Organized Social Complexity: Challenge to Politics and Policy 9781400869961

Table of contents :
Cover
Contents
List of Figures and Tables
Preface
Part 1. Explication of the Concept of Organized Complexity: Studies in Its Effects
Part 2. Approaches in Policy Analysis and Design
Part 3. Methodology: Some Implications for Research
Part 4. Prospects in the Study of Social Complexity
Bibliography
Index

Citation preview

Organized Social Complexity

Organized Social Complexity CHALLENGE TO POLITICS AND POLICY

Edited by Todd R. La Porte

PRINCETON UNIVERSITY PRESS PRINCETON, NEW JERSEY

Copyright (c) 1975 by Princeton University Press All Rights Reserved Library of Congress Cataloging in Publication Data will be found on the last printed page of this book This book has been composed in Baskerville Type Printed in the United States of America by Princeton University Press, Princeton, New Jersey

Contents LIST OF FIGURES AND TABLES PREFACE

PART ONE

Explication of the Concept of Organized Complexity: Studies in Its Effects

CHAPTER I by Todd R. La Porte

Organized Social Complexity: Explication of a Concept

CHAPTER II by Langdon Winner

CHAPTER III by J. Serge Taylor

Introduction The Concept of Organized Social Complexity Social Complexity as an Independent Variable Limitations of Complexity Some Properties of Organized Complexity Summary and Further Questions

xi xiii

3 3 4 10 13 15 17

Appendix I-A: Matrix Forms of Illustrative Structural Variations Appendix I-B: A Note on Graph and Lattice Theories Applied to Organized Complexity Technical Note by Daniel Metlay: A Quasi-Operational Definition of Complexity Selected Bibliography on Organized Social Complexity

35

Complexity and the Limits of Human Understanding

40

22 24

26

A Note to the Reader Section I: The Symptoms Section II: Conjectures Section III: Cures, Panaceas, and Further Dilemmas

40 40 49 60

Organizational Complexity in the New Industrial State: The Role of Technology

77

Technology as Potential Cause A Conception of Technology Galbraith Revisited

79 80 94 ν

Contents

Directions and Responses Conclusions

98 114

PART TWO

Approaches in Policy Analysis and Design

CHAPTER IV by John GerardRuggie

Complexity, Planning, and Public Order TheMarket TwoFormsofRationality The Politics of "Planning the Market" The "Impossibility Theorem" Complexity Public Planning Comprehensive Public Planning Incremental Public Planning The Paralysis of Complexity Public Order Cognitive Structure Ideological Framework Institutionalization Consequences Beyond Orderly Errors of the Past Stateless Theory Government as Derivative Planning as Implementation

CHAPTER V by Jay D. Starling

The Use of Systems Constructs in Simplifying Organized Social Complexity Systems Analogs As Conceptual Tools The Mechanical Equilibrium Perspective The "Mechanical" Equilibrium Perspective of Social Organization Mechanistic Perspectives and Increasing Social Complexity Homeostatic Perspectives Applications of Homeostatic Perspectives to Social Organizations Homeostatic Constructs and Increasing Social Complexity Adaptive Constructs: A Promising Perspective in Organizational Analysis

119 121 121 124 127 128 130 131 133 136 138 138 140 141 141 144 145 147 148

151 151 154 155 157 159 161 164 165

Contents

Adaptive Concepts and the Empirical Setting

PART THREE

Methodology: Some Implications for Research

CHAPTER YI

Analysis of Complex Systems: An Experiment and Its Implications for Policy Making Increasing Analytic Size: A Corrective to Simplifying? Human Limitation and Theoretical Issues Organized Complexity and Decomposition Determinants of a Model's Size or Analytic Complexity An Experiment in the Analysis of Organized Complexity Constructing a Model Increasing Analytic Size: An Example Partial Management Tactics Introducing Uncertainty Totals and Cross-Sections Implications of the Experiment Understanding the Problem Contexts Size and Research Implications Size and Organizations Implications for Policy Multiple Interpretations of Reality Increasing Chaos System Externalities Futures Command, Control, and Complexity's Burden A Concluding Note

by Garry D. Brewer

Technical Appendix: Specialized Programs and Sample Outputs

CHAPTER YII

by Daniel Metlay

169

175 175 177 179 181 185 185 187 193 197 198 199 200 201 203 204 204 205 207 207 208 210 211

On Studying the Future Behavior of Complex Systems 220 Complexity in Visions of the Future 220 An Approach to Analyzing Systems over Time 223 VlI

Contents

Assumptions for Simulation System Separability Near Decomposability: The Dominance of Straight Edges Simulating the Future Behavior of Complex Systems: The Intersystem Coefficients Closer to Actuality: Relaxing the Assumptions of Simulation Relaxation of Assumptions The Pandora's Box of Simultaneous Systems: Confronting the Unknowable Implications for Planning, Action, and Design Implications for Planning Appendix A: A Program to Compute the Rank and Power of a Dependence Matrix Appendix B: Error Ratios of Systems

CHAPTER YIII by Jennifer Nias

223 226 229

230 240 241 242 247 247 251 253

The Sorcerer's Apprentice: A Case-Study of Complexity in Educational Institutions

256

Systemic Components Dependence Rather Than Interdependence Participative Decision Making New Communication Channels Growing Complexity Complexity, Conflict, and Communication Organized Complexity Compared Information Technology Redundancy Complexity and Control Control within Complex Systems Inferences Drawn From This Study

257 258 260 262 263 266 266 269 270 271 272 274

PART FOUR

Prospects in the Study of Social Complexity

CHAPTER IX

Complexity as a Theoretical Problem: Wider Perspectives in Political Theory

281

Some Distinctions About Complexity

281

by Harlan Wilson

Types of Structural Complexity: Institutional, Situational, and Analytic Types of Institutional Complexity: Coercive aaid Normative "Organized Social Complexity": The Problem of Levels Some Theoretical Analyses of Complexity Coercive Simplicity and Normative Complexity: Hobbes and Burke Normative Simplicity: Rousseau The Defense of Coercive Complexity: Madison Institutional and Analytical Complexity: Durkheim and Theoretical Sociology Organization Theory and Complexity Complexity, Consciousness, and Theory Wider Perspectives? CHAPTER X by Todd R. La Porte

BIBLIOGRAPHY INDEX

Complexity and Uncertainty: Challenge to Action Constructs of Simplicity in the Face of Complexity Conceptual Challenge From a Policy Perspective The Challenge to Research Politics, Action, and Organizational Design The Politics of Uncertainty and Psychic Reassurance Policy Implementation as Error Making: Planning as Learning Policy and Planning in a New Mood Organization in Response to Complexity

List of Figures and Tables Figure 1-1 1-2 1-3 1-4 1-5 1-6 1-7 1-8 1-9 1-10 3-1 3-2 6-1 6-2 6-3 6-4 6-5 6-6 7-1 7-2 7-3 7-4 7-5 7-6 8-1 8-2 8-3 10-1 10-2

ATreeofDependence A Full Matrix of Dependence A Semilattice of Dependence Increases in Complexity: An Illustration Matrices Representing Three Forms of Social Complexity Five-Member System and Its Matrix Representation Examples of Dependence Matrices Hypothetical Organization Matrix Representation of Hypothetical Organization in Figure 1-8 Dependency Matrices "Organizational Significance" of a Technological Change The "Perfection Cycle" Verbal Representation of Illustrative Models Formalization of Model Flow Chart Model StructuralRepresentationoflllustrativeExample Input-Output Matrix — Structural Connections: Organized by Function Input-Output Matrix — Structural Connections: Organized by Spatial Location Matrix Representation of Simulated Systems Error Ratio of System B Approximated by System A Error Ratio of System C Approximated by System A Error Ratio of System D Approximated by System A Error Ratio of System E Approximated by System A Error Ratio of System F Approximated by System A Official Relationship in Certification Process — Committee Structure for the Certificate of Education Committee Structure for the B. Ed. and Certificate of Education Committee Structure of the Faculty of Education Types of Response in the Politics of Psychic Reassurance Types of Policy Perspectives and Criteria in a New Mood

8 9 9 13 22 28 29 31 31 32 90 91 184 185 186 191 192 195 228 253 254 254 255 255 259 261 275 347 350

List of Figures and Tables

Table 4-1 6-1 6-2 6-3 6-4 6-5 7-1 7-2 7-3 7-4 7-5 7-6 7-7 7-8 7-9 7-10 8-1

Strategies and Tests of the Planning Process Summary of Model Modifications General Design for Model Modification Structural Connectedness Matrices (Functional Organization) Raised to Powers 2, 4, and 7 Structural Connectedness Matrices (Spatial Organization) Raised to Powers 2 and 4 Structural Connectedness (Spatial Organization) Raised to Power 10 System A Coefficients System B Coefficients System C Coefficients Number of Nonzero Cells in Qri Residual Matrix 1 Residual Matrix 2 Error Ratio for System B Error Ratio for System C Summary of Figures in Appendix B Average Error in Submatrix Matrices and Ranks of Committees A and B

149 187 188 194 196 197 233 233 233 234 234 235 235 236 237 239 268

Preface

Some collections are brought together and issued rapidly. Others, such as ours, seek an integration which has a much longer gestation time. The origins of these papers go back to the Spring of 1969 when a small seminar group set to work exploring what could be made of the phenomenon of organized social complexity. This book grew from that quite special experience. As we struggled through many of the abstract formalizations from works we thought could assist us, the swirl of the People's Park tragedy swept over us. Sometimes the familiar smell of riot gas invaded the seminar room; sometimes our discussions reflected the frustration of events crashing about us just three blocks away. This was Berkeley's ugliest episode during its Time of Trouble, and it became a disturbing symbol of the consequences of planning without substance or effect and politics gone slightly mad. It seemed as though we were living some of the implications of the work we were doing in the seminar. Was it possible that the fabric of social relations could rupture badly in the face of simplistic perceptions of it? It seemed that it was . . . it seems that it is. Our effort has been to bring together an integrated set of papers reflecting a sustained conversation and exchange. The volume is dedicated to those who will come after us and who may take up some of the challenges we believe to be inherent in the increasing levels of organized social complexity that confront us. Seven of the papers are revisions of those begun in those initial explora­ tions. I knew of Garry Brewer's interest in a version of these problems. During the summer of the previous year we had exchanged enthusiasms and he had contributed an initial bibliography gleaned from his work at Yale University. Later I asked him to modify a portion of a longer paper so it could be included in this volume. Daniel Metlay was sufficiently intrigued by Brewer's effort to write a related chapter, the only one which self-consciously builds from another of the papers. All of the chapters, however, are vitally linked — despite the variety of contexts. To discover their collective unity and many points of congruence will profit the reader beyond the respective readings. During the process of development, I found encouragement from my colleagues in the Department of Political Science, Professors Ernst Haas, Warren Ilchman and Martin Landau, who at crucial times shared conversation and critiques which suggested that the substance would be of sufficient importance to justify the extraordinary efforts needed to bring

Preface

the papers together. The Institute of Governmental Studies has been invaluable in lending the support of its manuscript staff to the preparation of the several versions and reworkings needed for discussion drafts and for eliciting critiques from important readers. Catherine Winter has been especially competent in preparing the final draft. Others who have assisted ably are Linda Harris and Amy Alsbury. I thank them all. But I must reserve my highest thanks and praise for Mary Sapsis, who has been intimately involved with the project for the past two years. She has handled the problems arising from our own kind of complexity — in counseling with eight different authors, caring for countless details, supervising the preparation of the final manuscript, and finally getting it into the hands of the publishers. She has taught us all a great deal about the uses of language. For a variety of reasons both the authors and our readers will profit from her dedicated and professional efforts. Todd R. La Porte Berkeley, California February 1974

PART ONE Explication of the Concept of Organized Complexity: Studies in Its Effects

Chapter I Organized Social Complexity: Explication of a Concept TODD R. LA PORTE

Introduction One particularly striking aspect of modern political and social de­ velopment has been the capacity of men to construct social systems encompassing more and more groups. Our lives are bounded by agencies, organizations, combines, coalitions, and associations: networks of hundreds of connected groups and persons. In part this condition has been a selfmoving outgrowth of economic and technological progress which has stimulated proliferating organizational and social differentiation. In part men have intentionally linked group to group, organization to organiza­ tion, nation to nation in efforts to gather specialized and mutually required resources. National development of such resource capacity has been a major driving force of politics and commerce. Indeed, cooperation and mutual exchange provide the foundation of modern life and the consuming attention of public policy concerns. Our national penchant in solving public problems is through policies which increase the connections between groups and which tend toward mutual dependence among public and private organizations. One consequence of these increases in group connections — both spontaneous and purposive — has been the tightening of organizational dependencies affecting social dynamics and political movements. Another has been a rapid increase in the number of people and agencies affecting the day-to-day experiences of individuals. Closely related to this increase has been one in the number of surprises we encounter. They are generally disturbing surprises, caused by the interruption or frustration of our expectation by some hitherto unrecognized dependency. These surprises we often "account for" with the somewhat bewildered assertion, "It's a complex situation," implying that they are unaccountable. Somehow the unexpected occurs frequently, especially in matters of politics and social and organizational life. Perhaps such situations have always been unac­ countable, but at present they seem to affect more people in a shorter span of time. They seem somehow to have intensified. The pervasiveness

Todd R. La Porte

of such surprise-producing dependences is exploited by contemporary advertising. Repeatedly we are told that such and such a product or service will "uncomplicate" things: will "simplify" matters in the kitchen, "expedite" getting from here to there, "ease" the process of paying our bills by reducing our indebtedness to one loan compounded from the unwieldy burden of many creditors. The sensitive nerve endings of the advertising copywriter have intuitively gauged the degree to which the surprises of complexity prompt feelings of uneasiness and frustration within everyone. But advertising strategies and tired assertions that "things are complex" do little to provide satisfactory understanding of what is happening to us. We eagerly seek conceptions of the world which promise some explanation or insight about what we are experiencing. The conceptions available to us form a network of notions describing phenomena which are social, complex, and organized. This book is addressed to some of them and in some cases challenges their adequacy in the face of increasing levels of organized social complexity.

We have come to the collective conviction that the degree of social complexity, particularly that confronting modern industrial nations, has seriously eroded the quality of our traditional conceptions about social and political realities. Insofar as this is the case, the utility of our cause/ effect beliefs about these realities must be seriously questioned — especially the utility of those that are currently used as the basis for the analysis of public problems and the construction of policy proposals. Part One of this volume (Chapters I—III) includes an explication of the concept of organized social complexity, explores its impact on the human intellect, and ends with a discussion of what is often asserted as the major cause for its increase. Part Two (Chapters IV and V) is devoted to analyses of two theoretical systems — public planning and systems analysis — which, while intended to provide direct control over social matters, may be neither direct nor regulatory. Part Three (Chapters VI-VIII) emphasizes methodological aspects and research applications. Part Four (Chapters IX and X) takes both a retrospective and prospective view of theories responding to social complexity. The Concept of Organized Social Complexity

The term "complexity" appears in many areas of the social sciences, perhaps most often in the study of large scale, "complex" organizations. Very little, however, has been done to develop this concept so that the phenomenon intuitively ascribed to the term may be related to aspects of

Complexity: Explication of a Concept

social, political, or organizational life. In this chapter we shall attempt such an explication as an introduction to more particular considerations of the consequences for conceptual thought of increasingly complex organized social systems. In an important article titled "The Architecture of Complexity," Herbert Simon avoids a formal definition of complexity, suggesting only that complex systems are ones "made up of a large number of parts that interact in a nonsimple way."1 We shall attempt, perhaps more fool­ hardily, to advance beyond this generality by dealing with a particular kind of complexity, namely organized, social complexity.'1 In emphasizing organized complexity we are following the distinction made by Weaver between unorganized and organized complexity.3 Taken originally from formulations in the natural sciences, the former describes systematically unrelated elements, parts, or variables affecting the behavior or outcomes of systemic operations. These aggregates of randomly interacting elements, such as gas molecules under pressure, consumer behavior, and voters in general elections, are fruitfully described with statistical techniques. Despite the fact that each of the variables displays random behavior, each system as a whole has certain orderly properties which can be dis­ covered through probability analysis. Systems that are characterized by organized complexity, on the other hand, are those in which there is at least a moderate number of variables or parts related to each other in organic or interdependent ways. Systems, like the internal dynamics of living organisms, self-conscious social organi­ zations and chemical molecular reactions, for example, cannot be ade­ quately described through probability techniques and pose challenging conceptual and methodological problems.4 Our concern will be further limited to social systems possessing the characteristics of organized complexity. The most obvious empirical 'Herbert Simon, "The Architecture of Complexity," in General Systems Yearbook > 10 (1965), 63. 2The term "complexity" has at least two common-usage significations. Often it is used in a derivative sense to describe a situation that has so many aspects as to be unknowable or incomprehensible, prompting feelings of confusion. We are using it here in its stricter sense: to characterize phenomena in such a way as to distinguish a system which has many parts from a simple system which has only a few. In the next chapter, Langdon Winner pursues this distinction, noting how the former sense of the term has developed largely as a response to the latter sense, so that often " 'complexity' refers not so much to a neutral property of things in the world as to a kind of emotional response to certain perceptual and cognitive difficulties." 3Warren Weaver, "Science and Complexity," American Scientist, 36 (1948), 536—544. iIbid., pp. 537-539.

Todd R. La Porte

referents are social groups with conscious purposes, such as formal organizations or informal, but cohesive, groups and associations. Members of such systems will be defined as those persons engaged in relatively self-conscious interaction with each other, recognizing their common relatedness to one another within the system. For our purposes, the selfconscious characteristic is crucial; it is central to the requirement that interaction among elements be interdependent and systematic. Lacking this self-consciousness, aggregate behavior in social groups could just as well be unorganized. Self-conscious relatedness implies a distinction between perceived and unperceived relatedness. The former is based on the individual's recogni­ tion of his connections to others around him — his awareness that his activities directly impinge on the activities of others and theirs upon his. When dependence and connectedness are recognized, an individual is likely to base his actions on some reckoning of their effect on those involved with him. Unperceived relatedness exists when the structure of a situation, e.g., work structure, holds persons in remote and indirect but important relationship to one another. In these cases dependencies are not likely to be recognized, and an individual's actions are not likely to reflect a conscious concern for how they might affect others indirectly dependent upon him. Such actual but unrecognized dependence is revealed when the relationship falters: suburban homeowners' sudden recognition of their dependence upon garbage collectors when confronted with a garbagemen's strike; the city dweller's realization that there is an ad­ ministrator downtown who is crucial in the determination of his housing conditions. In this introduction, our concerns are mainly fastened on self-conscious, perceived relatedness rather than on social complexity of the unperceived variety. With these distinctions as a preface, we can now move to a working definition of organized social complexity. The degree of complexity of organized social systems ( Q ) is a function of the number of system components (Ct), the relative differentiation or variety of these components (Dj), and the degree of interdependence among these components (Ik). Then, by definition, the greater C1, Dj, and /¾, t h e g r e a t e r t h e c o m p l e x i t y of t h e o r g a n i z e d s y s t e m ( Q ) . A component of an organized social system is defined as a person or group occupying a position within the system and evincing these characteristics: (1) sufficient mutual agreement or consensus about this position so that he or she or it is the object of expectations and actions from other members and (2) recognition on the part of the person or group of the legitimacy of the others' expectations and positive response to those expectations, at

Complexity: Explicatioa of a Concept

least to the degree required for maintaining membership in and avoiding expulsion from the system.5 Differentiation of components is defined as the number of different social roles or positions within the system, based on the degree of mutual exclusiveness of the activities distributed among the roles in an organization.6 These differences are based, in turn, on those activities expected of a role occupant by other members of the system. To develop operational indicators of differentiation can become very difficult. Without accepting them as necessarily definitive, we could consider formal job descriptions to be such indicators; survey research instruments and techniques of analysis to determine high norm concensus might also be developed. The most difficult element of our definition is the interdependence of components. It is by far the most important and the least developed. Interdependence among persons or groups assumes varying degrees of reciprocal relationships between them. Interdependence means an exchange relationship of at least one resource between at least two persons. Inter­ dependent relationships can vary between any two members (a, b) exchanging resource rx as follows: 1. member A dominant over member B, i.e., B depends on A for some desired resource {a » b) ri 2. A and B mutually dependent upon one another for a resource both parties desire (« CL

IZ>

o

195

Garry D. Brewer

explicit;26 others have made it normatively explicit.27 Figure 6-6 shows the two versions of the model repartitioned into one aggregate and five spatial sectors. The input-output matrix form in Figure 6-6 has been operated as before. Outputs of versions change no. 3 and change no. 4 are summarized for the number of connections at the second and fourth powers or time periods in Table 6-4. Table 6-4. Structural Connectedness Matrices (Spatial Organization): Raised to Powers 2 and 4 Change No. 3, Migration Only

Change No. 4

INPUTS

O U T P U T

2 1 2 3 4 5

INPUTS

Σ Sect.

1

2

3

4

5

S

1

2

3

4

5

5 6 6 6 6 6

12 23 1 1 1 1

12 1 23 1 1 1

12 1 1 23 1 1

12 1 1 1 23 1

12 1 1 1 1 23

5 10 10 10 10 10

12 26 4 4 4 4

12 4 26 4 4 4

12 4 4 26 4 4

12 4 4 4 26 4

12 4 4 4 4 26

S Connections to the Power 2 INPUTS O U T P U T

Σ 1 2 3 4 5

75 42 42 42 42 42

86 118 40 40 40 40

86 86 86 86 40 40 40 40 118 40 40 40 40 118 40 40 40 40 118 40 40 40 40 118

INPUTS 105 131 131 131 131 131 115 159 81 81 81 81 115 81 159 81 81 81 115 81 81 159 81 81 115 81 81 81 159 81 115 81 81 81 81 159

S Connections to the Power 4 HerbertA. Simon, Administrative Behavior (New York: Macmillan, 1957) pp. 21-22. Simon contends that a basic choice must be made between spatial or functional orga­ nization. Various consequences flow from either choice. 27Jane Jacobs, The Economy of Cities (New York: Vintage, 1970), pp. 161-162. Jacobs contends, in arguing for the spatial organization, ". . . statistics [on urban spatial areas] contain a good deal of guesswork [because] they are collected according to categories of activities (e.g., services, transportation, manufacturing, construction, entertainment, 'other'), not according to actual destinations [and orgins] of the goods and services." 26

Analysis of Complex Systems

Several points are suggested in this reorganization by space instead of function. Separate spatial sectors may be analyzed individually, using the relative frequency of intra-connectedness indicated in the diagonal. The aggregate sector may be regarded separately (column 1), although con­ siderable analytical effort is apparently needed to understand the inter­ changes or flows between sectors to this aggregate. The specter of time wipes out these tentative suggestions for the manage­ ment of these analytical contexts. By the fourth power of the matrix, everything is connected to everything in change no. 4; and, by the tenth power, there are no less than 1,516,635 total links for version change no. 4. Distinctions between the main diagonal and other cells, which begin to blur at the fourth power, are effectively neutralized by the tenth power, as summarized in Table 6-5. Table 6-5. Structural Connectedness (Spatial Organization) Raised to Power IO Change No. 4 — Migration and Government Connected INPUTS 1

2

3 SECTORS

4

5

o" U 1 T 2 P 3 U 4

58330

54033

54033

54033

54033

54033

45287 45287 45287

43697 42161

42161 43697

42161

42161

42161

42161

42161 43697

42161 42161

42161 42161

45287

42161

42161 42161

42161

45287

42161

42161

42161 42161

43697

T

42161

43697

5

S Connections to the Power 10

Decomposability may be an adequate management device in the short run of, say, five periods or less; but for increasingly long periods of time, what Ando, Fisher, and Simon term "second order effects" — as ap­ proximated by connectedness — begin to dominate and reduce the effectiveness of decomposability tactics. Introducing Uncertainty

The results are not entirely clear, partly because resources were inadequate to make full Monte Carlo runs and partly because the model is fairly stable for the range of parameter settings at which it was operated. How­ ever, disturbing initial conditions apparently had more systemic effects than only disturbing the relationships: Disturbances of (±) 10% had more effect than those of (±) 5%, and the joint disturbance of initial conditions

Garry D. Brewer

and behavioral relationships was the most disruptive combination of all. These observations are based on calculated average differences between individual time series values in the uncertainty set of modifications and those from change no. 4, the similar but undisturbed version. One thing is clear: measurement error of parameters and initial conditions, a random­ ness more amenable to direct control than exogenous disturbances in individual relationships, has such pervasive and important effects on a model's outputs that one is well advised to minimize it. For example, with the identical structure and a maximum (±) 10% random disturbance to initial conditions, average differences as great as 42% between individual disturbed and reference run values were observed. Totals and Cross-Sections Correlation coefficients of all variable pairs were computed for each model modification. The first set was calculated for all ten sectors for the twenty periods in the analytic run. With an N = 200 for each variable and with relatively stable total outputs, correlations are significantly high for all pairs in all changes to the model. These may, in one sense, be mere artifacts of the observed, aggregate system stability. However, when the procedure was repeated for each of the twenty cross-sections for all ten variable pairs in each separate model modification, the stability of the correlation coefficients remained, though at somewhat reduced levels. In the overall case, stability apparently derives from the relatively large number of observations and smooth time series for all aggregated variables. At the cross-sections, stability apparently derives from an implicit equilibrium assumption made in the process of measurement,28 and from the limited numbers of structural interconnections that are effective at any given cross-section. To sum up, assuming a condition of disorganized complexity leads the analyst to obtain a large number of observations on a few variables at single points in time. One way of accomplishing this is to increase the number of disaggregations of a fixed number of variables. What results is a bias toward fine spatial detail in models.29 In the context of comparative politics, for example, the bias is toward simple aggregated measures of many nations at a cross-section in time.30 In either case, one's ability to 28See James S. Coleman, "The Mathematical Study of Change," in Methodology Social Research, ed. Hubert M. and A. B. Blalock (New York: McGraw-Hill, 1968), pp. 444-445. 29See David R. Seidman, The Construction of an Urban Growth Model (Philadelphia: Delaware River Valley Planning Commission, Plan Report jfl, n.d.), pp. 54-64. 30See Irma Adelman and Cynthia Taft Morris, "An Econometric Model of Change in Underdeveloped Countries," American Economic Review, 58 (1968), 1184-1218.

Analysis of Complex Systems

understand individual differences and behavior through time is slight. The analytic context is made complicated by increasing the number of con­ ceptually undifferentiated elements to satisfy a preference for a larger sample size. Conversely, assuming conditions of organized complexity implies that individual differences do matter. Time, interconnections, various conceptual elements, and diversity of structural configurations are each more important than the number of observations of one or a few elements. Considerations of complexity have long been at the root of much social science theory. How various simplifying choices are made at a very abstract level along various dimensions of size has considerable impact on the efficiency, economy, and efficacy of a particular symbolic formalization. If our simple analysis is indicative, understanding a formal symbolic model, the theoretical image it replicates, and the context purportedly described by model and image, declines rapidly as size increases. One loses control. Confidence in the symbol system's structure decreases as the number of elements, their interconnections, their relationships, and error in measurement increase. The central point is essentially this: At some level of size for a given model we decidedly lose the ability to make structural revisions, i.e., to improve the model theoretically. It is prudent, therefore, to minimize complicating the formulation with extraneous elements and to concentrate on the specification and structuring of indi­ vidual relationships and decomposed subsystems. How large should a model be? Small enough to be useful theoretically, but large enough to be realistic. The dilemma of the scientist is to select models that are at the same time simple enough to permit him to think with the aid of the model but also sufficiently realistic that the simplifications do not lead to [highly inaccurate] predictions .... The more complex the model, the more difficult it becomes to decide exactly which modifications to make and which new variables to introduce. Put simply, the basic dilemma faced in all sciences is that of how much to oversimplify reality.31 How might we withstand that dilemma, given the information generated in our short exercise?

Implications of the Experiment Three sets of related questions are suggested by these simple experiments. What do we require to understand how any environment or context 31Hubert BlaIock Causal Inferences in Non-experimental Research (Chapel Hill: University of North Carolina Press, 1964), p. 8.

Garry D. Brewer

defined in space and time can be simplified for various purposes of under­ standing, theorizing, and management? What difference does changing analytic size make for various research strategies? What are the relation­ ships between system size and the behavior and operation of organizations? Understanding the Problem Contexts

An immediate requirement is the integration of specialized information developed from multiple observational perspectives. But obviously this is easier said than done. There are very fundamental problems in such an ambition. Historians write beautiful stories, but the stories seldom help policy makers with present and future matters of moment. Economists look at the same world quite differently from the way anthropologists do. Munich taught us immutable lessons . . . that did not apply to Vietnam. "What's-good-for-General-Motors-is-good-for-America" . . . unless one happens to be killed or maimed by a Corvair. A context may be defined many ways: as loosely as sketching out aspects of a nation-state during a given era, for example, or as rigorously as detailing the state vector that produced our simple illustrative model. To puzzle over various specific definitional modes is not useful here, but several logical distinctions might be. A context that is well defined in space may change as it unfolds over time. Our model at time t = 0 is not what it became later at t = 1, 2, 3 . . . n. (See FORTRAN Code in Appendix.) Definition of a state vector is not identical for different observers. For instance, an economist's state vector for the United States between 1960 and 1970 will not be equivalent to an anthropologist's. For that matter, it is unlikely that it will even match another economist's. Reality is of course complex, and what any observer seeks out are those few aspects that interest him because of his training, his immediate purpose, or a host of other possible motives. The case for human simplification has already been made. As the absolute number of elements operating in a context increases, so too does the possible number of specialized interpretations to be derived from different observational perspectives. A context might be defined by elements in different spatial locations that have similar or common characteristics. A fundamental methodological bias of comparative studies, this definitional mode asks to what extent are two (or more) entities or contexts similar, and in what ways do they differ? The questions are complicated, for example, by political scientists, some of whom make comparisons for many national, spatial contexts at a single point in time and some of whom compare two or more nations at quite different points in time. In the first instance, this method has led otherwise reasonable men to utter absurdities such as "the more eco-

Analysis of Complex Systems

nomically developed a nation, the more democratic"; and in the latter, to the Munich-Vietnam fallacy. A corollary problem is that of the level of analysis: time is held constant, but one observes at different levels of spatial resolution or detail. Explanations that are viable at the national level lose significance at the personal level and vice versa. The simple General Motors-Corvair example illustrates the point. So we have the following as distinct definitional possibilities for a prob­ lem context: (1) the same spatial or operational entity at different points in time, (2) the same environment at identical times observed differently, and (3) the same time but different spatial entities taken compositely. What complicates this simplest of views is that many permutations and combinations exist. An approximation to some logical total of all possible contextual interpretations is enormous. Σ CI = f

-

l)]-[Om(Oro — 1 ) ] · [ S t C S t - 1)]}

(IV. 1)

where, = the number of contextual interpretations possible T = time unit, e.g., minute, day, year, etc. η = number of discrete units considered O = observer, each of whom has a unique set of perceptual filters m = number of discrete observers considered S = spatial sites considered, e.g., a nation, a person, a firm, etc. k = number of discrete spatial units considered

CI

Our experiment has pointed out, if nothing else, how much might be taken into consideration in different ways. Small wonder there exists in social science so little fundamental consensus on practical and research matters. Size and Research Implications

In an effort to capture the realistic essence of a problem context, common research practices include the indiscriminate gathering of ever more basic information32 and the construction of ever larger analytic models.33 While 32 See R. J. Rummel, "Indicators of Cross-national and International Patterns," American Politwal Science Review, 63 (1969), 127-147. 33 See, for example, these enormous efforts in (1) urban planning: Arthur D. Little, Community Renewal Programming: A San Francisco Case Study (New York: Frederick A. Praeger, 1966); (2) international relations: Morton Gorden, "Burdens for the Designer of a Computer Simulation of International Relations: The Case of TEMPER," in Computers and the Policy-Making Community, ed. D. B. Bobrow and J. L. Schwartz (Englewood Cliffs, N. J.: Prentice-Hall, 1968), pp. 222-245; and (3) war gaming: R. H. Adams and J. L. Jenkins, "Simulation of Air Operations with the Air Battle Model," Operations Research, 8 (1960), 600-615.

Garry D. Brewer

reasons for these practices are understandable, the costs of overcomplication may exact a toll that negates the benefits of increased "realism." The possibilities for a researcher to understand and manipulate a model decrease rapidly as the analytic size of his formulation increases. Increasing the time devoted to research can only partially compensate for the intrinsic problem. After some point, all the time and money in the world may not make a large and complicated formulation tractable.34 Capturing reality is an elusive business. "Just a few more facts" and "only a couple of additional subroutines" are directives that pave the road to disaster. A better approach is to have one or a select few research questions in mind before doing an analysis — either these get answered or they do not. They will at least provide the analysis with a clear, guiding purpose. Bigness is not a virtue: it may be pathological. At the very least, it probably indicates poor specification of the analytic questions. Bigness per se may be a cover-up and an excuse for inadequate thought either about problems or models. Acquiring and managing data to support large, finely detailed models cost a great deal in time, money, and human attention. One suspects that large data collecting ventures have become a subterfuge for deficient theoretical thinking. It is important to recall that decision makers do not operate with a large number of elements; they cannot, if one believes George Miller. Research could be productively expended in determining what elements and pro­ cesses decision makers do use under various contingencies. In other words, researchers could develop various explanations for the "coded," aggre­ gated, macroelements used by the policy makers and others participating in a social context over time. Decision makers are visceral theorists; their ad. hoc, operating theories should be seriously examined. Spatial models for individual, highly particular locations of demonstrated policy interest, for example, might be devised and continuously updated with survey or direct measurements. Concerted effort in the continuous collection of detailed information for one or a few spatial sectors having high and continuing utility for policy makers would enable a test of visceral theory as well as provide a base for theory building. Another tack could also be intriguing. Formalizing the versions of the macroaggregated concepts and processes routinely employed by decision makers could make more explicit the hierarchical reasoning by which policy makers relate general notions to highly particular spatial models. The meaning of the higher level is not accounted for by analysis of the lower. Upon what bases do decision makers suppose that results produced 34B. and S. Rome, Communication and Large Organizations (Santa Monica, Cal.: System Development Corporation, SP-1690/000/00, September 1964).

Analysis of Complex Systems

at the aggregate level (asymmetric control totals) are related to more specific, contained spatial configurations? Expanding the analytic time frame confounds analysis and greatly reduces predictive power. The role for preferred end state specification at future cross-sections in time seems viable. In plainer language, precise predictions at expanded time periods are highly speculative; therefore, use models to indicate what general structural modifications and be­ havioral implications are needed to reach desired and desirable future conditions. Let construction of a present configuration be directed toward some desired future configuration. The role for explicitly normative models is quite clear, indeed quite necessary. Size and Organizations

The difficulties of large systems hold for smaller organizations as well. The "artichoke effect" is where there exists "a proclivity to add features, add functions, and add interfaces — layer upon layer — onto existing systems. Each succeeding layer has less and less useful or tasty substance on it, until the outside layers merely add weight, complexity, and a prickly hindrance to reaching the core of the problem that someone wants solved."36 No solutions to the problem are readily apparent; however, understanding that it exists is a first step. In our terms, increasing organizational size generally increases specialization of function and the possibility for unproductive suboptimization. Increasing the number of links or connections in an organizational chain of command potentially increases the number of organizational decisions and outcomes, increases the level of uncertainty about which possibilities will in fact be selected, and, through fragmentation, ordinarily decreases the chances that any one individual either will know what is going on or will take responsibility for it. In gaming terms, with increased organiza­ tional size it becomes necessary to explore an ever greater decision space through repeated plays to develop some approximation of a representative strategy. However, resources, particularly time, are not usually expand­ able, and the chances for determining such a strategy quickly vanish. Crisis decision making exemplifies the problem at the limit. Increased uncertainty makes control over an organization and its surrounding environment increasingly problematic. In response, decision makers resort to extraorganizational sources and procedures: consulting firms, geomancers, and oracles at one level, and market manipulation and "gentlemen's agreements" at another. 36Association for Computing Machinery, "President's Letter," A C M 13 (1970), 173.

Garry D. Brewer

Several summary caveats are evident: • One should expect inconsistent and conflicting interpretations of complex social and problem contexts. • Competing interpretations may confuse one's understanding of a context by increasing levels of both insecurity and measurement uncertainty. • Diversity of unanticipated behavior should be expected with in­ creasing organizational size. • The prevalence of ad hoc and intuitive explanations increases with increasing contextual size. • Consensus on matters of fact and on recommendations for action decreases with increasing size. These suggestions and the policy implications discussed below assume that social systems take on many of the characteristics of the experimental model as they expand. This would result in significant difficulties for "inside participants" to understand their existential experience and would have important perceptual and behavioral consequences.

Implications for Policy Besides expanding the bases for misinterpretation, increased system size is likely to have significant impact on crisis or creative decision making. It is likely to cause, in surprising ways, increased interaction between an organization and its external environment, promoting what economists call externalities; and, hopefully, it will stimulate efforts to anticipate behavior in complex organized systems. Multiple Interpretations of Reality

A social system's complexity is a function of the number and form of relationships of considered elements. With increased social complexity one should expect to observe increases in both the number and the diversity of potentially competing system interpretations. Depending upon the general issues at stake and the perceptions of possible threats and pay­ offs held by concerned participants, these diverse interpretations may seriously hinder the operation of an institution involved with the system. Up to some manageable limit, such diversity is probably a positive at­ tribute. Problem solving requires one to consider a number of plausible, alternative courses of action and then to select that one having the greatest probability of success. For practical purposes, however, the limit is sur­ passed regularly in highly complex situations, with dyfunctional conse-

Analysis of Complex Systems

quences for most institutions. Under what conditions this occurs is an empirical question of some moment.36 At one level, decision makers necessarily rely on and select narrowly one or a few of these interpretations. Their reversion to "tried and true" behavioral paradigms, particularly in a novel or crisis setting, is a constant. Assuming that a present crisis can be managed "just like last time" may lead to failure if the problem context has changed and differs significantly from one's familiar perception. Incremental decision making has been explored elsewhere at length for the good reason that it probably approximates what goes on in most large organizations. Choices have to be made; and, following a course of least effort, one adjusts at the margin. The practice probably occurs more frequently with increased social complexity of both the organization and its surrounding environment. While incrementalism may suffice in the short run for ordinary, routine, steady-state decisions, contextual dis­ continuities or other basic structural adjustments may render it ineffective, irrelevant, or at the limit, pathological.37 Vacillation over "What-are-we-going-to-do?" may become the domi­ nant and debilitating modus operandi. Doing nothing in the face of competing and contradictory contextual interpretations is the choice by default made, one suspects, in many large organizational settings. For many mundane issues it may be a "right" choice, but certainly it is one made for the wrong reasons. Other possible reactions doubtless could be alleged; these few illustrate only several of the more important ideas held about size and organizational behavior. Let us concentrate on the case where stakes, threats, and payoffs are high and well understood, even if not formally defined. Increasing Chaos

Increasing a system's size may lead to chaos within an affected organiza­ tion, where no one view or recommendation prevails amid the clamor and no choices are made, or to authoritarianism, where only one or a select few recommendations are heard and acted upon.38 Perhaps a less extreme 36See A. L. Samuel, "Some Studies in Machine Learning Using the Game of Checkers" in Computers and Thought, ed. E. A. Feigenbaum and J. Feldman (New York: McGraw-Hill, 1963). 37One interesting empirical investigation that develops this theme to advantage is Geoffrey Clarkson, Portfolio Selection: A Simulation of Investment Trust (Englewood Cliffs, N. J.: Prentice-Hall, 1962). See also Ruggie's discussion in Chapter IV above. 38See Thornton B. Roby, E. H. Nicol, and F. M. Farrell, "'Group Problem Solving under Two Types of Executive Structure," Journal of Abnormal and Social Psychology, 67 (1963), 550-556, for illustration of these tendencies. But for another view, see J. D. Thompson, Organizations in Action (New York: McGraw-Hill, 1967).

Garry D. Brewer

variant of the chaos-authority model is the analysis-intuition one. Con­ fronted with a difficult and pressing problem, the intuitive decision maker usually opts to "fly by the seat of his pants," taking the consequences serially with little or no regard for interrelationships, priorities, or other potentially important subtleties. To the extent that one's intuition substitutes for a hearing of other perceptions of reality, the situation becomes authoritarian.39 The analytic decision maker, when likewise confronted, strives to collect more intelligence and more data, to do more analysis, and to hear out as many points of view as time permits. In the absence of choice, or in the event of vacillation about choice, such a style may become chaotic. Time is an important aspect of decision making. As the time available to make a choice decreases, both the search for alternatives and the oppor­ tunity to do analysis decrease and may force intuitive decision-making behavior to take over. Analysis yields to intuition. In a crisis setting the decision focus is narrowed and intensified on a select few alternative courses of action. The number of advisers consulted decreases, the amount of effective or new information brought to bear on the problem decreases. As the competitive aspects of the situation increase, analytic thoroughness and precision both fall by the wayside.40 Consider a setting where the time available for decision making is decreasing and competitiveness is growing, and suppose that there is an increase in the magnitude of the problem that absolutely must be considered. Likewise, suppose the levels of uncertainty about both the problem con­ text and possible outcomes for the various choices increase. As human, practical limits are approached, organizational fragmentation is likely to occur. With it, one must be prepared to deal with suboptimized, contra­ dictory and conflicting recommendations and points of view. All of these behavioral aspects exist in any event, but they are exacerbated in the high stakes, short-time, crisis setting.41 If there is an increase in the number and diversity of values at stake in organizations or other complex, orga39For industrial location decision making, to cite one important example, there is evidence that an intuitive and authoritarian style predominates. Decisions appear to be made subjectively, without benefit of analysis, by one or a few participants. See Management and Economic Research, Inc., for the OfRce of Regional Development Planning, Industrial Location as a Factor in Regional Economic Development (Washington: U. S. Department of Commerce, 1967). 40See, for example, G. T. Allison, Jr., Conceptual Models and the Cuban Missile Crisis, P-3919 (Santa Monica, Cal.: The Rand Corporation, August 1968). (Short version of the author's doctoral dissertation for the Department of Government at Harvard.) 41There is experimental evidence to support this view. See Mauk Mulder, "Comnunication Structure, Decision Structure and Group Performance," Sociometry, 23 (I960), 1-14.

Analysis of Complex Systems

nized systems, there will be increased opportunities for participants to be indulged or deprived. System Externalities

Because social systems exhibit properties of organized complexity, perturbations at one point in a structure may have effects elsewhere. Participants often perceive these effects as occurring "outside" of their particular system, and almost as often, are surprised by these externalities. Increased social complexity implies that whatever policies are adopted, both positive and negative externalities are likely to occur in increasing numbers. The unanticipated consequences of policy outcomes have to be accounted for; furthermore, because they are context specific, they may require quite different attention from one setting to the next. Sensing and evaluating the effects of such externalities are critical policy functions. The need for accurate information is basic to deciding whether effects are positive or negative, for whom, to what extent, and to what effect. Based on such assessments, other policies may be instituted to reinforce the desirable effects and to suppress the unwanted ones. Policy makers need to be alert to redressing and to terminating unneeded or outworn procedures and programs. Externalities are currently subjects of much greater moment than they were in the past. As social complexity increases in our political system more externalities appear and are recog­ nized. They represent potential political policy areas that may become serious issues, depending upon the number of participants and the magnitudes of deprivation or indulgence. Urban renewal in America of the 1960's is one striking example; others are easily noted. If nothing else, the renewal experience has shown the importance of knowing specific contexts well. General policies promulgated in the bureaus of the Department of Housing and Urban Development may bear faint resemblance to the specific deeds in the separate com­ munities where they are enacted. For instance, the negative externalities and consequent political activity that characterized the Boston renewal experience differed significantly from what happened in St. Louis or New Haven. Policy making, cast in terms of specific contexts, must be reoriented to anticipate, to sense, and to act on eventualities in those contexts. Anticipation is a key to the process. Whether there is now a reservoir of needed conceptual understanding is still very much in question. Futures

To anticipate the future and plan for it intelligently are desirable goals directly thwarted by the complexity of social systems. The effects of

Garry D. Brewer

increasing size, interconnectedness, and uncertainty all work against satisfactory social projection and effective policy invention. Simple extrapolations of trends about social systems may be an effective fore­ casting technique as long as system "inertia" is large, the number of relevant elements is small, the interconnections between elements are few and understood, and no untoward exogenous or structural changes perturb the context. In effect, so long as the system is relatively simple. Such crude extrapolations are what the incrementalist makes. But who can assert that we deal with simple systems? If one is interested in pricise information about behavior of even a single part of a complex system, extrapolation may be impossible. Because of the number and interrelationship of elements, more involved analytic formulations are required. Simulations, games, process models and related techniques each have a potential fro grappling with these kinds of complexity.42 When the degree of uncertainty about system structure and performance reaches intolerable or unmanageable levels, several organizational effects may be noted: (1) decision making becomes discernibly less orderly and predictable and tends to employ intuition, (2) the cost of information gathering and analysis increases, and/or (3) direct attempts to gain control over the environment increase. One decision-making style, under conditions of great or increasing uncertainty, emphasizes the collection and synthesis of additional in­ formation to attempt to reduce the level of uncertainty. Hiring a consulting firm or constructing large scale simulations are examples. Another style emphasizes efforts to control the context. Price fixing is one manifestation. Intuitive decision making appears to be particularly successful when supplementary efforts are made simultaneously to control the context. "One peek is worth two finesses" in social affairs as well as in a bridge game. How long either style remains effective in the face of increasing complex social systems is not entirely clear, but it is not likely to be very long. Command, Control, and Complexity's Burden

As those scholars who are concerned with processes of communications and political control remind us, the levels and interplay of loads, lags, leads, and gains have great importance for the efficiency, performance, and character of any social system. Load is the degree of stress, tension, or disequilibrium in a system. Lag is the time that a system takes to respond 42 Martin

Shubik and Garry Brewer, Systems Simulation and Gaming as an Approach to Understanding Organizations, P-4664 (Santa Monica, Cal.: The Rand Corporation, June 1971).

Analysis of Complex Systems

to a stimulus. Gain is the amount of action a system takes expressed as a ratio of outputs to inputs. Lead is the time between the present and the point in the future at which the state of the system can still be accurately predicted. Generally control in a system varies inversely with the degree of load and lag, directly with the amount of lead, and, up to the point of overcontrol or overresponse, directly with the amount of gain. Loading a system's decision-making apparatus with numerous, diverse perceptions and recommendations may reduce the prospects for consensual processes to operate. Attaining and holding a majority on one issue is rendered increasingly problematic, the greater the number of competing demands or loads requiring attention. In the situation where no one person really comprehends the whole, coordination and analysis dissolve. As systems become loaded, lag times tend to increase. "Priority" matters dominate the decision maker's attention, routine issues are delayed, and many matters are simply ignored. The condition of the judicial systems of the United States illustrates this well. When choices are finally made, it is often necessary to apply more gain than would have been required for a more timely decision. Brute force and coercion replace reason and intelligence as the dominant modes of operation. In the face of increasing uncertainty, lead times decrease — the time horizon for accurate predic­ tions contracts — rendering decision making all the more difficult. "Fire fighting" and "flights by the seat of the pants" replace reasonable problem solving and decision making. It is hoped that the many burdens of social complexity are a bit better understood as a result of the exercise drafted here. Remedial and preven­ tive measures are not so easily determined, although several activities offer interesting possibilities and could be explored in more detail. The integration of multiple, specialized perspectives is a key problem. The multimethod and multidisciplinary promises of the emerging policy sciences confront the issue squarely on the analytic front. To date, how­ ever, experiences have not borne out the promise. Proposals and several fledgling efforts to create and operate "decision seminars," a longstanding concern of Harold Lasswell,43 appear to be a conscious step in the direction of context-specific, disciplined analytic attempts to integrate diverse perspectives and purposes in problem solving. Experiences here too have been mixed but encouraging enough to elicit continuing participation and support. Efforts to increase lead time seem particularly appropriate. If the results have left something to be desired, the underlying motivation to forecast 43Harold

D. Lasswell, "Technique of Decision Seminars," Midwest Journal of Political

Science, 4 (1960), 213-236.

Garry D. Brewer

and do "futurology" is commendable. Time — unexpandable, precious time — may be the most critical element of all. While just about any activity that attempts to push the time horizon back is in principle justifiable, efforts to link large data processing procedures with analytic models or even conceptual frameworks merit special consideration.44 Augury notwithstanding, more decent information coupled with theo­ retically based, analytic formulations has to improve the present all too prevalent situation of short or no lead time decision making. A Concluding Note

The modest aims of this exploration belie the difficulty and importance of the issues. This has been only an attempt to join and redirect an old and continuing dialogue. It is hoped that a few hypotheses have been put convincingly enough to provoke thought and attention. Our immediate purpose is served if we have slightly aided the disentanglement and clari­ fication tasks that Charles Merriam so long ago called to our attention. 44Martin Shubik, "Symposium: The Nature and Limitations of Forecasting," Daedalus, 96 (1967), 941, 945-946.

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