform candidates (Key factors)

- Provide a participatory knowledge circulation place for safe consumption and discovery of new business

- Strengthen systematization capabilities (planning, building, coordinating, and managing multiple layers) in the IoT era

Fig. 4 Experimental study for the development of the Super Smart Social Service Platform

for conducting this simulation and deepening mutual stakeholders’ understanding. Figure 5 shows such a system with specific targets in the energy domain [30]. • Strengthen Systematization Ability in the IoT era: Needless to say, the leading 11 systems have IoT in them as well as a System of Systems (SoS) structure. In order to design these systems, the ability to adapt to new characteristics such as IoT and SoS is required. In addition, the social context in which value-driven design is of interest must be fully addressed. Figure 6 shows an example of system recognition in a global context shown by de Weck et al. [31] As shown in this figure, the target system needs to be recognized with multiple layers. It is desirable to develop a design theory that seeks social institutions and technology in an integrated manner. In the field of system sciences, initiatives for institutional design have begun, such as mechanism design, and furthermore, the challenge of system configuration theory based on increasing computational power [32, 33] is underway. These trends foresee the emergence of new design theories.

4 Concluding Remarks In order to give a bird’s-eye view of the issues in the systems approaches related to social systems, global trends in social systems approaches have been discussed

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Simulation needs

- Simulation results (system configuration) - Simulation needs survey

Model base for energy domain Environment

Citizen

Technology

Management services

Modeling & simulation services

Economy

Enterprise

Curator Simulation bas (domain ontology)

Government

Database for energy domain Materials

Researcher

Transportation

Behavior on energy value chain

- Domain ontology - Data model - Methods for data and models (optimization / identification)

Technology External DB

Real- world data

Production Consumption

Fig. 5 Participatory modeling and simulation place for energy domain [30] SYSTEM BOUNDARY 3

Fossil energy sources

Refineries

Energy policy

Renewable Energy sources

Power plant

Emissions

Supply chain

Gasoline stations

Infrastructure policy

Electrical grid

Recharging stations

Emissions

Gasoline vehicles

Tax policy

Electric vehicles

SYSTEM BOUNDARY 2

Fig. 6 Multi-layered recognition and synthesis of systems [31]

Range

Range

SYSTEM BOUNDARY 1

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and the required systems approach for the “Super Smart Society” proposed by the 5th Science and Technology Basic Plan, have been examined. Preliminary study has shown that two candidate platforms as successful key factors for the systems approach: Provide a participatory knowledge circulation place, and strengthen systematization capabilities in the IoT era, are promising and important for the Super Smart Society. Generally, it is desired to develop new social measurement methods as well as design theories in order to innovate the process of recognizing and managing complexity simultaneously. The advances of ICT will be expected to enable the realization of completely new systems approaches. Acknowledgements This paper is backed by discussions at the SICE “Super Smart Society Cocreation WG” and the Systems and Information Division “New Systems Approach Study Group for Realizing a Safe, Secure, and Comfortable Society (Smarter World)”. It goes without saying that the censure is to the author, but I appreciate the meaningful discussion. In addition, a part of this was supported by KAKENHI (No. 25240049). I would like to express my gratitude here.

References 1. T. Miura, T. Hamaokax, Introduction to Modern Systems Technology, Ohrmsha (2014) (in Japanese) 2. J. Kondo, Memorandum for systems engineering. Jap. Soc. Aeronaut. Space Sci. 17(181):77–84 (1969) (in Japanese) 3. Y. Fukushima, Lessons from PPBS and a road to policy science. J. Oper. Res. 25(5), 285–296 (1980) (in Japanese) 4. M. Ikeda et al., Study on SD in Japan and new directions. J. Japan Chap. Syst. Dynamics Soc. 7, 1–16 (2008). (in Japanese) 5. A. Ichikawa, Evolution of system science and technology in the society of instrument and control. Instrum. Control 50(8/9), 719–722 (2011). (in Japanese) 6. S. Tanaka, Roles and recent activities of TC systems engineering. Instrum. Control 50(8/9), 722–723 (2011). (in Japanese) 7. SICE (the Society of Instrument and Control Engineers), Special issue—Society directed by social simulation and service systems. Instrum. Control 52(7) (2013) 8. M. Funabashi, Social Issues requiring systems approaches. Instrum. Control 55(8), 665–670 (2016). (in Japanese) 9. NIC (the National Intelligence Council) Global Trends 2030, Alternative worlds (2012), https:// globaltrends2030.files.wordpress.com/2012/11/global-trends-2030-november2012.pdf 10. NIC (The National Intelligence Council) Global Trends, Paradox of Progress (2017), https:// www.dni.gov/files/documents/nic/GT-Full-Report.pdf 11. The Economist, Megachange: The World in 2050, Economist Books (2012) 12. CRDS/JST (Center for R&D Strategy, Japan Science and Technology Agency), in Panoramic View of the Systems Science and Technology Field, CRDS-FY2015-FR-06 (2015) 13. KAKEN, The development of agent-based simulation methodology for empirical research of international relations, https://kaken.nii.ac.jp/ja/grant/KAKENHI-PROJECT-24243023/ (2012) (visited on 30 Dec 2019) 14. L.J. Christiano, M. Eichenbaum, Nominal rigidities and the dynamic effects of a shock to monetary policy. J. Polit. Econ. 113(1), 1–45 (2005)

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15. M. Helcombe et al., Large-scale modeling of economic systems. Complex Syst. 22, 176–191 (2013) 16. RESAS Home Page (2015), https://resas.go.jp/#/13/13101 (visited on 30 Dec 2019) 17. GSS Home Page, http://global-systems-science.eu/ (2014) (visited on 30 Dec 2019) 18. European Commission, in Future and Emerging Technologies, Horizon 2020 Work Programme 2016–2017 (2015) 19. FuturICT Home Page, http://futurict.inn.ac/ (2012) (visited on 30 Dec 2019) 20. ESD Home Page, in https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=13340&org=NSF (2015) (visited on 30 Dec 2019) 21. SYS Home Page (2015), https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504788& org=NSF (visited on 30 Dec 2019) 22. C. Paredis, Program overview: engineering & systems design (ESD) systems science (SYS) (2014), http://www.nsf.gov/eng/cmmi/documents/NSF_ProgramBriefing_v1.13_201 41202.pdf 23. B.D. Lee, C. Paredis, A conceptual framework for value-driven design and systems engineering, in 24th CIRP Desgin Conference (2014) 24. NSF (the National Science Foundation) Dear Colleague Letter: Announcing Creation of the Engineering Design and Systems Engineering (EDSE) Program which Merges and Replaces the Engineering and Systems Design (ESD), System Science (SYS), and Design of Engineering Material Systems (DEMS) Programs (2017), https://www.nsf.gov/pubs/2017/nsf17146/nsf 17146.pdf 25. INCOSE, in Systems Engineering Vision 2025 (2014), http://www.incose.org/AboutSE/sev ision 26. SICE (the Society of Instrument and Control Engineers, Special Issue—Systems technology for realizing society 5.0. Instrum. Control 55(4), 282–302 (2016) 27. Cabinet Office, in Reference materials for the 5th science and technology basic plan (2016). https://www8.cao.go.jp/cstp/kihonkeikaku/5siryo/5siryo.html (in Japanese) 28. NIST (the National Institute of Standards and Technology), Framework for Cyber-Physical Systems, Release 1.0 (2016) 29. M. Funabashi, A reference model for service systems building transdisciplinary research community, in Proceedings of the Third Asian Conference on Information Systems (ACIS2014) (2014), pp. 465–471 30. CRDS/JST (Center for R&D Strategy, Japan Science and Technology Agency), in Progress Report: Towards Realization of System Building-type Innovation, CRDS-FY2013-XR-03 (2013) 31. O.L. de Weck et al., Engineering Systems, Meeting Human Needs in a Complex Technological World (The MIT Press, Cambridge, 2011) 32. H. Aoyama, K. Oizumi, T. Koga, Design Management with structural analysis of the product systems. Oukan 10(1), 22–37 (2016). (in Japanese) 33. S. Hasebe, Process synthesis using superstructures. Oukan 10(1), 38–46 (2016). (in Japanese)

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Dr. Motohisa Funabashi graduated from the Department of Applied Mathematics and Physics, the Graduate School of Engineering, Kyoto University in 1969, and joined Hitachi, Ltd. and worked for R&D on systems control at the Central Research Laboratory and the Systems Development Laboratory (1969– 2010). He was a Board Member (1992–1994) and a Vice Chairman (2003–2005) of the Society of Instrument and Control Engineers (SICE). Also, he served as a Visiting Professor, the Graduate School of Mathematical Sciences, the University of Tokyo (1996–1999), a Visiting Professor, the Graduate School of Informatics, Kyoto University (2003–2008), an Auditor of the National Institute for Environmental Studies (2007–2011), the Secretary-General (2009–2014) and a Director (2009-present) of the Transdisciplinary Federation of Science and Technology, a Senior Professor of Japan Advanced Institute of Science and Technology (2012–2017), and a Program Officer of the Low Carbon Technology Development and Demonstration Program of the Ministry of the Environment (2017–2020).

Growing Systems in Smarter World Hiroshi Kawakami

Abstract From the viewpoint of human–machine systems that regard artifacts and humans as a total system, this article discusses the direction of developing adaptive systems. The relation between artifacts and human is expected to develop from the phase of substitution to the phase of cooperation, but the adaptive systems are still in the phase of substitution. For predicting the new phase of adaptive systems in the near future, this article focuses on the natural biological phenomena called mutual growth. Based on the insights that the biological growth can be seen as objective phenomena of leaving traces of interaction, and that the interaction between artificial systems and humans involves human efforts, artificial systems are required to force users efforts in order to be grown mutually. The mutual growth is represented in a spiral structure. For establishing this structure, the direction of mutual growth is set to be the direction of the central shaft of the spiral and is determined based on the ecological notions. Namely systems are grown not toward enlarging with ingestion, but toward shaping up. Keywords Growing system · Smarter world · Super Smart Society · Systems science · System of systems · Adaptive system · Systems science

1 Introduction Looking at recent trends in adaptive and evolutionary systems, it is easy to foresee that adaptive systems, which change their behavior in the direction toward adapting to the environment and their users, permeate in daily life in the near future. It is Adapted from Hiroshi Kawakami “Growing System in Smarter World (written in Japanese),” Journal of the Society of Instrument and Control Engineers, Vol. 55, No. 8, pp.671–674 (2016). Partly reprinted by permission of The Society of Instrument and Control Engineers. H. Kawakami (B) Kyoto University, Sakyo, Kyoto 606-8501, Japan e-mail: [email protected] Kyoto University Advanced Science, Ukyo, Kyoto 615-8577, Japan © Springer Nature Singapore Pte Ltd. 2021 T. Kaihara et al. (eds.), Innovative Systems Approach for Designing Smarter World, https://doi.org/10.1007/978-981-15-6651-6_5

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expected to be a convenient life but if the direction of the development is wrong, the relationship between humans and systems can drastically shift in an undesirable direction. For example, the autonomous driving of automobiles is a topic to be discussed even now. If automatic driving becomes possible, under the social structure of system of systems (SoS) [1], a layer different from the technical aspect (e.g., the layer of security aspect) may declare that “the society prohibits manual driving because it is dangerous when it is mixed with automatic driving.” In that case, automobiles would be mere means of transportation, and those born in that era would not be able to imagine that driving was one of the leisure activities. A similar scenario can be drawn for systems that change their behavior. Namely considering only the reduction of human load and seeking smartness in that sense, unexpected impacts from different layers of the SoS may occur that change society toward unintended (in many cases, bad) direction. It is important to foresee how the “systems that change their behavior” in the near future are. The phenomenon of changing behavior is related to such keywords as adaptation, evolution, learning, and growing up. Among them, this article employs “growing up” as a keyword for examining Smarter World that is in a different direction from seeking only human load reduction.

2 Super Smart Society Versus Smarter World As frequently described in other commentaries in this special issue, the fifth Science and Technology Basic Plan for Japan [2] was approved by the Cabinet in early 2016. Among them, Super Smart Society (Society 5.0) is advocated. On the other hand, in January 2014, Smarter World research study group (abbreviation) was established in the Systems and Information Department of Society of Instrument and Control Engineers (SICE). Both Super Smart Society and Smarter World are coined words to image the coming society after the so-called smart society. From the sense of the word, Smarter World aims to make the world smarter in the same direction as the vector of the current smart world. On the other hand, it sounds like Super Smart is not assumed to be in the same direction as the current smart as long as the Super Smart exceeds the current smart. But in fact, the opposite is true. Super Smart Society is assumed to be based on AI and big data as basic technologies with utilizing the development of ICT and cyber-physical systems (CPS) including IoT. In the society, the range of autonomy and automation will expand to the area where they have not yet spread sufficiently. It is considered that the society will create new value and service, and the technology is assumed to be developed in the same direction as the current vector. On the other hand, Smarter World adopts the concept of a system of systems (SOS), and the viewpoint of looking at a system as a rhizome, instead of the general viewpoint that looks systems as trees where a system can be recursively decomposed into subsystems. In addition, Smarter World focuses on the spiral approach. Therefore,

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it is impossible to say that Smarter World will be placed on ahead of the vector of current technological development. This paper examines the difference between the meanings of super smart and smarter, using the keyword “grow”.

3 Yet Being Human-Centered 3.1 Being Interactive One of the differences between what the words “adaptation, evolution, learning” imply and the word “grow” implies is that whether traces exist or not. The trace of interaction makes us feel growth. Traces left on system side: Being physical would make it easier to leave traces. The difference between a wellworned house and a just old one is that traces of the maintenance remain. The change of texture and color of leather products are also traces of interactions with users. Some people are particular about adding tea stains to teacups. In order to leave a trace in cyberspace where physical presences cannot be expected, there is no other way except for creating them artificially by designers. A product called “Tamagotchi” had become popular in Japan. It was a bringing up simulation game on egg-like mobile hardware. Like this game, it is possible to leave traces of interaction on virtual creatures. But the traces were deliberately made. They are only within the expected combination range. This is not “personalization” as Norman calls in emotional design [3]. Traces left on user side: Norman, who made an epoch of usability design in The Psychology of Everyday Things (POET) [4], is one of the flag makers of human-centered design. Yet, in 2005, he declared “human-centered design is considered harmful [5]” and submitted the notion of activity-centered design. The notion sounds an alarm bell over designs that ignore the ability of humans to adapt to their environment including tools. This can be seen as criticism against the situation where adaptive systems are designed without criticism by the wrong understanding of human-centered design. As of 2020, searching “adaptive system” on Web results in finding only the systems that adapt to people. Searching “adaptable system” is also in a similar result. Still nowadays, systems that emphasize making it possible (or easy) for humans to adapt are rare. Among the traces that are left on the human side by human–system interactions, some are difficult to handle in engineering such as “memories.” However, adaptation and learning can be quantified to some extent by measuring performance.

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Personalization as a trace of interaction: In the same year as the activity-centered design was submitted, emotional design [3] has been published as what overlooked in POET [4]. “Personalization” is mentioned as one of the important issues in this research. Rather than “customize,” which is simply combining the available options or changing the provided parameters, “personalization” is selected as a key issue. To understand the author’s intention in this word, the author’s friend’s episode helps us. To make it personalized, his friend scratched the newly bought mobile terminal on the gravel. The device got wound uniquely in the world, but the friend only felt sad. To be personalized, the traces need to be the mark of long user–system interaction. On the other hand, “just scratching” was too shallow to be the user– system interaction.

3.2 Enabling to Consume Users Time and Effort Interactivity is based on the premise that users interact with the system. That consumes users’ time and effort. There may be some areas of research where it would be better to treat the consumption of time and effort as uniformly negative. In those areas, technologies are developed in the direction of eliminating such consumption. On the other hand, in the area where the emphasis is placed on interactivity, technologies should not be developed in such a simple direction. However, in general, if technology development progresses and once it becomes possible to save time and effort, the technology tends to be widely applied without considering the abovementioned differences between areas. But in the area where human commitment is essential, technology development should not hinder consuming time and effort. Reassurance and safety: At the end of the last century, for discussing how human interact with artifacts, the problem of partial automation is being studied in detail [6]. Regarding the causes of aircraft accidents, etc., the following problems were discussed: • • • •

transformation of human tasks, opportunity loss of “on the job training,” declining motivation and skill for tasks, and getting difficult in situational awareness.

By investigating the causal relationship between elemental factors of these problems, such fundamental bases of the problem were detected as “automation” and “partiality”. They are independent. In other words, to reduce accidents in a human–machine system, we can focus on “automation” or “being partial” as the culprit of problem occurrence.

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Setting “being partial” as the culprit is pursuing complete automation. However, this policy has not solved the problem even now. In these days, autonomous automobiles still cause traffic accidents. On the other hand, there is a policy to setting “automate” as the culprit. Even if a task can be technically automated, this policy dares to leave the operation to the user. This policy designs the human–machine systems so that the user can recognize the situation of the whole system and can commit to operational tasks. When a system of automation systems is complicated, recognizing the situation of the system as a whole is difficult. Unexpected situation happens at unexpected timing and surprises users (automation-induced surprise [7]). If users are feeling safe, the burden on the users is heavy when the situation gets into be unsafe. There is a policy for designing human–machine systems that dares not to reassure users. The policy places always on the users the most important task of comprehending and managing the entire system. That is one way to ensure safety. In this case, interaction is important for a user to be an operator, not just be a watcher. System grown by interaction: Let us analogically consider that “user is parent and system is a child.” The state in which the system automatically changes (adapts to the environment) without the user’s commitment (consuming time and effort) cannot be said to be a “growing” state. What we call system includes widely social systems. In the fifth Science and Technology Basic Plan for Japan, Super Smart Society is defined as a “society in which necessary goods and services are provided to those who need them and when they need them.” It seems that there is a unidirectional scheme in which society just provides and people just receive. Conversely, in the case of a “growing” system, the commitment of people to the social system is essential, and it is not unidirectional. As a result of the commitment, the social system changes (grows), and the growth of the social system is felt by the human side. Although it is difficult to develop beyond the current social structure, it is also difficult to imagine that the reality of the “world where people live livelily,” which is aimed by Super Smart Society, is the world where society just provides people with goods and services in one direction.

4 Designing Growing System There is a view that the concept of “design” is clearly established in the age of modern design that is based on industrialization in the nineteenth and twentieth centuries [8]. It shows the historical view as shown in Fig. 1. At first, the user of a tool was the one who produced it. Next, the people who use and those who produce were differentiated. Finally, thinking and making were differentiated, then designers emerged.

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Fig. 1 In the view that design was conceptualized in the age of industrialization, human tasks are considered differentiated

4.1 Design of the Mutual Growing Systems The division of labor between thinker and maker relieved the thinker of the constraints associated with making. Designers (thinkers) can concentrate on creative activities. Since industrialization has made mass production possible, we are freed from such local activities as thinking and making things for one’s own life. Thanks to this division of labor, in the twentieth century, society has been established in which many people felt materially blessed. It has been a long time since the merits and demerits of this industrialized society were discussed. The twenty-first-century design vision [9] released by the Science Council of Japan in 2003 includes the following recommendations: [Recommendation 2] Excellent artifacts are born from the continuous process in which producing (design and make) and using (live) are closely related to. The design process of the twenty-first century needs to expand significantly from making to nurturing.

It can be said that there is a basic scheme in which unidirectional relationship is between people and systems where people create and nurture systems. Nevertheless, I want to pay attention to that the keyword “nurture” is used. Such artifacts as Japanese gardens, for example, have been expected to keep, maintained by owners for a long time. Such artifacts as good old-fashioned European houses, for example, have not just an antique value, but a value that has been maintained for a long time by hand. From the historical view presented at the beginning of this section, artifacts created before the emergence of designers have been used by many people over time and have functional and beautiful forms [8]. The metaphor of growing up is appropriate for those kinds of change of artifacts. Such growing artifacts are luxuries that are not readily available nowadays. Speaking of luxuries, some people predict that autonomous automobile will become the standard soon and having a manual automobile would become a luxury of rich people. In the society in which necessary goods and services are provided to those who need them, and when they need them, having a kitchen at home and cooking oneself can be luxurious. In Smarter World, it is desirable to have systems that allow people to work on their own, including raising systems.

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4.2 Growing Systems Focusing Not on Elements But on Relationship Being a super system (System of Systems: SoS): According to the definition of SoS [1], the subsystems that make up an SoS must be operationally and administratively independent. That is, each subsystem can work solely and be managed independently. These features differentiate SoS from the standard systems. Generally, either SoS or standard systems are composed of multiple subsystems. According to this definition, the present social system is already SoS, and the smart society is also SoS as long as it is not designed in a top-down manner. Therefore, there is no point in declaring it as SoS, and most of the so-called large-scale complex systems are SoS. A universal methodology applicable to design and manage SoS, in general, is desired. To construct a systems theory in the case where individual elements of the whole system cannot be regulated in a top-down manner, rather than discussing how individual component systems should be in terms of element reductionism, it would be appropriate to focus on the relationship among component systems. Introducing ecological notions: Speaking of relationship, Ecology is a natural science that discusses relationships between organisms and environments and serves as a reference when focusing on relationships. It is well known that the ecological approach to visual perception [10] and ecological interface design [11] introduced ecological notions to such research fields as cognitive science and systems science where element reductionism had been dominant. In these approaches, the matter related to “growing up” can be summarized as “not increase but decrease.” In the era of good old-fashioned AI (GOFAI), it was believed that the more knowledge and experience one has, the more intelligent one becomes. It is an increasing strategy. On the other hand, in the ecological approach, learning is said to be allowing each individual to selectively tune only to the appropriate ones among the myriad of “possibility of actions (affordance)” that emerge upon interaction with the environment. That is to say, reduction strategies have been adopted. Table 1 shows a part of the keywords that are given by the members of the Smarter World Study Group. They were asked to clarify the difference between “adaptive systems using the current AI” and “systems that can be expected to grow in Smarter World” by submitting keywords. Unexpectedly, there is a lot of words that relate not to increase but to decrease in the growing systems side.

4.3 Growing in Spiral Structure How to grow up: In Sect. 3.2, based on the analogy of “user : system = parent : child,” we discussed that users’ commitment to the system is essential to “grow.” In the section, we looked

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Table 1 Contrasting current adaptive systems and future growing systems. To capture the outline of the “growing system,” the differences in the image from “adaptive system,” which is similar in the sense of changing behavior, were enumerated by members of the Smarter World Research Group. On the left is an adaptive system based on the current AI, and on the right is the image of the growing system desired in Smarter World Adaptive systems Growing systems Keep enlarge Be big and improve performance Increase Increase options Ingest to inside Individual versatility Pursuit of self-purpose Customization Function

Narrow down Understand the taste of adults Purify Narrow boundary Utilize outside Embed in the environment Be altruistic Personalization Content

broadly at social systems, but similar structures (child and parent grow up at the same time by interactive efforts) can be observed in individual systems that are components of social systems. Figure 2 shows a schematic representation of this structure. In the research group of Smarter World, such spiral structures have been adopted as a basic scheme of the systems approach. The details of the spiral structure are mentioned in a separate article, and this article only points out that a spiral structure can also represent how the system and users are mutually grown. As shown in the left half of Fig. 2, when only one side changes even if there is a bidirectional relationship, it becomes distorted. As shown in the right half of Fig. 2, a spiral structure can be used to represent a growing state in which the two sides interact bidirectionally and change.

Fig. 2 A schematic representation of the situation where the system and user grow up together. As shown in the figure on the left, if there is a bidirectional relationship but only one changes, the structure becomes distorted. The state in which they interact and change (grow) in both directions can be represented by a spiral structure as shown in the figure on the right

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Growing to be a bit bad: It is also important to discuss the direction of the spiral structure, i.e., the direction indicated by the central axis of the spiral shown in the right half of Fig. 2. Driving the spiral requires human commitment. Analogically stating, if the system (child) is a “good boy” who grew up along the spiral structure without requiring human (parent) load, the spiral will stop moving. For example, in a fully automated factory (good boy) where people become observers from operators, the function of Kaizen stops moving. Here, the fact that they are not “good boy” is rather seen as an opportunity to create value together. “Good boy” actually deprives people of the opportunity to create value together. However, it is also unacceptable that they are too bad to coexist in society. To trigger value co-creation, systems should be moderately bad. We call it “a bit bad” here as a slogan. If systems pursue only the convenience and functionality of people and over-adapt to the user, they cannot be a bit bad. That is to say, the system design is required to take into account how users exert their abilities to grow up. Good services also require users’ commitment. For example, there is a view [12] that the state in which the master and the customer co-create the value is called “struggle.” There is a Sushi restaurant where the master (Aruji) asks, “what would you like?” even though there is no menu. Customers find value and pay for a restaurant with a menu written in difficult Italian that does not make sense for Japanese customers. The essence of such services is seen in struggles. These masters are not “good children” who unilaterally provide convenience and functionality to customers.

5 Conclusion Making a system smarter does not mean just reducing the human load. Based on this idea, this article discussed the system that adapts, evolves, and learns in Smarter World with the keyword “growing up.” The desirable relationship between the artifact and people in Smarter World can be summarized as follows: • It is not an adaptive type that grows up on its own, but requires human commitment. • It is not a one-way education from people to a system but a two-way (interactive) one. • Not just one person or system grows, but changes mutually.

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References 1. M.W. Maier, Architecting principles for systems-of-systems. Syst. Eng. 1-4, 267/284 (1998) 2. Fifth Science and Technology Basic Plan. http://www8.cao.go.jp/cstp/kihonkeikaku/index5. html, in Japanese 3. D.A. Norman, Emotional Design (Basic Books, 2005) 4. D.A. Norman, The Psychology of Everyday Things (Basic Books, 1988) 5. D.A. Norman, Human-Centered Design Considered Harmful, Interactions, July+August, 14/19 (2005) 6. O. Katai, Interaction between people and systems and intellectual support. J. Jpn. Soc. Artif. Intell. 13-3, 339/346 (1998) (in Japanese) 7. Studies highlight automation ’surprises’; Aviation Week, 48/49, Feb 6 (1995) 8. T. Ishida (ed.), Introduction to Design Studies (Kyoritu Shuppan, 2016) (in Japanese) 9. Science Council of Japan, Design Vision Proposal for Artificial Design and Production in the 21st Century. http://www.scj.go.jp/ja/info/kohyo/18youshi/1804.html (2003) (in Japanese) 10. J.J. Gibson, The Ecological Approach to Visual Perception (Houghton Mifflin, 1979) 11. K.J. Vicente, J. Rasmussen, Ecological interface design. IEEE Trans. SMC 22, 59/606 (1992) 12. Y. Yamauchi, Servise as Struggle (Chuokeizai-sha, Inc., 2015) (in Japanese)

Author Biography Hiroshi Kawakami is a program-specific professor at Kyoto University and a professor at Kyoto University Advanced Science, in Japan. He received his Bachelor, Master, and Dr. Eng. degrees from Kyoto University. He started his career at Okayama University as an assistant professor. He joined Kyoto University, where he was an associate professor of Graduate School of Informatics. His research interests include systems design, where he has proposed FUBEN-EKI that stands for designing systems based on appreciating “benefits of inconvenience.” He received best paper awards of Transactions of the Society of Instrument and Control Engineering (1990, 2001, 2013), the Transactions of Human Interface Society (2009, 2018), and Journal of Society of Automotive Engineers of Japan (2014).

System and Information. A Viewpoint Toward a Novel Systems Approach Hajime Kita

Abstract With progress in information technology as background, creation of Smarter World to solve various problems attracts attention. As well as various elementary technologies, novel systems approaches are also needed to construct society wide smart system as cyber-physical systems (CPS) or system of systems (SOS). For that we have to consider collaboration of science and technology both of system and information. In this article, with an overview of knowledge in the both fields, the author discusses several viewpoints as hints for novel systems approaches. Keywords Knowledge of system and information · Model · Treatment of information · System structure · Platform

1 Introduction Progress in science and technology has improved human life and expanded their activities rapidly. However, it also has brought about serious problems such as the explosion of population, environmental problems and poverty to the modern society as well. Recently, approaches of using information technologies to such problems with a keyword of ‘smart’ attract attention. Government of Japan proposes a vision of ‘Super Smart Society’ or Society 5.0 in the 5th Science and Technology Basic Plan with several applications as examples [1]. In this article, we call such vision as ‘Smarter World’ considering improving processes of smartness. Progress in information and communication technologies is the background of this vision. Technology forecast in integration on semiconductor known as Moore’s Law has been kept for long years. Such large progress of information technologies in computing, memory and communication that change their performances in number of digits has occurred. Now, the global computer network connected with the Internet and services using them has been emerged . Computers have expanded from organiH. Kita (B) Institute for Liberal Arts and Sciences, Kyoto University, Yoshida-Nihonmatsu, Sakyo, Kyoto 606-8501, Japan e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 T. Kaihara et al. (eds.), Innovative Systems Approach for Designing Smarter World, https://doi.org/10.1007/978-981-15-6651-6_6

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zational mainframes, to desktop personal computers and mobile terminals. Further, Internet of Things (IoT), a network of many machines with computers connected each other is appearing. It brought about cyber-physical systems (CPS) that connect the real world of physical processes and virtual world of information processes. It also expected to construct system of systems (SOS) to achieve higher functions by connecting separate systems. As aforesaid, progress in elementary technologies gives expectation to achieve Smarter World i.e., to construct solutions for social problems. One of key issues to achieve it is science and technology to construct cyber-physical systems or system of systems (SoS) as artificial and to create social values through their operate as novel systems approaches for Smarter World. However, it has not been well clarified as concrete subjects. So as to discuss such novel systems approaches, first we overview the knowledge of science and technology both in the fields of ‘system’ and ‘information’ based on undergraduate level curricular. Then, we pick up several viewpoints to consider science and technology of system and information to create ‘Smarter World.’1

2 Science and Technology of System and Information For achievement of the Smarter World, we have to recognize requirements to science and technology of system and information. First, we examine similarities and differences of the both.

2.1 Similarities of Science and Technology of System and Information Sciences and technologies of system and information are recognized close fields. For example, as the cybernetics proposed by Wiener [2] they shared viewpoints in their dawning. Science and technology of system have made progress along with availability of computer on the one hand, and on the other hand in applications of computers, viewpoint of system has been needed as shown in the word of ‘information system.’ A common feature of them is their generic nature. Transdisciplinary Federation of Science and Technology in Japan defines such science and technology as follows and promotes research in the field as a federation of relating academic fields [3].

1 Adapted

from Hajime Kita “System and Information. A Viewpoint toward a Novel Systems Approach (written in Japanese),” Journal of Society of Control and Instrument Engineers, Vol. 55, No. 8 (2016) . Partly translated by permission of The Society of Instrument and Control Engineers.

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Fig. 1 System and information, similarities and differences

Transdisciplinary science and technology are fundamental academic knowledge with logic as its normative principle and promote collaboration of various disciplines through integration of natural sciences, humanity, social sciences and engineering so as to create novel social values. [Supplement explanation] It includes disciplines such as statistics, simulation, optimization, informatics and design theory to treat society, human, environment, life and management of organization.2

In system science and technologies, target issue is abstracted through modeling as a system, and then generic methods such as mathematical approaches are then applied to it for analysis and synthesis. On the other hand, information science and technology such as computer provide generic method to handle information, such as collection, transfer, accumulation and processing of information. It also provides theoretical basis of computation. See Fig. 1.

2 This

quotation is originally in Japanese, and translated by the author.

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2.2 Difference of Sciences and Technologies of System and Information System and information are established as different disciplines. It means they have different knowledge. Science and technology of system have mainly treated physical systems, or systems as particular machine or closed one in, e.g., factory, and discussed them with modeling, simulation or optimization. They have also treated decision making of human connecting to social sciences such as economics and management. Further, since the movement of cybernetics [2], they also have possessed a viewpoint of investigating both artificial systems and natural living systems. Movement of evolutionary computation has focused ‘evolution’ and ‘emergence’ as mechanisms to be applied in construction of artificial systems. Major application fields of science and technology of system have been conventional engineering fields, and hence, science and technology of system have basic characteristics of mathematical approaches for quantitative treatment of target systems. On the other hand, science and technology of information have been sought implementation of computing and communication with digital technologies having physical foundation in electronics and construction of functions with software. Theoretical foundation of computation and communication has been also studied. Science and technology of information are also generic as well as those of system, and it cannot create social values without particular application in some fields. However, it also has disciplinary nature of constructing computer and information systems with basis of electronics. For example, architecture of information systems with hardware and software as core technologies has been discussed, and both the concepts of centralized systems and distributed systems have been attracted attention along with progress of technologies such as semiconductor and communication networks.

3 Knowledge of System and Information Body of knowledge in particular field is utilized through its internalization by scientists and engineers. Hence, undergraduate level curricular in the field gives an important view to discuss it because they provide scientists and engineers with foundation of their thinking. This section overviews knowledge in system and information referring to curricular in relating fields and discusses that they are closely related on the one hand, and but not well connected on the other hand. It gives hints to think about science and technologies for creation of smart societies.

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3.1 Knowledge Treated in Education of System Knowledge of science and technology of system may not be well established as an undergraduate level major subject. It may be taught in embedding rather concrete subjects such as electrical engineering and mechanical engineering. Recently, ‘design’ attracts attention as new subject in education [4, 5]. It also has generic nature as a subject similar to system science and technology. Here, we list topics included in the special issue ‘Think of Systems, Think with Systems’ in Journal of Society of Instrumentation and Control Engineers [6]. This issue is organized aiming at use for training of freshmen in graduate program in the system field and for that in companies in the field. System Modeling: Introduction of Dynamical Systems/System Structure/Discrete Event System/Decision-Making Theory under Uncertainty—From Economic Psychology to Integrated System Science/Object-Oriented Modeling System Analysis and Design: Optimization/System Thinking for Learning, Evolutional Systems/Simulation Technology/Hearts of Statistical Mechanical Approach/ Idea Generation Method System and Human: Reliability and Trust of Human-in-the-Loop Systems/Multiobjective and Social Games/An Introduction of Agent-Based Social System Theory—As a Basis for Problem-Solving/Managing Organizations. These show interests in system science and technology. ‘Object-oriented modeling’ has been picked up considering implementation as information systems, and ‘idea generation method’ has been picked up considering connection to recent trend in education of design. Instrumentation and system control have not been treated explicitly while they are important issues in this field. It was because similar special issues on these fields were issued in this journal. Further, above topics are picked up rather system science field, and we also consider more pragmatic knowledge in system engineering, e.g., [7].

3.2 Knowledge Treated in Education of Informatics In the field of informatics, Information Processing Society of Japan proposes standard curricular based on those proposed by ACM, and current version is called J17 [8]. Further Science Council of Japan also reported reference in curriculum design/development for disciplinary quality assurance in university education [9]. J17 proposes curricular in the following five fields: • • • • •

CS: Computer Science IS: Information System SE: Software Engineering CE: Computer Engineering IT: Information Technology

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Additionally, body of knowledge for general education of informatics (GE), undergraduate level education independent of major field is also proposed3 Among the above, curricular of CS is considered a core in the field, and it proposes knowledge areas as follows: Algorithms and Complexity, Architecture and Organization, Computational Sciences, Discrete Structures, Graphics and Visualization, Human–Computer Interaction, Information Assurance and Security, Information Management, Intelligent Systems, Media Representation, Networking and Communication, Operating Systems, Platform-Based Development, Parallel and Distributed Computing, Programming Languages, Software Development Fundamentals, Software Engineering, Systems Fundamentals, Social Issues and Professional Practice

It shows a curricular to learn stacking knowledge in hardware and software of computer technology with background theory of computer sciences.

3.3 Connection of Knowledge of System and Information We see intelligent systems and human interface as commonly appearing topics in education of both system and information. However, considering the future systems that tightly combine virtual and real worlds as CPS, we also have to see the topics that are paid little attention in either fields. Looking at curricular for informatics shown in J17, small attention is paid to ‘(feedback) control’ taught in control engineering and and ‘decision making’ taught in system science. In the curricular of J17, control is introduced only in CE, and decision making only in IS, but not well treated in the other areas. In other words, in curricular in science and technology of information, interest in utilizing information by human or by machine is not paid largely. The author has devoted himself in planning and operation of information systems in campus. Sometime, system engineers in charge of such system did not have a viewpoint of ‘closed system’ to confirm systems’ behaviors, while it is a common sense in the system field. On the other hand, in education in engineering except information area including science and technology of system, education of information systems and development of large-scale software are limited. In education of engineering, computer simulation and computer programming, numerical computation and algorithms as basis for simulation are commonly taught. However, teaching on information systems and development of software for them are not well considered to cope with increase in activities in virtual space. In Japan, basics of science and technology of information are commonly taught as general education in undergraduate programs. In 2015 and 2016, survey of such 3 From elementary and secondary education to general education in university in Japan, it puts small

importance on education of pragmatic subjects. Education of informatics is compulsory in junior and senior high school and one of such pragmatics subjects.

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courses was conducted [10, 11]. In the survey, topics taught in the courses were asked along the body of knowledge in general education (GEBOK) on information proposed by IPSJ. The survey results showed that information systems and modeling were not well treated while they were listed in GEBOK. While the reason why these topics are not taught well is unclear, and there may be restriction of class hours and level of content. The survey also showed that many teachers do not have background of informatics. Hence, it suggests that there may be another reason that such teachers do not have interests and knowledge in these topics. In thinking of novel system approaches, collaboration of the specialists in system and information is necessary. For promotion of such collaboration, to share their knowledge will be needed, and the author proposes the following: • In the field of information, commitment to the physical world will increase, and automation and optimization will be needed. Fundamental knowledge of control and decision making is intrinsic, and it will make collaboration with scientists and engineers in system field effective. • In the field of system, for collaboration with scientists and engineers in information to actively create expanding virtual world, scientists and engineers in system should have fundamental knowledge in design of information systems and development of large-scale software.

4 Viewpoints Toward Novel Systems Approaches So as to make Smarter World that tightly connecting virtual and real worlds with IoT and CPS, we need to seek novel systems approaches connecting knowledge in system and information. The authors propose several viewpoints for it.

4.1 Models One of the key practice in system science and technology is utilization of ‘models.’ That is, we construct models to understand targets and commit to the target through thinking with the model. In informatics, modeling is also an important practice. We have to recognize usage of models as important key practice to challenge Smarter World. For example, in ordinary X-ray imaging, we put X-ray on one side of the object to be investigated, and penetrating X-ray is detected on the other side, and develop a image of transparency. Sometime, we take several images by changing direction of X-ray for detail diagnosis. For further diagnosis, we use computer tomography (CT). In CT, ‘tomography’ is assumed as a ‘model’, and it is estimated through computation that fulfills con-

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straints of penetration in various directions. It should be noted that by introduction of tomography as a model to be estimated, we can construct such methodology. For creation of future Smarter World, we need to construct models considering the above way of thinking and utilize them. Agent-based model (ABM) is studied as a promising method to treat social systems. Concerning ABM, models can be categorized into three, i.e., abstract models, middle range models, and facsimile models, and usage of models is different by the type [12, 13]. Recently, studies of facsimile types that can be used in quantitative evaluation of concrete alternative policies are increasing. Progress in computer technology enables simulation of such type of large-scale models. However, in construction of such models, we face difficulty in model parameter estimation, i.e., configuration of many agents in the model, because of limitation of social survey due to cost for it and availability of data in detail gathered in survey. The authors propose a population construction method that fulfills several publicly available statistic results with random sampling under constraints [14, 15]. Availability of data is increasing due to open data policy by national and local governments, but it may not be possible to directly construct ABM with the data, and we need a systematic approach to construct ABM using available data.

4.2 Treatment of Information In information processing, total performance may be discussed quantitatively on the capacity of storage and processing speed. However, information itself should be treated separately except handling of copy. In use of information in society, we face many problems due to conflict among members in society, e.g., protection of privacy, organizational secrets, adequate use of intellectual properties in control of circulation and utilization of information, avoidance of plagiarism and establishment of traceability of information. Conventionally, technical limit such as difficulty in making copy, restriction of transmission and time constraints in processing have been naturally restricted of use of information and made control easy. In modern society, digitalization of information made its treatment easy and vast amount of information can be stored and processed. Public key cryptography provides infrastructure of secure communication over computer networks. However, we still need large manual operation in management and control of information, and we need to further systematic treatment of information to make it efficient.

4.3 System Structure In thinking of future systems that support society, a viewpoint of system structure is also important. A particular system is constructed seeking technical and economical rationality in a given environmental condition. However, evolution of such systems

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(a) Layer-type Platform

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(b) Interaction-type Platform

Fig. 2 Two types of platform

in society is rather emergent. We need to develop science and technology of system observing change of social system and clarifying merits and demerits of them. Under globalization, industry forms worldwide supply chains. Information communication systems as an infrastructure that support such activities yield various layered services from fundamental ones to applied ones. Here we think a keyword ‘platform’ which appears in the 5th Science and Technology Basic Plan. As a pioneering work, Deguchi payed attention to layered structure of the information system and captured it as platforms [16]. Importance of such platform is its economic characteristic called network externality [17] as well as its characteristic as infrastructure. Network externality is economic merits that increase in number of users increases the merits of each user. ‘Economy of Scale’ is a supply side merit created by size of the system, but ‘network externality’ is a demand side characteristics in size. Increase in number of particular platform increases the merits of each user, and then the platform attracts more users. Consequently, it grows to a winner-take-all structure. Negoro et al. classified platform into two types, i.e., layered type and interaction type [18, 19]. See Fig. 2. This figure is drawn so as to make the characteristics of these two types clearer based on the figure shown in [19]. [18] explains layered-type platform as the products and services that combined with a variety of complementary products services serve as the foundations that achieve the functions demanded by customers

and listed OS, smart phones, game consoles, iTunes as examples. Other than information technology, public utilities share such characteristics, and among then electric power system is an excellent energy supply that provides customers with various functions with equipments (complementary products). Further, social institutions such as language, unit system and other standards share similar characteristics. As for another type, interaction-type platform is products and services that provides a forum for conscious interaction within player groups and between groups

and listed net auctions, Internet communities, booking sites, credit cards and electric money [18].

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Internet enabled people and organizations connect flatly and widely in society, and at the same time it lowered processing cost in administrative information processing, and long-tail market that have products and services of large in variety and small in each size emerged [20]. However in such flat society, to connect various needs and seeds is difficult. Search engines such as Google enables to connect huge information resources provided as Web sites and huge information search needs of users. Similar structures are also shown in electronic commerce, job matching, etc., and interaction-type platforms play an important role. In interaction-type platforms, large amount of data both form needs side and seeds sides are accumulated in the platform. Through extraction of knowledge from such big data, the value of the platforms increases. As well as platforms themselves, we also should pay attention to the change of business that utilizes platforms. As an example, a company Misumi [21] is a platform business of e-commerce site of mechanical parts. Nakazawa et al. pointed out capability development of the parts suppliers in large in product variety, small in lot size and short in delivery time [22].

5 Conclusion The 5th Science and Technology Basic Plan by the of Government of Japan proposes a vision of ‘Super Smart Society’ or ‘Society 5.0.’ It requires system science and engineering to construct whole society. It should be considered a big change of requirements. Requirements are changed from engineering of a closed system for particular purpose such as Appolo program in 60s–70s to very complex open systems that cover the whole society. This article discussed the possibility of novel systems approaches through overview of both knowledge in system science and engineering and knowledge in computer science and informatics recognizing the trend of systems that couple physical space and cyberspace more more tightly. The author would like to seek novel systems approaches based on this discussion.

References 1. Government of Japan: The 5th Science and Technology Basic Plan (Jan 22, 2016) 2. Norbert Wiener, Cybernetics, or, Control and Communication in the Animal and the Machine, 2nd edn. (MIT Press, Cambridge, 1961) 3. Transdisciplinary Federation of Science and Technology, Setsuritsu-Shushi. http://www.trafst. jp/aims.html. Last access 18 Jan 2020) (in Japanese) 4. T. Kurokawa, Daigaku, Daugakuin Ni Okeru Dezain Shikou (Design Thinking) Kyouiku. Sci. Technol. Trend 9–10, 10–23 (2012) (in Japanese) 5. T. Ishida, Introduction to Design Studies (Kyoritsu, 2015) (in Japanese) 6. H. Kita et al. (eds.), Special issue ‘think of systems, think with systems,’. J. Soc. Instrum. Control Eng. 46(4) (2007) (in Japanese)

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7. INCOSE: System Engineering Handbook, 4th ed. (Wiely, 2015) 8. Information Processing Society Japan, Karikyuramu-Hyojyun J17. https://www.ipsj.or.jp/ annai/committee/education/j07/curriculum_j17.html. Last access 18 Jan 2020 9. M. Hagiya, Defining informatics across Bun-kei and Ri-kei. J. Inf. Process. 23(4), 525–530 (2015) 10. S. Okabe, Ippan Jyohou Kyouiku No Zenkoku Jittai Chousa (2). IPSJ Mag. 55(12), 1400–1403 (2014) (in Japanese) 11. S. Okabe, Ippan Jyohou Kyouiku No Zenkoku Jittai Chousa (1). IPSJ Mag. 56(1), 94–97 (2014) (in Japanese) 12. N. Gilbert, Agent-Based Models (SAGE, 2008) 13. S. Takahashi, Resolution and validation of agent-based models. J. Soc. Control Instrum. Eng. 52(7), 582–587 (2013) (in Japanese) 14. S. Hara, H. Kita, K. Ikeda, M. Susukita, Configuring agents’ attributes with simulated annealing, in Agent-Based Approaches in Economic and Social Complex Systems VII, T. Murata et al. eds. (Springer, Berlin, 2013) 15. J. Fukuta, H. Kita, Evaluation of a method for deciding agents’ attributes for an agent-based population estimation model. Trans. Inst. Syst. Control Inf. Eng. 27(7), 279–289 (2014) (in Japanese) 16. H. Deguchi, Network merits and industrial structure. J. Jpn. Soc. Manage. Inf. 2(1), 41–61 (1993) (in Japanese) 17. M.L. Kats, C. Shapiro, Network externalities, competition, and compatibility. Am. Econ. Rev. 75(3), 424–440 (1985) 18. T. Negoro, K. Kato, A strategic model of non-technological advantage between platforms— mechanism of winner-take-all and countermeasures in software products. Waseda Bull. Int. Manage. 41, 79–94 (2010) (in Japanese) 19. T. Negoro, S. Ajiro, An outlook of platform theory research in business studies. Waseda Bus. Econ. Stud. 48 (2012) 20. C. Anderson, The Long Tail: Why the Future of Business is Selling Less of More (Hyperion, 2006) 21. http://www.misumi.co.jp/index.html. 12 Apr 2016 22. T. Nakazawa, T. Fujimoto, J. Shintaku, Monodukuri No Hangeki, Chikuma (2016) in Japanese

Author Biography Hajime Kita received his B.E., M.E. and D.E. degrees all from Kyoto University. Currently, he is Professor of Institute for Liberal Arts and Sciences of Kyoto University. He also serves as Director General of Institute of Information Management and Communication of Kyoto University. His research interests are social simulation and general education of informatics in universities.

Interpenetration of System Borders Mediated by Human Activities: Weaving Trees with Rhizome Katsunori Shimohara

Abstract Toward the understanding and design of System-of-Systems (SoS) that consists of functional and complex interactions and cooperation of independent systems, we introduce “Rhizome” as a concept to weave independent systems, almost all of which have tree-type structure, into SoS. We discuss the significance and potential of the Rhizome-typed systems approach to complement the so-called treetyped systems approach. While the tree-typed approach employs hierarchical structure and functional partition in inter-systems, the Rhizome-typed approach should promote change and/or difference generation in systems without postulating hierarchical structure and functional partition. Emphasizing the significance of people element in SoS, we should take the bottom-up standpoint in systems design for SoS, and it is pivotal to incorporate people element into the bottom-up systems approach, i.e., the Rhizome-typed approach. From a viewpoint of the Rhizome-typed systems design, we propose an interpenetrative SoS model to achieve interpenetration between system borders as a dynamical mechanism that mediates interactions and cooperation of systems based on human activities. Keywords Interpenetration · System borders · Rhizome

1 Introduction In order to build the so-called Society 5.0—a human-centered society that balances economic advancement with the resolution of social problems by using a system that highly integrates cyberspace and physical space [1], it is indispensable to establish a systems approach toward System-of-Systems (SoS), which effectively achieves functional collaboration and/or combination of multiple independent systems. Here, SoS denotes a group of independent systems that work and function together as a whole Adapted from Shimohara [16]. Partly reprinted by permission of the Society of Instrument and Control Engineers. K. Shimohara (B) Doshisha University, Kyoto 610-0321, Japan e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 T. Kaihara et al. (eds.), Innovative Systems Approach for Designing Smarter World, https://doi.org/10.1007/978-981-15-6651-6_7

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system through interactions and cooperation between them. SoS includes aspects of meta-systems such as organization and discipline, planning and implementation, and operation and management as a target of research [2, 3]. As a matter of fact, most of human activities nowadays, from social and economic lives to daily life, deeply depend on various systems. Human activities freely traversing over systems network them. Human activities drive the relation between systems and influence their performance. In other words, people intermediate the interactions and cooperation between systems. The borders between systems constrain peoples’ activities, whereas people substitute coordination between system borders. In order to effectively operate those systems and enrich human activities, it is indispensable to grasp, understand, and design such social systems as SoS. In other words, a new systems approach toward SoS should be devised. Toward the understanding and design of SoS, we introduce “Rhizome” [4, 5], which Gilles Deleuze and Pierre-Félix Guattari advocated, as a concept to complement the so-called tree-typed systems approach, and discuss the significance and potential of the Rhizome-typed systems approach. While the orthodox tree-typed systems approach employs hierarchical structure and functional partition as the basic principle for ordering, the Rhizome-typed systems approach does not postulate hierarchical structure and functional partition but makes much of change generation as mechanisms for ordering. Emphasizing the significance of people element in SoS, in this paper, we should take the bottom-up standpoint in systems design for SoS. And we believe that it should be pivotal to incorporate people elements into the bottom-up systems approach, i.e., the Rhizome-typed approach. From a viewpoint of the Rhizome-typed systems design, we also propose an interpenetrative SoS model to achieve interpenetration between system borders as a dynamical mechanism that mediates interactions and cooperation of systems based on human activities.

2 Targetting SoS 2.1 What Is SoS? SoS denotes a group of independent systems that work and function together as a whole system through interactions and cooperation between them, and it includes aspects of meta-systems such as organization and discipline, planning and implementation, operation, and management, as shown in Fig. 1. For example, not only biological systems such as brain and cells, the Internet/WWW, Cloud, IoT (Internet of Things), infrastructures such as electricity, water and sewerage, and traffic, and other social systems but also their operations and management should be included into SoS.

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Fig. 1 Concept of SoS

The current situation where everything in the world has been connected to the Internet and accelerated to be intelligent and functionalized, in a sense, drives everything toward SoS. Therefore, the system science for SoS and research on a new systems approach for SoS have attracted the attention of researchers. On the other hand, however, it has been one of the basic ideas in system science and engineering so far that a system consists of sub-systems, and the concept of SoS itself is not new. For example, Maier listed the independence of operations of elements as well as their management as the definition of SoS [6]. The operational and managing independence conditions require that the element system itself should be independently operated and managed even if the SoS is decomposed. Also, DeLaurentis emphasizes three directions towards SoS, namely transdisciplinary trend crossing over the research fields, blending of heterogeneous systems, and networking of independent systems [7]. In addition, the research on complex systems with micro-macro dynamics essentially includes the idea of SoS as its research perspective, in which a global order or structure emerges from the local interactions between elements and the emerged order or structure affects their interactions in turn. However, even in cases where network modeling with elements as nodes and relationality as links is introduced into complex systems, such as that in our research, we may have been unconsciously captured by the system view based on hierarchical structure and functional partition as the ordering principles.

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2.2 Types of SoS Depending on the degree of independence of operation and management, SoS can be divided into the following four types [8]: • Directed—Directed SoS is created and managed to fulfill specific purposes, and the constituent systems are subordinated to the SoS. The component systems maintain the ability to operate independently; however, their normal operational mode is subordinated to the centrally managed purpose. • Acknowledged—Acknowledged SoS has recognized objectives, a designated manager, and resources for the SoS; however, the constituent systems retain their independent ownership, objectives, funding, and development and sustainment approaches. Changes in the systems are based on cooperative agreements between the SoS and the system. • Collaborative—In Collaborative SoS, the component systems interact more or less voluntarily to fulfill the agreed-upon central purposes. The central players collectively decide how to provide or deny service, thereby providing some means of enforcing and maintaining standards. • Virtual—Virtual SoS lacks a central management authority and a centrally agreed-upon purpose for the SoS. Large-scale behavior emerges—and may be desirable—but this type of SoS must rely on relatively invisible mechanisms to maintain it. Interchange and direct operations of railways, which are operated and managed by different companies, are a typical example of Acknowledged SoS. MaaS (Mobility as a Service), which enables people to seamlessly and efficiently move by combining a variety of transportation options without worrying about route planning, parking, and car maintenance, is an example of Collaborative and Virtual SoS. Based on a promising prospect that various agents such as people, organizations, and enterprises will be able to dynamically form various bottom-up SoS by using IoT (Internet of Things) and smartly utilize them in the future, we focus on the systems approach for Collaborative, Virtual, and their hybrid SoS, which we call Emergent SoS.

2.3 Human’s Activity Mediated and Driven SoS Most human activities nowadays, from social and economic lives to daily life, deeply depend on various systems. In reality, however, they exist and work independently, and there is little cooperation among them except for the ones preliminarily formed based on Service-Oriented Architecture (SOA). It means that human activities drive the relation between systems and influence their performance. In other words, people intermediate the interactions and cooperation between systems. The borders between

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Fig. 2 Trajectory of people as Nomads traversing the forest of systems

systems constraint peoples’ activities, whereas people substitute the coordination between system borders. Figure 2 depicts the situation where our social world consists of independent systems. We may assume the world to be a forest of systems because almost all independent systems have a tree-type structure. Moreover, we should recognize that human activities freely traversing over systems network them. In order to effectively operate those systems and enrich human activities, it is indispensable to grasp, understand, and design such social systems as SoS.

3 Perspective of Rhizome-Typed Systems 3.1 What Is Rhizome? “Rhizome” advocated by Deleuze and Guattari denotes the concept in which the only linkage always connecting, deviating and crossing over exists without a center ruling the whole, hierarchy, symmetrical rules, and the concept seems to oppose the tree with all features which the so-called order possesses in nature [4]. Deleuze and Guattari summarized the features of Rhizome as the following principles [5, 9]; (a) Junction and Heterogeneity: Rhizome can make junction with each other at any arbitrary point on the Rhizome. The feature of the Rhizome is different from tree and root that fix a point and/or an order, and this feature makes a system and its state flexible and ever-changing. (b) Manifold principle: Rhizome is a sort of manifold. There is no rigid distinction between the subject and the object, let alone measurable oneness. That is, Rhizome cannot be related to any oneness at any case and/or situation.

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(c) Disconnect without semantics: Rhizome can be disconnected at any time at any point and place. (d) Map building principle: While hierarchical structure can be composed with copying and duplication, Rhizome should be a sort of map which cannot be composed with copying. The map can be assembled or decomposed, connected or disconnected, and reversed at any dimension.

3.2 Viewing System as Rhizome Figure 3 shows images of tree-typed and Rhizome-typed structure. Here we would like to view systems as Rhizome, in comparison to the tree which possesses all features of ordering, as follows [4]: (a) Centricity: There is a central stem in the tree. (b) Regularity and Symmetry: The stem is supported by symmetrical roots and has symmetrical branches. (c) Hierarchy: There is hierarchy or order according to the distance from the central stem. (d) Recurrence and Similarity repeated from stem to branch, and from branch to twig. The above-mentioned features such as centricity, regularity and symmetry, hierarchy, recurrence, and similarity are points of view that we have made much of in designing systems so far. In relationality design, we grasp, understand, and design a complex system as a network by modeling an element of the system with a node and relationality between elements with links. In this sense, we should be familiar with the network-oriented system view. However, the above-mentioned Rhizome-typed system view reminds us of the fact that how strongly we might be captured by the tree-typed system view unconsciously. The Rhizome-typed system view brings us a cue to think of and seek Fig. 3 Images of tree-typed and Rhizome-typed structure

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for a new possibility and meaning of order formation different from the orthodox and traditional system view. Here, we consider the features (b) and (d) of the principles of Rhizome, mentioned in Sect. 3.1. That is, Rhizome is a sort of manifold, and its dynamics of everchanging is focused. It means that discarding the system view to grasp a system static and steady, we should employ the system view in which changes and differences generated in systems should be dynamically organized. Especially, on the point that the changes and differences should include “unperceivable” entities, we could find some similarities with informatics in which the functionality and significance of information and relationality are investigated. The “unperceivable” entity implies not only that it is not a static and steady entity but also that some entity as an actor who perceives it should not be any constraint to the generation of changes and differences.

3.3 Rhizome-Oriented Systems Approach for SoS Here, once again, we emphasize the significance of ‘People element’ in SoS. Most human activities nowadays, from social and economic lives to daily life, deeply depend on various systems. In other words, human activities freely traversing over systems network them, drive the relation between systems, influence their performance, and eventually intermediate the interactions and cooperation between systems. It can be also said that the borders between systems constrain peoples’ activities, while people substitute the coordination between system borders. In addition, we focus on the systems approach to Collaborative, Virtual, and their hybrid SoS, which we call Emergent SoS. Therefore, we should take the bottom-up standpoint in systems approach to SoS, and it is pivotal to incorporate people elements into the bottom-up systems approach. Here we would like to discuss the relationship between relationality-oriented systems and Rhizome-oriented systems view as a new systems approach towards SoS. In relationality-oriented systems, in addition to the network modeling with elements as nodes and relationality as links, we employ evolutionary methodologies to generate some changes in the function of nodes and in connection of links and to investigate what and how such changes influence on system behavior [10– 12]. That is, we have utilized hypothesis-generative abduction as one of the systems approach. The emphasis on dynamics and continuous development with changed generated by evolutionary methodologies as well as its network modeling itself is much closely related to the Rhizome model rather than tree model. So what has lacked in the system view and modeling so far? One possible answer might be that we have limited change generation to the system’s structural and functional features from the beginning. In other words, we have employed a priori structure-based and functionality-based modeling, and

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systems approach related to these modeling so far. That is, we might unconsciously and implicitly postulate a sort of framework and/or rules concerning the system’s structure and functions. Rhizome-based system view suggests to us ideas that such a framework and/or rules should spontaneously emerge, disappear, and/or move in the dynamical process of generating change and difference, and that any framework and/or rule should not constrain generating change and/or difference. Moreover, we might also unconsciously take the perspective of the subjective logic in which its position, place, and predicate where the subject and/or the object belongs to should be treated as the properties attributed to the subject [13]. Rhizome-based system view demands not the subjective logic but the predicate logic in which the subject and/or the object should be treated as the properties attributed to its position, place, and predicate. The predicate logic has consistency with the concept of Rhizome that has an emphasis on the process continuously generating changes and differences, and that does not necessarily require the subject-based framework. In other words, we should focus not on humans, tangle, and intangible artifacts, which intermediate interactions and cooperation between independent systems through traversing the world composed of these systems, as the subject, but on difference generated by them. We might have an image in which nomad traversing a desert forms a way as a sort of order by making their trajectory as different as shown in Fig. 2. In Fig. 2, you may assume the space of SoS consisting of individual systems as a forest, and dashed-lines as trajectories of Nomads traversing the forest of systems. Who are Nomads? Nomads are we people who make living by using these systems. Based on the discussions above, we would like to propose an idea as Rhizomebased systems approach to position relationality networks between humans, tangible and intangible artifacts as a mechanism of differentiation to intermediate interactions and cooperation between systems [14], as shown in Fig. 4. In the upcoming era of IoE (Internet of Everything) in which everything including people, things, information, data, processes, and devices can be networked and connected, relationality networks generated through people’s social, economic and/or living activities should play a role of subsuming the interactions and cooperation between individual systems, as shown in Fig. 4.

4 Interpenetrative SoS Model 4.1 Interpenetrative Border Between Systems We have discussed Rhizome-oriented systems approach for SoS, however, it is quite difficult for the existing individual system to take Rhizome-oriented systems approach except for that linkage between systems is designed and built beforehand as SOA (Service-Oriented-Architecture). In reality, a system has been designed and

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Fig. 4 Image of Rhizome-typed systems approach for SoS: trails of people as nomad traversing the forest of systems drive differentiation, and the integration of trails would spontaneously form a way of connecting systems

built individually and independently based on its own postulated users, needs, and usage. On the other hand, it is also a fact that most of the humans’ activities from social and economic live to daily live nowadays depend on various systems. It means that, in a sense, humans’ activities intermediate interactions and cooperation between the existing independent systems, and humans treat and resolve problems in linkage between systems. Therefore, we could see it the essence that people’s social and living activities themselves drive SoS, and dominate its performance and effect of SoS, as shown in the upper of Fig. 5. Thus, we could model that humans’ activities universally traversing over systems each of which has been individually built with the tree-typed architecture are linking and networking those systems, and then we should understand and utilize the dynamics and functionality of such relationality network as mechanisms of Rhizomeoriented ordering [15]. Here, we would like to propose the interpenetrative SoS model

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Fig. 5 Interpenetrative model of system borders

to make interpenetrative borders that mean to merge both systems’ boundary conditions and achieve information sharing on the overlap between systems [14], as shown in the lower of Fig. 5. For that purpose, it is necessary to clarify the problems caused by the current SoS as a mere aggregate of systems through grasping and analyzing the reality of people’s living and activities and to seek how to achieve information sharing between systems.

4.2 Time Dependency and Spatial Selectivity When we model interpenetrative borders between systems, we have to consider the following two points on interactions and cooperation between systems; time dependency and spatial selectivity in interpenetration of SoS. Figure 6 shows the time dependency in interactions and cooperation between systems depending on patterns of people’s system utilization. It means that time-dependent behaviors of individuals’ intermediate interactions and cooperation of systems. Figure 7 shows spatial selectivity in interpenetration of SoS. People seem to have several borders of self-and-others, switch, sometimes overlap, and control these borders dynamically and naturally. Under the sense of values depending on the

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Fig. 6 Time dependency on interactions and cooperation between systems

Fig. 7 Spatial selectivity in interpenetration of SoS

dynamical control of the self-and-others borders, as shown in Fig. 7, people should recognize, understand, and judge a thing and/or situation, and make a decision and an action. It means that interactions and cooperation between systems should be made depending on individuals’ dynamical control of their self-and-others borders and that the modeling of interpenetration of SoS should reflect such dynamical properties.

4.3 Systems Approach for Interpenetrative SoS Model An idea on how to achieve interpenetrative SoS model which has the abovementioned properties is to introduce agent-based approach. Especially multi-agent systems that incorporate evolutionary methodologies should be applicable, because interactions between people, tangible and intangible artifacts including places and/or

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events, and elements of individual systems can be modeled and simulated even in dynamical situations. In addition to MAS (Multi-Agent System)-based approach, a systems approach that circulates analysis, synthesis, and abduction should be taken in order to implement the proposed interpenetrative SoS model, as shown in Fig. 8. One of indispensable conditions for that purpose is that functions consisting of every system should be modularized and open as resources available for the outside of system borders. Secondly, data on relationality between people and those modularized system functions should be collected, and then real diverse utilization of SoS should be revealed. Some typical utilization patterns should be analyzed and synthesized as a sort of framework so as to maximize the effect and utilities of SoS. Through such framework design, streamlining can be achieved to some extent. However, it is important to focus on differences, which indicates a possibility of unexpected utilization, covered by the existing framework. In order to follow such a change of utilization, abduction as a mechanism to generate some hypothesis should be useful for developing newly revised framework. The effect of the revised framework should be verified through prototyping. Thus, new data brought by such a process of framework design can be collected. The proposed penetrative SoS would be self-organized and emerged by repeating the above-mentioned circulation of systems approach. In reality, interpenetration especially related to hardware between system borders should need enormous time and expense, and the fact can be a realistic problem. On the other hand, considering that every processing for an individual system is carried out on the cloud, interpenetration related to software between system borders tends to be much easier, as shown in Fig. 9.

Fig. 8 Systems approach toward interpenetration of system borders

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Fig. 9 Interpenetration of system borders on the cloud

5 Conclusion Aiming to understand and design System-of-Systems (SoS) composed of interactions and cooperation between independent systems, in this paper, we have introduced the concept of “Rhizome” to complement the orthodox Tree-based systems approach, and have considered its significance and possibility of Rhizome-based systems design and approach. Rhizome-based systems approach emphasizes on generating changes and difference, and on self-organizing them, without postulating hierarchical structure and functional partition. In the sense that Rhizome-based systems approach is quite different from the traditional orthodox Tree-based systems approach with hierarchical structure and functional partition as the basis principle, we could expect to find its significance and new possibility. We have emphasized the significance of people element in SoS, in this paper, and the idea to incorporate people element into the Rhizome-typed approach as the bottom-up systems approach. From a viewpoint that people’s social and living activities themselves drive SoS, and dominate its performance and effect of SoS, we have proposed the penetrative SoS model which enables penetrative borders and information sharing between system through grasping and analyzing the reality of people’s living and activities. It is expected that this model could show a form of effective and developmental SoS according to the reality of people’s living and activities, which could not be achieved from the independent system-driven systems approach. In other words, this is an attempt to propose a new form of systems design to involve people and even their senses of value into the systems design, and it should have some significance to bring new frontier to system science.

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References 1. https://www8.cao.go.jp/cstp/english/society5_0/index.html 2. Toshiya Kaihara, Katsunori Shimohara, System of systems concept and super smart society. J. SICE 55(4), 288–290 (2016) 3. T. Kaihara, New systems approach towards the realisation of society 5.0. IEEJ Trans. Electron. Inf. Syst. 137(8), 997–1000 (2017) 4. K. Uno, Deleuze: Philosophy of Flux (Koudansya, Tokyo, 2012) 5. G. Deleuze, F. Guattari, Rhizome, Translated by K Toyosaki (Asahi Press, Tokyo, 1987) 6. M.W. Maier, Architecting principles for systems-of-systems. Systems Engineering 1(4), 267– 284 (1999) 7. D. DeLaurentis, W. Crossley, A Taxonomy-Based Perspective for System of Systems Design Methods, in Paper 925, IEEE Conference on Systems, Man, and Cybernetics (2005) 8. J.S. Dahmann, System of Systems Characterization and Types, Systems of Systems Engineering for NATO Defence Applications, NATO STO, EN-STO-276-01, pp. 1–14 (2015) 9. T. Imada, Beyond network theory: rhizomic view of systems. Financial Review 26, 52–68 (1993) 10. K. Shimohara, Designing Relationality: Towards Relationality-Oriented Systems Design, in Proceedings of the Int. Conf. on Humanized Systems 2010 (ICHS2010) (2010), pp. 24–29 11. K. Shimohara, Designing relationality. Design Engineering (JSDE) 43(11), 609–615 (2008) 12. K. Shimohara, Relationality Design and Relationality -oriented Systems Design, in Advances in Knowledge-based and Intelligent Information and Engineering Systems (KES2012) (2012), pp. 1962–1971 13. S. Kido, Philosophy of Place — Existence and Place — (Bungeisha, Tokyo, 2003) 14. K. Shimohara, Relationality Design for System of Systems–Tree-typed versus Rhizome-typed Systems Approach, in 10th Asian Control Conference (ASCC2015) (2015), pp. 2540–2544 15. Katsunori Shimohara, Boundary and relationality perspective systems approach toward designing system of systems. Proc. SICE Annual Conf. 2019, 491–494 (2019) 16. Katsunori Shimohara, Interpenetrative model of system borders based on human activity – weaving trees with rhizome. J. SICE 55(4), 680–685 (2016)

Katsunori Shiomohara He received the B.E. and M.E. degrees in Computer Science and Communication Engineering and the Doctor of Engineering degree from Kyushu University, Fukuoka, Japan, in 1976, 1978, and 2000, respectively. He was Director of the Network Informatics Laboratories and the Human Information Science Laboratories, Advanced Telecommunications Research Institute (ATR) International, Kyoto, Japan. He is currently a Professor at the Department of Information Systems Design, Faculty of Science and Engineering, ant the Graduate School of Science and Engineering, Doshisha University, Kyoto, Japan. His research interests include human communication mechanisms, evolutionary systems, human–system interactions, and socio-informatics.

Toward Modeling Learning Behavior from a Micro–Macro Link Perspective Shingo Takahashi

Abstract This chapter describes the adaptive process in System of Systems (SoS) approach, which is a critical factor in the development of a Super Smart Society from a micro–macro link perspective. The adaptive process is primarily produced from the interaction between the individual and systems levels, which is considered as a micro–macro loop composed of two learning processes: goal-seeking behavior by individual-level single-loop learning that does not modify the internal model of an individual system, and systems-level double-loop learning that modifies the internal model of an individual system and shares the internal models throughout the whole system. The micro–macro loop forms a three-dimensional spiral process rather than a planar loop process, and drives the SoS adaptation process. Keywords Cybernetics · Learning · Organizational learning · Situated learning · Agent-Based social simulation

1 Introduction This chapter comprehensively describes learning behavior from a cybernetic perspective, which would be required to achieve a Smarter World, such as Society 5.0. Smarter World is a society with advanced and sustainable social infrastructures for autonomous and dynamic value network creation (see Preface). Learning behavior is often considered as a process for increasing individual knowledge and problem-solving ability. However, the relationship between learning behavior and the development of a Super Smart Society or Society 5.0 is not well

Adapted from Shingo Takahashi, “Toward Modeling Learning Behavior from a Micro-Macro Link Perspective (written in Japanese),” Journal of The Society of Instrument and Control Engineers, Vol. 55, No. 8, pp. 686–691 (2016). Partly translated by permission of The Society of Instrument and Control Engineers. S. Takahashi (B) School of Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo, Japan e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 T. Kaihara et al. (eds.), Innovative Systems Approach for Designing Smarter World, https://doi.org/10.1007/978-981-15-6651-6_8

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understood. In the system of systems (SoS) approach, which is critical for the development of a Super Smart Society, whole systems with multiple functions consist of multi-layered and complex social systems, and research problems of concern include cooperation and coordination between these systems. A Super Smart Society can be realized by developing the ability of the whole system to adapt to the environment in which the complex social systems constituting the whole system change in complex ways, or various uncertainties exist inside and outside the system. For a complex social system to adapt and evolve, a comprehensive system that includes social systems must explicitly incorporate autonomous decision-makers at each system level. Thus, it is necessary for complex social systems to deal with improvement of the adaptive ability of system behavior from the viewpoint of not only internal interaction but also various interactions, such as between elements of systems at different levels, between systems, and between elements and systems. Learning concepts play a central role in capturing the improvement of adaptive ability in a comprehensive system, such as a Super Smart Society. This chapter discusses concepts of learning at both the individual and systems levels, as well as the relationship between them, using a systems approach based on cybernetics. Learning has long been a major research topic in various fields, including learning psychology, cognitive science, and educational psychology [1–3]. Typical learning concepts in these fields include operant conditioning and reinforcement learning as behavioral approaches, and learning, as knowledge changes based on individual learning processes, such as memory, proficiency, and trial and error learning. In the field of artificial intelligence, machine learning methods have mainly been developed for individual systems, such as reinforcement learning [4, 5]. In contrast, in recent years, learning research has been more focused on constructivism and metacognition, involving approaches focused on situations in which the learning subject is involved [2, 3, 5]. Although a comprehensive explanation of the details of each of these approaches is beyond the scope of this chapter, the chapter attempts to provide an explanation of learning in various conventional fields at the individual and systems levels, as well as the micro–macro links between them. In previous research, conventional learning perspectives and methods have typically focused on learning behavior only at the level of the individual. However, in addition to learning at the individual level, improvements of the adaptive ability of systems behavior at other levels can also be realized by learning at the systems level, and by the interaction between the individual and systems levels. To achieve this aim, it is important to comprehensively consider the modeling of learning behavior in a complex social system, as well as learning behavior at the individual level. To comprehensively consider the modeling of learning, it is necessary to consider learning at the systems level as a sharing process in the system, as well as learning as an intrinsic process of each subject. In this chapter, the concept of learning in the field of cybernetics is introduced as comprehensively as possible, and individual learning and systems-level learning are discussed from the perspective of single-loop learning and double-loop learning,

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which are concepts developed in organizational learning by Argyris et al. 6). Conventional learning research methods, including approaches in which learning is considered as a change in individual knowledge, and situational approaches, can then be interpreted from the same perspective. The author then integrates conventional learning concepts from the perspective of micro–macro links and consider the future direction of modeling of dynamic learning that can be modeled as the interaction between individual learning and systems-level learning.

2 The Concept of Learning in Cybernetics Systems thinking provides a way of grasping the characteristics that occur as a whole by focusing on the relationship between various factors. This idea existed as long ago as ancient Greece, and was also advocated by Aristotle [7]. Subsequently, a cross-disciplinary approach to organized complexity issues evolved into the socalled systems movement. Cybernetics has been one of the central ideas supporting the systems movement for the last 50 years [8]. Cybernetics is a concept for system control initially developed by Wiener, and was subsequently developed further by Ashby et al. [9, 10] as an approach for controlling systems to achieve intended purposes. Figure 1 shows the basic conceptual model of cybernetics from a decision-making perspective. A process is considered as a target to be controlled, which is modeled as an input/output system. The process receives input from the external environment and outputs the results of the process to the environment. The decision-maker predicts and estimates the input to the process via a feedforward mechanism. The system observes the output of the process, feeds it back to the decision-maker who uses it for decision-making. Decision-making is the control of the system to achieve a given goal by the decision-maker making a decision in the process. From an engineering

Fig. 1 Basic conceptual model of cybernetics

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perspective, the process from input to output is an object to be controlled, and is managed by rules of control. The features of cybernetics are primarily used to model the process as the target of decision-making, and, importantly, to model the decision-making that acts on the process to explicitly grasp decision-making as a comprehensive integrated system. In cybernetics, to cause a system to perform a desired action, the first step is to evaluate the degree of deviation from a predetermined purpose of the output of a process to be controlled. The decision-maker determines the amount of operation required to correct the gap and acts on the process. The predetermined purpose provides the “goal” or “solution” to be achieved for the system. If the deviation between the output and the goal becomes greater with a previous operational amount because of fluctuation of the external input, the decision is updated to a new operational amount that can correct the deviation. From a cybernetics perspective, this series of steps, which represents correction of the error to output the correct answer, is considered learning. In cybernetics, learning to correct an error in relation to a given goal is called negative feedback. Ashby 9) called systems behavior guided by negative feedback “first-order adaptive behavior”, whereas Maruyama [11] called it “first-order cybernetics”. Ashby is considered one of the most influential cyberneticians, and defined the basic concepts of cybernetics from a broad perspective, as well as laying the foundations for the use of cybernetics in various fields, such as management and learning [10, 12]. Although cybernetics originally focused on systems behavior guided by negative feedback to achieve a goal, in cases where environmental changes are so unexpectedly large that the system cannot adapt within the given range of error correction, it is necessary to change the domain in which the system acts [9]. Such adaptive behavior of a system, in which its behavior field is modified, is known as second-order adaptive behavior or second-order cybernetics [9, 11]. The adaptive behavior of the system can be accompanied by positive feedback that intentionally amplifies deviation from the target during the adaptive behavior process. First-order adaptive behavior in cybernetics corresponds to single-loop learning in organizational learning, and second-order adaptive behavior corresponds to doubleloop learning in organizational learning. Interestingly, Argyris reported that these learning concepts were borrowed from Ashby’s adaptive behavior concept of cybernetics [9]. From the perspective of individual and systems-level learning, single-loop learning corresponds to individual learning, and double-loop learning corresponds to systems-level learning. Double-loop learning is also a central concept that supports the situated learning concept defined by the learner’s whole activity system [5]. In the following sections, individual learning and systems-level learning are described in terms of double-loop learning.

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3 Individual Learning from the Single-Loop Learning Perspective Single-loop learning was proposed by Argyris et al. as a framework for organizational learning in the field of organizational behavior research, which explores individual behavior in organizations [13, 14]. Single-loop learning is defined as changes in the strategies of actions to improve the performance of task processing in an organization. Improvement requires the establishment of criteria that define the improvement. However, single-loop learning does not change the values or norms that provide the basis for improvement. Single-loop learning typically aims to improve organizational performance by changing only the strategies focused on achieving a given goal. This corresponds to negative feedback in cybernetics. Argyris reported that the concept of single-loop learning borrows from the concept of negative feedback [13]. In a singleloop learning approach, learning is performed as a cycle that determines a strategy to achieve a given purpose, evaluates the result after performing it, improves the deviation from the purpose, and makes the next decision. This improvement demonstrates that the plan-do-check-act (PDCA) cycle in an individual is functioning. This process involves a series of cycles that first determines the alternative with reference to the objective (plan), implements it (do), evaluates the result (check), and, if there is deviation from the objective, corrects and improves it (act). In single-loop learning, the goal is given before learning takes place. Thus, it is implicitly assumed that the structure of the environment is stable, from planning to implementation. However, the environment constantly changes, and the input to the system has different characteristics from the situation in which the environment is constant. Therefore, the system needs to adapt to the changing environment to achieve the goal. To achieve the goal, it is essential for the decision-maker to have an internal model describing the environment. This fact is conventionally known as the internal model principle in the field of systems control [15]. Single-loop learning from the aspect of an individual learning system can be considered as the learning of an agent that is an intelligent autonomous decisionmaking subject [5, 16, 17] The role of an agent model in recognizing the environment and deciding on actions to perform on the environment is important for learning. This model is created internally by the agent system itself, and is called an internal model. Although interactions with other agents play an essential role as social abilities of the agent, single-loop learning does not necessarily include the concept of interaction with others. Interaction with other agents becomes essential in double-loop learning, as described below. The internal model of an agent expresses the agent’s perception of the situation surrounding the agent. The situation is unique to each agent, and the internal model represents the individual situation. In that sense, agents are individually embedded in the situation via internal models [18, 19] (Fig. 2). In behaviorism, learning is defined as a change in behavior [2], and behavior that responds to a stimulus changes as a result of learning. Classical conditioning involves learning in which a new stimulus induces a previously acquired behavior

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Fig. 2 Model of agent individually embedded in situation

[2]. Although classical conditioning is based on conditional responses, learning is considered to be performed purposefully [1]. In operant conditioning, actions occur because of internal causes, and learning is performed by reinforcing the actions using their results as new stimuli. Latent learning using cognitive maps is also performed in maze learning. Stimuli used when the behavior is reinforced (also known as discriminative stimuli or reinforcers) can be considered as a form of negative feedback. Learning with cognitive maps involves the formation of an internal model of the situation [14]. In behaviorism, the research object is observable behavior. Hence any changes in such unobservable objects as knowledge cannot be considered learning. In contrast, cognitive science deals with knowledge as a subject of information or information processing, and considers learning as a change in the knowledge possessed by an individual [2–5]. When learning is seen as a change in knowledge, the process of change is realized in various ways, such as memory, becoming skilled, trial and error learning, and understanding explanations. However, focusing on fixed knowledge regarding facts or procedures is not always appropriate when dealing with learning as a process of knowing or thinking. In the field of machine learning, researchers have examined various learning systems, such as learning using decision trees, Bayesian networks, hierarchical neural networks, explanation-based learning, case-based learning, and reinforcement learning. These learning approaches are typically composed of a model that determines how sensors collect data from the environment, an actuator that acts on the environment, and an algorithm that obtains a model from the data [19]. Models include classifiers, knowledge bases, and reinforcement signals. Learning systems are characterized by algorithms such as decision trees, Bayes’ theorem, backward propagation, deductive inference, search functions, and the action-value function.

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Studies in the field of machine learning have focused on data, models, and algorithms, and, from a systems learning perspective, can be categorized as single-loop learning of individual systems. However, recent studies in machine learning have used the concept of an evolutionary system involving interactions among multiple agents, which can be regarded as double-loop learning, as described below, rather than single-loop learning. Individual learning is a system performed by a single subsystem that is a component of the system. For example, in supervised learning, the difference between the current situation and the target value as the correct answer is evaluated, and methods for approaching the correct answer are searched for and executed. This is basic process underlying negative feedback approaches. In unsupervised learning, input data are structured (modeled) to improve the agent’s perception of the situation. So far in this chapter, single-loop learning as individual learning has been described, using an approach based on negative feedback to decide on a policy for achieving a goal, without changing the interpretation framework (expressed as an internal model). This learning can be performed not only for individuals but also for the whole system (or organization). In other words, sub-goals are assigned to each subsystem to achieve the purpose of the whole system, and each subsystem determines a policy to improve error correction according to its own sub-goal, and integrates the policies of the subsystems for execution as the policy of the whole system. Even in the SoS framework, daily operations when the environment is stable are executed as single-loop learning of the whole system. Based on this notion, the term “individual” used in this section means that a system learns as a “single” system in the sense of using only its own internal model without changing it, rather than interacting with other systems.

4 Situated Learning from a Systems-Level Perspective As discussed in the previous section, single-loop learning is learning that achieves a purpose by correcting errors without changing the interpretation framework. We classify single-loop learning as individual-level learning. In contrast, double-loop learning is systems-level learning that is superordinate to individual learning, in which the value system and interpretation framework itself provide the criteria for decision-making by the system [9]. As described by Argyris [13], the concept of double-loop learning is defined as an analogy from Ashby’s concept of adaptive systems [9]. The value system and interpretation framework are constructed from the recognition of the situation. They are maintained in the system as an internal model representing the system’s perception of the situation involved. This means that double-loop learning can be considered even at the individual system level. A typical feature of double-loop learning at the individual level is that the internal model held by the system is modified or improved to adapt to environmental changes. If the internal model does not change, a systems-level behavior in a specific period of time can be

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Fig. 3 Single-loop and double-loop learning

represented as a trajectory in a certain field of the system variables. If the system changes its perception of the situation and essentially modifies the internal model, the behavioral domain of the system itself will change. An outline of the relationship between individual-level and systems-level learning, and the relationship between single-loop learning and double-loop learning is shown in Fig. 3 [21]. Double-loop learning at the individual level can be effectively performed as a whole only after double-loop learning has taken place at the systems level. The environment surrounding each subsystem is not only different, but each subsystem is individually embedded as an activity system in which the activities of each subsystem are performed. The environment surrounding each subsystem is not only different, but each subsystem is individually embedded in the environment as an activity system in which the subsystem’s activities are performed. Each internal model is an individually embedded representation of the environment. The behavior of each subsystem is determined with reference to the individually embedded internal model. The individually embedded internal model is modified by double-loop learning at the individual level of each subsystem, then shared by the whole system through double-loop learning of the whole system. This means that the shared perception of the situation is constructed from individual perception of the situation, and that the system acts under the shared perception of the situation. Thus, an important feature of learning at the systems level is that each subsystem shares their views of the situation by interaction between subsystems in the situation of the whole system.

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In contrast to the traditional view of learning as a process of acquiring structured knowledge, recent conceptions of learning propose that learning progresses through interaction with others in situations, representing a situational approach in learning science [5, 22]. It should be noted that various studies have focused on situations in which the learner is embedded. For example, in constructivism, there is an assimilation of how a child understands the world in which they interact. The structure of intelligence is considered to change by adjusting how the experience after assimilation is integrated into the learner’s understanding. In addition, constructivism focuses on the process of creating connections with what a learner already knows, and considers learning from the perspective of how to gain knowledge for oneself and how to have an affinity. In situationally embedded learning, as reported by Lave et al., learners in an apprenticeship situation initially participate in the situation as if they were engaged in peripheral work, and learn how to do the work. Subsequently, learners can participate in the situation legitimately if they can become able to fulfill their full roles. Although the concept of the internal model is not explicitly included in the learning embedded in the situation, it can be seen to involve a process of sharing the internal model of the method of working in the whole system [3, 5, 23]. In the field of logic, Barwise et al. formulated situational semantics in the 1980s, in which the interpretation of the world and the meaning of sentences differ depending on individual situations. In usual formulations of semantics in predicate logic, the interpretation function that gives the interpretation of a sentence is outside the model. In situational semantics, situations are explicitly formulated as part of a model [24]. The importance of the concept of metacognition has been also highlighted in the field of educational psychology [2]. Metacognition is cognition that describes one’s thoughts and cognition as a form of information that can be manipulated by the subject [5]. Metacognition is important because it is possible to learn information for manipulating the situation by recognizing one’s own cognition. If cognition, as a form of manipulable information, is considered as an internal model, metacognition includes the process of evaluating and modifying the internal model. Although the situational approach of learning is concerned with situation and cognition, and there is a slight difference among the researchers regarding how learning proceeds, it can be largely regarded as systems-level learning in the situation of an activity system [5]. This concept of the activity system includes a whole situation in which various actors are active, and corresponds to the concept of the whole system described earlier in this chapter. Engeström explicitly regards systems-level learning in the situational approach to be a form of double-loop learning, as proposed by Argyris. The current chapter takes the same position as Engeström, proposing that the contexts of both systems science and learning science indicate that double-loop learning is central to systems-level learning.

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5 System Learning and Problems from a Micro–Macro Loop Viewpoint In the sections above, the internal model was argued to play an essential role in the learning of a system. In particular, double-loop learning at the individual level that modifies the internal model, and learning at the systems-level that shares the internal models modified by the individuals constituting the system, are essential for learning behavior. This framework also influences the situational approach that has recently been proposed to be important in the field of learning sciences. The modification process of the internal model at the individual-level and the sharing process at the systems-level can be considered using the so-called micro– macro loop framework. The term micro here does not merely refer to an individual subject. Rather, from the viewpoint of the SoS approach, the relationship between micro and macro refers to the relationship between the whole system and the subsystems composing it, and can be considered as a learning process of the whole system and individual systems. In an individual system, single-loop learning by PDCA is performed to achieve the goal according to the internal model that the individual system holds. The internal model of an individual system is modified by double-loop learning to adapt to changes in the environment during the improvement process after implementation. The internal model of an individual system is modified by double-loop learning to adapt to changes in the environment during the improvement process after implementation. The shared vision induces the rules and behaviors that individual systems must follow, and regulates the behavior of an individual system (Fig. 4). In this series of processes, individual systems whose behavioral decisions are regulated by norms derived from Fig. 4 Micro–macro loop

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a shared vision behave differently from the original individual system that provided the shared vision. In other words, the learning process of the micro–macro loop should be considered as a spiral process that changes the system structure. Regarding the dynamic characteristics of the modification process of the internal model at the individual level and the internal model sharing process at the systems level, models using genetic algorithms have been proposed, but have not always been sufficiently modeled [25]. Nonaka et al. [26] developed the SECI (Socializaion-ExternalizationCombination-Internalization) model of knowledge conversion, which is similar to the double-loop learning concept in organizational learning. The SECI model conceptually represents the dynamic creation of knowledge in an organization via four knowledge conversion modes: socialization, externalization, combination, and internalization. As Nonaka pointed out, the SECI model and loop learning of organizational learning are conceptually similar in the sense that they both focus on individual mental models and the process of sharing them in the organization. In contrast, the SECI model has some unique characteristics, including being transformed into shared and formalized knowledge. Our framework focuses more on the learning situation itself rather than on representational knowledge. In addition, the SECI model is a conceptual model, and the way in which its process is driven is demonstrated by actual examples and case studies only. Thus, our framework has not yet been modeled to express the dynamic characteristics of the process. It is currently unclear whether the dynamic characteristics of the process of the learning situation of the micro–macro loop are well modeled by behaviorism, cognitive science, or learning concepts in machine learning, which are mainly regarded as single-loop learning processes, or by recent contextual approaches, which are considered to represent double-loop learning, or even conceptual models of knowledge creation such as the SECI model proposed by Nonaka et al. Considering the learning concept of the comprehensive approach in the SoS approach, modeling of the dynamic representation in the learning situation of this micro–macro loop should be developed as one of central problems of research.

6 Conclusions This chapter described the adaptive process in the SoS approach, which is a critical factor in the development of a Super Smart Society from a micro–macro link perspective. The adaptive process is primarily produced from the interaction between the individual and systems levels, which is considered as a micro–macro loop composed of two learning processes: goal-seeking behavior by individual-level single-loop learning that does not modify the internal model of an individual system, and systemslevel double-loop learning that modifies the internal model of an individual system and shares the internal models throughout the whole system. The micro–macro loop forms a three-dimensional spiral process rather than a planar loop process, and drives the SoS adaptation process.

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The concepts described in this chapter may still be insufficient. Even from a micro perspective, this chapter does not simply regard “people” as micro, but considers them to be elemental systems for the whole system. However, this issue remains unclear, and future research should examine the micro concept in more depth, as well as the concepts of macro and the micro–macro link. To further study the modeling of the learning behavior of the system, breakthrough modeling techniques for the micro concept, macro concept, and the micro–macro link are required. To this end, it is necessary to elucidate the connections of such concepts with actual social systems. For learning using micro–macro links, it is particularly important to first empirically analyze successful cases of real-world social systems.

References 1. M. Jitsumor, S. Nakajima, Psychology of Learning (in Japanese), Science-sha (2000) 2. M. Koyasu, S. Tanaka, T. Haebara, Y. Ito, Educational Psychology (3rd ed.) (in Japanese), Yuhikaku (2015) 3. R.K. Sawyer (Ed.): Cambridge Handbook of the Learning Sciences (2009) 4. G. Weiss, (ed.): Multiagent Systems—A Modern Approach to Distributed Artificial Intelligence (MIT Press, 1999) 5. N. Gilbert, Agent-Based Models (SAGE Publications, 2008) 6. R.K. Sawyer, in Cambridge Handbook of the Learning Sciences, pp. 63–87, in P.H. Winne, R. Azevedo: Metacognition, A. Collins, M. Kapur, Cognitive Apprenticeship, pp. 109–127. J.G. Greeno, Y. Engeström: Learning in Activity, pp. 128–150 (2014) 7. Aristotle, Politics (Japanese Translation by K. Ushida (Kyoto University Press, 2001)) 8. P. Checkland, Systems Thinking, Systems Practice (Wiley, 1981), (-Includes a 30-year retrospective (1999)) 9. W.R. Ashby, Introduction to Cybernetics (Chapman & Hall, 1956) 10. W.R. Ashby, Design for a Brain (Wiley, 1960) 11. M. Maruyama, The second cybernetic: deviation-amplifying mutual causal processes. Am. Sci. 5(2), 164–179 (1963) 12. S. Beer, Brain of the Firm (McGraw-Hill, 1972) 13. C. Argyris, D.A. Schön, Organizational Learning II (Addison-Wesley, 1996) 14. F. Ando, Organizational Learning and Cognitive Map of Organization (in Japanese) (Hakutoshob, 2001) 15. B.A. Francis, W.M. Wonham, The internal model principle for linear multivariable regulators. Appl. Math. Optim. 2(2), 170–194 (1975) 16. M. Wooldridge, Introduction to Multiagent Systems (Wiley, 2002) 17. R. Axelrod, M.D. Cohen, Harnessing Complexity (The Free Press, 1999) 18. S. Takahashi, Foundation of Systems (in Japanese (Baifukan, 2007) 19. S. Takahashi, Approach to Problems on Organizational Learning by Agent-Based Organizational Cybernetics (in Japanese), The Japan Association for Social and Economic Systems Studies, No. 28, pp. 9–15 (2007) 20. T. Terano, Learning, evolution and systems thinking (in Japanese). J. Soc. Instrum. Control Eng. 46(4), 274–279 (2007) 21. R. Espejo, W. Schuhmann, M. Schwaninger, U. Bilello, Organizational Transformation and Learning-A Cybernetic Approach to Management (Wiley, 1996) 22. H. Kato, Design of educational system by situational approach (in Japanese). J. Soc. Instrum. Contr. Eng. 34(2), 122–130 (1995)

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23. J. Lave, E. Wenger, Situated Learning—Legitimate Peripheral Participation (Cambridge University Press, 1991) 24. J. Barwise, J. Perry, Situations and Attitudes (MIT Press, 1983) 25. S. Takahashi, Evolutionary Learning in Agent-Based Modeling, in Discrete Event Modeling and Simulation Technologies H.S. Sarjoughian, R.E. Celler (ed.) (2001), pp. 297–314 26. I. Nonaka, H. Takeuchi, The Knowledge Creating Company (Japanese Translation ed.) (Toyo Keizai Shinbumsha, 1996)

Shingo Takahashi is a professor at Department of Industrial and Management Systems Engineering, Faculty of Science and Engineering, Waseda University. He received BE in Management Engineering in 1984, and MSc and PhD in Systems Science in 1986 and 1989 respectively from Tokyo Institute of Technology. His research interests include Social Simulation, Social Systems Science, Soft Systems Approach, and Mathematical General Systems Theory.

Understanding Disruptive Innovation Through Evolutionary Computation Principles Takao Terano

Abstract This chapter explores the nature of innovation from the perspectives of evolutionary computation principles. So far, the disciplines of innovation have been mainly discussed in management science literature, however, some of the recent articles address the transdisciplinary characteristic of innovation-related issues from system science standpoints. In such discussions, evolutionary computation, which is a flexible but strong computational methodology inspired by biological evolution, has had one of the major roles to explain the disruptive innovation phenomena in new businesses, organizations, or new products. This chapter surveys the ideas of innovation with evolutionary computation from management, computer, system, and biological sciences. Then it discusses the system requirements for open or free innovation. The chapter concludes some comments on the strategies to accelerate the technical innovation processes. Keywords Transdisciplinary innovation · Evolutionary computation · Complex systems · Agent-Based modeling · System creation

1 Introduction This chapter deals with the topics of innovation principles from the perspectives of evolutionary computation. The contents have come from both our discussions at the Smarter World Research Group [1] and my own experience on Agent-Based Modeling research [2–5]. So far, the creation of innovative business, organizations, or products has been considered to be the issue in the field of management science,

Adapted from Takao Terano “Evolutionary Computation Approach to Understand Mechanisms of Interdisciplinary Innovation (written in Japanese),” Journal of The Society of Instrument and Control Engineers, Vol. 55, No. 8, pp. 692–697 (2016). Partly translated by permission of The Society of Instrument and Control Engineers. T. Terano (B) Chiba University of Commerce, Chiba 272-8512, Japan e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 T. Kaihara et al. (eds.), Innovative Systems Approach for Designing Smarter World, https://doi.org/10.1007/978-981-15-6651-6_9

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focusing case analyses and implications from traditional activities. There are surprisingly few studies that discuss the nature of innovation from a systems science perspective. For example, in Artificial Intelligence literature, they only discuss how advanced artificial intelligence technologies are used for innovation from a technical perspective, but do not discuss the nature of innovation, itself. In this chapter, we introduce the nature of innovation, which is derived from the observations of biological systems and their evolutionary mechanisms, as well as the similarities and properties of innovation. We introduce the contents of the representative four books, all of which apply the framework of complex adaptive systems and evolutionary computation, which have been developing rapidly in recent years. By introducing these, we would like to get some hints on how to achieve “open innovation” or “free innovation”. The structure of this paper is as follows. First, in Sect. 2, in order to clarify the concepts of innovation, we introduce the work of Cristensen et al. [6–8], and Von Hippel et al. [9, 10] in management literature. Sections 3–6 survey the contents of four books by Goldberg [11], Axelrod and Cohen [12], Arthor [13], and Wagner [14]: Goldberg’s book [11] discusses how evolutionary computation is related to innovation; A book by Axelrod and Cohen [12] argues that the concept of harness is important to deal with complex social and organizational problems and use biological evolutionary methods as a means to achieve it; Arthor [13] uses the idea of Learning Classifier Systems in Genetics-Based Machine Learning [15] to explain emergent phenomena in economics and discusses the relation between emergent behaviors and technological evolution; Wagner [14] focuses on the link between complex chemical reaction networks in living organisms and technological evolution. In Sect. 7, we discuss the requirements for a system in which “open innovation” or “free innovation” is likely to occur, based on these existing studies. In Sect. 8, some concluding remarks will follow.

2 A Brief Survey on Innovation in Management Science Literature One of the most representative innovation studies in the field of management is the enormous work by Christensen et al. [6–8]. In these studies, he classifies innovation in firms as sustaining and disruptive innovation. Sustaining innovation is a way to continuously improve the performance requirements of the core market by continuing to deliver higher performing, more profitable products to customers at a faster pace than their changing needs. Such a type of innovation is a normal way, which companies routinely carry out in the sense of making a good product even better. Disruptive innovation, on the other hand, refers to a type of innovation in which an entirely new product is introduced to the market as a new business, even if the performance is initially inferior in what existing customers value the most, and then a

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new market is formed in a position far removed from the core market and the existing market is displaced. In general, successful innovators continue to thrive by continuously improving the products they put into them. However, there are many cases in which the existing products are so fixated on the next innovation that they fail to respond to the next stage. In the old days, for example, Kodak was obsessed with film cameras, and more recently, Sharp was obsessed with large LCD TVs. Such phenomena, as Christensen describes, is called the “innovators’ dilemma”. To avoid this, he states that (1) they should not be too obsessed with past experience; (2) innovation is relative to the environmental change; (3) choice is important but not everything, and (4) the role of top management is important in promoting innovation. Such an idea is reminiscent of the process of dinosaur prosperity and its extinction in biological evolution. Just as species that have continuously evolved and thrived during periods of moderate environmental change have failed to respond to rapid environmental change. On the other hand, a series of studies by Von Hippel et al. [9] proposes the concept of “open innovation” as a move to democratize innovation rather than keeping it within the enterprise. His book argues that advanced users should be considered as innovators to help companies innovate. This means erosion by users of corporate activities, but it is important for companies to address and apply the following methods: (1) Productize the innovation by the user, then produce it in a customized way; (2) Platformize the toolkit used for product design and sell that platform; and (3) Focus on products or services, which complement innovation by users. Such a method might be applicable well to disruptive innovation-oriented venture firms such as the ones in Silicon Valley. However, in some sense, the method also fits for sustained innovation activities, as seen in the recent development of smartphone applications by small firms. Actually, in his recent book [10], Von Hippel extends his idea of “open innovation” to “free innovation” paradigm. He emphasizes that the traditional household activities carried out by end-users have much more important roles than the ones by industrial sectors. Riffkin’s book “zero marginal cost society” [16] is a further development of this idea. Here, he argues that efficiency and productivity have been increased to the extreme and the cost of producing goods and services is becoming zero. This infrastructure is called the new “Internet”. He states that there are three essential elements in his word “Internet”: communication media, logistics for transporting products, and energy sources for information and transportation. He also states that a new society should be achieved when all three interact elements would interact with each other and would operate as a single total system. As a result, he explains that a decentralized and cooperative IoT and sharing economy will be achieved from the era of centralized organizations. The concept of disruptive innovation is re-discussed by Downes and Nunes in their book: “Big Bang Innovation” [17] with reference to the recent activities of Google, Uber, and others. They argue that products and services subject to “big bang innovation” will have a rising “shark fin” type life cycle, rather than the traditional “bell

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curve” one. They compare this process to the evolution of the universe and divides it into four stages: the “singularity,” where fluctuations occur from empty space, the “big bang,” where rapid inflation occurs, the “big crunch,” which is followed by contraction, and the “entropy,” which leads to thermal equilibrium. This idea is interesting because it points out the importance of the speed of innovation, the importance of platforms, the scale beyond national borders, and the importance of ecosystems that include other products and services, although there is a slight discrepancy in the use of the technical terms. Based on the brief survey in the management science literature, in the following, we introduce the research by discussing the relationship between innovation and evolutionary computation.

3 On the Design of Innovation in Computer Science In the area of computer science, David Goldberg, who is one of the pioneers of Genetic Algorithms, titles his last textbook on evolutionary computation as “Design of Innovation” [11]. The book summarizes his group’s work on the latest genetic algorithms up to that time. Even for those who read it today, the contents are thoughtprovoking. The claim, discussed in the first chapter, that only with genetic algorithms can we construct theories of innovation, such as inventions and discoveries, and that a computer-based system of inventions and discoveries can be realized, is interesting in discussing innovation. For example, he describes the first successful airplane by the Wright brothers was realized with only the right combinations of the right technical elements available at that time. Today, this principle is widely recognized in the methods of evolutionary computation and genetic algorithms (allegedly inspired by the evolution of living organisms). A proper understanding of the evolutionary computational process makes it clear how innovation can be achieved. In this book, the algorithms developed by them are uniformly described in the following way. First, to apply evolutionary computation, we should (1) prepare building blocks as components of a given problem, and (2) be able to provide a sufficient variety of them. Secondly, (3) there must be a mechanism to grow the combination of building blocks at a sufficient rate. In addition, (4) finalize a way to determine what is a good building block, and (5) prepare a mechanism to replace the good building blocks. This is merely a rephrasing of the evaluation, selection, and selection mechanism common to genetic algorithms, with an explicit concept of building blocks. In order to promote innovation, especially open innovation, it will be important to have a clear awareness of the building blocks as basic technologies, and to share the idea of combining, growing, and evaluating them.

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4 Harnessing Complexity: Key for Innovation In the field of management and organizational literature, the concept of harness is more essential than control. In system engineering literature, in general, control is a method to identify the structure of a target system and maintain or change the system in a predetermined state in response to changes in input or disturbance. From the viewpoint of control theory, it seems quite natural to formulate the target system in a formal way. When using the term control, a system designer is required, either explicitly or implicitly, to have a complete understanding of the target system. However, in the context of management science, control is a very strong word, that is, it is a concept in which “a more powerful person forcibly changes the will and behavior of a weaker person”. On the other hand, the original meaning of harness is “to manage horse movement with a harness,” which is translated to mean “to make effective use of natural forces.” Axelrod and Cohen’s argument in their book [13] is that in order to make this harness operational, it is important to use the concepts of the evolution of living things or to utilize the concepts which have become apparent in the study of evolutionary computation. In the following, we describe the methodology for harnessing complex adaptive systems according to the claims of the book. There are three central concepts here: maintaining variation, facilitating interaction between agents, and setting appropriate selection or evaluation criteria. First, it is important to maintain diversity for innovation. The main idea for this is that diversity can be maintained by duplicating information on the functions, capabilities, and environment of each component. However, the exact copy must not be required here. On the contrary, the point is to inevitably admixture “errors”, i.e., partial diversity, in copying. From the point of view of biological evolution, this corresponds to the fact that mutations change the nature of individuals, and that diversity is maintained by having redundant genes. From the standpoint of evolutionary computation, it corresponds to the active introduction of mutation operators that change a part of the object with a certain probability when generating an individual. The next important concept is to introduce a mechanism to allow interaction to take place in an environment. This means that if individual system elements already have partial solutions, better solutions can be easily generated by combining them as building blocks. From the perspective of biological evolution, the realization of interaction corresponds to sexual reproduction from multiple individuals with redundant genetic information. This is expected to result in the expression of a dominant trait in the offspring. This supports the aforementioned building block hypothesis that a better solution can be obtained by combining better partial solutions. The nonlinear operation of remaking the combination of partial solutions facilitates the arrival of a superior solution. The mating operator in evolutionary computation is equivalent to this. The third important concept is what kind of metrics and evaluation scales should be set. Maintaining diversity and promoting interaction alone does not lead directly

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to innovation. This, in turn, becomes a criterion for selection, giving the “right” way for the important ones to remain in the innovation. From the perspective of biological evolution, this corresponds to natural selection. What is important here is the idea that the evaluation scale itself is also determined by the environment. On the other hand, from the perspective of evolutionary computation in pursuit of optimization, the problem of selection and selection is divided into two parts: how to define the evaluation function, and how to maintain a good population by adopting a generational change model. Particularly in corporate and organizational problems, it should be recognized that there are always multiple valid solution spaces, and no solution is unique.

5 Technology and Innovation In economics area, Santa Fe Institute (SFE) has proposed new scientific methods of complex systems economics, which emphasize dynamic processes, in addition to the traditional equilibrium concept of economics. Arthur, one of the key people at SFE, has written a book [13] on technology and innovation based on the theory of evolution and generation. He defines technology as (1) a means to achieve human desires, (2) a method of practice and assembly of components, and (3) a collection of devices and engineering that would serve a culture. Furthermore, he argues that the common nature of all technologies is that they “evolve by combining the right components”. That is, new technologies emerge from a combination of existing technologies, and therefore existing technologies are used as building blocks to shape the next one, and this process continues. Here again, the building block hypothesis described earlier is supported. And it is the evolution of technology itself that causes innovation. The source of technology, he said, is any phenomenon that is physical, social, or involves information. Therefore, he argues that the development of science and technology to measure phenomena is necessary for innovation. In other words, in order to advance innovation, it is necessary for each organization to maintain and develop its own basic science. And, again, the existence of target domains that exploit different phenomena makes technology a means to an end. To facilitate this, modularity, recursivity, hierarchy, and sharing of technologies are necessary. On the one hand, the exchange of the internal structure of the system and the deepening of the system configuration will result in an increase in the complexity of the system itself, resulting in an increase in its scope of application and a lock-in phenomenon that may lead to an innovation dilemma. Arthur has developed a transaction simulation model that applies Learning Classifier System [15] as an experimental environment for reproducing complex phenomena in economics, and in this book, he states that this simulation environment has become an appropriate testbed for innovation studies. Learning Classifier System is one of genetics-based machine learning systems. It has a rule of If-Then

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form as problem-solving knowledge, the effectiveness or value of the rules is changed by reinforcement learning, and finally, Genetic Algorithms are applied to generate and evolve the rule. It is interesting to note that Brian Arthur’s argument that the learning system itself, with its combination of these elements, is directly linked to the consideration of technology and innovation.

6 Acquiring Robust and Optimized Results Wagner discusses the problem of technological innovation in Chap. 7, in his book [14], which describes recent biology progress and, in particular, the nature of gene and reaction circuits in cells. Based on the discussion of how the function of the network of enzymatic reaction systems in a cell is acquired, he focuses on the process by which nature acquires new traits in biological evolution and the similar nature of the technological innovations. First of all, it is interesting to note that the network of complex enzymatic reactions is characterized by a large number of nodes and a small network diameter. This will ensure that local changes in the network can substitute functions and help to maintain a robust biological system against environmental changes. This is similar to the important role of dense networks of companies and their engineers, such as those found in Silicon Valley, in fostering innovation. The second is the fact that trial and error or the generate and test methodology has been the driving force of biological evolution. In fact, there is often a “failure” in the evolutionary process in which a species goes extinct, leading to a number of failures in innovation. In the evolutionary process, there is a phenomenon called exaptation, in which organs that originally occurred for one purpose are diverted to different purposes. This is often the case in innovation as well. There is always more than one solution to a similar problem in business and/or engineering domains. Furthermore, he describes that living organisms often adapt to a new environment by rearranging the functions of existing proteins. This is similar to the pattern of innovation in which old functions are leveraged to realize new products and services. The components found in organisms are highly modular and can be easily reused. This is also an essential property for the aforementioned open innovation.

7 Discussion In this section, based on the above survey in various domains, we would like to present how we consider the creation of open and/or free innovation from the perspective of evolutionary computation in the following three points: management-related issues, computational complexity issues, and the speed of environment changes versus innovation activities.

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The first important perspective is that of management-related issues. For the creation of open and/or free innovation, it is necessary to facilitate the recombination of components or building blocks by commonization of module designs and interfaces. This yields the necessity of some standardization activities. This cannot be resolved only by a technology-only approach. International negotiations, including those of other companies and organizations, will be necessary. Moreover, the powers of new products and services are very weak in the situation before disruptive innovation. In experimental biology, genetic modification of E. coli is relatively easy to carry out, but the resulting modified E. coli will be outclassed by wild-type E. coli in a normal environment. For this reason, we put antibiotic resistance genes into the E. coli that we want to modify and conduct experiments in an environment where wildtype E. coli cannot live in a medium that contains antibiotics. In addition, modified E. coli that survive in such an environment might be reintroduced to the original E. coli due to the robustness of the gene network, if the modified E.coli keep left intact. The innovators’ dilemma also occurs in nature. To address this, it is important to set an appropriate evaluation function for the outcomes of the innovation. In reference, the importance of an appropriate evaluation function is discussed using the setting of a human national treasure as an example. This is because the living national treasures (masters of traditional technology) in Japan would otherwise be weeded out and disappear. The second important issue is to deal with the computational explosion problems, which often occur in combinatorial problems known in computer sciences. In areas where modularity and recombination of modules can be easily achieved, innovation is easier due to obtaining a large number of combinations of candidates. However, there are difficult areas where this is not the case. If we would have advanced software development environments and the platforms for innovators, it would be easy to successful innovation. On the other hand, innovations in agriculture with artificial breeding techniques, and/or innovations in pharmaceuticals are difficult to achieve with conventional methods, because the definition of modules is not clear, to begin with. In these areas, technological innovation would play a major role. The progress of the Internet platform, for example, has made it extremely easy for businesses to innovate. In addition, some years ago, innovation in the field of mechanical machining was considered difficult, but with the spread of 3D printers, it has become possible to produce small quantities of products with complex shapes. The potential for new innovation is expected to be very high with these innovations. The third important perspective is the speed of environmental change versus the speed of innovation. When the environment is changing slowly, sustaining innovation is sufficient, and policies of choice and concentration are more likely to be effective. Constructive innovation, where system elements are gradually and hierarchically built up from the lower levels, becomes appropriate. In various fields, there is a need for mechanisms in which modules are defined in a constructive way. In the recent information and communication technology domains and the business management domains, the speed of the changes is so fast (dog years) that sustaining innovation strategies cannot work well. In such domains, the number of

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trial and error or generative inspection trials is very important. This is exactly where evolutionary computational methods come into their own. In particular, the diversity at the genotype level, which is not expressed as a phenotype, plays a major role. For example, the continueing business policies at Google or Ideo, in which they set aside time for activities outside of business duties, are one of the excellent ways to keep and increase the diversity for innovation. On the other hand, as Barabasi explains in his recent book [18], in the networkoriented society, the environmental randomness would affect the success performance of any business and/or academic domains. Their network analysis with huge data has revealed that very few attempts would yield high-performance successes. Furthermore, in the realm of fast change and big bang innovation, it is especially important to have many trials and frequent generational changes. The innovation expected from venture businesses nowadays has this nature. It is necessary to first promote modularization, next to explore various possible trials, and then to evaluate them appropriately. In particular, in open and/or free innovation in the service domain, co-evolution between service providers and service recipients is the key. This is known as the importance of value co-creation in the service science domain. New system methodologies such as soft system approaches play a major role in this domain because of the lack of clarity in the problem formulation and the nature of the solution.

8 Concluding Remarks In this chapter, we have introduced the research on disruptive innovation in interdisciplinary areas, which we started to investigate after the discussion of the Smarter World Research Group in this book [1]. It is very interesting that prominent researchers in business administration, computer science, organizational science, economics, and biology have discussed issues of innovation in relation to their own disciplines in their books and that they have a great deal to do with evolutionary computation principles in the way they relate to their own disciplines. This is why the essence of innovation becomes clearer through the interdisciplinary exchange between various studies. In this chapter, we have described our views on the deep relationship between innovation and evolutionary computation by developing the relationship between business and research on measurement, control, and artificial intelligence that we have recently published [3]. We, researchers and engineers involved in systems science and engineering, must continue to discuss with researchers, experts, and users in various domains and face the challenges of innovation head-on. We hope this chapter would be a good opportunity for this purpose.

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References 1. T. Kaihara, H. Kita, S. Takahashi, (eds.) Innovative Systems Approach for Designing Smarter World (Springer, 2020) 2. T. Terano, This Is How I Feel About Complex Systems. in [3] (April, 2019), pp. 1–7 3. F. Koch, A. Yoshikawa, S. Wang, T. Terano, (eds.) Evolutionary Computing and Artificial Intelligence Essays Dedicated to Takao Terano on the Occasion of His Retirement (Springer, 2019) 4. T. Terano, Gallery for Evolutionary Computation and Artificial Intelligence Researches: Where Do We Come from and Where Shall We Go, in S. Kurahashi, H. Takahashi (eds.): Innovative Approaches in Agent-Based Modelling and Business Intelligence. Agent-Based Social Systems Book 12 (Springer, 2018), pp. 1–8 5. B. Stephan Onggo, L. Yilmaz, Franziska Klügl, T. Terano, C.M. Macal, Credible AgentBased Simulation—An Illusion or Only a Step Away?, in Proc. the 2019 Winter Simulation Conference, N. Mustafee, et al. (eds.) (Dec. 2019) 6. C.M. Christensen, The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail (Harvard Business School Press, 1997) 7. C.M. Christensen, E. Raynor, Sensing What’s Next: Using the Theories of Innovation to Predict Industry Change (Harvard Business School Press, 2004) 8. C.M. Christensen, S.D. Anthony, E.A. Roth, The Innovator’s Solution: Using Good Theory to Solve the Dilemma of Growth (Harvard Business School Press, 2003) 9. E. Von Hippel, Democratizing Innovation (MIT Press, 2005) 10. E. Von Hippel, Free Innovation (MIT Press, 2016) 11. D.E. Goldberg, The Design of Innovation: Lessons from and for Competing Genetic Algorithms (Kluwer, 2002) 12. R. Axelrod, M.D. Cohen, Harnessing Complexity (The Free Press, 1999) 13. W.B. Arthur, The Nature of Technology -What it is and How it Evolves (Free Press, 2009) 14. A. Wagner, Arrival of the Fittest—Solving The Evolution’s Greatest Puzzle (Oneworld Publications, 2014) 15. R.J. Urbanowicz, J.H. Moore, Learning classifier systems: a complete introduction, review, and roadmap. J. Artif. Evol. Appl. vol. 2009, Article ID 736398, 25 pp 16. J. Rifkin, The Zero Marginal Cost Society: The Internet of Things and the Rise of The Sharing Economy (Griffin, 2015) 17. L. Downes, P.F. Nunes, Big Bang Disruption Strategy in the Age of Devastating Innovation (Portfolio/Penguin, 2014) 18. A.-L. Barabasi, The Formula: The Universal Laws of Success (Little, Brown and Company, 2018)

Takao Terano is a professor, Chiba University of Commerce, Professor Emeritus, Tokyo Institute of Technology, and the University of Tsukuba. He is also at the position of Invited Researcher of the National Institute of Advanced Industrial Science and Technology (AIST), Japan. He received BA degree in Mathematical Engineering in 1976, and M. A. degree in Information Engineering in 1978 both from the University of Tokyo, and Doctor of Engineering Degree in 1991 from the Tokyo Institute of Technology. His interests include agent-based Modeling, Knowledge Systems, Evolutionary Computation, and Service Science. He is a member of the editorial board of major Artificial Intelligence and System science-related academic societies in Japan and a member of IEEE, and the president of PAAA.

Smartification of Social Infrastructure for Efficient Power and Energy Use Keiichiro Yasuda and Ken-ichi Tokoro

Abstract This article presents an overview of the history of power systems and describes the features of power systems from a systems engineering perspective, as well as discussing smart grids and smart communities as social infrastructure for efficient power and energy use. This article also outlines the latest trends in the research, development, and pilot-testing of smart grids and smart communities both within Japan and abroad. Keywords Power and energy system · Social infrastructure · Smartification · Smart grid · Smart community · Demand response

1 Introduction Amid global-scale environmental issues, depletion of fossil fuels, and other societal challenges, public concern related to the building of new power and energy systems as social infrastructure for the next generation has skyrocketed, as seen by the growth of sustainable cities; i.e., cities that aim to become sustainable by promoting energy conservation and recycling. This situation has prompted Japan and other countries to attempt to create new, sustainable, and environmentally-friendly power and energy systems and local communities through the mass introduction of renewable energies and the adoption of novel information and communications (IC) technologies [1, 2]. Adapted from Keiichiro Yasuda and Ken-ichi Tokoro “Smartification of Social Infrastructure for Efficient Power and Energy Use (written in Japanese),” Journal of The Society of Instrument and Control Engineers, Vol. 55, No. 8, pp. 698–703 (2016). Partly translated by permission of The Society of Instrument and Control Engineers. K. Yasuda (B) Graduate School of Science and Engineering, Tokyo Metropolitan University, 1-1 Minamiosawa, Hachioji-shi, Tokyo, Japan e-mail: [email protected] K. Tokoro Energy Innovation Center, Central Research Institute of Electric Power Industry, 2-6-1 Nagasaka, Yokosuka-shi, Kanagawa, Japan e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 T. Kaihara et al. (eds.), Innovative Systems Approach for Designing Smarter World, https://doi.org/10.1007/978-981-15-6651-6_10

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Such initiatives involve electrical energy for vast geographical areas and thermal energy for more limited geographical areas. However, as the majority of specific smartification initiatives, both domestic and outside Japan, are for next-generation electrical energy systems with next-generation advantages and features, this study focuses on the smartification of social infrastructure related to electrical energy. Power and energy systems involve two main approaches: electrical power engineering, which recognizes the individual component technologies behind the generation, transformation, transportation, and consumption of electrical energy; and electrical power system engineering, which identifies those functions as a single system. However, the breakdown of the self-regulation characteristics of loads, triggered by an increase in constant-power loads (exemplified by inverter loads), failure of the stable structure of systems due to a decline in the synchronizing power in their grids, and recent rapid advances in IC technologies have transformed these systems into more than simple electrical energy systems. Such systems are becoming more largescale, complex, and interdisciplinary systems that integrate the arts and the sciences, with greater emphasis on their characteristics as IC systems and social networks. In addition, in contrast to pre-liberalization power systems, in which power generation was managed in an integrated way by the electric company, and demand was managed locally by consumers, recent years have seen a shift toward systems that use mixed management on both the supply and demand sides. Smart communities as social systems, in which the homes, industries, facilities, traffic networks, and public services in the local community are connected to an energy system based on a smart grid, can be considered as a type of “System of Systems” (SoS). This study first provides an overview of the features of power and energy systems and how they have developed and changed over time from a systems engineering perspective, before (1) discussing smart grids and smart communities as social infrastructure for efficient power and energy use and (2) outlining the latest trends in the research, development, and pilot-testing of smart grids and smart communities both within Japan and abroad.

2 Overview of Power and Energy Systems This section discusses the history of power systems from their advent until now while describing the importance of maintaining the supply–demand balance of a power system in terms of stability and power quality (frequency and voltage) and explaining the features of power systems from a systems engineering perspective [3].

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2.1 Features and Historical Development of Power Systems as Systems Power systems involve a closely connected mechanical system and electrical system. Early power systems, which used DC (direct current) generators and DC distribution circuits to provide power to electric lamps, gave way to systems that use AC (alternating current) generators to distribute and transmit AC power. This, in turn, increased the share of power loads using three-phase AC rotating magnetic fields (induction motor loads), in addition to the existing electric lamp loads. There are two types of power: real power and reactive power. Real power does the actual work, whereas reactive power acts as a lubricant to smooth the flow of power. From the perspective of these two types of power, power systems have contrasting natures. When real power changes, it immediately sends fluctuations through the entire system as modulations in frequency; thus, power systems are very global in terms of real power. In contrast, reactive power is generated locally by reactors or condensers and its consumption stays within that locality; thus, power systems are very local in terms of reactive power. Frequency and voltage, the main qualities of power, have important and wellknown characteristics. In old power systems, which were dominated by electric lamp loads and induction motor loads, an increase in grid frequency causes power consumption to increase and vice versa; this characteristic is known as the self regulation effect of the frequency dependence on load. In addition, an increase in a load’s power consumption causes the reactive power to increase, which also causes its voltage to decrease; however, when the voltage of the load itself decreases, the power it consumes and therefore the reactive power also decreases. This describes the self -regulation effect of the voltage dependence on load. Thus, the behavior of power systems has a dual nature, in that it is global in terms of real power and frequency, but local in terms of reactive power and voltage. Attempts to decrease the transmission loss in power system transmission networks led to the adoption of AC circuits, which have the unique characteristic of relatively little resistance in their power lines compared to reactance. This is why the voltage in power systems, which is related to the reactive power, is dominant and the effect of real power is relatively small. Furthermore, the main generators in power systems are synchronous generators; all synchronous generators connected to a power system must generate power synchronously to maintain the stability of the system. If any generator accelerates for any reason, it must be returned to its normal position. A power system’s ability to do this is called its synchronizing power. Although this power varies depending on the operational state of the generator (i.e., the real and reactive power), it is an inherent characteristic of synchronous generators. Because the existence of this synchronizing power results in a close relationship between a power system’s frequency and real power, as well as maintaining system stability, it maintains the system’s supply–demand balance, which in turn maintains its frequency.

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By maintaining the important qualities of frequency and voltage for power, power systems exploit the fact that the relationships between real power–frequency and reactive power–voltage are stronger than those between real power–voltage and reactive power–frequency. That is, maintaining the important qualities of frequency and voltage for power can be almost independently controlled by the real power and reactive power, respectively. Frequency control, which uses the formerly close relationship between real power and frequency, is called load–frequency control or real power–frequency control, whereas voltage control, which uses the latter close relationship between reactive power and voltage, is known as voltage–reactive power control. For example, because fluctuations in frequency caused by supply–demand imbalances in a power system immediately spread throughout the entire system as fluctuations in frequency, engineers have been able to maintain the supply–demand balance in power systems (i.e., their frequency) by measuring these frequency fluctuations and using control systems, of which governor mechanisms are a representative example. Before the liberalization of electricity that began in the 1990s, there was almost no renewable energy, which is typified by wind and solar power generation and influenced by climatic conditions. This left the generation of real and reactive power in power systems under the control of electric companies, who succeeded in achieving control of power grid frequency and voltage under uniform management systems based on the strong relationships between real power–frequency and reactive power– voltage. Thus, despite being large-scale complex systems, traditional power systems were arguably well-structured systems whose stable operation was easy to maintain through (1) the existence of the self -regulation characteristics of loads and synchronizing power of synchronous generators; (2) the global and local nature of power systems in terms of real power–frequency and reactive power–voltage, respectively, due to the strong relationships between real power–frequency and reactive power–voltage; and (3) the uniform management systems of electric companies.

2.2 Changes in Power and Energy Systems As stated in Sect. 2.1, power systems used to be well-structured systems whose stable operation was easy to control and maintain due to the self-regulation characteristics of loads and the synchronizing power of synchronous generators. However, from the 1980s onward, there was an increase in constant-power loads, as exemplified by inverter loads, which led to a decline of this effect. This decline is an important issue that is directly connected to the difficulty of controlling frequency and voltage in power systems. In Japan, the Greater Tokyo Area suffered a so-called “voltage instability phenomenon” in July 1987, in which the voltage decreased in affected areas, despite the fact that sufficient power was being generated, there had been no accident, and voltage was being controlled without issues. This event led to a supply failure of approximately 8 million kW.

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In Japan, the liberalization of electricity that began in the 1990s made it possible for power-generating facilities owned by entities other than electric companies to be connected to existing power systems, which led to the establishment of wholesale power exchange. Recent years have also seen the continued growth of renewable energies, such as wind and solar, whose power generation is influenced by climatic conditions. This could cause a decline in the synchronizing power of power system grids, which may make it more difficult to successfully control frequency and voltage and maintain stability when compared to the uniform management systems of electric companies. On the other hand, rapid developments in IC technologies and system technologies are expanding the potential to operate systems in more advanced ways than ever before. For example, in the future, it will be possible to use smart meters and other new sensor devices to obtain Big Data (massive quantities of accurate data) about energy consumers and distribution facilities. Plans are already underway to analyze and leverage these data so that suppliers will not only be able to check the status of the grid and manage energy use, but also provide information to energy users for the management or incentivization purposes, implement cooperative control with consumer energy management systems (EMSs), and optimize energy use over entire vast supply areas (so-called “total optimization”). The deterioration of the well-structured nature of power systems, induced by electricity liberalization and the recent introduction of renewable energies, as well as the desire for greater profitability and stability, has led to the proposal of (1) smart grids, which are high-quality, highly efficient, and highly reliable power supply systems (“grids”, i.e., power transmission networks) that use IC technologies and system technologies to combine distributed generation (such as solar power generation) with information about consumers; and (2) smart communities, which are next-generation energy–social systems that develop next-generation traffic systems, cities, and even new lifestyles based on smart grids [1, 2]. Thus, power systems have developed and changed over time. Now, due to the recent manifestation of global-scale environmental issues, fossil fuel depletion, and other societal challenges, as well as the rapid development of IC technologies, we are witnessing the beginning of an era of unprecedented transformation, commonly termed “smartification.”

3 Smartification of Power and Energy Systems This section provides (1) an overview of smart grids and smart communities as social infrastructure for efficient power and energy use and (2) an outline of the current transformation of power and energy systems.

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3.1 Smart Grids and Smart Communities In April 2010, a public–private council called the “Smart Community Alliance” was established in Japan, which comprised private companies/organizations and the Ministry of Economy, Trade, and Industry (METI), with the New Energy and Industrial Technology Development Organization (NEDO) as its secretariat. The same year, the METI also recruited areas to participate in its “Next-Generation Energy and Social System Pilot Project.” The cities of Kitakyushu (Fukuoka Pref.), Toyota (Aichi Pref.), and Yokohama (Kanagawa Pref.) were chosen, as well as Kyoto Prefecture, and the pilot projects began. These are just some examples of the enthusiastic initiatives being undertaken across the public and private sectors1), 2) . Specific plans for new, sustainable, and environmentally-friendly power and energy systems and local communities include [4–6]: (i) Smart grids, which are high-quality, highly efficient, and highly reliable power supply system networks (“grids”) that use IC technologies and power storage technologies to combine distributed generation (such as solar power generation) with information about consumers; (ii) Smart communities, which are next-generation energy–social systems that involve the development of next-generation traffic systems, cities, and even new lifestyles based on smart grids. Generally, smart communities are social systems that connect the homes, industries, various facilities, traffic networks, and public services in the local community to an energy system based on a smart grid. Rather than being mere consumers of energy, smart communities aspire to generate, store, and use energy in a “smart” way in order to foster a way of life with a low environmental impact.

3.2 New Power and Energy Systems As stated in the previous section, existing power systems have predominantly been considered and studied as electrical energy networks. However, smart grids allow power systems to go beyond the level of mere electrical energy networks to even be considered information networks. In other words, the study of power systems has become an interdisciplinary field that intersects with engineering fields. In the future, power systems are expected to become more like networks subject to regulation, networks as power markets, and networks as individuals and organizations. As a result, they are transforming from electrical energy systems to energy information systems and further to systems integrating both the arts and sciences. The new “System of Systems” (SoS) concept in systems engineering is defined as follows: (i) A system created by gathering multiple components that are managed locally rather than globally to provide a single service;

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(ii) An entire system that activates autonomously distributed groups of systems when required for particular functions. In contrast to pre-smart grid power systems, in which power generation was managed in an integrated way by the electric company and demand was managed locally by consumers, recent years have seen a shift toward systems that use mixed management on both the supply and demand sides; i.e., systems with SoS-like characteristics. The smartification of power and energy systems within these new environments, which is predicated on the enthusiastic introduction of wind, solar, and other renewables, intends to introduce the latest IC technologies to maintain and improve the stability of supply and profitability. From a systems engineering perspective, the following concepts are important for achieving this smartification: (i) Ensure and expand structural flexibility: The development and introduction of storage batteries and controllable household appliances that can absorb the effect of unstable power sources whose power generation is influenced by climatic conditions and be integrated with other infrastructure. (ii) Improve functional flexibility: The use of new demand-side management and demand response based on two-way information sharing through the introduction of smart meters. According to these two concepts, in addition to existing power sources, unstable power sources whose power generation is influenced by climatic conditions will also require unprecedented new structural and functional frameworks to maintain their supply–demand balance at a high level. The remainder of this article outlines the latest trends in the research, development, and pilot-testing of smart grids and smart communities as social infrastructure for efficient power and energy use, both within Japan and abroad.

4 Trends in Smart Communities Many social experiments have been conducted around the world with regard to smart communities. This section will present an overview of some pilot tests of smart communities in Japan.

4.1 Objective of Pilot Testing To date, electric companies in Japan have maintained a supply–demand balance by adjusting the amount generated to the amount consumed. However, with the mass introduction of renewable energies, this supply-side-only response will become inefficient. The introduction of renewable energies will increasingly cause the utilization

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rate of thermal power plants to fall; therefore, securing the power supply to cope with peak usage hours, of which there are only dozens per year, is no longer particularly efficient on a community-wide level. Moreover, as thermal power plants cannot be turned on instantly, some thermal power plants with extra capacity will need to be kept on standby in order to absorb the unpredictable output fluctuations of renewable energies. However, too many will lead to greater fuel usage and CO2 emissions. The use of unpredictable renewable energies requires pilot tests. In smart communities, the power transmission system of the electric company (supplier) and the generation systems and devices of the home (consumer) will be connected to each other through IC technologies to enable power-sharing. The introduction of smart meters will enable two-way information sharing between consumers and suppliers, which will make it possible to stabilize the unstable output of renewable energies by controlling consumers’ power storage devices and household appliances. In addition, maintaining the supply–demand balance not only by suppliers adjusting their supply to demand but also by consumers curtailing their demand, should enable efficient power generation on a community-wide level. In Japan, local governments, residents, and private companies in four areas (the cities of Yokohama (Kanagawa Pref.), Toyota (Aichi Pref.), and Kitakyushu (Fukuoka Pref.), as well as Keihanna Science City (Kyoto Pref.)) have worked together to conduct pilot tests as part of the “Next-Generation Energy and Social System Pilot Project,” whose aim is to establish the various technologies required in smart communities and to create highly efficient social infrastructure. Additionally, “Next-Generation Energy Technology Pilot Projects” are being conducted in nine areas in Japan to supplement the aforementioned four pilot tests. The objective of these projects is to conduct community-based testing to establish the use of highly versatile technologies, including renewable energies that take advantage of local resources such as wind, geothermal, and forest resources.

4.2 Pilot Technologies Among the techniques and technologies that have been piloted for smart communities are two broad methods of system control. One is the Energy Management System (EMS) technologies, which are intended to optimize a community’s energy use as an SoS through IC technologies. The other is demand response techniques and technologies, which efficiently maintain the supply–demand balance by indirectly incentivizing people to change their power demand. What follows is an overview of each item tested as part of the “Next-Generation Energy and Social System Pilot Project.”

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Using EMS to Optimize Energy Use

The following EMSs were tested, which are intended to optimize a community’s energy use as an SoS through the use of IC technologies. • HEMS (Home Energy Management System) HEMSs facilitate efficient energy use by (1) managing home solar power-generating devices, storage batteries, electric vehicle (EV)/plug-in hybrid electric vehicle (PHEV) charging stands, and home electrical appliances (air-conditioning, TV, etc.) in an integrated way, and (2) advising consumers on how to use energy more efficiently. The use of EV/PHEV as storage batteries to absorb excess solar power in HEMSs was also tested. • BEMS (Building Energy Management System) BEMSs (1) manage electric and heat energy and (2) achieve the “local production and consumption of energy,” through the effective use of an integrated electricity storage system that encompasses the logistics necessary for commercial facilities. BEMSs were also tested for their ability to optimally regulate supply and demand within the community based on (1) information about current energy use obtained from networks that link homes, schools, convenience stores, etc. within a community with transport modes such as cars and trains; (2) estimates of the community’s power demand based on predictions of the climate and consumer behavior. • CEMS (Community Energy Management System) CEMSs (1) take information transmitted from HEMSs, BEMSs, and EV charging management systems (power consumed, gas consumed, solar power generated, etc.) to understand the energy demand for the entire community; and (2) fully exploit renewable energies without waste by proposing a plan for optimal energy use within a community, spreading it to the EMSs of its homes and buildings, then requesting compliance from the consumers. CEMSs were also tested for their ability to control the timing at which power is supplied by generating solar energy within the community and charging/discharging to storage batteries, PHEV, and EV, in order to standardize power throughout the community and reduce load on the system’s power.

4.2.2

Demand Response

Also tested as part of the pilot project were demand response techniques and technologies, which efficiently maintain the supply–demand balance by indirectly incentivizing people to change their power demand. Generally, either economic methods are used to guide people’s behavior through financial incentives, or data-driven methods are used to provide data to guide people’s behavior. Despite the previous adoption of demand-side management, in which consumers directly control their use

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of air-conditioning and other devices as required due to the supplier discounting the price of electricity, demand response does not involve the direct control of devices but instead provides people with data and financial incentives to change their behavior and ultimately change power demand. The most representative form of economic demand response is dynamic pricing, in which the price is increased during peak time slots when the supply is expected to be tight. This causes a peak demand to decrease and the efficiency of supply to increase. Although electric companies do provide pricing menus for different time slots, these prices are fixed in advance based on the season and time of day and do not change from day to day. In contrast, in dynamic pricing, prices are dependent on the changing supply and the demand or market conditions from moment to moment. Typical forms of dynamic pricing include [7]: • Critical Peak Pricing (CPP) Prices are raised to above those during peak time slots on days when the supply is expected to be tight. This is intended to reduce capital expenditure by decreasing power during the dozens of peak hours in a year. Consumers will be notified on the day before a critical peak is called. There may also be limits on the number of days in a year when critical peaks can be called or the number of consecutive days they can be called. • Real-Time Pricing (RTP) The portion of the price that is assigned to expenses for power generation is fixed to the previous day’s wholesale power market price or the previous day’s estimated real-time market price. This presumes the existence of a wholesale power market. As the supplier passes the wholesale power price on to the consumer, it is simple to design the pricing; however, it has been noted that consumers incur a relatively greater risk because of the price changes by the hour. • Peak Time Rebate (PTR) Consumers who reduce their consumption during critical peak times will be paid a rebate of a corresponding amount. Normally, the amount to be compensated is subtracted from a baseline consumption, which is calculated as if there were no rebates. For example, the average actual consumption for several days before a critical peak is called might be used as the baseline. Consumers will receive a rebate if they reduce their consumption; even if they do not, they will not be charged more than the normal price. On the topic of economic demand response, Japanese electric companies announced in 2016 that they would begin providing pricing menus. For example, the Hokuriku Electric Power Company announced in April 2016 that it would provide a service called the Setsuden tokutoku plan, which is equivalent to PTR [8]. Furthermore, representative forms of data-driven demand response include providing information about deals and bargains or printing coupons in order to guide people’s behavior. Among the forms of demand response, PTRs present a challenge

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in that sufficient capital must be secured to offer the rebates. To that end, pilot tests were conducted to efficiently encourage behavior changes with the minimal capital requirement; this was achieved using a technique from behavioral economics called a “nudge” [9] that changes people’s behavior in predictable ways. For example, in one experiment, people were given coupons that they could use at nearby stores during times of increased power in order to “nudge” them to go out, which would thereby reduce peak power usage. Although not based in Japan, one famous pilot study of data-driven demand response in the United States is Opower’s energy efficiency service, which compares consumers’ energy use with that of other households on their electricity bill, along with tips on how to save energy, in order to encourage energy-saving behavior. This service uses a unique data processing algorithm to divide consumers by their energy consumption attributes and create customized messages for each household. It then shares energy-saving tricks and compares consumers’ energy consumption with that of the entire community, that of their neighbors, and that of similar households. As an example of how they might be customized, high-consumption households might receive a message that emphasizes the negative consequences of not conserving energy, such as “You are using more electricity than your neighbors. Over a whole year, this would cost you an additional $X.”

5 Challenges in Creating Smart Social Infrastructure In demand response, not only do suppliers maintain the supply but consumers control their demand in order to maintain the supply–demand balance; incorporating this is important for creating a smart social infrastructure that makes effective use of the unstable power provided by renewable energies. However, to use demand response on a daily basis in social infrastructure, the following challenges must be addressed. First, a highly precise method is required for predicting the effect of demand response. Because demand response does not directly control air-conditioning and other devices, unlike demand-side management, and involves “nudging” people to change their consumption indirectly, its effect is unpredictable. For example, if PTR is called during a critical peak time, it is uncertain how many people will reduce their demand and the extent to which power consumption will decrease. However, using demand response for the efficient use of renewable energy requires the ability to predict its effect beyond a certain level of precision; if the effect of demand response cannot be predicted with the same precision as the renewable energy output, this will only increase the number of variables when determining optimal energy use. There also needs to be a way of continually sustaining the effect of demand response over time. The effects of both economic and data-driven methods of demand response have been reported to decrease as people get used to or tired of it [10]. Of course, with economic demand response, it should be possible to sustain its effect by progressively increasing the penalties or incentives. However, adopting this method could also result in a community whose low-income members will no longer be able

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to use electricity during a CPP- or RTP-induced surge in electricity prices. Also, in the case of PTR, continually increasing the rebate amount requires securing a source of capital for those rebates. If most of the rebate capital is recouped through electricity prices, this may result in a situation where the wealthy will profit, as they can afford expensive storage batteries that will considerably decrease their power consumption during peak times. For example, Opower’s aforementioned energy efficiency service continually praises successful energy-saving households by printing smiley faces and energy-saver rankings on their power bills in an attempt to prevent them from losing interest in energy conservation; however, other mechanisms besides price revision are required to sustain the effect of demand response. The above two challenges are related to the unpredictability of human behavior; however, accurately predicting human behavior will surely prove to be just as much of a challenge in the future as it is now. As such, cyclically developing a “menu” of response programs that adjust to people’s behavioral changes as they get used to or tired of the response programs may be an effective way to create a smart social infrastructure. For example, i.

Design a menu of demand response programs based on people’s current behavior (providing data, setting prices, etc.) ii. Implement that menu of demand response programs iii. Measure the effect of those demand response programs iv. If the obtained effect differs from what was expected, reexamine the menu.

6 Conclusion This article presented an overview of the history of power systems and described the features of power systems from a systems engineering perspective, as well as discussing smart grids and smart communities as social infrastructure for efficient power and energy use. It was noted that such systems are becoming more than just electrical energy systems; they now exhibit characteristics of information and communications systems that take them beyond engineering subfields and should instead be interpreted as systems that integrate the arts and sciences or “Systems of Systems” (SoSs). This article also outlined the latest trends in the research, development, and pilottesting of smart grids and smart communities both within Japan and abroad. In particular, it discussed, in detail, two pilot-tested methods of demand response that aim to efficiently maintain the supply–demand balance by indirectly incentivizing people to change their power demand. These methods include an economic method that uses financial incentives to guide people’s behavior and a data-driven method that provides data to guide people’s behavior.

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References 1. Ministry of Economy, Trade and Industry (METI), Smart Grids/Smart Communities. http:// www.meti.go.jp/policy/energy_environment/smart_community/ 2. Japan Smart Community Alliance (JSCA), About JSCA. https://www.smart-japan.org/ 3. Y. Sekine, Power Systems Engineering. Denki Shoin (1976) 4. Y. Hayashi et al., Key technologies to make smart grids a reality. J. Inst. Electr. Eng. Jpn., 132(10), 678–701 (2012) 5. T. Nitta et al., Smart cities and electricity. J. Inst. Electr. Eng. Jpn., 133(12), 788–829 (2013) 6. K. Hidaka et al., Smart cities, information processing society of Japan. J. Digital Pract., 5(3), 171–230 (2014) 7. T. Hattori, N. Toda, Current status and prospect of demand response programs for residential customers in the United States: evaluation of the pilot programs and the issues in practice. Research Report Y10005, Central Research Institute of Electric Power Industry (2011). https:// criepi.denken.or.jp/jp/kenkikaku/report/detail/Y10005.html 8. Kurabeyo! Denki Ryokin (Electricity Price Comparer). http://denkihikaku.net/ootedenryoku/ rikuden.html, accessed 7 Apr 2016 9. H. Komatsu, K. Nishio, Applicability of ‘Nudge’ as information provision for energy and electricity conservation: energy reports for the US households as a case example. Research Report Y12035, Central Research Institute of Electric Power Industry (2014). https://criepi. denken.or.jp/jp/kenkikaku/report/detail/Y12035.html 10. A. Laskey, O. Kavazovic, Energy efficiency through behavioral science and technology, XRDS: Crossroads. The ACM Magazine for Students—Green Technologies. From Pollution to Pixels, vol. 17(4), pp. 47–51 (2011)

Keiichiro Yasuda (Regular member) Born in 1960, Keiichiro Yasuda completed his doctoral program in Electrical Engineering at the Graduate School of Engineering, Hokkaido University, in 1989. The same year, he became an Assistant Professor in the Faculty of Engineering at the Tokyo Metropolitan University. In 1991, he became an Associate Professor in the Faculty of Engineering at the Tokyo Metropolitan University and, since 2006, he has been a Professor in the Graduate School of Science and Engineering. He is engaged in research on systems optimization and power systems engineering. He holds a Doctor of Engineering and is a member of societies such as the Society of Instrument and Control Engineers, the Institute of Electrical Engineers of Japan, the Japanese Society for Evolutionary Computation, and the IEEE. Ken-ichi Tokoro (Regular member) Born in 1963, Ken-ichi Tokoro completed his doctoral program in Administration Engineering at the Graduate School of Science and Technology, Keio University, in 1989. The same year, he joined the Central Research Institute of Electric Power Industry (CRIEPI), where he is now engaged in research on optimization in power projects at CRIEPI’s Energy Innovation Center. He holds a Doctor of Engineering and is a member of societies such as the Society of Instrument and Control Engineers, the Institute of Electrical Engineers of Japan, the Operations Research Society of Japan, and the Japanese Society for Evolutionary Computation.

The State of Art and Future Direction on Smart Home Systems Takashi Nishiyama

Abstract Ambient intelligence is a system technology that embeds sensors, computers, and actuators into the environment without making the user aware. The system estimates the situation and behavior of the user in the environment and provides useful support to the user through interaction with the system. Ambient intelligence can be applied to various environments, one of which is considered a house environment called a smart home. This report introduces overseas and Japan commercialization and research cases on smart home technology. Keywords Ambient intelligence · Smart home · Home energy management system · Health care system

1 Introduction Research and development of ambient intelligence have been actively promoted in Japan and overseas. Ambient intelligence means that a system consisting of sensors, computers, and actuators is embedded in the environment so that a user is not conscious of the system [1]. And the situation and actions of the user in the environment are assessed by the system to provide useful support for the user through the interaction with the system [2]. Smart home technology is ambient intelligence targeting the living environment, and its promising application fields are: (i) Home energy management system, HEMS that maintains the comfort of the living environment while monitoring the power consumption behavior of residents, (ii) security/healthcare system that manages Adapted from Takashi Nishiyama “The State of Art and Future Direction on Smart Home Systems (written in Japanese)”, Journal of the Society of Instrument and Control Engineers, Vol. 55, No. 8, pp. 710–713 (2016). Partly translated by permission of The Society of Instrument and Control Engineers. T. Nishiyama (B) Advanced Technology Development Center, Life Solutions Company, Panasonic Corporation, 1048 Kadoma, Kadoma City, Osaka 571-8686, Japan e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 T. Kaihara et al. (eds.), Innovative Systems Approach for Designing Smarter World, https://doi.org/10.1007/978-981-15-6651-6_11

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the security, safety, and health of residents, and (iii) social computing systems such as communication tool among users [3]. Among these three application fields, some systems have already been commercialized, and smart home technology is expected to become more familiar in the future. In the next Sect. 2, we will introduce the cases in Japan and overseas that have already been commercialized in the smart home technology. In Sect. 3, we will introduce examples of research on smart home technologies both in Japan and overseas. Section 4 describes the future of smarter homes that will be more intelligent, with reference to ecological perspectives aimed at harmony between nature and the environment [4], followed by the summary in Sect. 5.

2 Current Status of Commercialization of Smart Home Technology In reviewing the current state of commercialization of smart home technologies, they can be broadly divided into two categories shown in Table 1, referring to the above three application fields. That is, they are safe/secure and comfortable/convenient. The former is further divided into two fields: security and monitoring the elderly, and the latter is divided into two fields: HEMS/automatic home appliances and conversation terminals/software. In the following, along with this classification, an overview of the notable commercialization cases will be introduced individually.

2.1 Overseas Commercialization Cases In the security field, Indiegogo’s Cocoon is considered a home security device installed at home [5]. It has microphones to detect low-frequency sound among the non-audible region of humans and to judge abnormalities and notifies the user’s smartphone when abnormalities are detected. Since Cocoon has learned the normal sound in the house in advance, it notifies only when it detects an abnormal sound such as noise. There are also HD cameras and motion sensors embedded. In the field of monitoring the elderly, there is Medical Alert System supplied by Medical Guardian Inc. [6].It is a so-called watching system for the elderly, a service that connects to a 24-h operator simply by pressing a button if the user feels something unusual while wearing it. This allows the user to receive an emergency response. There is also a GPS-equipped type that can be carried around when going out and position information is obtained. In the field of HEMS/automatic home appliances, there is a thermostat by Google Inc. [8].Currently, it is the third version, which learns the user’s life pattern and favorite room temperature, and generates a cooling and heating schedule. This will reduce the cost of heating and cooling. Also, when a camera system is installed

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Table 1 Commercialization example of smart home technology Safe/secure Security

Comfortable/convenient Monitoring the elderly

HEMS/automatic home appliances

Conversation terminals/software

Overseas • Indiegogo Inc.: Cocoon [5]

• Medical Guardian Inc.: medical alert system [6] • Royal Philips: emergency call service [7]

• Google Inc.: Thermostat (3rd Gene.) [8] • Opower Inc.: energy saving solution services [9]

• Amazon Inc.: Echo [10] • Google Inc.: Google Assistant [11]

Japan

• Zojirushi Corp.: • Panasonic Corp.: • Softbank Corp.: monitoring using Smart HEMS Robot ‘Pepper’ thermos [12] • Sharp Corp.: [18] • Panasonic Corp.: • OMRON Corp: mobile type robot Mimamori Net ‘Robophone’ services [13] supporting • Konica Minolta comfortable sleep Corp.: care [19] support system • Panasonic Corp.: [14] good night • Paramount Bed navigation Corp.: bed with service [20] leaving sensor [15, 16] • Informetis Corp.: monitoring services with power sensor [17]

• Secom Corp.: rush service etc. • Alsock Corp.: same as above • Panasonic Corp.: security sensors

separately, if the user turns off the thermostat during the stay outside, the camera will automatically turn on and enter into the monitoring mode. Opower Inc. provides cloud-based energy-saving solution services to public institutions [9]. It is characterized by analyzing user’s power consumption data and providing a power-saving message from the user’s viewpoint based on the behavioral science called Nudge [21].Opower Inc. was acquired by Oracle Inc. in 2016, and the effectiveness of the power-saving message technology has been verified in a largescale demonstration project of CO2 reduction in 300,000 households nationwide in Japan [22]. In the conversation terminal/software field, core technologies have been developed through a human interface called AI speakers. As AI speakers, there are two representatives called Amazon’s Echo [10] and Google’s Google Assistant [11].The both companies have developed a platform to connect AI speakers and home appliances, and allowed many users to use them. And they collect and analyze data related to the behavior of residents in the house, and create new value corresponding to each individual. Now, the two other GAFA companies, Apple Inc., and Facebook Inc. also release their AI speakers.

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2.2 Commercialization Cases in Japan There are many familiar products and services in the security field in Japan. On the other hand, in the field of monitoring the elderly, commercialization has been steadily started since the early 2000s, and the system using Zojirushi thermos is a long-standing commercial service [12]. Panasonic Corporation has also provided a monitoring service for the elderly living alone called Mimamori Net [13], which consists of a motion sensor and a transceiver released by the former Matsushita Electric Works Co., Ltd. since 20021 .In recent years, many monitoring systems not only at home but also in facilities for the elderly have been developed and commercialized. For example, Konica Minolta Corporation has developed a resident activity detection solution for nursing care facilities called ‘care support system’ [14].This is a system that detects the behavior of residents in the facility with a non-contact sensor and notifies the care staff through the smartphone. The system uses a moving body detection sensor utilizing near-infrared rays and a sensor detecting micro-body movement via microwaves. This enables the system not only to detect getting up and getting out of bed but also to detect the fall and the presence/absence of slight body movement due to breathing. Paramount Bed Corporation has developed a technology called ‘CATCH’ that realizes the detection of leaving a bed with high-accuracy. This is the technology in which a strain gauge attached to the output shaft of an actuator detects a change in load due to the movement of a cared person, and is embedded to be commercialized in the bed [15, 16].This kind of system is expected to increase in number through the anticipation of a further aging society. In the field of HEMS/automatic home appliances, Panasonic Corporation is developing a so-called smart HEMS that connects solar panels and various home appliances in the house. Here, a distribution board measuring the power consumption for each branch circuit is used as a core home appliance [18].It has features such as visualization of power consumption in each room and control of home appliances collectively while going out. Since the power consumption data is also history as a result of the resident’s operation for home appliances in the house, analyzing the data can give an overview of the resident’s behavior. The waveform measured by the main circuit of the distribution board is the total power used in the house, and the waveform of each device having an individual frequency is superimposed and integrated. Therefore, in principle, it can be separated into the power waveforms of each device. Informetis Corporation, which developed this separation technology, has partnered with Tokyo Electric Power Company to provide monitoring services for the elderly living alone whose child household is staying away [17]. This service only installs a power sensor in the home of the elderly and does not use a camera, thus the psychological burden on the elderly is small who are watched over. There are also devices measuring the user’s sleep time and services supporting comfortable sleep. OMRON Corporation has commercialized a sensor placed on the 1 Currently,

the service has been terminated.

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bedside measuring sleep time [19]. The sensor monitors the user’s falling asleep by detecting the movement of the bedding due to the user turning over. When the user wakes up, pressing the sensor button stops the monitoring and displays the sleep time. ‘The good night navigation service’ by Panasonic Corporation is a smartphone application for air conditioners controlling the temperature from falling asleep to waking up [20]. When the user puts the smartphone on the bed and sleeps, the sensor inside the smartphone detects turning over and changes the set temperature for the air conditioner in conjunction with the room temperature sensor inside the AC.

3 Current Research Trends Related to Smart Home In reviewing the latest research trends related to smart homes, the classification in Table 1 is followed. In addition, common basic research utilizing smartphone built-in sensors, which has been increasing in recent years, was also included in the classification and divided into the framework shown in Table 2. Some of the notable studies are outlined below.

3.1 Research Trends in Overseas Regarding monitoring the elderly, around 2000, there were many studies that installed infrared sensors at some locations in the house to detect abnormalities of the elderly living alone. Recently, researches aiming at dementia judgment are increasing in number by grasping behavioral disorders. Hayes et al. of the Oregon Health and Science University published an experimental study in 2008 under the hypothesis that walking speed and activity fluctuate with dementia [23]. They installed motion sensors and contact sensors (door opened/closed) in the home of 12 elderly people aged 65 or older, collected and analyzed data for about half a year, and compared the 1 m walking time of the subject. As a result, people with MCI (Moderate Cognitive Impairment) fluctuate twice as much as healthy people. Cook and colleagues at Washington State University published in 2013 a largescale experimental study on the elderly suspected of such as dementia [24]. When the elderly have dementia, they spend more time performing basic activities such as cleaning than they are healthy. Based on this hypothesis, Cook et al. asked 263 elderly people: healthy, MCI, or dementia, to try eight types of basic behaviors specified by the experimenter for one hour. Through that time, sensor data such as motion and door-opened/-closed is being collected. Since the camera image was also obtained during the experiment, the experimenter prepares a dataset by tagging facts to the sensor data. As a result of machine learning and cross-validation, the correlation between the activity estimated from the sensor data and the activity based on the

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Table 2 Latest research examples related to smart home Safe/secure

Comfortable/convenient

Common platform technology

Monitoring the elderly

HEMS/automatic home appliances

Conversation terminals/software

Behavior Estimation using built-in Sensor

Overseas 【Dementia judgment】 • Oregon Health and Science Univ., Hayes et al. [23] • Washington State Univ., Cook et al. [24]

【Behavior estimation by power analysis】 • ETH Zurich, Kleiminger et al. [25]

• IBM Inc., Watson 【Smartphone [26] utilization】 • Yonsei Univ., Lee et al. [27] • Pennsylvania State Univ., Sun et al. [28] • Georgia Institute of Technology, Thomaz et al. [29]

【Anomaly detection】 • Osaka Pref. Univ., Fukunaga et al. [30] 【Dementia Judgment】 • Tokyo Univ., Mori et al. [31] • Shibaura Institute of Technology, Abe and Inoue [32]

【Behavior Estimation by Power Analysis】 • Kyoto Univ., Matsuyama et al. [33] 【Power-saving advice by Power Analysis】 • Osaka Univ., Higashino et al. [34]

• Kyoto Univ., Minoh et al. [35] • Panasonic Corp., Nishiyama et al. [36]

Japan

【Smartphone utilization】 • Osaka Univ., Higashino et al. [34]

experimenter’s observation was 0.54, and the discrimination rate between healthy and demented elderly was 73%. In the conversation terminal/software field, IBM’s Watson is famous and has been commercialized. Watson receives questions from people, searches for answers on the Internet, and responds appropriately. They learn by finding correlations and patterns in sentences given as questions [26]. On the other hand, there is an increasing number of studies that utilize acceleration sensors built into smartphones and smartwatches with the aim of collecting data naturally as common basic technology. Estimating the location in the house and the behavior there is considered to be effective for HEMS control etc. Lee and his colleagues at Yonsei University in Korea have noted that people’s location and behavior can be related. They collected the location of smartphones, sound, and vibration data from smartwatches to estimate behaviors [27]. In the experimental environment, the position estimation accuracy is 87%, but there remains a problem that people who remove the smartwatch at home cannot use it.

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There is also a study by Sun et al. of Pennsylvania State University, which also analyzes sounds captured by smartphones and detects signs of illness in everyday life [28]. In this study, sneezing, coughing, nasal congestion, and gargle were distinguished from sounds, and the accuracy of these four classifications was more than 80% (16 people, 204 days, in a real environment). However, the hurdles for practical use are considered to be high, such as verification of estimation accuracy in every place as where a smartphone is carried or placed, and privacy infringement due to sound collection.

3.2 Research Trends in Japan In the field of monitoring the elderly, in Japan as well as overseas, the viewpoint of research has shifted from detecting abnormalities to determining dementia. Mori et al. of the University of Tokyo have noted that dementia causes abnormal behavior such as a decline in ADL (Activities of Daily Living) and wandering at night. They hypothesized that this behavior would be recognized through pyroelectric sensor data, and placed many such sensors in the actual elderly home and collected data for one year [31]. They applied their behavioral change detection algorithm to the collected data, which allowed them to detect the abnormal behavior. However, there also included outing days. Inoue et al. of Shibaura Institute of Technology have noted on behavioral disorders caused by dementia. That is, they focus on abnormal behaviors such as forgetting to take a bath or to close a faucet, frequent going to a toilet, etc. They propose an algorithm recognizing those behaviors based on data obtained from sensors installed at home [32]. They also introduce a method to consider the onset probability of dementia when individual abnormal behavior occurs, which enables their algorithm to make a comprehensive judgment. In the HEMS field, Higashino et al. of Osaka University are conducting their research on generating power-saving advice through power data analysis [34]. They hypothesized that demonstrating power savings and those effects would encourage users to save power. An electric power sensor was installed at the home of a family of three, collecting data for a little over a month in late summer and confirming its effectiveness. In the field of conversation terminals/software, Panasonic’s Nishiyama et al. are conducting their research on a question–answering system for control tasks that were performed by agents in charge of each family member with an emphasis on the utility of each family member, aiming to be an agent trusted by users [36].

4 Towards Smarter Home The current status of commercialization and research and development of smart homes has been outlined through examples. It will become increasingly important

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how to integrate technologies that are being developed individually as a whole system called “Smarter Home” in the future. Here, in order to realize ‘Smarter Home’ considered to be a more intelligent and integrated whole system, we will introduce a vision based on the ecological point of view [4]. Crowley and colleagues have proposed a realistic approach to making the house itself an autonomous robot. That is, they start observing the house itself from the inside to solve internal problems. At that time, the viewpoint of “homeostasis” is introduced, and attention is paid to the environmental state of the whole house, and the state of the house itself, for example, the opening and closing of windows and doors and the state of equipment. They propose the development of “Smarter Home” that aims to maintain the comfort of residents by detecting these conditions and integrating those control. Equipment state detection has already been realized as visualization of power consumption on individual home appliances, water, and gas consumption. In the future, it is expected that the amount of garbage discharged and the amount of dust accumulated in each room will become more visible. This enables not even a fully autonomous system but a semi-autonomous system to be realized in which a resident could be aware of an unusual change. This is considered useful and helpful. The intellectual services that ‘Smarter Home’ could provide are classified into the following four categories: (i) tool services such as home appliances, (ii) housework services such as cleaning, washing, shopping and cooking, (iii) advisory services for resident behavior in the home, and (iv) media services such as entertainment and communication. For each service, based on the past progress of technological development up to now, future possibilities on developing each service are considered. This enables them to analyze the individual characteristics of the tasks consisting of each service. They further argue that R & D on fully automated systems seeming technically difficult is not efficient, such as fully automated cooking systems. Based on this perspective, for the individual tasks that compose each of the four services, we can enumerate the tasks performed by humans now. The tasks are classified into two categories: tasks that can be easily automated by replacing humans with autonomous systems, and tasks that are technically difficult to automate. The latter is a task that could be performed and worth to be done by a human, so it is worth leaving to humans [37]. Assigning tasks to autonomous systems or humans is an important issue to be considered. This leads to the realization of a “Smarter Home” composed of a group of smart objects2 [38] proposed by Crowley et al. We believe that this enables residents to receive useful and meaningful services. The individual smart objects will be realized by employing IoT technologies [39] and artificial intelligence technologies such as deep learning [40] which is to learn feature expressions of the target world.

2 An object that has its own ID, knows its location, has a function to detect the environment in which

it is placed, and is connected to a network.

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5 Conclusions It has been discussed on the technical singularity, in which artificial intelligence exceeds human intelligence [41] and human intelligence could be always realized by programs [42]. The so-called AGI (general-purpose artificial intelligence) [43] might be introduced to a Smarter Home. However, when integrating systems such as smarter homes, it is important to assume the value that people living there should originally enjoy and receive, and to utilize artificial intelligence and IoT as a technical tool.

References 1. M. Weiser, The Computer for the 21st Century (Scientific American, 1991), pp. 94–104 2. J.C. Augusto, P. McCullagh, Ambient intelligence: concepts and applications. ComSIS 4(1), 1–28 (2007) 3. D.J. Cook, How smart is your home. Science 335, 1579–1581 (2012) 4. J.L. Crowley, J. Coutaz, An Ecological View of Smart Home Technologies, Ambient Intelligence. Lecture Notes in Computer Science, vol. 9425 (Springer, 2015), pp. 1–16 5. https://www.indiegogo.com/projects/ 6. https://www.medicalguardian.com/products/ 7. http://www.hmservice.philips.co.jp/app/webroot/service/ 8. https://nest.com/thermostat/meet-nest-thermostat/ 9. http://opower.co.jp/ 10. https://www.amazon.com/Amazon-SK705DI-Echo/dp/B00X4WHP5E 11. https://robotstart.info/2018/05/04/smart-home-with-the-google-assistant.html 12. http://www.mimamori.net/ 13. http://internet.watch.impress.co.jp/www/article/2002/0910/mimamori.htm 14. https://www.konicaminolta.com/jp-ja/care-support/service/nursing-home-solution/ 15. T. Hatsukari, T. Shiino, S. Murai, The reduction of tumbling and falling accidents based on a built-in patient alert system in the hospital bed. J. Sci. Lab. 88(3), 94–102 (2012) (in Japanese) 16. http://www.paramount.co.jp/contents/949 17. https://www.informetis.com/anshin/ 18. http://sumai.panasonic.jp/aiseg/hems/about.html 19. http://www.healthcare.omron.co.jp/product/hsl/hsl-001.html 20. https://panasonic.jp/nemuri/oyasuminavi.html 21. H. Komatsu, K. Nishio, Applicability of ‘Nudge’ as information provision for energy and electricity conservation: Energy reports for the US households as a case example, Report of Central Research Institute of Electric Power Industry, No. Y12035 (2013) (in Japanese) 22. https://www.oracle.com/jp/corporate/features/pr/moe-nudge-project-oracle-utilities/ 23. T.L. Hayes, F. Abendroth, A. Adami, M. Pavel, T.A. Zitzelberger, J.A. Kaye, Unobtrusive assessment of activity patterns associated with mild cognitive impairment. Alzheimers Dement. 4(6), 395–405 (2008) 24. P.N. Dawadi, D.J. Cook, M. Schmitter-Edgecombe, C. Parsey, Automated assessment of cognitive health using smart home technologies. Tech. Health Care 21(4), 323–343 (2013) 25. W. Kleiminger, C. Beckel, S. Santi, Household occupancy monitoring using electricity meters. Proc. of Ubicomp ’15, pp. 975–986 (2015) 26. D. Ferrucci, E. Brown, J. Chu-Carroll, J. Fan, D. Gondek, A. Kalyanpur, A. Lally, W. Murdock, E. Nyberg, J. Prager, N. Schlaefer, C. Welty, Building Watson: an overview of the DeepQA Project. AI Magazine (2010)

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27. S. Lee, Y. Kim, D. Ahn, R. Ha, K. Lee, H. Cha, Non-obstructive room-level locating system in home environments using activity fingerprints from smartwatch, in Proceedings of Ubicomp’15, pp. 939–950 (2015) 28. X. Sun, Z. Lu, W. Hu, G. Cao, SymDetector: detecting sound-related respiratory symptoms using smartphones, in Proceedings of Ubicomp’15, pp. 97–108 (2015) 29. E. Thomaz, I. Essa, G. D. Abowd, A practical approach for recognizing eating moments with wrist-mounted inertial sensing. in Proceedings of Ubicomp’15, pp.1029–1040 (2015) 30. S. Aoki, M. Onishi, A. Kojima, K. Fukunaga, Detection of a solitude senior’s irregular states based on learning and recognizing of behavioral patterns. IEEJ Trans. Sens. Micromachines 125(6), 259–265 (2005) (in Japanese) 31. T. Mori, T. Ishino, H. Noguchi, T. Sato, Y. Miura, G. Nagami, M. Oe, H. Sanada, Life pattern estimation of the elderly based on accumulated activity data and its application to anomaly detection. J. Robot. Mechatron. 24(5), 754–765 (2012) 32. Y. Abe, M. Inoue, Early detection system of senile dementia by behavior sensing. Proc. FIT2014 4, 299–300 (in Japanese) 33. Y. Yamada, T. Kato, T. Matsuyama, Human behavior estimation from power consumption patterns of appliances over smart tap network. Technical Report of IEICE, pp. 1–6 (2011) (in Japanese) 34. S. Nakamura, S. Shigaki, A. Hiromori, H. Yamaguchi, T. Higashino, A study on designing smart advisor in daily life. Technical report of IEICE, pp. 1–6 (2015) (in Japanese) 35. M. Minoh, Human daily life support at a ubiquitous computing home. J. Japanese Soc. Artif. Intell. 20(5), 579–586 (2005) (in Japanese) 36. T. Nishiyama, R. Nakajima, T. Sawaragi, Home ambient intelligent agent with the ability of explaining to users, in Proceedings of the Human Interface Symposium, CD-ROM (2015) (in Japanese) 37. K. Naitou, H. Kawakami, T. Hiraoka, Design support method for implementing benefits of inconvenience inspired by TRIZ. Proc. Eng. 131, 327–332 (2015) 38. L. Atzori, A. Iera, G. Morabito, From “Smart Objects” to “Social Objects”: The Next Evolutionary Step of the Internet of Things, in IEEE Communications Magazine, pp. 97–105, Jan 2014 39. A. Dohr, R. M-Osprian, M. Drobics, D. Hayn, G. Schreier, The internet of things for ambient assisted living, in Proceedings of the 7th International Conference on Information Technology, pp. 804/809 (2010) 40. Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521(28), 436–444 (2015) 41. R. Kurzweil, The Singularity is Near (Penguin Books, 2005) 42. Y. Matsuo, Does Artificial Intelligence exceed Humans? Kadokawa (2015) (in Japanese) 43. B. Goertzel, C. Pennachin, Artificial General Intelligence (Springer, 2007)

Takashi Nishiyama is a Senior Researcher at the Advanced Technology Development Center, Life Solutions Company, Panasonic Corporation in Japan. He received the B.E., M.E., and D.E. degrees in Precision Mechanical Engineering from Kyoto University, Japan in 1986, 1988, and 1994, respectively. His current research interests include ambient intelligence and its applications to health care fields.

System of Systems Approach to Multiple Energy Systems Kazuyuki Mori, Toshiyuki Miyamoto, Shoichi Kitamura, and Yoshio Izui

Abstract This chapter presents two energy trading methods for efficient energy use, saving energy cost, and reduction of CO2 emissions. These methods can be achieved the objectives of the entire system consisting of many systems that pursue own benefit and CO2 emission reduction targets by coordinating a number of energy control systems that trade a plurality of multiple energy resources. Keywords Smart grid · Smart community · System of systems · Energy management system · Auction · Alternating direction method of multipliers

1 Introduction In the future, the super-smart society called Society 5.0 in Japan, a large number of systems (equipment/systems/people) are organically connected to cooperate as a

Adapted from Kazuyuki Mori, Toshiyuki Miyamoto, Shoichi Kitamura, and Yoshio Izui “System of Systems Approach to Multiple Energy System (written in Japanese),” Journal of The Society of Instrument and Control Engineers, Vol. 55, No. 8, pp. 714–718 (2016). Partly translated by permission of The Society of Instrument and Control Engineers. K. Mori (B) · S. Kitamura Advanced Technology R&D Center, Mitsubishi Electric Corporation, 8-1-1, Tsukaguchi-honmachi, Amagasaki, Hyogo 661-8661, Japan e-mail: [email protected] S. Kitamura e-mail: [email protected] T. Miyamoto Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan e-mail: [email protected] Y. Izui College of Engineering, Kanazawa Institute of Technology, 7-1 Ohgigaoka Nonoichi, Kanazawa, Ishikawa 921-8501, Japan e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2021 T. Kaihara et al. (eds.), Innovative Systems Approach for Designing Smarter World, https://doi.org/10.1007/978-981-15-6651-6_12

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system of systems (SoS). This is expected to create new values, such as more efficient energy use, more robust systems, and more convenient users. For example, in the wholesale electric power exchange market in Japan, the exchange of value is carried out by the electric power suppliers through the electric power exchange trade. In the future, due to the progress in the deregulation of electricity, gas, and heat, energy resources to be traded will be diversified. However, it was not always clear whether new value could be created while balancing conflicting values, such as reducing prices and CO2 emissions, through the trade of multiple energy resources. This paper introduces, as an example of basic studies, two energy trading methods for achieving efficient energy use, saving of energy cost, and reduction of CO2 emissions. These methods can be achieved the objectives of the entire system consisting of many systems that pursue own benefit and CO2 emission reduction targets by coordinating a number of energy control systems that trade a plurality of multiple energy resources.

2 Value Creation in Multiple Energy Systems As the liberalization of electric power and gas further progresses in the future, as shown in Fig. 1, the generation, distribution, and transformation systems of electric power, gas, heat, hydrogen, CO2 emission rights, renewable energy, and unused energy will be networked, and it is expected that the multi-energy system will be constructed together with the consumers using energy. It is expected that these various types of energy resources will be efficiently used in various energy trading markets such as wholesale, retail, and regional markets, new markets will be created, and systems will be developed.

Fig. 1 Value creation in multiple energy systems

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The data measured and traded in this energy trading market is utilized for analysis, design, and control of the trading market in the virtual world on the computer, and is reflected in the system of the real trading market. The opening of new trading markets, in turn, is expected to create new businesses related to energy trading markets (e.g., business audits, value certification, value exchange, and value creation). However, a multi-energy system composed of a plurality of systems is a system of systems, and it is difficult to predict the overall behavior due to the interaction of the systems. Therefore, a new system approach is required to analyze/devise a mechanism or system for predicting/controlling the overall behavior by modeling/simulating the system on a computer based on input/output data of each system for continuously creating new value.

3 Cooperation and Coordination of Energy Management Systems 3.1 Optimization in Energy Management Systems In smart grids and smart communities, the Energy Management System (EMS) has a mechanism for predicting demand, procuring, generating, and distributing energy required for demand in order to provide energy to consumers stably and efficiently. EMS has functions specific to applications, from the load dispatching center for electric power companies to the home EMS (HEMS) for energy management of households. However, the basic principle of efficiently utilizing energy is the same. This section describes how to optimize EMSs by exemplifying Community EMS (CEMS) for regional energy management and management. In regions with CEMS, as shown in Fig. 2, regional energy suppliers procure energy from electricity suppliers called power producer and supplier (PPS) and gas suppliers gas outside the CEMS, generate electricity and heat through energy generation facilities in the CEMS, and efficiently supply energy to EMSs, which are consumers, by utilizing storage facilities and heat storage facilities. CEMS aims to predict the electricity and heat energy demand needed in the region and to optimize (minimize) energy costs, energy consumptions, and CO2 emissions. For this purpose, energy flows in CEMS are formulated as optimization problems, and optimal operation plans of energy supplying systems are obtained and operated using optimization methods.

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Fig. 2 Energy flow model in CEMS

3.2 Coordination and Cooperation in the Energy Management System (1) Overview In recent years, attention has been paid not only to the optimization of energy supply systems, such as smart grids and smart communities but also to a mechanism for making the entire energy grid more efficient in cooperation with customer systems by coordinating a plurality of EMSs from the viewpoints of global warming countermeasures and energy security. In this section, as a mechanism for coordinating a plurality of EMSs, we introduce our study cases based on a multiple price auction and a single price auction. (2) Multiple price auction of multi-attribute products Multiple price auctions is a mechanism for energy trading between energy suppliers and consumers based on the priorities of commodity prices and bid times and is used in the forward market and the time-ahead market of wholesale electricity trading. The authors propose a trading method of a commodity with two attributes, electricity (or heat) and CO2 emissions [1]. In this method, consumer can determine the supplier, price, and each quantity of electricity and heat with different CO2 emission coefficients, and the trading price differs for each supplier. Though the effect of the cooperation between the systems does not reach the centralized system which can manage the whole system, it has the feature that the speed of the trading is increased and it is easy to reflect the intention of the customer with respect to the trading. Each energy trading uses the value ν per unit of energy.

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ν = λα + μβ Here α is the unit price of energy, β is the CO2 emissions intensity, and λ, μ, and ν are parameters presented by consumers. In this method, a customer creates an energy market by presenting four items of demand D, λ, μ, and ν. Suppliers bid for prices α, CO2 emissions intensity β, and supply amounts, and suppliers who bid for smaller values of ν successively win the energy market until they satisfy the demand of the customer. If the deal of trading does not close, the customer can invite the suppliers to bid by indicating a new value of ν. Further, in the present method, by appropriately presenting the values of λ and μ by the customer, it is possible to control the amount of emissions of the customer. (3) Single price auction A single price auction is a trading system in which a trade is made at a price at which demand and supply are balanced based on bids from all customers and suppliers. This auction is characterized in that the equilibrium price is the optimal transaction price, but the bidding needs to be repeated until the price at which the demand and the supply are balanced is reached for clearing [2]. The method can open a trading market on a customer-by-customer basis as described in Sect. 2 or one trading market for all customers and suppliers. Equations (1)–(3) formulate the problem of commodity exchange in the market as a problem that minimizes the sum of the objective functions of each participant under the condition that demand and supply are balanced in each market m. Suppose that the set of market participants is I = {1, 2, …, n}. Let the decisionvariable vector of the participant be the domain of the x i , x i be the convex set X i and the objective function be the convex function f i : X i → . Here the set of decision is a partial vector of x i , where variables is disjoint. M is the set of markets and x (m) i is the decision-variable for m ∈ M of participants i ∈ I . In the market m, a x (M) i (m) > 0 is called participant with x i < 0 is called a supplier, and a participant with x (m) i a customer. The optimal solution to this problem is x*. min



f i (xi )

(1)

i∈I

s.t. xi ∈ X i (i ∈ I ) 

xi(M) = 0

(2) (3)

i∈I

This optimization problem has been sought by techniques such as MarketOriented Programming (MOP) [4], Dual Decomposition Method (DDM) [2], and Alternating Direction Method of Multipliers (ADMM) [3]. Our experiences indicate that ADMM convergence is excellent [3]. Note that the objective function must be a narrow convex function as a condition for convergence of the solution in MOP

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and DDM. In Sect. 4, we describe an auctioning system in which customers and suppliers converge their prices to equilibrium points by repeatedly bidding on the market, using the ADMM of establishing a trading market for each demand.

4 Creation of Value Through Cooperation and Coordination Among Regional Energy Management Systems 4.1 Overview This section shows that a regional energy management system (CEMS) which consists of a plurality of EMSs can operate energy more efficiently than a single CEMS by providing energy management to other CEMS through markets [5, 6]. As shown in Fig. 3, there is an energy-interchange between the three CEMS through two markets. As shown in Fig. 2, each CEMS has a combined heat and power supply system (CHP: Cogeneration of Heat and Power) and heaters as energy generation facilities, an electric storage system (Battery) and a heat storage system as energy storage facilities, and supplies energy to three EMSs of customers.

4.2 Formulation (1) Energy flow model in the CEMS The models in the CEMS are represented by Eqs. (4)–(16).

Fig. 3 Energy trading model among CEMSs

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(4)

s.t. 0 ≤ E(t) ≤ E max , ∀t ∈ T,

(5)

E g (t) ≤ a0 G(t)2 + a1 G(t), ∀t ∈ T,

(6)

Hg (t) ≤ a2 G(t), ∀t ∈ T,

(7)

G min ≤ G(t) ≤ G max , ∀t ∈ T,

(8)

Ce (t) = ae Ce (t − 1) − Ceo (t), ∀t ∈ T  ,

(9)

Ce min ≤ Ce (t) ≤ Ce max , ∀t ∈ T,

(10)

Ch (t) = ah Ch (t − 1) − Cho (t), ∀t ∈ T  ,

(11)

Ch min ≤ Ch (t) ≤ Ch max , ∀t ∈ T,

(12)

Hh = h 1 He ,

(13)



E ems, j = E + E g + Ceo − He − Me ,

(14)

Hems, j = Hg + Cho + Hh − Mh − H,

(15)

f (x) = ppT E + peT Me + phT Mh + pgT G,

(16)

j∈J

 j∈J

where T = {1, 2, …, |T|}, T  = T \{1}, E max represents the maximum value of contract power, E(t) represents the amount of electricity, E g (t) represents the amount of electricity generated by CHPs, G(t) represents the amount of gas input, H g (t) represents the amount of heat generated, a represents the characteristics of the equipment, Gmin and Gmax represent the lower and upper limits of gas input, C e (t) represents the amount of electricity stored, C eo (t) represents the amount of discharges, C h (t) represents the amount of heat storage, C ho (t) represents the amount of heat radiation, E max and E g represent the lower and upper limits of H g (t), and Gmin and Gmax represent the lower and upper limits of C e (t). The heaters generate heat H h from the power H e in an efficient h1 , J is a set of local energy management systems EMSs within the CEMS. The demand for electricity and heat in each EMS j is represented by E ems,j and H ems,j , respectively. CEMS must generate electricity and heat and procure

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them from the market to meet each demand. M e and M h represent the amount of electricity and heat procured from the market, respectively. H, H e, and H h represent the amount of heat discharged, respectively. pp , pe , ph and pg represent the unit price of purchased electricity, market price of electricity, market price of heat and unit price of purchased gases, respectively. x = (E, G, M e , M h , C e , C h ) is the determinant of the EMS j. f (x) represent the cost function of the E ems,j . (2) CEMS trading models Inter-CEMS trading models are represented by Eqs. (17) and (18). 

Me, j = 0

(17)

Mh, j = 0

(18)

i∈I

 i∈I

An algorithm for determining the transaction amounts M e,i , M h,i and the unit prices Pe,i , Ph,i of the respective participants i by ADMM is expressed by the following equations. 

   ρ k 2  k+1 k+1 k , Mh,i + Me = arg min f (x) +  Me,i − Me,i Me,i 2 2 2  ρ k   k + Mh,i − Mh,i + M h  , ∀i ∈ I, 2 2 k

pek+1 = pek + ρ M e , k

phk+1 = phk + ρ M h , where M e is the average of Me , M h is the average of Mh , ρ is the penalty variable, and k is the repeat variable. Note that the main residual r and the dual residual s for confirming the convergence of the ADMM algorithms are expressed as follows. |re (t)|2 = M e (t)2 < ε, ∀t ∈ T, |rh (t)|2 = M h (t)2 < ε, ∀t ∈ T, k−1 k−1 k k 2 se (t)22 = ρ(Me,1 (t) − Me,1 (t), . . . , Me,|I | (t) − Me,|I | (t)) < ε, ∀t ∈ T, k−1 k−1 k k 2 sh (t)22 = ρ(Mh,1 (t) − Mh,1 (t), . . . , Mh,|I | (t) − Mh,|I | (t)) < ε, ∀t ∈ T,

where ε is a convergence determination index close to zero.

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Table 1 Unit price of electricity in time of use [Yen/kWh] Time

1–8

9–13

14–16

17–22

23–24

CEMS1

15

15

15

15

15

CEMS2

8

20

25

20

8

CEMS3

10

18

18

18

10

4.3 Simulation Results Table 1 shows the unit price of electric power for each CEMS used for simulations, and Fig. 4 shows the electric power demand and the thermal demand. Figure 5 shows the electric power trading unit price and heat trading unit price determined as a result of the trading, Fig. 6 shows the trading volume and Fig. 7 shows the effect obtained by trading.

Fig. 4 Demands of each energy

Fig. 5 Clearing prices in each market

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Fig. 6 Trading result of each energy

Fig. 7 Created value by energy trading

Figure 7 shows that a comparison result of the energy costs between when the demand is satisfied in each CEMS and when the demand is satisfied by energy trading among CEMSs. As a result, it can be seen that all CEMS can reduce energy costs by energy trading among CEMSs. This energy cost reduction is due to a reduction in the amount of waste heat discharged to the atmosphere, i.e., a reduction in the amount of waste inherently.

5 Conclusions This chapter has shown that in various markets of energy resources, many participants may create the value of effective utilization of energy resources and reduction of energy cost by carrying out multiple energy trading. And, as a mechanism for realizing the trading, we presented our study cases. In the future, we expect that a wide variety of energy resources will be traded in a wide variety of markets in mutual cooperation, the markets will be revitalized, and new value creation including measures against global warming will be promoted.

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References 1. T. Miyamoto, T. Kitayama, S. Kumagai, K. Mori, S. Kitamura, S. Shindo, An energy trading system with consideration of CO2 emissions. IEEJ Trans. EIS 125(10), 1514–1521 (2005) 2. E. Aiyoshi, K. Masuda, Basic knowledge for market principle: approaches to the price coordination mechanism by using optimization theory and algorithm. IEEJ Trans. EIS 130(4), 534–539 (2010) 3. S. Boyd, N. Parikh, E. Chu, B. Peleato, J. Eckstein, Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1–122 (2010) 4. Y. Muroda, T. Miyamoto, K. Mori, S. Kitamura, T. Yamamoto, A multiple period problem in distributed energy management systems considering CO2 emissions. Trans. SICE 47(4), 200–208 (2011) 5. M. Okada, T. Miyamoto, S. Kitamura, K. Mori, Y. Izui, Operation planning of community energy management system considering inter-community energy trade, in IEEJ International Workshop on Sensing, Actuation, and Motion Control, IS2-6 (2016) 6. T. Miyamoto, M. Okada, T. Fukuda, S. Kitamura, K. Mori, Y. Izui, Distributed day-ahead scheduling in community energy management systems using inter-community energy trade. IEEJ Trans. Electr Electron Eng. 13(7), 858–867 (2018)

Kazuyuki Mori received the B.E. and M.E. degrees in industrial administration from Tokyo University of Science in 1985 and 1987, respectively, and the D.E. degree in electrical engineering from Osaka University in 1998. He joined Mitsubishi Electric Corporation, Amagasaki, Hyogo, in 1987, where he is currently engaged in research on discrete event systems, production scheduling, systems optimization, and energy solution. At present, he is a chief researcher in the Advanced Technology R&D Center. Dr. Mori is a member of the Institute of Electrical and Electronics Engineers, the Institute of Electrical Engineers in Japan, the Society of Instrument and Control Engineers, and the Institute of Systems, Control, and Information Engineers. Toshiyuki Miyamoto received the B.E. and M.E. degrees, both in electronic engineering, from Osaka University in 1992 and 1994, respectively, and the D.E. degree in electrical engineering from the same university in 1997. From 1997 to 2003, he was a Research Assistant at the Graduate School of Engineering, Osaka University. In 2000–2001, he was a Visiting Researcher at the Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, USA. He is currently an Associate Professor at the Division of Electrical, Electronic and Information Engineering, Osaka University. His research interests include the theory and applications of concurrent systems and multi-agent systems. Dr. Miyamoto is a member of the Institute of Electrical and Electronics Engineers, the Institute of Electronics, Information and Communication Engineers, the Institute of Systems, Control, and Information Engineers, and the Society of Instrument and Control Engineers. Shoichi Kitamura received the B.E. and M.E. degrees in biophysical engineering from Osaka University in 2000 and 2002, respectively, and the D.E. degree in electrical engineering from the same university in 2013. He joined the Advanced Technology R&D Center, Mitsubishi Electric Corporation, Hyogo, in 2002, where he was engaged in research on the factory energy management system. At present, he is engaged in research on the smart grid and smart community-related technologies. Dr. Kitamura is a member of the Institute of Electrical Engineers in Japan.

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Yoshio Izui received the B.E., M.E., and D.E. degrees in electrical engineering from the University of Tokyo in 1981, 1983, and 1986, respectively. He joined Mitsubishi Electric Corporation, Amagasaki, Hyogo, in 1986. Since 2018, he has been a Professor in the Department of Electrical and Electronic Engineering, College of Engineering, Kanazawa Institute of Technology. His current research interests include smart grids and smart community-related technologies. Prof. Izui is a member of the Institute of Electrical and Electronics Engineers, the Institute of Electrical Engineers in Japan, the Institute of Electronics, the Information and Communication Engineers, Information Processing Society of Japan, the Society of Instrument and Control Engineers, the Institute of Systems, Control and Information Engineers, and the Japanese Neural Network Society.