Human Forces in Engineering 9783110535129, 9783110534726

Table of contents :
Preface
Contents
Acknowledgements
List of Contributors
Engineering in the modern world
Psychology
Socio-political analysis
Engineering economics
The economics of climate change
The leadership challenge for engineers
Concluding remarks

Citation preview

Aleks David Atrens, Andrej Atrens (Eds.) Human Forces in Engineering

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Human Forces in Engineering Edited by Aleks David Atrens, Andrej Atrens

Editors Dr. Aleks D. Atrens The University of Queensland [email protected]

Prof. Andrej Atrens The University of Queensland [email protected]

ISBN 978-3-11-053472-6 e-ISBN (PDF) 978-3-11-053512-9 e-ISBN (EPUB) 978-3-11-053526-6 Library of Congress Control Number: 2018934735 Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the Internet at http://dnb.dnb.de. © 2018 Walter de Gruyter GmbH, Berlin/Boston Typesetting: Integra Software Services Pvt. Ltd. Printing and binding: CPI books GmbH, Leck Cover image: John Lund / Blend Images / Getty Images ♾ Printed on acid-free paper Printed in Germany www.degruyter.com

Preface The modern world has become a very complex working environment for engineering professionals. Engineering projects tend to be larger, involve more components, and operate in a context of high societal expectations. Engineers are also expected to quickly adapt to substantial technological and social changes. More information is available than ever before, and this can be a double-edged sword. Deeper knowledge expands the realm of what can be achieved, and extensive data enables more-informed decisions. However, the massive quantities of information available means that considerable effort is required to synthesise the information into a plan of action, and to understand the implications in a social context. Despite this increased complexity, engineers have an exceptional, perhaps unparalleled capacity to change the world for the better. A disproportionate number of engineering graduates end up in senior management or other leadership positions, and many take on important non-engineering roles. Vital to their success are their technical skills (often seen as the core skill of engineering) but also, crucially, an understanding of the societal setting within which engineering takes place. Engineers that are able to leverage their technical and analytical abilities with an understanding of the social context have traditionally been enormously successful, both professionally and in terms of broader impact. This book originated from a recognition that this exceptional capacity of engineers can be enhanced with an understanding of the ‘human forces’, the phenomena that underpin and govern human interactions. The focus is on sociopolitics, psychology, economics, and leadership, as key features of the social context that guide the actions of individuals, groups, and society. This book consequently aims to provide overview knowledge of each of these disciplines as they affect engineering. The key ideas were assembled through consultation with domain experts from each field, with an aim to provide information on the key critical insights, and how these might be practically applied by engineers. We aim to provide a framework for the knowledge about human forces that is vital to achieving outstanding success. This framework is intended to provide the basis for future learning necessary to guide high-level strategic decisions, manage teams of diverse skillsets in complex environments, and to excel at the interface between a technical discipline and non-scientific fields.

https://doi.org/10.1515/9783110535129-201

Contents Acknowledgements  List of Contributors 

 IX  XI

Aleks D. Atrens, Andrejs Atrens Engineering in the modern world 

 1

Aleks D. Atrens, Alexander K. Saeri Psychology   35 Brian Head Socio-political analysis 

 79

Peter Knights Engineering economics 

 97

John Quiggin The economics of climate change 

 123

Trevor Grigg The leadership challenge for engineers  Concluding remarks 

 157

 145

Acknowledgements Thank you to all those without whom this work would not have been possible. In no particular order, thank you to: Ian Cameron for providing useful advice regarding resilience engineering and process safety. Penny Sanderson for her helpful feedback. Greg Birkett for prompting some of the ideas that led to the initial formulation of this work. Victoria Hampton for useful discussions and advice. David Mee and Ross McAree for useful and insightful discussions. And finally thank you to our anonymous referees who provided useful feedback regarding the technical content of the chapters within this book.

https://doi.org/10.1515/9783110535129-202

List of Contributors Aleks D. Atrens The University of Queensland [email protected]

Peter Knights The University of Queensland [email protected]

Andrejs Atrens The University of Queensland [email protected]

John Quiggin The University of Queensland [email protected] [email protected]

Trevor Grigg The University of Queensland [email protected] Brian Head The University of Queensland [email protected]

https://doi.org/10.1515/9783110535129-203

Alexander K. Saeri The University of New South Wales [email protected]

Aleks D. Atrens, Andrejs Atrens

Engineering in the modern world Abstract: Why should engineers read a book describing ideas and concepts that are far different from the core practices of engineering? This introductory chapter explores why this knowledge is crucial to engineering as a professional discipline in the modern world. First it explores what engineering actually involves at a practical level, and the role of non-technical abilities in the success of professional engineers. Secondly, it examines the changes that have taken place in the world over recent centuries, and how these changes increase the professional challenge for engineers, and enhance the usefulness of broad non-technical capabilities. Finally, it introduces the human forces as key sources of knowledge for the professional engineer: psychology, sociopolitics, economics, and leadership. Key concepts: Engineers’ social role; non-technical skills in engineering; changes in population, wealth, technology, complexity, and expectations; power as a vehicle for change; the human forces as levers of power. Key ideas: 1. Engineering has always been an interface between society and science/technology. Each engineering project aims to provide a technical solution for a societal need. 2. Engineers have always needed the non-technical knowledge and skills to operate effectively in this interface. The most effective engineers are those that can identify and provide a cost effective technical solution to a previously unmet need. Those that do so have typically become rich and prosperous. 3. The modern world is complex. Larger populations have increased the scale of most engineering projects. Increased wealth and advances in technology have increased our expectations, including of what engineering can achieve. These factors have increased the complexity of engineering work, including individual projects, organisations, and the social context in which engineering occurs. 4. In the complex modern world, non-technical knowledge and abilities are more important than ever. 5. Changes to the world are effected by the use of power and influence to modify the actions and goals of others. Power in society is wielded through the human forces, which influence human actions. Study of psychology, sociopolitics, economics, and leadership provides key insights into how power arises and is used in the modern world. An understanding of the key concepts from these disciplines will assist engineers throughout their professional careers. https://doi.org/10.1515/9783110535129-001

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1 Engineering roles More than half of the work done by engineers is non-technical. This is true even of engineers who have specialised, technically-focussed roles. Engineering is often viewed as primarily a technical profession. Indeed, even engineers tend to view it this way. However, ‘Engineering’ as a term for a type of work is an extraordinarily broad classification. It covers many different technical disciplines (civil, mechanical, materials, chemical/process, electrical, software, mining, and others). Even more importantly, the range of actual jobs engaged in by engineers is even broader. Engineers work in a spectrum of roles from those that are highly specialised and technical (e.g. research, design), to those that are primarily management (e.g. company directors or executives, project managers). Work within this spectrum can have varying levels of technical involvement: consider for example the different levels of technical knowledge involved in operations, sales, asset management, maintenance, or quality assurance. Generally, both engineers and others tend to identify engineering roles by their technical aspects. That is, the ‘engineering’ part of a job is typically seen as the element that requires the analytical application of scientific understanding to resolve particular questions or problems. Engineers are seen as either being engaged almost solely in this type of activity, and that other tasks they do as part of their work are considered to be ‘non-engineering’. This is a misinterpretation of what engineers actually do. While the ability to apply engineering science to analytical work is a fundamental component of engineering roles, ‘non-engineering’ tasks are also fundamental. They have always been a critical component in ensuring the success of engineering activity. Consider engineering design as an example. Design engineering is a very technically-focussed area of engineering, and many would expect that 100% of a design engineer’s time would be spent on technical activities. The reality is different. Design engineering is, usefully, a job that has been studied in some depth, and so there is quantitative information available, from observational studies, work monitoring, or timekeeping diaries. In general terms, these studies show that substantial amounts of design engineers’ time is engaged in tasks that they themselves regard as ‘non-technical’ activities. For example Figure 1.1 shows the results from one study in which only 40% of work time was spent on technical and non-social activities. A total of 37% was spent on non-technical & social and non-technical & non-social work. This result is similar to other studies, which have reported large amounts of design engineers’ time is spent on non-technical tasks (e.g. 55%, [2] 30–40%, [3]). In these types of studies, non-technical work can include activities such as communication (with non-engineering and engineering staff), planning, project management (of self and others), or motivating other staff. Figure 1.1 also highlights the social element of engineering. Getting engineering tasks accomplished inevitably involves communication (face to face, phone, or

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40%

30%

20%

10%

0%

Technical & Social

Technical & Nonsocial

Non-technical & Social

Non-technical & Non-social

Figure 1.1: Percentages of working time of design engineers categorised by social or technical nature; data collected from 78 engineers over 20 days, based on task categorisation at a random time within each hour [1].

email) with other staff (engineers and non-engineers) particularly in relation to information gathering, processing, decision-making, and reporting. As an engineer, you often need other people to do work that is critical to the success of your own tasks. However, you generally have no leverage on others, and need to encourage and persuade them to produce what is required. It’s evident that all engineering roles, even those in the most technically-focussed areas of engineering, involve substantial non-technical activities. These non-technical tasks are critical to successfully delivering the desired outcomes of an engineer. The social and non-technical aspects of engineering practice are often only superficially addressed in engineering education. The implication is that most engineers are expected to develop these abilities through experience. The most successful engineers do so. Others may not, and their careers or job satisfaction may suffer as a consequence. Part of the impetus behind this book is to provide knowledge to assist in developing skills in these areas. That is, to introduce engineers to the key concepts and ideas that will assist them in their work, such as: – An understanding of basic aspects of psychology with respect to social interactions and the cognitive biases of individuals; and how these can be used to advantage, or how others will use these to their advantage or your disadvantage; – Broader perspectives on aspects of society that may enable better decision-making (i.e. a better understanding of politics, economics), so that your project scope is better focussed for success; – Frameworks for strategic decision-making and investment decisions, which may help with both engineering decisions, but also personal choices (e.g. choosing between job or career options, or major purchasing decisions such as a house).

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1.1 Engineering projects For individual engineering roles it is clear that even a technically-focussed engineer does a lot of non-technical work, and that the quality of the ‘non-technical’ work may be critical to an engineer’s professional success and career. What is the role of non-technical work in engineering projects, teams, and organisations? Engineering projects involve substantial non-technical components. This is different from an individual engineer’s work: while an individual technical worker needs to spend their time on non-technical things like communication, motivation, and planning, there is an additional requirement for non-technical work at the level of a project overall. At the project level, there is consideration of project-level management, logistics, external communication, engagement with stakeholders such as community, politicians, owners; fund-raising and financial considerations; laws, regulations, and consideration of social context, public perception, and strategic positioning. While the technical components of engineering projects are clearly fundamental, the non-technical elements of a project are also critical. Consider project scoping, which is the precise clarification of desired project outcomes and preferences as to how they should be achieved. If projects are scoped adequately at the outset, this assists enormously in getting things right the first time. This avoids duplicated effort, delays related to correcting errors or misconceptions, and most importantly, ensures that the project actually meets the needs of the client or end-user. A project can involve precise and skilled technical work, but nonetheless fail catastrophically to meet the needs of a user or society if it is inadequately scoped. Stakeholder engagement can also be extremely important: if particular interests (of e.g. the local community, client, politicians, or special interest groups) are not explored, and if those groups are not communicated with about the nature of the project, they can be a source of massive delays, or they can even block projects completely. Expending effort throughout the project on ensuring that groups external to the project do not raise barriers to its success is a critical part of successful projects. On the other hand, informed and engaged interest groups may become important champions of projects that are aligned with their desires. Why not delegate these types of non-technical tasks to non-engineering workers? Sometimes this is possible. However the technical and non-technical aspects of engineering projects are often closely inter-twined, and each may depend on the others. This means that the non-technical tasks can only be delegated if they are understood. If they are not understood in relation to the technical tasks, disastrous results are likely. The implication is that engineers must be involved in these critical non-technical components: either directly, or by working in close collaboration with non-technical staff, itself a form of non-technical work. In practice, large amounts of non-technical work is required in any engineering project, with engineers often being responsible for the consequence of how the

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non-technical work is managed. The consequence is inevitably direct involvement of engineers in the non-technical aspects (particularly in project management roles), or through close working relationships with non-technical project staff.

1.2 Engineering in society From examining both individual engineering jobs and engineering projects, the picture is clear: engineering work is much broader than technical engineering analysis applicable to a particular project or task. The same is true if engineering is considered at the broadest sense, by evaluating ‘engineers’ as those who have studied engineering, instead of just those currently employed in an engineering role. Most trained engineers (i.e. those with an engineering degree) don’t work in engineering roles. Many work in science and tech-related areas. Many work in jobs that are primarily non-technical. For example, the USA National Academy of engineering reported that 50% of engineers and scientists work in non-engineering/science roles, and that a further 25% work in roles that are only engineering/science-related [4]. They also reported that 70% of engineers and scientists work in management, sales, or administrative roles [4]. In Australia, Engineers Australia reported that only 60% of engineers work in engineering roles [5]. Furthermore, the engineering profession is highly-valued in management and leadership roles. Engineers are less than 5% of university graduates in the USA, and less than 9% of graduates in Australia. But they disproportionately contribute as corporate executives (e.g. 20–24% of top-performing CEOs are engineers), [6, 7] the wealthy (22% of the world’s 100 wealthiest people studied engineering), [8] and leaders (12% of 1709 leaders identified from 30 different countries were engineers) [9]. The critical implication is that by combining the technical analytical capabilities provided by engineering, with a good understanding of non-technical aspects of the world, engineers can be extremely successful within or beyond the engineering profession.

1.3 Opportunities in engineering Why does an engineering degree and engineering work experience position engineers so effectively to be valuable in non-engineering roles, and in management or leadership positions? Ultimately, it is not because engineering is a technical discipline specialised in the application of scientific principles. It is because engineering fills an interfacial role: engineers work at the interface between social needs and technological capability. Engineering is an interpretation function, which consists of:

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Assessing what is needed by a client or society; Analysing what is physically possible by applying their technical understanding; Evaluating what is economically possible in the social context; and Conceiving an idea that fits into the conjunction of need and possibilities (i.e. where 1, 2, & 3 overlap) Navigating the complex modern social world to translate that idea into a physical reality.

This gives us a clearer view of engineering, as a cohesive set of individual tasks building toward a tangible outcome desired by a group of people, generally with particular goals. Engineers may not always control this entire process. Some elements, such as the particulars of the need or determination of a desired outcome may be determined by politicians, corporate management, government departments, communities, etc. Nonetheless, engineers have a key role by virtue of their technical ability: they are the only ones with the ability to understand key physical and logistical constraints. The individual tasks in the project may be technical (necessitating engineering input), non-technical but tied to technical constraints (also requiring engineering input), or non-critical non-technical (which can be delegated). Ultimately there needs to be communication across the interface between the technological aspects and the non-technical aspects. Because the technical aspects can be conceptually challenging, engineers are often best-positioned to bridge the communication gap between the technical and non-technical aspects. And engineers who are competent in these broader skills consequently tend to be more successful (i.e. the ones that end up in top management or leadership positions). This provides some insight as to why engineers are not simply applied scientists who work on socially-important problems. Instead the engineering profession is one concerned with providing the interface between society and technology: translating the ideas of governments, corporations, groups, and individuals into physically realised changes in the world. Engineers that recognise the above can make use of their broader knowledge of the world to benefit their careers, profit, etc. This book aims to support them by providing a framework of knowledge to enhance their non-technical skills.

2 The modern world Effective practice of engineering has always involved non-technical skills. However, the world has changed in some particular ways over the last century, and as a result, non-technical skills are more important than ever. In the following pages, we’ll take a look at some of the major trends over past centuries, and their implications for engineering practice.

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One perspective of the world and human society is that despite superficial changes, the nature of the world, human society, and human interactions is the same as it has always been, i.e. that “there is nothing new under the sun.” The alternative and currently fashionable viewpoint is that the world has changed recently, and in particularly dramatic ways. The reality is somewhere in between. Most human systems have substantial inertia, which tends to slow and smooth changes over time. Human society has undergone substantial changes, but most of these take decades or centuries to propagate. The modern world is best understood by seeing how the world has changed. The world has always been changing, and many changes are a matter of degree. In many ways people have not changed much over the previous centuries, and so while some aspects of the social world have changed, much is the same as it has always been. Because engineering is primarily a social function (providing an interface between society and science & technology), many aspects of engineering have not changed since the first engineering projects thousands of years ago. The nature of the social interactions involved in planning, negotiation, communication, and financing of engineering projects in classical antiquity (e.g. construction of bridges, roads, public buildings, war machines) were in fundamental ways similar to the social interactions in modern engineering. However, some things have changed dramatically, and it is useful as an engineer to understand how the world is different compared to 10, 100, or 1000 years ago, so that they can better operate in the present world. In particular, it is useful to be aware of changes in: – Demographics – population, wealth; – Technology and human capabilities; – Social expectations; and – The resulting impacts of these factors on scale and complexity, particularly in the context of engineering work and projects. These aspects of society have always been in a state of change. However there have been some fairly dramatic changes in the last 10–200 years. We’ll take a look at each in turn, and then consider what it means for engineering overall.

2.1 Population Population has increased dramatically over the course of human history, particularly in the last century. This is most evident when the population change is examined in absolute terms. However, the human population has always been increasing, and in many senses, the nature of engineering work (in terms of e.g. building new buildings and infrastructure) is dependent on the rate of change. This can be easily visualised with population on a log-linear graph, as in Figure 1.2, where the slope indicates the rate of change. It is clear from this approach that not only is the total population larger

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Population (millions)

100000

10000

1000

100

10 5000

4000

3000 2000 1000 Years before present

0

–100 200 100 0 Years before present

Figure 1.2: History of world population over the past 5,000 years; the points indicate estimates from various data sets, [10–22] the dotted line represents the mean of these estimates, the solid line represents the United Nations projections of future population to 2100 [23]. A long-term trend of population growth is evident, which accelerated starting about 500 years ago, and became particularly rapid over the past 60 years. Population growth is slowing and is projected to plateau over the course of this century to 10 billion.

now than previously, but the population growth rate has also been above long-term historical values in recent centuries and particularly in the past 60 years. This is sometimes a source of anxiety, particularly regarding resource allocation and sustainability of a population growing at a continually exponential rate. However population growth should probably not be a cause for concern or anxiety. Recent increases in population growth appear to be primarily due to reductions in child mortality as countries become wealthier, which is subsequently also counteracted by a trend toward lower birth rates in wealthy countries (driven by literacy and women’s rights). The overall result of these trends is that countries are expected to follow a ‘logistic’, ‘sigmoid’, or S-shaped population curve as they progress through this transition. As a consequence, the total world population is expected to stabilise during the mid-21st century at ~10 billion, as shown by the estimates of future population (projected by the United Nations) shown in Figure 1.2. Nonetheless, population growth has massively increased demand for material goods, food, and infrastructure. It has consequently been a major underlying driver for the demand for engineering skill and expertise. It has also lead to large increases in the scale of engineering projects. Essentially any infrastructure project, such as a road, dam, factory, or power plant, has undergone a substantial transition in scale over the past 100 years. This has pushed the boundaries of engineering capabilities and driven new accomplishments in engineering science and technology. The world’s growing population also poses a range of current engineering challenges. The needs of larger and more complex cities, and the ability to provide food,

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water, electricity, and other infrastructure services to a rapidly growing population involves substantial engineering requirements. Larger populations themselves have been a driver of other increases in complexity: larger societies have enabled scientific and social advances, which in turn increases economic growth, all of which contribute to increased social and technical complexity of engineering projects.

2.2 Wealth & income There have also been large increases in wealth over the past few centuries. This is most visible in terms of personal income, which is better-quantified and is generally related to personal wealth. These changes appear particularly dramatic if charted in absolute terms, but to examine the differences in society now compared to previously the rate of change is more important, which as with population is best explored using a logarithmic scale. As shown in Figure 1.3, there have been dramatic changes in personal income over the last 200 years. Figure 1.3 examines average income, but income isn’t distributed evenly: there are large disparities in income and wealth between individuals, groups, countries, and regions. The most obvious disparities are between regions with Africa and Asia being substantially less wealthy than Europe or North America, with South and Central America being somewhere in between. Figure 1.4 provides one approach to conceptualising the disparities between and within regions, by examining a statistical distribution of wealth. It also gives an indication of how income and the distribution of income has changed in the past 200

Gross Domestic Product per Capita (1990 International Dollars)

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Figure 1.3: World gross product per capita in 1990-equivalent inflation-adjusted international dollars over past 5,000 years [24]. This shows the dramatic increase in humans’ ability to produce wealth that has taken place over the most recent centuries.

Population-weighted Frequency

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Remainder Latin American Countries Sub-Saharan Africa OECD

1000 Income (2005 USD)

China Other Asia India World 1820

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Figure 1.4: Distribution of annual income per person worldwide in 1820 vs. 2011 [25, 26]; coloured areas correspond to regional populations, and the area under the thick black line corresponds approximately to the income distribution of the entire world’s population in 1820. Over the last 200 years, the human population has become substantially wealthier, and poverty has been reduced in absolute terms. Large disparities in wealth distribution within and between regions remain.

years. It shows a population-weighted distribution of personal income for different regions of the world (so the different coloured areas are proportional to population, and with relative height at any point being proportional to the population at that income level. As evident from Figure 1.4, there are extraordinary differences between e.g. the OECD1 and India. Also shown in Figure 1.4 is a line showing the overall distribution of income for the whole world in 1820, demonstrating the extraordinary progress between 1820 and the present. Nearly half the world’s population (and nearly all who live in the OECD) now has a lifestyle that would have been unimaginable to most of the population in 1820: essentially an income equivalent to the top 1% of the population from 1820. At the lower end of the scale, life is better for the poorest as well. Most of the poorest in the OECD have a quality of life comparable to the wealthy in 1820. The disparity of wealth within regions and individual countries (i.e. the broad width of the distribution and sub-distribution in Figure 1.4) has been a topic of substantial recent interest amongst economists, politicians, and sociologists. There is a concern that these disparities might be a source of potential conflict between countries, and a source of political instability within countries. It is also thought that the

1  The Organisation for Economic Co-operation and Development (OECD) is an intergovernmental ‘club’ of 35 moderate-high income developed countries, essentially Western & Central Europe, North America, Turkey, Chile, Australia, Japan, South Korea, and New Zealand.

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disparities of wealth may themselves be detrimental to future economic growth. The argument for this is fairly straightforward: those less wealthy generally have poor access to health care and education. This prevents them from being able to achieve their potential. For example, high child mortality and poor access to education may mean that children with the potential to be brilliant scientists end up doing menial work, or die. Some wealth disparity is probably unavoidable: included within measures of wealth inequality within countries are the differences in wealth and income between the young and old (which includes increases in income or accumulation of personal assets with age), which are difficult to accurately adjust for. What does this all mean? It’s unclear how wealth inequality or disparity will change in the future, and what its affects will be on society, but it may become an increasingly important political consideration and consequently an important consideration for engineering projects. Most of the improvements in personal wealth have been driven by advances in technology, and the ability of engineers to translate technology into societally-useful outcomes. One of the most important implications of the growing wealth has been to change expectations. As humans have become wealthier – through a transition from subsistence farming to rudimentary urban living to a modern society ‒ our exposure to risk of death, disability, or even discomfort has decreased. And our expectations, particularly of risk, have changed accordingly.

2.3 Technology Advances in human technology have driven increases in human population and wealth. Technology is now fundamental to meeting the needs and desires of a large and affluent population. Often technological advances are thought of as abrupt step-changes. But past technological advances are probably less discontinuous than how they are often framed. It is rare that a single idea or concept instantaneously enables improvements to our way of life. Most often, technological advancements are incremental changes on existing ideas, or applications of existing concepts or objects in slightly different ways. However, a small series of incremental improvements can lead to a tipping point, where a technology becomes clearly preferred compared to other options. When this tipping point is reached, a technology can rapidly become dominant. Even then, progressive uptake within a society may be gradual if there are high ‘replacement costs’ (to change from the existing technology to the new technology), and historically spread of technology between societies has been slow. As an example, consider the steam engines, a technology recognised as a pivotal technology in driving the industrial revolution, i.e. retrospectively one of the most important human inventions. The potential use of steam in motive power had been recognised for hundreds of years before the first commercial steam engines, and by

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the time of the first ‘commercial’ engine there were other practical instances of steam being used as a source of motive force (including in fountains, mining pumps, and simple mechanical devices). The first commercial engines themselves were seen as a barely worthwhile improvement on existing energy sources (human labour, animal labour, or environmental energy sources such as wind or water), due to extremely low efficiencies (and consequent high energy needs). It was only after further incremental improvements upon existing concepts (decades after initial commercial application) that a point was reached where the steam engine was a definitively preferable source of motive power. It is thought that despite the concept of steam power being present hundreds of years earlier, the higher-quality steels and more-precise machine tools of 18th century England were fundamental to the idea being put into practice in a worthwhile way. Depending on where one counts the first ‘discovery’ of the motive power of steam, the technology took hundreds of years to develop, and many decades to propagate around the world. After a technology becomes definitively preferable to alternative options, it will propagate until it has filled all the possible applications it is suited for (unless it is first supplanted by an even better technology). Propagation can happen quickly or slowly depending on perceived advantage, and at least historically was limited by geographic constraints. Figure 1.5 shows some examples of technology propagation within the last century, demonstrating the variability that can occur between different technologies and regions, and some instances of long lead-times between initial ‘discovery’ and perceptible uptake. 100 90

% Ownership

80 70 60 50 40

USA Electricity USA Radio USA TV Mexico Electricity India Electricity World Mobile Phones World Internet

30 20 10 0 1865

1890

1915

1940 Year

1965

1990

2015

Figure 1.5: Technology uptake for different technologies and regions [27–30]. This figure allows comparison of how rapidly different technologies were adopted in different regions, typically following a curve with time that is s-shaped (or ‘sigmoidal’) with time. Technologies that are more useful tend to have rapid uptake, but this can be slowed by lack of wealth or other social barriers to technology adoption. The shape of the curve changes accordingly. Not all technologies reach 100% adoption, as they may be replaced by newer, better options.

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One point of interest is that with rare exceptions (Roman concrete, Damascus steel, Greek fire), technology has rarely regressed or been lost. Historically there have been periods where in localised regions knowledge and utilisation of technology has changed (for instance in Europe following the fall of the Roman Empire), but technological concepts remained known and in use in other geographic regions. Partly because of the inaccurate popular view of technological advances as sudden ‘Eureka’ moments, and partly due to the recent wealth and population changes over the past few centuries, technological progress is often framed as having been historically fairly limited for a long time, followed by a period of rapid advancement. As outlined above, this is probably not accurate. Many of the most fundamentally important technological ideas (e.g. farming, fire, construction, boats, the wheel) are extremely old. Other technologies had periods in which they saw limited use due to local lack of knowledge or need (e.g. sanitation). So the history of human technological development is best understood as a longterm accumulation of knowledge. Any person working in the role of an engineer in the past few millennia needed to be able to work with existing and new technologies of the time, and often needed to come up with incremental improvements themselves to be able to accomplish their goals. So in an absolute sense, engineers have always needed to work with technology, and there has always been technological progress. However, while technological improvements have been present throughout human history, some of the technological advances in the past two centuries are particularly important, and have changed the work of engineers. Some of the most notable are health technologies including antibiotics, vaccination, anaesthetics, and a range of pharmaceutical products; energy conversion technologies including heat engines (the ability to convert heat into mechanical work), electricity, and refrigeration; transportation technologies including railways, the internal combustion engine, and flight; synthetic fertilisers; telecommunications technologies such as the telegraph, radio, telephone, and the internet; and industrial robotics, including the use of mechanical and electronic calculating machines, leading up to modern computers. These technology changes have been important drivers of the other changes in the past centuries, and are also directly responsible for changes in the practice of engineering. Many aspects have changed dramatically, but there are three major areas we’ll explore: energy utilisation, connectivity in terms of travel & communication times, and health.

2.3.1 Energy Utilisation Energy utilisation has been expanded continuously by humans throughout their existence. Taking the long term view, one must consider fire (burning biomass), agriculture, and the use of animal power as key steps in the long-term increasing use of energy to achieve human goals. In this context, there is a long-term trend of gradual

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increase in energy consumption throughout human history as these technologies became available, propagated through and between societies, and were sequentially improved through technological advances. Societies based around hunting and gathering used about 10 MJ/person/day [31, 32]. Basically all of that energy use was for survival: for baseline energy consumption and acquiring more food, without much left over for anything else. Close to 100% of able-bodied individuals in these societies were involved in food production. The discovery of fire enabled the use of biomass (wood) as an additional source of energy for cooking (including food drying for preservation) and heat. Energy use likely varied between locations depending on climate and fuel availability. Definitive data is not available; widely used but unjustified estimates are an increase to about 17 MJ/person/day, [33] but contemporary fuel use suggests values of 25–37 MJ/person/ day [34]. Regardless, the additional energy available from fuel allowed humans to survive in a broader range of climates, and allowed the population to expand (i.e. feed more children who couldn’t yet find food themselves). Some of that additional energy allowed time to be spent on crafting of tools, clothing, etc, beginning the long process of technological advancement. Nonetheless, almost everyone in these societies was still primarily engaged in food production. From 10,000 to 2,000 years ago, the progressive adoption of agriculture expanded available energy. Toward the end of that time-frame, established agrarian societies probably used about 25–46 MJ/person/day [35]. This was from a combination of human food and animal fodder to support labour, and biomass (primarily wood burning) for heat, cooking, and small-scale industry such as pottery or metalwork. The increased energy supply (in the form of surplus food) meant that the entire population of agrarian societies did not need to be dedicated to food production. Agrarian societies could support a small population surplus, which filled other societal needs: craftsmen, warriors, priests, and rulers. This allowed the emergence of complex societies. It is in these societies that engineering developed: a profession of workers who made their way by construction, building bridges, equipment, devices, and war machines. These societies could also better support learning and retention of information, allowing better retention of knowledge across generations and hastening incremental development of technology. Over the most recent two millennia, agrarian societies expanded their ability to use energy through technological advances, such as heavy ploughs, efficient horse harnesses, use of animal energy for milling and processing, irrigation technologies, and expansion of metalworking. Thus more advanced agrarian societies had more energy available. Complex agrarian societies in Europe and Asia increased their energy use to about 50 MJ/person/day [36]. This was still almost entirely sourced from human and animal muscle energy (provided via food), and wood as a source of biomass energy. Despite these increases in energy availability, most of the population still worked to produce energy in the form of food. For example in England by 1500, this was around 60% of the population; in other complex agrarian economies, it was likely 70–80% [37].

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The advances of the past 200 years have been even more dramatic. The use of fossil fuels decoupled energy availability from arable land area, and expanded the scale on which societies could engage in industry. The steam engine’s ability to convert thermal heat into mechanical force meant that energy could be more readily used to fulfil a broad range of social needs. This combination lead to a dramatic expansion in energy use as developed countries industrialised. For example, in England and Wales, which first progressed through the industrial revolution, energy use increased from 50 MJ/person/day in c1500 (as a complex but primarily agrarian society) to 140 MJ/person/day in 1800 (before the steam engine), to 560 MJ/person/day in 2000 [36], see Fig. 1.6. This increase was almost entirely sourced from fossil fuels, which replaced wood burning and animal muscle power as sources of energy, as well as most human muscle power. Feeding the human population is still ~5–10% of the energy budget in modern industrial societies, [38] and human energy is still used for productive purposes (although generally for cognitive work, social interactions, or tasks which are difficult to automate, rather than physically strenuous labour). It can be a bit hard to conceptualise how we each as members of a developed society manage to use 500 MJ/person/day of energy. One way to get a feel for these numbers is to consider an estimate of the energy that a relatively wealthy person might use over the course of a year, and average that out on a per-day basis. Table 1.1 gives some reasonable estimates of this type of energy budget, for a person living in the UK [39].

Per capita energy consumption (MJ/day)

500 England & Wales 400

China India

300 200 100 0 400

350

300

250 200 150 Years Before Present

100

50

0

Figure 1.6: In the long-run, human history has involved substantial increase in energy consumption. The main series shows the change in energy consumption in England and Wales, the initial location of the industrial revolution and the first country to transition through a rapid increase in energy consumption due to the addition of fossil fuel energy supply. The energy consumption in China and India are also shown, as illustrations of two particularly populous countries in different stages of a transition to an energy-intensive economy [31, 35, 36, 40–42].

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Table 1.1: Estimates of a typical daily energy budget of a relatively prosperous person living in the UK; determined by averaging annual energy usage on a daily basis per person [39]. Item

Energy Consumption, MJ/person/day

Travel via car: average consumption for 50 km/d travel

144

Travel via plane: 1 intercontinental trip, averaged over 1 year

108

Heating and cooling, average house & workplace

133

Light, home & workplace

14

Gadgets (e.g. phone, TV, radio, computer)

18

Food, including farming & fertilizer Stuff (e.g. drinks & their containers, packaging, furniture, houses, roads, infrastructure, drinking water & desalination, imports) Transporting things Public services: including defence, universities Total:

54 173 43 14 701

This means that in modern industrialised societies, the energy available on a per-person basis is approximately 50 times that available to hunter-gatherers. To put this another way, one can conceptualise the individual energy usage of individuals in a modern society, of ~500 MJ/person/day as each being able to have 50 labourers working for them (at 10 MJ/person/day). The modern world isn’t quite like having 50 people working for you, though. We can use energy in ways that was extremely difficult with human or animal labour, in that mechanical equipment can provide higher peak power, and higher forces, and technological advances such as flight, refrigeration, electricity, or computation allow the use of energy in ways that was previously impossible or inconceivable (or that would have seemed like magic). These changes in energy availability meant that a further social transformation was possible: only a few percent of the population in industrialised countries work in agriculture, as they can harness other energy sources to do the necessary mechanical work. In developing countries, this transition has not yet been completed. For example, in the Bashkurit Valley, an agricultural area of Ethiopia, energy use is 121 MJ/person/ day. This is from ~14 MJ/person/day in human food, 82 MJ/person/day in animal fodder, 24 MJ/person/day in biomass (wood and dung), and 0.5 MJ/person/day in petroleum [43]. Different countries, and regions within countries, sit across a spectrum, with some areas still being primarily agrarian. Energy has been fairly inexpensive historically. While our use of fossil fuels is more efficient than using human muscle, we don’t use energy from these fossil fuel sources as efficiently as is possible. There’s substantial scope for decreasing energy consumption. There’s been substantial progress in this area from developing

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countries in terms of decreasing energy use per capita (note the ramp downward in the most recent years for England & Wales in Fig. 1.6), and decreasing energy use per amount of wealth created. In fact, most of the increases in wealth have actually happened after the fairly rapid ramp up in energy use. This implies that countries currently going through this energy and wealth transition (i.e. developing countries) will not necessarily reach energy use per person equalling that of current developed countries, even after achieving similar levels of wealth and quality of life. What does this mean for engineering? Many engineers work on harnessing and using energy for the benefit of society. Engineers have enormous amounts of energy available for engineering tasks compared to the case previously. Energy is nonetheless expensive, and there is increasing social pressure to use it efficiently. Not all countries have the same levels of energy available, and in some regions there will be an expectation of using a larger share of human labour for example.

2.3.2 Connectivity Connectivity has also continuously expanded through technological advances. Improvements in transport allow a person to travel around the world in days, and allow shipping of massive quantities of material around the world in days or weeks. The maps shown in Figure 1.7 provide a perspective of the magnitude of these changes. Both maps show isochronic travel distances from London (that is, how long it would take to reach particular locations on the map). What should be immediately apparent is that the change is essentially multiple orders of magnitude. Now, essentially nowhere is completely isolated. As a consequence, engineering projects can be based in much more remote locations than previously possible. This connectivity is a driver behind development of remote sites, the ability for fly-in fly-out work, and for engineers to do design, consulting, or management work without continuously being onsite. Advances in telecommunications have complemented the travel changes. One could map out the delay in communication similar to the isochronic travel map; in the 1700s a map of communication times would look very similar to the 1800s isochronic travel map. With radio, telephony, satellites, and other communications advances of the last century, modern communication between any two points on Earth is essentially instantaneous. Much like the travel map, these changes have occurred over essentially the past 100 years. These improvements in communications speed have led to globalisation of engineering work. For example it is now possible to have multiple engineering teams at different sites collaborating on a single project (e.g. a team in each of Australia, India, and the USA), or to have engineers remotely monitor sitework from the other side of the world.

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1881 Travel Time:

< 10 days

10–20 days

20–30 days

30–40 days

> 40 days

2016 Travel Time:

< ¾ day

> ¾ day

> 1½ days

Figure 1.7: Maps of isochronic travel distances (time of travel) from the origin of London, UK, in 1881 and in 2016 [44, 45]. The first map shows the substantial travel time required between different parts of the world in the late 19th century. Technological advances (powered flight and the internal combustion engine allowing cars) have dramatically reduced travel times, such that almost anywhere in the world can now be reached within a couple of days (the only exception being particularly remote and inhospitable areas).

2.3.3 Health & Life Span Technology has dramatically improved our ability to prevent or treat disease, and to heal the injured or sick. The advances in medicine, public-health-related infrastructure, and increases in wealth in general have increased life expectancy, as apparent in Figure 1.8.

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90 80 Expected Life Span

70 60 50 40 30 20 10 0 1810

At Birth At Age 20 1860

1910 Year

1960

2010

Figure 1.8: Changes in expected life span of white females in the USA over the last 200 years [46, 47]. Rapid social improvements reduced child mortality in the 1800s, consequently increasing expected life span at birth. Further advances in technology over the course of the 1900s have steadily increased the expected life span. These improvements are similar in other OECD countries, and for males (although the life expectancy of the latter is varied over the same time-frame due to a larger influence of mortality in war).

Initial improvements in life expectancy were largely due to reductions in child mortality. Figure 1.8 shows that between 1850 and 1910 life expectancy of a child rose substantially. Over the same time frame, the life expectancy of adults increased by only small amounts. Subsequent technological advances, including antibiotics and other anti-infective agents, anaesthetic agents enabling major surgery, medical imaging (X-rays, ultrasound and magnetic resonance) improving diagnostic accuracy, a range of pharmaceuticals, and a more comprehensive understanding of the interactions between human physiology and disease processes, have increased the human expected life span. These improvements now mean that a typical individual has much higher expectations for a long and relatively healthy life than previously. This, and other changes in typical expectations of the average person, have important implications for engineering practice.

2.4 Expectations Engineers have always had to operate in a social context, and needing to meet the expectations of the general population is not new. However there are, particularly in wealthy societies, very different expectations of engineers and engineering projects now compared to even a few decades ago, and certainly compared to 200 years ago. These changes in expectations are most evident in terms of:

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Safety; Environmental impact; Social impacts; and Community engagement.

These changes have largely occurred over the past hundred years, but are most evident over the past 50. Figure 1.9 shows the mortality rate from work-related causes, for different industries. Evident here is the rapid reduction in mortality in what were previously hazardous jobs. Similar trends are present for mortality rate due to external causes, as shown in Figure 1.10, for Australia. ‘External causes’ means all non-disease causes of death, so includes accidental death in the workplace, but also includes death due to e.g. nonwork accidents, poisoning, suicide, or murder. This change in expectations of safety is not limited to occupational safety. This expectation has spread throughout society to include other sources, for example accidental death in motor vehicle accidents as shown in Figure 1.10. It also extends to much larger scales, for example substantial efforts have been taken to avoid or reduce death rates from natural disasters, as shown in Figure 1.11. This is not to say that there has never been some expectation of safety: death rates 10× or 100× higher than historical values would likely have been unacceptable to potential workers. What has changed is the baseline level of risk that is seen as

Fatalaties per 100,000 workers per year

1000

100

10

Mining Average Construction, Transport, Agriculture, Forestry, and Fishing, Average All Other Industries Average

1 1880

1900

1920

1940 Year

1960

1980

2000

Figure 1.9: Work-related mortality rate in the USA over time [48, 49]. The rate of industrial fatalities has reduced by more than an order of magnitude, at least in the mining industry, for which comprehensive data is available. Data for other industries over recent decades has shown a similar declining trend. A logarithmic scale of fatality rate is used for easier visualisation of the data. Criteria changes cause the small step-change for 1972–1973.

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 21

Deaths per 100,000 persons per year

120 100 80 60 40 20 0 1900

All external causes Motor vehicle accidents 1920

1940

1960 Year

1980

2000

2020

World Annual Death Rate (per 100,000) from Natural Disasters

Figure 1.10: Mortality rate in Australia from external causes (i.e. excluding all disease, but including accidental death in the workplace, trauma, poisoning, suicide, murders) over the past century [50]. A decline in the death rate by a factor of two is evident. 50 45 40 35 30 25 20 15 10 5 0 1900

1920

1940

1960 Year

1980

2000

2020

Figure 1.11: Worldwide death rate from natural disasters over the last century [51, 52]. A decline in death rate due to natural disasters of an order of magnitude has been achieved over the past century.

acceptable. In many instances in modern wealthy societies, the acceptable rate of workplace injuries or fatalities is now seen as zero. These changes in expectations are not limited to health & safety issues. They extend to social and environmental impacts (particularly of, but not limited to, engineering projects), for example the environmental impacts of dams, or the noise effects of construction, or the aesthetic impacts of infrastructure. This includes situations where those concerned with the impacts are not personally affected. Social

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or environmental concerns may overcome potential economic benefits (a particularly evident example is resistance to city councils re-zoning inner suburban residential areas to higher density). These changes in social expectations have consequently become extremely important to manage proactively in engineering projects. Often we conceptualise environmental impacts in terms of poor waste disposal practices and chemical spills, and consequently think of environmental damage as being a problem of recent centuries. But there are many examples of past practices that were massively environmentally or socially damaging in ways that are unacceptable today. Some of these practices were acceptable due to lack of knowledge, but many were previously seen as acceptable regardless: – Predatory species such as wolves were nearly completely exterminated from continental Europe as they were seen as detrimental to livestock and human safety, whereas now there is interest in reintroduction of these species. – Many whale species were hunted as a source of oil, leading to massive reductions in their populations, an activity that is now nearly universally seen as unacceptable. – Historical structures are routinely preserved now, due to being as inherently socially valuable. Historically, old buildings were often used as a cheap source of building materials. For example, bricks from ancient buildings in Rome were used as a source of building materials for much of the middle ages. – Enabling the international spread of plant or animal species for recreational (rabbits and foxes initially being introduced to Australia for hunting), economic (cane toads introduced to Australia to control native sugar cane beetles), or other purposes was seen as acceptable; whereas now the environmental consequences are deemed unacceptable. These changes in societal expectations are hard to quantify other than by looking at particular examples such as those above. One other possible alternative is to examine changes in law and regulation, as a very indirect quantification of society’s expectations. Figure 1.12 shows the number of pages of the USA code of regulations over the past 100 years. While the code of regulations covers a broad range of topics (not only safety, environment, and social interactions), the change provides a clear indication of a much more comprehensive amount of societal control over acceptable and unacceptable action. What do these changes in societal expectations mean for engineers? They mean firstly that any particular engineering activity is likely to involve additional project management functions compared to 100 years ago, such as community engagement, regulatory compliance, and health and safety monitoring, and environmental impact evaluation. There is an expectation to carefully control occupational health and safety issues, and minimise external impacts, whether they are related to safety, the environment, or social effects. Secondly, there is a heightened expectation for engineers to closely interact with any community associated with an engineering project. That is, above and beyond minimising impacts, there is an added expectation to under-

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Pages of USA Code of Federal Regulations

200000 160000 120000 80000 40000 0 1920

1940

1960

Year

1980

2000

2020

Figure 1.12: Pages of USA Code of Federal Regulations over the past century [53]. A dramatic increase is evident over time. This is an indication of the finer control that government has taken over a range of different activities, including those involved in most engineering work.

stand community needs and expectations, educate the community regarding risks and mitigation strategies that will be used, and potentially to ensure that there are benefits to the community as a matter of principle.

2.5 Scale & complexity As a consequence of the various changes to population demographics, wealth, technological capabilities, and societal expectations, engineering projects have grown larger and more complex. How substantial have these changes been? One aspect to consider is change in the scale of engineering. This can be examined by looking at the largest examples of engineering projects. For example, Figure 1.13 shows the scale of the tallest buildings and longest bridges over the past 200 years, showing a clear increase of more than an order of magnitude. Similar changes are visible in typical rather than extreme examples: Figure 1.13 also shows the average tonnage of freight ships over the same period, which has increased by 2 orders of magnitude. These changes in scale are examples: a similar trend can be seen in a wide variety of engineered systems. Concurrent with the changes in scale, the complexity of engineering projects has also increased. Complexity can be tricky to quantify compared to scale. Figure 1.14 provides one approach to quantifying the changes in complexity over the past two centuries: it shows the typical number of parts of engineered physical items, and the lines of code in the Microsoft Windows operating system. These quantities don’t directly correspond to the complexity, but are related to it. There has been an increase of many orders of magnitude in these proxy measures of complexity over the past two centuries.

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100000

Dimension

10000

1000

100

10 1800

1850

1900 Year

1950

2000

Figure 1.13: Dimensional increases in scale of engineered items over the previous 200 years have typically been 1-2 orders of magnitude; black diamonds – skyscraper height (m); [54] open squares – suspension bridge span (m); [55] black circles – average freight ship size (gross tons) [56]. Similar changes would be apparent with most arbitrarily chosen metrics related to humans’ built environment or industrial output.

That approach quantifies complexity in terms of changes of a technical nature, but does not capture increase in complexity from the context within which engineering project operate – i.e. the social, political, and economic systems with which this book is primarily concerned. Changes of complexity in those terms are very difficult to quantify. Approaches that can be taken examine features such as: – Organisational, social or political structures, the complexity of which can be quantitatively evaluated by measuring the interconnectedness or arrangement of different functional parts (or ‘topology’). For example, more divisions of a company or more lines of reporting or communication within a company is a measure of increasing complexity. – The complexity of manufactured objects can be measured by a similar approach, by considering the number of component systems required. For example, a modern car, compared with a car from the 50s, might include air-conditioning, motor-controlled doors and windows, pollution control, a rear-view camera, collision sensors, a heads-up-display, a GPS system, and some automated-driving functions. – International complexity can be explored by the evaluation of business deals, trade quantities, or social networks (e.g. measuring the total amount of global trade, as in Figure 1.15, or quantifying the international social or professional links). – Sociopolitical complexity can be evaluated by quantifying rules and regulations, or the amount of effort required to comply with them (e.g. quantifying staff hours spent on legal or regulatory compliance).

Engineering in the modern world 

1.E+10 1.E+09 1.E+08

Number

1.E+07 1.E+06

 25

Number of Parts (Single Items) Number of Parts (Mass-Produced Items) Number of Parts (Small Items) Transistor Count (Consumer CPU) Lines of Code (Windows OS)

1.E+05 1.E+04 1.E+03 1.E+02 1.E+01 1.E+00 1800

1850

1900 Year

1950

2000

Figure 1.14: Dimensional increases in complexity of engineered items over the previous 200 years have typically been many orders of magnitude [57–59]. Manufactured objects have increased in number of components by a few orders of magnitude. The precision manufacturing of miniaturised electronic parts has allowed complexity increases of many orders of magnitude. These changes do not encompass the further conceptual increases in complexity through the integration of different systems (such as incorporating complex electronics alongside complex machinery in e.g. aircraft).

Global Trade Exports (Billion $)

100000 10000 1000 100 10 1 1800

1850

1900 Year

1950

2000

Figure 1.15: Total trade exports worldwide for the past 200 years [60, 61]. There has been an increase of more than three orders of magnitude over the past 200 years. This is an indication of the substantial increase in international trade, itself an indication of increasing complexity in supply chains. Most complex objects in modern society contain components sourced from many different countries.

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These different approaches won’t be explored in further detail here: the quality of data available is variable, and none of these various measures individually provides a precise measure of complexity. Overall, however, the general trend is clear: professional engineering operates in a more complex environment today than previously. For any particular engineering project or task, there is a greater likelihood that it will involve: – Multiple disciplines; – Multiple sites; – Teams or team members from different cultures or backgrounds; – Non-engineering staff; and – Varied measures of performance or success. All of these provide a greater need for engineers with command of non-technical aspects of engineering: the human forces.

2.6 Effect on engineering practice So what does all of this mean for the practice of engineering? It makes professional engineering more challenging, multi-disciplinary, and complex. Consider an engineering project example, of one of the oldest types of engineering projects – a bridge. Many of the important aspects of this type of project were well established back in the origins of engineering in antiquity: – The bridge would have technical requirements: span, load-bearing capacity, resilience/longevity; – There would need to be a minimisation of construction and material costs; and – It would need to meet political or social needs (i.e. of the community or ruler). A bridge built in modern times will likely have these same requirements. However these might be expanded with any or all of the following: – Increased requirements in terms of span & capacity (more people, cars / transport/ freight); – Broader range of technological options available (which would need to be compared); – Environmental and social impact assessments; – Broader stakeholder management (e.g. not just the ruler but also community/ special interest groups, investors, governing board, etc); – Incorporation of other technologies e.g. water, lighting & power, sensors and monitoring for maintenance, corrosion control of metal parts, control systems and automation, surveillance/security; and – Safety requirements – during construction and operation. The more complex needs and requirements of modern engineering projects mean that the professional engineer must have mastery beyond technical skills. An understanding of the human forces is a key component in addressing these new needs.

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3 The human forces This book is about the ideas that we’ve termed ‘human forces’: sociopolitics, psychology, economics, and leadership. In an engineering context, forces act on physical objects and cause particular outcomes or changes. Similarly, human forces act upon humans and affect their actions and behaviour. Knowledge of the human forces provides insight into what determines human behaviour. Important to the idea of human forces is the concept of power, the ability to influence or control behaviour. To understand the human forces is to understand the nature of power in society, and how it is used. As such, it is useful to have some overarching concepts of power. There are many different ways to think about power, but a useful framework is to consider three different types: 1. Coercive power 2. Influential power (sometimes referred to as ‘soft’ power) 3. Economic or financial power Coercive power is the ability to direct or control the behaviour of others through the threat (and application) of punishment. This includes the type of power wielded through legal systems and by governments (i.e. the threat of fines, imprisonment, or other penalties), or used by those in positions of authority (e.g. the ability to demote or fire personnel). Coercive power is particularly important in terms of sociopolitics, and an engineer should understand the power structures that exist within the governments of countries where they work. This is not a form of power frequently used by engineers, and is not generally part of an effective management strategy. However it is important for engineers to understand this form of power, particularly in situations where it is used in non-transparent or corrupt ways. Economic or financial power resides in the capacity to influence behaviour through the offer of a reward in exchange for action. This type of power is particularly important as a mechanism for organisations to enact change on the world. In many ways the ability to use this power is straightforward; but the key lies in being financially literate so as to be able to wield this power efficiently and effectively. Financial assets can be viewed as a tradable form of power. In this view, the objective is often to enact desired changes on the world with minimal use of resources (i.e. minimal cost). This is a frequently-used form of power for engineers, who will often be either directly making financial decisions, or providing the underlying analysis on which those decisions are made. Consequently a knowledge of both the language of economics and finance, and a robust understanding of the principles of these decisions is important for a professional engineering career. Influential power is the ability to change others’ behaviour without threat or offer of reward. It instead involves inspiration, persuasion, motivation, or reputation to convince others that a particular course of action is the best option. At first glance, this type of power might seem to be limited to politicians, celebrities, or other leaders. But this is the type of power that is used by individuals every day in

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most interpersonal relationships. This is the most frequently used type of power, and the dynamics of social influence underpin many interactions at the level of society, between small groups. This is a key underpinning theme in sociopolitics, psychology, and leadership. The human forces have associated bodies of knowledge related to how power is used within the modern world. An understanding of the key concepts from these disciplines will assist engineers throughout their professional careers. The following pages provide a brief overview of the different human forces, and the key concepts from each field.

3.1 Sociopolitics Sociopolitics is a discipline concerned with understanding the relationships between an individual, their society, and their government or political system. It is concerned with understanding the role of power and influence; governance, institutions, and policy; laws and regulations; cultural perspectives and differences; and the nature of political systems. In terms of power and influence, sociopolitics is concerned with power as the ability to decide what actions are to be taken by groups of individuals ‒ either by ‘hard power’: force, coercion, or money; or by ‘soft power’: persuasion, inspiration, encouragement. Sociopolitics is interested in understanding where power resides (i.e. in particular individuals or groups; or in particular positions or roles), and in how it is wielded, and with what intent (i.e. understanding the motives or goals of individuals and collectives of individuals). These aspects of power may not necessarily be immediately apparent: individuals, social structures, laws, and institutions can superficially appear to function with one purpose or manner, and when explored in detail may have a different underlying intent and different consequences. Understanding and interpreting subterfuge is an important aspect of understanding the human forces. In terms of engineering, understanding sociopolitics is particularly valuable to inform engineering projects in relation to political systems. In international projects an engineer should consider the implications to the project of the country in which the project will exist: consider risk (due to political/social changes), regulations (safety, environment), cultural perspectives (safety, environment, social consultation, social/ religious practices, etc). Engineers might consider: – Personnel safety, risks, regulations, and cultural expectations ‒ the ethical approach is to aim to make things ‘as safe as in a western country’. – Political control & interventionism: is there a risk of changes in government regulations, risk of government seizure of assets, or potential for change in project approval processes? – Rule of law and role of corruption: are laws contravened by those that are powerful, or by those with money? There is an expectation in most Western countries

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– –

–

–

 29

that citizens working in overseas nations (ex-patriots or ‘expats’) abide by the laws of their home country, or may be prosecutable on return; this is particularly relevant regarding issues such as bribery or other forms of corruption. Transparency of government processes: e.g. in relation to approvals, compliance to regulation, permits, or environmental standards. Cultural perspectives: work practices, hours, pay, social norms, interpersonal behaviour, expectations of social contribution or role of community needs in major engineering projects. Laws and regulations ‒ are there particular laws that apply to the project? Are there different customs in how the law is applied? Are there particular local socio-cultural practices that are enshrined in law? Ethical concerns: engineers may become disheartened if they feel that the project has ultimately done no good for the community in which it is placed. A guide to prevent cognitive dissonance (‘that uncomfortable feeling when you have behaved in a manner that conflicts with your personal ethical beliefs’) is to try to do what would be expected of an engineering project in your home country, as a minimum.

An understanding of the general principles of sociopolitics informs all engineering projects, not just those operating in unfamiliar international contexts.

3.2 Psychology Psychology is the study of the mind and behaviour. It is concerned with: – What governs how we act, at an individual level, as a person within a group, and between groups; – How we think, and the particular features of human cognition; and – How we interact with our surroundings, including the natural environment, but probably more importantly, with the built environments and human-made systems. This book provides an introduction to some of the concepts within the discipline of psychology. In particular, an exploration is provided of the influence on engineering projects of: – Expertise: what determines expertise? How does one become an expert? Can people be an expert on any topic? Under what conditions is expertise possible? How can one recognise an expert and their limits? Why and how do we make errors, and can experts make errors? – Human interactions: social factors that influence how an individual perceives the actions of others, and the way they might respond, from agreement/alignment through to neutrality, opposition, or obstruction. Of particular interest is how this affects teams of diverse individuals or the relationships between different teams.

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Human factors & resilience engineering: what human cognition and interaction implies for safety, and how our understanding of how best to build safe, resilient engineered systems has advanced.

The implications for engineering include for example whether experts on a particular topic or discipline are likely to have useful insights or ability to predict outcomes, which depends on the field of their expertise.

3.3 Leadership, strategy, and decision-making The study of leadership is concerned with an individual’s role in influencing, persuading, and inspiring others, (i.e. the ‘soft power’ of leadership), and the process of making important decisions with substantial implications (i.e. strategic decision-making). This field of study is ultimately also concerned with the ethical considerations and character traits of people given a decision-making role, including perspectives of their responsibility, culpability, and obligations (in a modern context, particularly with regards to their role in crises). This discipline is concerned with questions such as: – How does one make “good” decisions? – How do leaders make change occur? – How can one become an effective leader? Engineers are much more likely than typical university graduates to take on positions of responsibility in middle, senior, or executive management, where the soft power of strategic communication and persuasion have primacy over technical considerations. Consequently this book provides an introduction to these aspects of the study of leadership. It includes a consideration of: – What leadership is; – Current and future challenges for leaders in modern times; and – Particular issues for leadership within engineering.

3.4 Economics Economics is concerned with the social interactions that govern human activity related to material incentives. It is a system for evaluating activities related to production, distribution, and consumption of resources, goods, and services. It is interested in particular in understanding individuals’ voluntary decisions regarding the exchange of material goods or their time (i.e. providing services); both at the scale of individuals (e.g. game theory, bartering, negotiation, etc), and at large-scale (e.g. markets, trade, supply, demand, & prices). In terms of the impact on engineering, this book discusses economics from two perspectives:

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1.

2.

 31

Investment decision-making: the underlying principles that govern commercial or business decisions. This includes issues such as supply, demand, prices, markets, capacity, economic growth, inflation, and interest. The economics of climate change: as a lens to interpret a range of social problems that have an engineering aspect and an economic context. This chapter includes a discussion of a number of economic concepts that are very important in the engineering profession, using climate change as an example to explore how they relate to engineering problems. These include the issue of common property (things that have no single owner and can be used – or damaged – by anyone) and economic approaches to managing common property such as taxes and regulation, and how the different approaches can affect outcomes.

4 Study questions – – – –

–

Explain the role of non-technical skills in the engineering profession. Explain the most important societal trends in the last century, and how have these affected the practice of engineering. Discuss the changes in energy use over the past hundred years, and how this varies between different countries. What challenges does this pose in the future? Population growth is expected to level out over the course of the next century. Explain why this might occur, and what factors might change the course of future population growth. Investigate a large engineering project. Which human forces had an important role in the success of the engineering project? How did engineers involved in the project interact with their societal setting?

Further reading Pinker, Stephen. (2001). The better angels of our nature: why violence has declined. Viking: New York. This book provides an excellent overview of some historical trends, and how they have changed our world for the better. Rosling, Hans (2010). Global population growth, box by box. TED Talks: New York. Available from: https://www.youtube.com/watch?v=fTznEIZRkLg This short video provides an excellent overview of past and future trends in population growth. Rosling, Hans (2013). Don’t panic – the truth about population. Wingpsan Productions, Open University, & Gapminder Foundation. Available from: https://www.youtube.com/watch?v=FACK2knC08E This longer video explains global population trends in depth, and explores the social factors that influence population growth through both statistics and personal stories. The Economist. (2017). The World in 2018. This yearly series published by the economist provides a useful overview of current global trends, including political, social, business, economic, and technological considerations.

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References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23]

[24] [25] [26] [27] [28]

[29]

Robinson MA. How design engineers spend their time: job content and task satisfaction. Des Stud 2012;33(4):391–425. Hales C. Analysis of the engineering design process in an industrial context. Cambridge, UK: University of Cambridge, 1987. Lowe A, McMahon C, Culley S. Information access, storage and use by engineering designers – part 1. Eng Des 2004;March/April:30–2. Guenther RS, Didion CJ. Advancing diversity in the US industrial science and engineering workforce: summary of a workshop. Washington, DC: National Academy of Engineering, 2014. Kaspura A. The engineering profession: a statistical overview. Barton: Engineers Australia, 2015. Ignatius A. The best-performing CEOs in the world. Harv Bus Rev 2014;92(11):48–56. Flynn PM, Quinn MA. Economics: good choice of major for future CEOs. Am Econ 2010;55(1):58–72. Rajna T. What degree will make you rich? Approved Index, 2015. British Council. The education pathways of leaders: an international comparison, 2015. Population Reference Bureau, 2017. World population data sheets. Washington, DC. The World at Six Billion. In: United Nations Department of Economic and Social Affairs Population Division, editor, 1999. World Population to 2300. In: United Nations Department of Economic and Social Affairs Population Division, editor, 2004. Haub C. How many people have ever lived on Earth? Popul Today 1995;23(2):5–6. Maddison A. The world economy: development centre of the organisation for economic co-operation and development, 2006. US Census Bureau: World Population, 2017. Klein Goldewijk K, Beusen A, Janssen P. Long-term dynamic modeling of global population and built-up area in a spatially explicit way: HYDE 3.1. Holocene 2010;20(4):565–73. Clark C. Population growth and land use. London: Macmillan, 1967. Durand J. Historical estimates of world population: an evaluation. Population Studies Center, University of Pennsylvania, 1974. Thomlinson R. Demographic problems: controversy over population control. Belmont: Dickenson Pub. Co, 1975. Biraben JN. An essay concerning mankind’s evolution. Paris: National Institute for Population Studies, 1980. Tanton J. End of the migration epoch? Soc Contract 1994;4(3):162–74. McEvedy C, Jones R. Atlas of world population history. London: A. Lane, 1978. United Nations, Department of Economic and Social Affairs, Population Division. World population prospects the 2017 revision: Key findings and advance tables. New York, Working paper No. ESA/P/WP/248: United Nations, 2017. De Long J. Estimates of world GDP, one million BC – Present, 1998. Lakner C, Milanovic B. Global income distribution: from the fall of the Berlin wall to the great recession. World Bank Econ Rev 2016;30(2):203–32. Roser M. Global economic inequality OurWorldInData.org2017. Available at: https:// ourworldindata.org/global-economic-inequality. Accessed 17 April 2017. Cox WM, Alm R. The economy at light speed: technology and growth in the information age and beyond. Dallas: Federal Reserve Bank of Dallas, 1996. Pachauri S, Brew-Hammond A, Barnes DF, Bouille DH, Gitonga S, Modi V, et al. Chapter 19 – Energy access for development. Global energy assessment – toward a sustainable future. Cambridge and New York: Cambridge University Press, 2012:1401–58. World Bank. Mobile cellular subscriptions (per 100 people). World development indicators, 2016.

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[30] World Bank. Individuals using the internet (% of population). World development indicators, 2016. [31] Pontzer H, Raichlen DA, Wood BM, Mabulla AZP, Racette SB, Marlowe FW. Hunter-gatherer energetics and human obesity. PLOS ONE 2012;7(7):e40503. [32] Katzmarzyk PT, Leonard WR, Crawford MH, Sukernik RI. Resting metabolic rate and daily energy expenditure among two indigenous Siberian populations. Am J Hum Biol 1994;6(6):719–30. [33] Cook E. The flow of energy in an industrial society. Sci Am 1971;225(3):135–42. [34] Wood TS, Baldwin S. Fueldwood and charcoal use in developing countries. Ann Rev Energy 1985;10:407–29. [35] Malanima P. Energy consumption in the roman world. In: Harris WV, editor. The ancient mediterranean environment between science and history. Leiden: Brill, 2013:13–36. [36] Warde P. Energy consumption in England & Wales 1560–2000. Naples: Consiglio Nazionale delle Ricerche, 2007. [37] Allen RC. Economic structure and agricultural productivity in Europe, 1300–1800. Eur Rev Econ Hist 2000;4(1):1–25. [38] World Bank. Employment in agriculture (% of total employment). World development indicators, 2016. [39] MacKay DJ. Sustainable energy – without the hot air. Cambridge: UIT Cambridge, 2009. [40] World Bank. Energy use (kg of oil equivalent per capita). World development indicators, 2016. [41] Ramachandran P. Food consumption patterns in India. Bull Nutr Found India 29(2):1–5. [42] Ge K, Chen C, Shen T. Food consumption and nutritional status in China: achievements, problems and policy implications. Food Nutr Agric 1991;1(2/3):54–61. [43] Haas W, Andarge HB. More energy and less work, but new crises: how the societal metabolism-labour nexus changes from agrarian to industrial societies. Sustainability 2017;9(7):1041. [44] Galton F. On the construction of isochronic passage-charts. Proc R Geog Soc Monthly Record Geogr 1881;3(11):657–8. [45] Rome2rio. 2016 Isochronic travel times 2016. Available at: https://www.rome2rio.com/labs/ isochronic-travel-times/. Accessed 17 April 2017. [46] Arias E, Heron M, Xu J. United States life tables, 2012. Nat Vital Stat Rep 2016;65(8):1–64. [47] Hacker DJ. Decennial life tables for the white population of the United States, 1790–19001. H Methods 2010;43(2):45–79. [48] Improvements in Workplace Safety – United States, 1900–1999. MMWR Morbidity and Mortality Weekly Report 1999;48(22):461–9. [49] Mine Safety and Health Administration. MSHA fatality statistics. Arlington: United States Department of Labour, 2017. [50] General Record of Incidence of Mortality (GRIM) books [Internet]. Australian Institute of Health and Welfare, 2017. [51] Roser M. Natural Catastrophes: OurWorldInData.org, 2017. Available at: https://ourworldindata. org/natural-catastrophes/. Accessed 10 April 2017. [52] OFDA/CRED International Disaster Database. In: Disasters CfRotEo, editor. Brussels: Université Catholique de Louvain, 2017. [53] Code of Federal Regulations Total Pages and Volumes 1938–2014 Federal Register; The daily journal of the United States Government, 2015. [54] Wikipedia. List of largest office buildings, 2017. [55] Wikipedia. List of longest suspension bridge spans, 2017. [56] Alderton PM, Rowlinson M. The economics of shipping freight markets. In: Grammenos C, editor. The handbook of maritime economics and business, 2nd ed. London: Routledge, 2010:181–216.

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[57] Ayres RU. Computer Integrated Manufacturing: Revolution in Progress. London: Chapman & Hall, 1991. [58] Lisensky G. Moore’s law 2016. Available at: https://chemistry.beloit.edu/classes/nanotech/ moore.html. Accessed 11 April 2017. [59] Wikipedia. Source lines of code 2017. Available at: https://en.wikipedia.org/wiki/Source_lines_ of_code. Accessed 11 April 2017 [60] Federico G, Tena-Junguito A. A tale of two globalizations: gains from trade and openness 1800–2010. London: Centre for Economic Policy Research, 2016. [61] World Trade Organisation. World trade in 2010. World Trade Report 2011.

Aleks D. Atrens, Alexander K. Saeri

Psychology

Abstract: This chapter provides an introduction to key elements of psychology relevant to engineers. Psychology is the science of human mental states and behaviour. It aims to explain and predict beliefs, decisions, and actions of people as individuals and in groups. A working knowledge of psychology will assist with: identifying biased or erroneous reasoning and improving decision-making; promoting effective teamwork and coordination; understanding effective leadership; and recognising the importance of ‘human factors’ in designing resilient engineering and technology systems. Engineers appraise challenges and design solutions. Understanding and predicting human mental states and behaviour is essential for accurate appraisal of a challenge, and effective design of a solution. Humans form part of every system, either embedded (e.g., a required behaviour within a process), individually (e.g., as a designer of the system), or socially (e.g., as a member of a team that must coordinate action). Humans cannot be ‘designed out’ from a system. Even if humans are not embedded within the system itself, human judgement, decision-making, and social processes are still inextricably linked with who engineers are, and what they do. Key Concepts: Modes of cognition; cognitive heuristics and biases; expertise; group dynamics; norms; the psychology of leadership; the psychology of safety; human factors; resilience engineering. Key Ideas: 1. A useful model for human cognition is that judgements involve two modes of thinking: a fast, effortless, reflexive or intuitive mode of thinking (System 1), and a slower, effortful, and deliberative mode of thinking (System 2). 2. System 1 thinking relies on learned associations (heuristics) to make fast decisions from limited information. It is very powerful, but is also prone to a range of errors (biases). As a consequence, human judgement in general is prone to characteristic errors. 3. Expertise is the capacity to make accurate and useful judgements in a specific domain. An expert can integrate their experience and situational cues to quickly envisage a likely answer, or a path forward to make progress on a solution. 4. Expertise can be developed only in an environment with regular and valid cues, and is based on extensive experience using these cues for rapid and accurate feedback. Expertise may be difficult or even impossible to develop in chaotic environments with low-quality feedback (high noise to signal ratio), or long delays between action and feedback. https://doi.org/10.1515/9783110535129-002

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5. Expertise is domain-specific. Application of expertise across domains can be misleading, as confidence in one’s judgement is a poor indicator of accuracy and it can be difficult to distinguish between useful and useless intuitive judgements. 6. Social identity theory states that people define themselves not only in terms of “I” (as individuals), but also in terms of “we” (as members of social groups). Every individual has many group memberships and thus many social identities. When team members endorse and work for the benefit of a shared social identity, they will better coordinate their actions and are more likely achieve shared success. 7. A strong shared social identity can be a double-edged sword. We care about a social identity like we care about our personal identities. We will seek to uphold, protect, or enhance our group’s beliefs and values. Upholding a group’s values and distinctiveness may be positive in that it promotes coordination and good behaviours within a group. But it can also be negative in that protecting your group’s values and distinctiveness may mean conflicts with other groups. 8. The psychological view of leadership is determined by the context of the group, including the group’s shared social identity and purpose, its internal structure, and the environment within which the group operates (e.g., other competing or cooperating groups, availability of resources). This view differs from the traditional concept of leadership as arising from traits or behaviours such as power or charisma. 9. A leader in the psychological sense represents the group’s interests to other groups, makes decisions on behalf of the group regarding its purpose/goals, actions to attain the goal, and persuades group members to contribute to achieving the group’s goals. 10. Leadership is conferred by followers and is maintained by successfully leading the group. Leadership is likely to be lost by acting against the interests of the group, deviating from group norms, or not advocating for the group’s goals to those outside the group. 11. Humans are embedded in every engineering system. A system may appear technically reliable but be vulnerable to unexpected external hazards beyond the scope of its design. 12. Existing safety approaches tend to design ever more complex engineering controls to remove or restrict human ‘error’ in a system. It may also be vulnerable to unexpected internal hazards if its design does not account for the bounded rationality, and group dynamics of its operators. 13. A human factors approach seeks to incorporate a psychological understanding of how humans interact with designed systems to improve performance and safety. 14. Resilience engineering recognises that the human operator exists to provide resilience and adaptability and seeks to empower them to act appropriately in a system.

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1 Introduction From the outside, psychology may appear to be a clinical science or profession, focussing on the individual and their functioning. In this view, clinical psychologists seek to assist individuals with their mental illness or depression, and sports psychologists seek to improve athletic functioning. But the science of psychology is concerned with the understanding of the mind in general, and the understanding of human behaviour – of individuals and of groups. Every system has a human element, whether it’s an engineering system, a political system or a social system. Most systems operate within a social context, taking into account regulatory environments, policy ideas, and organisational or team culture. Even carefully-designed mechanistic systems have an inescapable human element, in that all the details of the system are intimately tied to the decisions of the creators. Understanding thoughts and behaviour can help to shed light on how systems work, so as to improve systems, or identify when they might be going wrong. This chapter introduces three areas of psychology of relevance to engineers. First, judgement and decision-making, and the development of expertise. Second, group dynamics, focussing on how individuals can lead groups and the way that groups make decisions. Finally, it addresses the relationship between human-system interactions and systems failure, and how systems can be improved through ‘human factors’ and resilience engineering.

2 Judgement and decision-making Our laws, politics, and many social structures are largely based on the assumption that people can and generally do make rational decisions. We have an impression that decisions are based on an even-handed interpretation of available information. This implies that when someone makes a mistake or a poor choice, they either were given the wrong information, or that they have some inherent incapacity: perhaps a lack of correct training, or maybe a lack of competence to make correct decisions – ‘poor judgement’. This is often evident in criticism when something goes wrong – people talk about ‘human error’, attributing mistakes to poor training, poor judgement, or a lack of correct information. Such individual-focused explanations are incomplete and can be misleading. All of us, whether more or less skilled, have cognitive features that shape the way we process information. And the way that we process information and reach decisions is not perfectly rational. That by itself may not seem like much of an insight. But there are regularities in our irrationality. Even clever people can make errors in their thinking, and these errors tend to happen in predictable ways. We can then try to identify situations or types of judgements that could be influenced by our irrationality, and to design systems that work around them.

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As people gain more skills or knowledge, typically they expect their decision making will improve – i.e., their error rate will be reduced as they gain expertise. While expertise does provide benefits, everyone has features of how they process information that can lead them to wrong conclusions. These features are what psychologists refer to as heuristics and biases.

2.1 Modes of thinking One of the approaches that psychologists use to understand human cognition is to conceptualise two different modes or styles of thinking (dual process theory) [1–3]. The first is an automatic, intuitive mode which involves rapid, seemingly effortless thought [3–5]. This mode of thinking, often referred to as System 1, is very powerful and allows quick and generally-accurate decision-making. However, as an associative and pattern-matching system, it is prone to specific errors in situations for which it is not well-suited. The other mode of thinking is slower, conscious, and analytic [3–5]. This more deliberative type of cognition is often referred to as System 2. It is a mode of thinking that requires more cognitive effort and more time. An example (from Frederick [6]) can illustrate the differences in processing between these two systems. Read the following question, and give your answer out loud: “A bat and a ball cost $1.10 in total. The bat costs $1.00 more than the ball. How much does the ball cost?”

What was your answer? A System 1 approach may be as follows: the two items together cost $1.10; the bat is associated with one dollar; after a dollar is removed, there’s 10 cents left; the ball must cost 10 cents. Is that right? When asked this question, a substantial proportion of respondents will give this answer. But if we break the question down using a System 2 approach, it’s not that the bat costs one dollar by itself, it’s that it costs one more dollar than the ball. So the ball must cost 5 cents, and the bat must cost one dollar and 5 cents. If we check the snap judgement that the ball costs 10 cents, we add the cost of the ball (10 cents) to the cost of the bat ($1.10, i.e. $1 more than the cost of the ball) to get $1.20 and realise that the snap judgement was incorrect. It’s easy when first seeing this type of question to immediately jump to the snap answer of 10 cents. Did you go through this same thought process? If so, you’ve just seen an example of how you can use System 1 to quickly answer a question, but potentially (as in this case) incorporate an error in the process. If you did get the correct answer, that’s great. But don’t assume that you are immune to these types of type of cognitive shortcuts. The same type of cognitive processing that leads to the incorrect answer is what allows us to recognise an emotion in a photograph, drive a car, catch a ball, or read and write.

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System 1 thinking uses heuristics to incorporate previous knowledge through association, and typically is engaged in pattern matching or pattern recognition. It’s sometimes referred to as instinct or intuition, and has been denigrated as ‘a machine for jumping to conclusions’. It behaves as a fill-in-the-blank judgment process, whereby given a query or stimulus it produces an almost reflexive response. It’s been characterised as an effort-saving approach to cognition. So what’s happening cognitively in the ball and bat example above? The query is phrased in such a way that prompts people to reinterpret the query, recognise that it sounds familiar to other familiar mathematical problems, and reframe or substitute the true query with a similar, easier problem. This results in a subconscious conclusion that the question is actually stating that the bat costs $1, and asking what the remainder is. This then becomes the foundation of the answer to the question. An important point is that this cognitive behaviour is not a quirky trick. It doesn’t only happen in contrived examples. Humans engage in these types of shortcuts all the time. Often it is tremendously advantageous in terms of the cognitive effort required. Suppose a person is driving on a road and the car in front brakes: it slows, and its brake lights shine. The driver behind doesn’t need to think about the relevant road legislation, or begin to solve the equations of motion to determine the course of action. They take a mental shortcut: they see the brake lights, and they depress the brake pedal. This is actually a computationally difficult problem: there has been interest in developing self-driving cars for nearly a century, [7, 8] but only in the most recent decade has the technology seemed plausible. Automatically replacing a computationally difficult problem with rapid, associative cognitive reasoning allows us to solve this challenge. The mental shortcuts of System 1 thinking are crucial to being able to function. Without the cognitive ability to rapidly process vast amounts of sensory information and make responsive decisions, many of our everyday actions would be impossible. This mode of reasoning is important in intellectual, problem-solving based activities too. It allows us to recognise existing or ‘good-enough’ solutions to the problems we face. So System 1 thinking is not a problem of cognition. While it can be a source of error, it is a feature of how we think about the real world, so that a lot of the time when we encounter a situation, we are able to jump rapidly to an answer, because most of the time, we need a quick answer. In some situations, it is important to deliberatively analyse the problem. Using a System 1 mode of thinking can erroneously replace a complex problem with a simple one, and we’ll make a mistake. This type of error is not related to intelligence or conscientiousness; it is an automatic error that we can make by inappropriately applying the System 1 mode of thinking to a problem that it’s not suited for. System 2 is a more deliberative and complex mode of thinking that is more effortful and more analytical, and enables us to reason through problems in which System 1 thinking will lead to mistakes. It’s slower, and operates by processing information

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in a rule-based way. If we’ve learned many different rules for a situation, then we can apply those rules in a structured way to determine a solution. Consider mathematics. If we know mathematical rules, we can apply them to some set of data or information, and arrive at a particular answer. In the previous example of the cost of the bat and the ball, algebraic equations could be used to represent the unknown costs. Solving those equations will then determine the price. This mode of thinking is not perfect. Firstly, it’s subject to resource constraints. If there’s unlimited time available to work through an analytical process, it’s useful. But if there’s a need for an immediate decision, or even for the approximate answer to a complex problem within a few minutes, it is not suitable. It also requires knowledge or training about the relevant steps in reasoning. This might be algebra as in the example of the bat and the ball above, or might be much more extensive or complex analytical knowledge (or reference information). Finally, the method of analysis with a System 2 mode of thinking also depends on having the correct analytical approach to solve the problem correctly. With very complex problems, we can make a related but different error to by misconceptualising a complex problem as a much simpler or more familiar problem (e.g. oversimplifying or assuming important aspects have a negligible effect). This idea has been recognised since the 1800s, and gives us the more recent proverb: “if all you have is a hammer, everything looks like a nail.” It is important to note that System 1 is influenced by our previous experiences, and this mode of thinking is closely involved with the development of expertise. As we increase our experience of relevant situations, our System 1 thinking becomes quicker in being able to reach a decision. Consider for example Fig. 2.1. What emotion is the person in the centre of the figure feeling? This isn’t a trick question. It’s immediately obvious that the person feels happy. Most people, if asked to explain, will mention a big smile, the crinkling around their eyes, or the contextual information about the scene, each of which reinforces the judgement of happiness. But it’s not the case that people who are seeing this image are using all of that detailed information about the scene in a kind of rule-based fashion. We don’t methodically look at details and step through a rule-based process to reach the conclusion about the person being happy. It’s a near-instantaneous judgement. This is an example of where the System 1 mode of thinking is effective, allowing us to infer an emotion from even a momentary glance, and even monitor emotions in real-time (e.g., such as when asking for a favour or breaking bad news). We have the cognitive structure to make these judgements quickly, and a massive amount of experience – decades – of detecting emotions in other persons, due to its importance in facilitating social interactions. In contrast, a different example: in the image of the chessboard in Fig. 2.2, how could white checkmate in one move? This is a much simpler visual scene than the previous photograph. There are only a few symbols present. The rules for chess are much simpler than the rules to

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Figure 2.1: What emotion is this person feeling?

Figure 2.2: How can white checkmate in one move?

interpret a face to try to determine if someone is feeling a particular emotion (especially if trying to judge whether the expression is genuine). But the ability to form an instantaneous judgement from looking at the chessboard is much less likely, because we have much less experience playing chess than recognising emotions. This chess

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puzzle was actually selected to be relatively straightforward – perhaps readers with some chess experience will experience that the solution immediately comes to mind. A common misconception is that System 1 is a non-mathematical type of thinking that jumps to conclusions, and that System 2 is an analytical mathematically-oriented type of thinking. However System 2 thinking can be entirely non-mathematical, and System 1 can involve mathematics. Consider for example multiplication tables. Most readers, if asked the product of 6 and 6 (i.e. 6 × 6), will answer the question not by counting up six sets of sixes, but instead will just immediately ‘know’ that the answer is 36. This happens because the times tables are a predictable system: with sufficient experience, we recognise that specific inputs have a specific output, and we can make an intuitive jump directly from input to output using our cognitive capabilities to shortcut the intermediate mathematical steps. We often develop ‒ with sufficient experience ‒ similar abilities to quickly simplify more advanced mathematical problems. The dual process theory is not the only model for understanding cognition; others include the cognitive continuum theory (where all cognitive processes can be characterised as being on a continuum between analytic and intuitive thought); [9, 10] and characterisation of human decision-making behaviour into skill-, rule- and knowledge-based modes [11]. However, the dual process theory is useful as a conceptual framework for engineers to understand human mistakes and expertise.

2.2 Heuristics and biases There are a range of other features of our cognitive processing that lead to patterns of thinking (and can in some situations lead to errors). Some examples of these heuristics and biases are: – Availability heuristic: [12] people judge the frequency or likelihood of things according to how readily they come to mind. For example when asked about the relative likelihood of different causes of death, people will often think they are more likely to die from causes that are emotionally salient, such as terrorism, than innocuous causes such as slipping and falling in their home. In actual fact, people are much more likely to die from being struck by a falling bookcase than by being killed by a terrorist [13]. – Representativeness heuristic: [12, 14] people will tend to categorise people or events based on how representative the person or event is of a particular category. This happens regardless of the how common or rare the category is. For example, suppose you were told about a hypothetical person Luke, who is 25, single, tattooed, has a prominent facial scar, and is often unwashed. Which do you think is more likely: that Luke is an arts student, or that Luke is an arts student that has previously spent time in prison? Often when posed with this type of scenario, people will tend to say the latter is more likely. However this is impossible: the former category includes everyone in the latter category, and so the former must be

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more likely. This is a specific example of the representativeness heuristic called the Conjunction fallacy. In general, this feature of human cognition indicates that our innate ability to understand and utilise probability and statistics is very limited. Loss aversion: [15] when given a choice between a mild but certain outcome and an extreme but uncertain outcome, people will generally choose the certain outcome if the outcome is beneficial, but the uncertain outcome if the outcome is detrimental. That is, people tend to prefer certainty regarding gains, but will gamble to minimise losses. Negativity bias: [16] related to but slightly different from the above, events or things that have a negative effect on people tend to be remembered more readily or have a greater cognitive or emotional effect than positive events or things. For example people will perceive losing $10 as a more significant event than finding $10. Affect heuristic: [17, 18] emotional responses to information can influence decisions, including deliberative decisions. Rather than considering the merits of an argument or the robustness of an evidence source, this heuristic leads to a judgement on the basis of “how does this make me feel?” Confirmation bias: [19] once people have a concept or answer in mind, they tend to look for evidence that supports that concept or answer, rather than looking for evidence that might be contrary (the latter arguably being much more useful). They tend to also discount or ignore contrary evidence. Simulation heuristic: [20] people tend to judge an event or outcome as more likely if they find it easier to conceptualise that event or outcome. Scarcity heuristic: [21] people tend to assign value to things based on a perception of how difficult it is to get possession of an item, as opposed to how inherently valuable or useful it might be to them. This heuristic is part of the basis for why people will often judge the quality of a product based on the price (i.e. that something expensive must be good), even though that may not actually be the case. For example, it has been shown experimentally that people will report higher enjoyment of more expensive wine than less expensive wine. This type of result can be reproduced even if the wine used is identical, and experimental subjects are simply told it is more or less expensive.

Much like the general modes of thinking, there are instances where these heuristics and biases are useful in allowing us to rapidly make correct decisions. But there are also many situations where they can lead us astray. We’re not going to explore any of the above in detail, but we will look at one more feature of cognition, anchoring.

2.3 Anchoring Anchoring is a feature of our cognition in which our thought processes tend to assign disproportionate emphasis to an initial response, guess, or concept. The first answer

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or idea that comes into a person’s head is weighted more highly relative to other, later, information [12]. This doesn’t mean that a person can’t change their mind given further information. But initial information or judgements are assigned disproportionate weight and can bias later decision-making. For example: Firstly: Is Australia’s annual water use more or less than 100,000 gigalitres? Secondly: What is Australia’s annual water use?

The answer people give to the first question isn’t that important. What’s important is that the number provided in the first question alters how people respond to the second question. When groups are asked these types of questions, their estimate is influenced by a number that has been stated. That is, the estimate of the annual water use would be higher if a larger number was used in the first question, or would be much smaller if e.g. 1,000 gigalitres was listed in the first question. This may seem a bit crazy: how could irrelevant information influence later judgements? The answer is that this information is not quarantined as irrelevant. Instead, it becomes a reference point against which future judgements are calibrated. If a range of values is specified, then subsequent judgements are more likely to fall within that range. This cognitive feature even occurs in the cognition of those with expert knowledge of the area, or those from analytical disciplines [22, 23]. From a practical point of view, this means there are some risks in having irrelevant numbers available when trying to solve a problem, particularly if the problem isn’t being approached analytically. If the example question is answered using reference data on Australian water consumption, or an analytical approach (e.g. multiplying your individual water consumption by the Australian population), then this type of error can be avoided. But if the estimate is not analytical, it’s prone to being influenced by other numbers that engage with people’s thought process, regardless of whether they are relevant. This isn’t a suggestion to clean off whiteboards before starting work on a technical problem. But it’s important to be aware that anchoring comes in all sorts of forms. For example, if a manager asks if seven days is enough for a project, they have implicitly created a mental anchor that will influence how long other staff might expect the task to take, and any answer will be influenced accordingly. This can lead to big problems, such as substantial underestimates in how long a project might need.

2.4 Anchoring in brainstorming Anchoring is not just constrained to numerical estimates [12]. Brainstorming is a classic technique for generating ideas or solutions to a problem. The expectation is that everyone involved has the chance to share their independent ideas. But consider how anchoring can influence this process. The first idea proposed creates an anchoring point. Subsequent proposals by others are considered in comparison to or

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variations upon that idea. Or the first couple of ideas proposed seem to stake out the universe of possible solutions for that issue. Closed brainstorming provides a solution to such anchoring. In closed brainstorming everyone independently writes down their ideas on the topic or problem. Only then does the group convene to discuss everyone’s ideas. This provides independent idea generation, upon which further collective idea generation and refinement can build. If working on a problem as a team, once the problem has been defined clearly, it can be useful to work on it independently before coming together to share ideas. Not only does this sidestep the issue of anchoring, it also allows harnessing of the wisdom of the crowds. This is the idea that multiple independent estimates or proposed solutions may converge toward a better answer than a single estimate (or multiple estimates anchored to an initial estimate). In the example of Australia’s water use, a wisdom of the crowds approach would be to convene a group of people to each independently estimate water use and take the average or median of the group’s estimates, while also carefully avoiding biasing the individual estimates with reference to other numerical values. One strategy to reduce the influence of anchoring is to defer trying to answer a question immediately, and instead focus on properly considering the question or problem. To get a good estimate of how long something might take, or how expensive it might be, avoid ‘first guesses’ as these may affect any analysis through anchoring.

2.5 Implications & recommendations So what can be done about these features of our cognitive processing that can lead to important errors? – System 1 and System 2 are each important in accurate and timely engineering practice. If time is available, a systematic and careful methodical approach can help in avoiding errors in judgement. If inexperienced with a specific question under consideration, using reference data rather than perceptions of past experience can be more reliable in estimating time-frames or costs. – People can’t necessarily detect biases in their thought process even if they have a good conceptual understanding of the types of cognitive mistakes that can be made. However, knowledge of cognitive biases can assist in identifying others’ errors in judgement. It can also help in recognising when others have framed information or communication to intentionally exploit these biases and heuristics for manipulation or deception. – Having others ‘check your working’ by asking for their feedback on decisions can improve the decision-making thought process. – Closed brainstorming, where team members work individually before sharing answers can assist in minimising the effects of anchoring in group decision-making.

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3 Expertise The development of expertise involves both the accumulation of domain knowledge and the ability to make skilled intuitive judgements [5]. Skilled intuitive judgements can be thought of as the transition from a methodical rule-based System 2 mode of thinking into a faster and still-accurate System 1 mode of thinking. An expert can intuitively solve problems within their domain; a novice would need to use System 2 thinking. An expert can also intuitively recognise the approaches and analytical tools that will be appropriate for a problem (potentially even one that is complex or ill-defined). In this way, expertise is not simply intuitive judgement, but includes the use of intuitive judgement to guide analytical thought. This means that an expert is likely to be substantially faster, and that a novice may have other difficulties besides speed. A novice may not even know where to start, or may omit important information from their decision-making process and consequently may reach an incorrect or suboptimal conclusion. A novice engineer who is designing a piece of equipment would use a deliberate, multi-step procedure to determine the appropriate material for that equipment. They would look up material properties – strengths, toughness, hardness – as well as temperature limits, chemical resistances, costs, and so on. After successfully completing this procedure many times and building experience for a range of different applications, the engineer’s judgements will become less effortful and more automatic: System 1 thinking can take over. Having developed expertise in material selection, the engineer can rapidly recognise that, for example, carbon steel and not stainless steel is appropriate for a given set of requirements. Similarly, an expert engineer could glance at a laboriously-produced report and see immediately that it makes unreasonable assumptions, or where outputs of a calculation are impossible. By amassing domain-relevant information and understanding the rules or cues that structure the application of that information to domain-specific problems, the expert reduces reliance on external resources or tables or textbooks to provide analysis of the situation. An expert can integrate their experience and the situational cues to quickly envisage a likely answer, or a path forward to make progress on a solution.

3.1 Preconditions for expertise Expertise can develop through experience in a specific system or domain where valid and reliable cues allow for accurate and timely feedback. Amassing sufficient experience requires time interacting with the system to develop an understanding of how that system behaves. One widely-circulated [24] but dubious [25] figure is that it takes 10,000 hours to develop expertise in a scientific or professional field. But the mere duration of exposure is not sufficient for the development of expertise. The first precondition for developing expertise is that interacting with the system gives rise to accurate and timely feedback.

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Consider for example someone learning to play the piano. Imagine that they are learning on a piano that is missing all its strings, so that it makes no noise, so there is no feedback on what striking a given key sounds like. No matter which key is pressed, there will be no indication if what they did was right or wrong. The learner may get better at striking keys and increase their hand dexterity, but they will find it impossible to become an expert pianist this way, because interacting with the system does not give rise to feedback. Now imagine they learn at a second piano, but this second piano is designed to produce sound only after a long delay. Every time a key is struck, there is an hour wait before the sound plays. This delay would dramatically hinder learning of how the system works. It is difficult to develop expertise in any system without timely feedback. Finally, imagine they learn on a third, normal, piano. Hitting a key instantly produces a sound, and each key produces a consistent sound. Striking a note with consistent force produces a consistent note or timbre. Finally, the learner is receiving accurate and timely feedback. This is a system in which they can develop expertise. The second precondition for developing expertise is that the system itself provides valid cues, and that the cues are sufficiently regular that it is possible to determine their validity. Suppose someone decided to become an expert at picking winning lottery numbers. It doesn’t matter how much experience they accumulated in picking lottery numbers. The feedback after each lottery is accurate and timely. But the underlying random nature of the system prevents any development of meaningful ‘expertise’, as there are no valid cues. There is no cause-effect occurring within the system from which the judgement-feedback process can extract understanding. So expertise cannot possibly be developed. It would be like trying to learn to play piano on an instrument where each key produced a different random note each time it was pressed. It might be obvious that one cannot develop expertise in picking lottery numbers. But this limitation extends to other systems that have extremely weak or non-existent relationships between known information and outcomes of interest – where the validity of the cues is in question, or the regularity of the cues is unclear. ‘Chaotic’ systems fit into this category. These systems are those in which the outcomes are extremely sensitive to input conditions. This can be the case in systems that are physically deterministic, but where our ability to measure the necessary data about the current state of the system is impossible. The archetypal example is the weather. We can make fairly accurate predictions about the weather in the short-term (of hours-days), but precise long-term prediction, e.g. if it will rain on December 6 next year, is not possible. In general long-term prediction into the future can be difficult to do accurately in many circumstances for similar reasons. Outcomes can be dependent on very uncertain variables, or even on unidentified variables. Many real-world systems, where expertise is highly valued, may not involve as clear a cause-effect relationship as a piano. If there is substantial ‘noise’ in the system (i.e., valid but irregular cues), it can be slower or more error-prone to develop expertise in that system.

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A contentious example is predicting future stock market prices. How could an expert predictor of stock prices be identified? Stock markets are immensely complicated systems. An overwhelming amount of information is available about prices, financial indicators and strategic positioning of companies contained in their annual reports, and a swath of news is available about companies, the market setting, and political interactions. The relationship between the available information and future prices is complex. Is it possible to develop expertise in predicting stock prices? Some may argue that a careful analysis of the above information allows for the identification of valid cues amongst the noise. But because there is so much information available, it is difficult to identify which information is useful in making predictions, and which is not. And feedback through the market mechanism, while it can be rapid, is often not accurate. It is easy to be correct about a specific prediction (e.g. that a stock price will increase) for the wrong underlying reasons. In this way, it is very easy for an inaccurate mental model of the system to develop. That is, by being lucky, people can develop the misconception that they have developed expertise. This makes it difficult to identify expertise in stock market prediction. Success in past trades doesn’t necessarily provide useful information. Being slightly better than chance at predicting future stock prices is sufficient to make substantial profits. But suppose a group of people picked stocks at random. If that group is not trivially small, some number will appear to have performed better than chance, just as some will have performed worse. Because there are so many people trading on the stock market, including professional traders, the information on an individual trader’s past success (or failure) might be due to their understanding of the market, or it might be an artefact of a complex system without valid or regular cues. What does this all mean? Because stock predicting involves a complex and uncertain underlying behaviour of the system, combined with poor quality feedback in terms of the accuracy of one’s mental model of the system, it is difficult to distinguish someone that has developed an actual understanding of the underlying behaviour from someone who has not but believes they have. This is an illustration of the general problem of expertise in systems that do not have rapid and accurate high-quality feedback processes. Expertise may not be possible to develop within these systems, and even if it is possible for it to develop, it may be very difficult to distinguish it from non-expert judgement. It also illustrates how confidence in one’s judgements is a poor indicator of one’s actual expertise. Because expertise is built on experience with a single domain, it is domain-specific. Development of expertise may involve the development of some general abilities (e.g. becoming an expert piano player might involve also becoming expert at reading music which may be transferred to other areas) but it does not provide general expertise in other topics. Being an expert software engineer does not make someone an expert manager or strategist; being an expert piano player does not make someone an expert guitar player. This may seem obvious, but because of the close association between expertise and

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intuitive thinking, it can be deceptively easy to apply expertise to contexts beyond which it is reliable. The boundaries of a person’s expertise can also be difficult to determine, both for others and the expert themselves. If a person is working in an area that is related to but not the same as their specific expertise, they may feel confident in their abilities. Their pattern-matching, associative System 1 approach to dealing with issues in the area of their specific expertise readily integrates information and provides solutions to issues in the adjacent area. But they may be as unaware as a novice of the cues specific to that system, and thus make costly mistakes. For example, if a project combines many different processes or areas of knowledge, having expertise in one aspect of the project is not necessarily sufficient to make accurate judgements about the project overall. If the project involves a novel combination of elements, expertise in the combination of all aspects may not be available, and using expertise available on individual aspects only may be necessary. However, what is critical is that expert advice or predictions in such a situation are likely to be less reliable. And of course, while an expert is likely to have better performance in their area of expertise than a novice, they are still people, and subject to same biases and heuristics as everyone. This is the fundamental basis by which they are prone to applying their expertise to areas beyond which it is applicable. So when getting expert advice or opinion, it is critical to ensure that the expertise is relevant to the problem that is to be solved. Preferably this extends to all aspects of the problem. Caution is needed in multidisciplinary work. Someone with expertise in one area may not necessarily be able to make accurate judgements about the project overall. But as with problems at the boundary of expertise, an expert in a multidisciplinary project may not recognise their errors when applying their expertise to other areas of the project. As a consequence of all these features of expertise, it is important to consider when seeking expert advice: – Does the area of expertise or system of interest have a fundamental regularity that gives rise to valid cues? – Has the expert had substantial experience in the area engaged with those cues? – Did the experience include rapid feedback? – Did the experience include accurate feedback? – Is the expertise being applied clearly within its bounds? If the answer to any of the above questions is ‘no’, then the expertise should be viewed with some scepticism.

3.2 Decision-making algorithms If expertise development requires a regular system with valid cues and rapid and accurate feedback, then couldn’t some type of algorithm ‒ a computational system,

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neural network, decision-tree flow-chart, or something similar ‒ outperform fallible human judgement? We might expect that if human expertise works by implicitly or explicitly determining the underlying principles of a regular system, then an algorithm that works by the same underlying principles should be able to do it as well. Or put more bluntly: can a machine do my job? Machine learning, deep learning, and applied artificial intelligence have made extraordinary advances in the development of artificial expertise, including in areas that typically have been considered the domain of humans (e.g. speech recognition, facial recognition, spatial reasoning, and strategic games such as chess). In health care, there is considerable interest in using algorithms to improve decision-making and patient outcomes [26]. One area where algorithms seem to do well is very noisy environments where any signal is very much attenuated, such as discovering regularities across massive datasets or in novel environments. An algorithm’s judgements can be more accurate than a human expert because it is less susceptible to heuristics and biases, and can be less-prone to over-interpreting the limited information that is actually present (e.g. as in the stock market example). It is likely that computer code and algorithms will replace humans in domains where humans are prone to errors. Looking ahead, algorithms will likely take over a great many things that currently humans do much better, faster, or more cheaply. One advantage in dealing with human experts, despite their potential failings, is that they have expertise in navigating the social world. This may assist in providing credibility to a course of action or persuading important stakeholders to engage. Similarly, human experts may be better positioned to explain their decision-making process and judgements in a manner that is clear to others (or in a way from which others can learn). It’s useless having a perfect recommendation for how to structure a project if nobody believes you, if you have no political clout, or if you’re unable to persuade others toward the preferred course of action. So there may be advantages to using a human expert even in a hypothetical scenario where an algorithm has objectively better quality of judgement. The other consideration is that the best option is a combination of algorithmic and expert decision-making. The very best chess players in the world now are not computers by themselves. They are human-computer teams [27]. An algorithm can be paired with a person who can compensate for failings in the algorithm and who can make expert judgement calls on when and where to apply the findings of the algorithm. Similarly the algorithm can overcome the cognitive errors and computational limitations of human thought. A possible future scenario is that, much like machinery during the Industrial Revolution, decision-making algorithms will augment human ability rather than replacing it entirely. One concluding message for any ambitious professional is that understanding how to work with computer code and algorithms is likely to become even more important [28]. Understanding how they behave, when they work well, when they fail, and how to handle situations where they fail (i.e. knowing how to do things without computer aid if necessary) are likely to be useful skills.

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4 Group dynamics Engineers do most of their work in groups or teams. Even when working independently, they often need to communicate their outputs or the implications of their work to a team. The systems they design will likely be used by teams. Understanding the group dynamics that underpin successful and unsuccessful teams is crucial to engineering practice. But students (and professionals!) frequently report that working in groups is difficult, tedious, or frustrating: not everyone is pulling their weight, or there is competition over the direction of the group, a lack of leadership (or competition over leadership), uncertainty or disagreement over the task at hand, or the sense of being ‘dragged down’ by the failure of the group as a whole. Engineering students recognise that they’ll have to work in groups professionally, but may believe that it’ll be different once they’re working because they’ll have more effective colleagues, or that their work will always be better-scoped with clearer tasks, or somehow that a professional environment will lead to better coordination than university assignment work. The reality is that, within any career, there are inevitably teams or groups that don’t coordinate well, clashes between individuals, and projects that are difficult, poorly scoped, or hard to coordinate. Psychology can provide some insights into how to better work together when there are disagreements or personality clashes; and how managers can create or manage effective and productive groups and teams. It’s not always possible to make human social interactions run smoothly, but psychology provides a force to nudge things in the desired direction. One approach that is sometimes taken by groups struggling with coordination is to work as individuals. Each individual takes on the tasks they’re most skilled at, they complete that work individually, and then the team attempts to integrate all the individual tasks when they’re completed. This process is an attempt to solve a problem of ineffective coordination by trying to avoid coordination entirely. Nonetheless, it can often seem like a viable approach, and one in which, if all the team members are individually brilliant, might lead to good, or at least acceptable results, if the scope and requirements of the project are clearly specified at the start and don’t change during the conduct of the project. This may be possible in a university setting or if the tasks can easily be broken down and allocated to independent operators, and no communication needs to happen within a team to successfully complete the project. But in most professional contexts this is a fantasy. So less coordination isn’t an effective solution to this problem. A group that works well together can accomplish much more than a group of individuals who are individually brilliant but can’t coordinate their work. In fact, the most successful teams are very effective at coordination [29], such that they can compensate for each other’s strengths and weaknesses.

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4.1 Shared social identity and team coordination Effective coordination in groups and team requires three elements. First, there must be an agreed upon and shared idea of the group’s purpose. This may be specified ahead of time (e.g., an engineering device that gets built/designed to fulfil a purpose) or motivated by a need (e.g., an engineering device that doesn’t work well any more – how can we make a better one?). Second, team members must agree on how to accomplish that purpose. Even if group members all agree that the device needs to meet particular requirements, each member may have a different idea of how to ensure the requirements are met. Finally, the group members must be motivated to achieve the shared purpose. Imagine a project work group where there is no shared idea of the group’s purpose, no one can agree on how the group can accomplish its work, and members are not motivated to work on this non-existent shared goal. This doesn’t mean that the members are necessarily lazy, or incompetent. However, it does mean that the group members are considering their potential work on the project through the lens of their personal identity: how individuals define themselves (e.g., their values, motivations). Work might still get done, if one or more of the group members defines themselves as conscientious or ambitious, or personally believes that the project is worthwhile, or believes they will be personally rewarded (or punished) on the basis of the project’s outcome. That is, each person may work on the project to the extent that doing so aligns with their personal identity. But the willingness of each individual to contribute can change rapidly depending on their individual circumstances or incentives. Coordination in such a group is fragile. According to psychology, a shared social identity is key to unlocking effective coordination [30, 31]. Where personal identity emphasises the individual, social identity emphasises the group. Just as values and motivations (e.g., ambitiousness, perceiving the project as worthwhile) can form part of a personal identity and shape individuals’ actions; values and motivations may also form part of a shared social identity and shape groups’ actions. The extent to which group members endorse and work for the benefit of a shared social identity, the more effectively they will operate as a team to achieve their shared purpose. A strong shared social identity is exemplified in successful sports teams or military groups, where achieving the group’s aims may require considerable investment (and even sacrifice) of its constituent members’ personal identities (e.g., for personal status or personal safety). However, a strong shared social identity may also have negative consequences. A group that has a really strong sense of identity has a risk of becoming insular [32]. The group works and coordinates internally very effectively, but begins to lack trust or coordination with people outside the group. This is what is referred to in organisations as forming ‘silos’ where individual teams within an organisation reject, dismiss, or distrust information or opinions from other teams. Groups are prone to this when their goal or purpose is not aligned with the goals or purpose of the institution, or

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where they are in direct competition with other groups. This is effectively the same type of coordination problem that happens within groups, but occurring between different groups. This means that setting up competing groups or company divisions can be very counter-productive. This can lead to the shared purpose of a work group being to block other work groups’ efforts to make progress, or to do “whatever it takes” to maintain the group’s relative status in an organisation, including harassing or bullying members of other groups. Members of such a team may coordinate extremely effectively to do so, leading to worse outcomes than toxic individuals acting alone! A related idea is that of tribalism, in which members of a group are likely to dismiss ideas from those outside the group, regardless of the merit of the ideas themselves. One can see this behaviour quite often in politics where party members tend to be more likely to only get information from sources they see as being receptive or aligned with their chosen political party. Even if the shared purpose of a group is “good” in that it is non-destructive to other groups in an organisation or is aligned with a larger strategy, a lack of coordination between groups may still limit the effectiveness of groups and teams. The other risk is that an extremely cohesive group can lack a diversity of opinion, leading to a phenomenon known as group‒think [33]. In this case, a group is so lacking in diversity of perspectives that every group member agrees with the group’s intent, purposes, and processes to such an extent that there is no longer potential for the group to recognise and correct for errors. Group members are unwilling to be critical about aspects of the group that could be changed or improved. Most frequently when this occurs, it is because individual group members are unwilling to appear to be at odds with the rest of the group. Achieving a shared social identity, and ensuring that the content of that identity (its purpose) is aligned with a wider strategy can be difficult to achieve in practice. Improving cohesion or coordination within groups while avoiding siloing or groupthink can be a complex balancing act. One approach for improving coordination within groups is to ensure that team members understand and agree with the team’s overall purpose from the beginning. This can obviously be difficult in situations where team members have very different individual motivations, such as in randomised student teams, or teams dictated by external factors. If a potential team member does not endorse the team’s shared purpose or their role in achieving that purpose, then it may be necessary to negotiate (1) the shared purpose, (2) their role, and/or (3) their membership on the team. Resolving these disagreements early is essential to ensuring that the shared social identity remains intact among all team members. Early efforts to rationalise team composition and resolve conflicts is the best approach to minimise wasted effort and confusion or disengagement by other team members, and is often counter to our natural tendencies toward avoiding social disagreement or conflict. Team building activities are popular in corporate and professional environments, and are typically aimed at increasing commitment to a social identity among team

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members [34, 35]. Coming up with team names, participating in icebreaking exercises, songs or chants, role-playing, or trust-building exercises are all common approaches. However, the effectiveness of these exercises in promoting a shared social identity is very dependent on the context of the activities and the environment in which the team will operate, and the extent to which they sincerely address the group’s shared purpose and members’ roles in achieving that purpose. Evaluation of team, rather than individual outcomes should be considered carefully. In teams without an existing shared social identity, this approach can lead to further splintering as team members may focus on ‘free riders’: that if the group is only judged collectively on their performance, then individuals will have no reason to contribute and can get all the benefits with none of the effort. However, if team members understand how they can (and are expected to) contribute to achieve the team’s overall purpose, then this strategy can further enhance members’ commitment to their team [36, 37]. One implication from the importance of shared identity is that changing the composition of an established team can be difficult or counterproductive. Development of social identities, including an ability to trust and rely on other members, can take substantial amounts of time; additions to a team or replacement of team members involves loss of potential productivity as the new team members take time to understand their role in the group and existing team members must adjust their understanding of the shared purpose to accommodate the new member. Improving coordination within groups requires an understanding of how groups internally manage themselves – including setting and changing a shared purpose – through norms and leadership.

4.2 Norms A norm is an unwritten rule about the nature of acceptable social action within a group [38, 39]. These can operate at the level of a team, an organisation [40], or at the level of society (forming an element of what might be generally referred to as ‘culture’). In the context of an individual team, there will be a range of norms that govern acceptable behaviour. For example, a team is likely to have norms about criticism of the team’s actions or actions of a team member. It may be appropriate to criticise other team members in private, but not in public. It may be that criticism can be given directly (“this design calculation is incorrect”) or must be given indirectly (“This might be a silly question, but does this equipment design meet the specifications?”). In some teams, personal criticism may be accepted (e.g. “you have made a mistake in your design calculations”), but it is generally never effective to criticise a person rather than a specific issue. Different teams will view different types of action as acceptable or unacceptable. Norms are a way of managing behaviour within a group and can be considered an expression of its shared social identity. A group member who acts in contravention of a

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group’s norms signals their disengagement with the shared social identity, and may be criticised by other group members or even excluded from the group. Norms may regulate behaviour in ways that are beneficial or harmful to group performance, depending on context and the group’s shared purpose. For example, a norm of politeness and deference may be very useful for a customer service team. On the other hand, a norm that discourages diversity of opinion or honest feedback on analysis of group decisions may be destructive for designers of a system to handle nuclear waste. Tensions may exist between different desirable team behaviours, making the desired set of norms either a balancing act or situation-dependent. For example, a group may have a norm of hard work, and a norm that each group member should avoid making others look bad. This may result in a maximum threshold for individual productivity, to avoid other team members appearing unproductive. Another example is that team norms that support critical decision-making generally result in higher quality decisions relative to consensus-based decision-making [41], but criticism can instigate inter-personal conflict, reducing group performance. However, there are some general principles of which team behaviours are more likely to produce good outcomes. Teams that avoid conflict generally have better social cohesion and performance [42, 43], and norms that minimise conflict are generally desirable. Group norms which promote open communication and effective resource sharing are likely to enhance productivity, particularly in the context of a strong shared identity. Norms that promote the reliability of each individual team member (i.e. that they will do what they say), and emphasise planning around resource usage (i.e. that team members consider the time [44] and other resources required for particular tasks) tend to lead to better performance. Critical decision-making is desirable, involving robust critical analysis of possible options, as this can reduce the likelihood of erroneous decisions. Ideally this does not become a source of team conflict. One approach to minimise conflict related to decision-making is to have norms that break the link between individual status and the ideas or actions that the team pursues. If the group judges individuals’ perceived value on the success of their ideas, the individual becomes more inclined to receive criticism of the idea personally, to unreasonably defend their idea against useful critique, or to champion a course of action even after serious drawbacks become apparent. An alternative is for collective ‘team ownership’ of ideas and actions. This may also strengthen a shared identity, and encourage proposal of more diverse ideas, and may also facilitate open disclosure of mistakes or errors. Another approach to minimise conflict arising from decision-making is a norm that supports team cohesion. An example would be that all group members support the outcomes of decisions. This approach encourages groups to resolve disagreements about courses of action early, and then subsequently provides the group with the best chance of success, even if all the team did not agree with the choice. This group norm is used with varying success by governments in the Westminster System (used in e.g. United Kingdom, Japan, India, Australia, and Canada), where ‘cabinet collective responsibility’ [45] dictates that cabinet ministers must publically support all cabinet decisions.

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A frequent feature of team-building exercises is the activity of discussing norms and codifying them as a set of rules or ‘team charter’. This type of activity misses a crucial opportunity to improve team success. While it might assist in building a shared team identity, typically minimal emphasis is placed on establishing team norms that are aligned with team performance. Establishing positive group norms at a group’s conception is substantially more effective than trying to change entrenched norms [46]. Norms can be established by statements (“what we say”), but are proven by actions (“what we do”). In the long run, actions influence behaviour more consistently than statements, and also may have reputational consequences if the group is seen as hypocritical. Leaders within groups may be able to influence norms, although this is complicated in that acting contrary to group norms risks losing leadership recognition within the group. Effective groups have a strong sense of shared identity and individual group member commitment to the group’s shared purpose, which is the most important determinant of effective group coordination. This commitment must be balanced with diversity of opinion, and norms allowing internal criticism, without these features of a group leading to discord. Ultimately all of these aspects of group dynamics are subject to complex interpersonal interactions that are understandably difficult to control or manage. A managers’ best approach to nudging a group toward the right balance of group dynamics is to establish important norms early, clearly communicate their expectations of the team and its objective or purpose, and to select team members that are likely to align with the purpose.

4.3 Institutional dynamics Psychological theories of group dynamics also apply at the larger scale of organisations (whether corporations, government or political institutions, or international bodies), which may contain many smaller groups [47]. Just as teams of individuals have shared goals, an organisation composed of many groups or teams will have organisation goals, as well as organisation norms [40]. Clarity regarding organisational goals and expectations can actually be really important. It can sometimes seem managerial ‒ in the pejorative sense ‒ for organisations to spend time on documents such as mission statements, which often seem overly general and vague. But the generous way to think about these is as an important, if not always successful, attempt to establish a shared social identity by explicitly describing the institution’s purpose and specifying its norms [48]. One challenge is that these documents are often not solely for internal use. They often also provide an external positioning statement and so can have a marketing function to potential customers or prospective employees. If public mission statements / strategic plans are misaligned with the actual purpose of the institution as perceived by group members, then such statements can be counterproductive [49]. If

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teams or individuals within the organisation perceive the mission statement is not aligned with the true behaviour of the organisation (or its leaders), then these can be counterproductive, leading to a loss of shared identity [50]. Actions of managers or executives can be very powerful in destroying shared organisation identity if they’re misaligned with an organisation’s stated purpose.

4.4 Implications & recommendations What does this mean for practical engineering purposes? – Coordination within teams is a function of team members’ shared social identity. Establishing clear shared objectives can help to promote that identity. – If a team’s purpose is disputed by one or more of its members, resolving the dispute is essential to maintaining a shared social identity and preserving longterm coordination. – Groups are sensitive to both statements and actions, and group norms can be promoted by deliberate action to establish them. – Groups are most sensitive to change during group formation, and so it is worthwhile to put effort into developing group norms early. There are a range of norms that are likely to be desirable to promote under most circumstances.

5 Leadership & psychology The modern psychological perspective on leadership focuses on the key elements we have already discussed – the establishment and management of a group’s shared social identity, and balancing team cohesion with effective norms. Understanding how leaders enhance or undermine this identity is relevant for all groups, whether they are small work teams, organisations, or countries. Lay theories of leadership often focus on personal qualities such as charisma, social intelligence, dominance, and drive or conscientiousness [51]. The popular trope of a great leader being ‘born, not made’ emphasises that leadership emerges from innate qualities possessed by an individual. In organisations, resources and time have historically been spent in trying to identify and empower these natural leaders. However, consider the implications of group dynamics and coordination. Different groups may have widely different purposes, may allocate roles to fulfil that purpose quite differently, and may use different strategies to motivate members. Group norms may also differ substantially between groups. For example, groups engaged in an audit or a misconduct investigation may have norms focussed on closely following procedure, in contrast to a technology startup. In the case of Uber, a dominant, growth-at-all-costs style of leadership led to exceptional success followed

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by exceptional dysfunction as the shared identity and purpose of Uber changed from startup to multinational technology company [52]. The psychological perspective on leadership instead emphasises the importance of shared identity [53]. Leaders must understand the shared identity and norms of a group to effectively influence and reshape its purpose, allocate work roles, and motivate its members to achieve a shared purpose. Because a shared identity emerges from the specific context of each group, including its members and history, a purely personality-based approach to leadership is insufficient. Also insufficient is a purely rule-based approach such as prioritising fairness over performance (or vice versa). Instead, leadership arises from followership. A leader can only shape and influence to the extent that others follow. A leader is one who embodies the shared social identity, is closely-aligned with the group’s purpose, and who champions the group’s interests. In the simplest sense, embodying the shared social identity may mean appearing as an archetype of the group members. A group of scientists is more likely to endorse a leader who emphasises proposing and testing hypotheses, and is willing to change their mind when confronted with contradictory evidence, compared to a passionate and emotion-driven person who feels that once a decision is made, it must be followed through to its ultimate end. A group member who works actively toward the group’s purpose and objectives, making highly visible contributions or successes toward shared goals is also more likely to be viewed as a leader [54]. These contributions could be diligent effort expended toward the group’s goals, but might also mean dealing with those external to the group to advocate on behalf of the group and represent its interests. Group members are likely to follow a leader who is seen as being ‘representative’ of the group’s shared social identity or purpose [55]. There might be others within a group with more forceful personality, or who are invested with power or status by the wider organisational structure, but if they aren’t as finely attuned to the overall interest of the group, they are less likely to be recognised as the group’s leader. Someone who is not seen as adhering to the group’s purpose or norms, or who has views at the extremes of the group overall, is less likely to be seen as a leader.

5.1 Implications of psychological leadership What are the implications of these features of the psychology of leadership? The first is that the psychological leader of a group may not be the same person as the organisational leader. This can lead to conflict when a team member who is viewed by the team as the team’s leader (in a psychological sense) disagrees with the team’s manager (in an organisational sense). A manager is invested with the power to determine a team’s actions, but if the team doesn’t agree with the manager’s judgement then team members are likely to disengage, and the performance or chance of success will decrease. The second is that because a psychological leadership role is tied to group identity and norms, it can be lost by deviation from the group’s identity and norms. For

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example, a group might form with a purpose of working hard to complete a project. If the team leader is lazy and does little work, they will quickly lose their ability to lead or influence the group, or the group output may significantly decrease. The third is that a psychological leadership role is also tied to advocating for the group’s best interests when dealing with others outside the group. A group will lose faith in a leader who is seen as accepting outside directives which are against the interests of the group. A good leader will also represent the group’s interests to other groups. They’ll stop external influence from compromising the group’s mission to the extent they can. One consequence of these implications is that changing the identity, purpose, or norms of a group can be difficult, even for managers empowered to coerce behaviour or group members perceived as embodying the group’s shared identity. Changing a group’s purpose or processes requires reshaping the shared social identity to fit the new purpose while also positioning the change as consistent with the existing social identity. A major change in strategic direction, or a merger between teams or organisations that threatens existing social identities (e.g., change of core business) may cause alienation or disengagement. However, too light a touch in reshaping a shared purpose may be insufficient to overcome the inertia of group members’ understanding of the group’s identity (e.g., an organisational restructure or rebranding that leaves most people in the same roles but called something different). A final implication is that a psychological leadership role depends on context ‒ on the nature of the group, and the setting within which the group operates. A leader who can effectively coordinate a team where the norm is to ‘move fast and break things’ may not effectively coordinate a team where the key purpose is to document meticulous compliance with regulatory requirements, regardless of their personal charisma. It can be desirable for an organisational manager to also have a psychological leadership role within their team, as it may give them greater ability to motivate and guide their team. Consequently the most appropriate selection for a managerial role can be closely dependent on the team that they will lead. One conclusion from an engineering perspective: attention to details regarding personality and behaviour can be critical. A one-size fits all approach is unlikely to be successful. Psychological leadership is conferred by willing followers. A manager can’t be a dictator who only does what’s in their own interests if they want people to listen to what they’re saying. Leadership is as much a process of persuasion as it is a process of coercion. Leaders get to choose the direction of the team but if no-one’s willing to follow, then they’ve lost the leadership. How then might an ambitious young engineer take a leadership role? Drawing on the psychology of leadership suggests the following (non-exhaustive) pathways: – Capitalising on unmet needs, – Creating and “selling” a shared purpose, and – Using resources to build a team

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As a member of a group with needs that are not currently being fulfilled, they could demonstrate leadership (i.e., gain followership) by advocating for those needs. If they demonstrate a more effective ability to champion the group’s cause than existing leadership, group members may look to them for purpose and cues on how to behave instead of the existing leadership. This type of approach was used successfully in the past during the formation of labour unions. That is, by recognising workers’ unmet needs (e.g., fairer pay, minimum safe working conditions) and championing for them, workers followed unions’ directions over their own employer (e.g., stopping work). Politicians who voice the views of an otherwise unrepresented segment of the population (“populists”) can become successful leaders in the sense that they may win votes and even elections. The disadvantage of this path to leadership is that there is limited control over the shared identity and norms of the group. This type of leader may be trapped by the views and values that brought them to power, acting as a mouthpiece for their followers without being empowered to reshape those views or alter the purpose of the group. Another pathway to leadership is for a person to recast their personal purpose as a shared purpose, and persuading others to support that shared purpose as part of a shared social identity. Founders of political parties would credit their success to this type of pathway. Successful inventors, social thinkers, and businesspeople might also describe their pathway to leadership in similar terms. This type of path to leadership has some advantages, as it allows for flexible choice of personal objectives. The clear disadvantage is that becoming a leader is largely dependent on others being inspired by those personal ideas and goals. This almost certainly requires good ideas, effective persuasion skills, and probably also has a large element of luck in terms of being in the right place at the right time. Finally, an aspiring leader might build a team using financial or other resources (e.g., status, access to equipment). In possessing resources to hire (or otherwise select) team members, there is an opportunity to select others who may be predisposed toward a particular purpose or objective. Resources may thereby be leveraged to assemble a team motivated to work toward a shared purpose. Organisational managers who have built new departments or divisions may use this type of approach. For the team to be ultimately successful nonetheless requires effective application of the principles related to psychological leadership. One disadvantage of this pathway into leadership is that it relies on resources, often provided by others, who must then be convinced as to the merit of objectives or goals. The resources may have a range of limitations or constraints (e.g. limitations regarding selection of a team), or the team itself may be subject to external influence from senior management. Any path to leadership requires careful application of skill. Opportunity or resources alone are not sufficient. For example, someone who bullies or sets team members against each other, someone who cannot strategise effectively, or someone who cannot persuasively represent the team’s interests to external parties, will not lead successfully regardless of resources or shared purpose.

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Ultimately, leadership skills are often difficult to develop, and certainly hard to learn from theory alone. It is important to take opportunities to learn to influence teams when possible, and reflect on the experiences of the interpersonal relationships and group dynamics that determine the behaviour of teams.

6 System failure & safety Humans are fundamentally involved in every human-built system, whether as users, operators, designers, or implementers. This is true of technical engineering systems, and of political, regulatory, social, and many other systems. System failure is inherently related to decisions that are made by humans at the design, implementation, operations, or management levels. It is for this reason that psychology is critical to understanding system failure (and how systems operate successfully without failure most of the time). For engineered systems, failure often results in harm to people, damage to equipment, and lost performance. All too often, the historical (and contemporary) response to systems failure involves: – Blaming humans associated with the error; and – Adding preventative measures (often engineering controls) to prevent the same specific mode of system failure from recurring. Each of these approaches may appear effective in that they seek to identify and “fix” the faulty element of the system. But a focus on human error and adding engineering controls has negative long-term consequences for system performance. Lay theories of human cognition connect systems failures to poor judgement, defects of character, recklessness, or simple incompetence of operators or other personnel. However, humans generally have no desire to make mistakes. Mistakes that do arise are generally associated with deeper issues within a system. These might involve pressures on personnel that push them beyond safe operation of the system, or complex interactions between operators and the system that predispose toward cognitive errors. As we’ve already learned, cognitive errors can arise as part of the fundamental nature of cognition, regardless of experience, education, or intelligence, or even the level of effort or ‘care’ taken. Blaming individuals involved in an accident can prevent determining which other factors contributed to the system failure. In most instances, it is unethical to blame individuals and teams when they had no intent to cause system failure. Blame is also unlikely to prevent those individuals or teams from making the same type of error in the future. Adding new preventative controls is also problematic. In the typical view of systems failure, humans are the source of error. In this view, people can be prevented from causing errors in the system if sufficient engineering controls and fail-safes are in place: rules for operators or users to follow, limitations of access to controls or

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physical pieces of equipment, or automated elements that eliminate, override, or counteract human actions. The idea is that if all possible sources of error are controlled, then nothing can go wrong. When systems are simple, with straightforward linear relationships between a cause and the undesired outcome, this strategy may be effective. For example, simple measures such as safety barriers (e.g. in public transport stations) can prevent harm from inattention. However, accumulation of barriers or system controls as a reactive response to failure is insufficient for complex engineered systems. Firstly, new controls may be added in reaction to system failure and may be motivated by time or political pressure (e.g., lost revenue while a system is non-working). If this occurs before contributing factors to the failure are understood, the new controls may not be effective at preventing subsequent failures of the same type. Secondly, the accumulation of barriers to system failure may incrementally increase the complexity of the system, leading to increasing tightness of coupling between system components. This can lead to system controls, and barriers to failure, themselves contributing to future failures, through human complacency, conflict from complexity, and inflexibility (or “brittleness”). Human complacency typically arises in a highly controlled system, where a deep set of engineering controls defend against hazards. The human user may never be aware of the hazards that the system is designed to defend against. This can lead to shortcuts or skipped procedural steps when interacting with the system, in the mistaken belief that the system is completely safe. Computer pop-up warnings are an everyday example of this. In most instances, there are a number of barriers that prevent viruses or Trojans from infecting computers, including virus scanners, automatic updates to patch vulnerabilities, and pop-up warnings requiring user input. In most instances, these warnings appear in entirely benign situations, and even when not benign, the other barriers prevent a harmful outcome. Computer users quickly get into the habit of automatically clicking the option that removes the warning and allows them to continue with their work. In the rare instances where the warning is relevant (i.e. other barriers will not prevent the virus), the user is in the habit of ignoring the warning, and opens the virus-infected file. In this way, the barriers are not independent, and the presence of one barrier may alter the effectiveness of others. Conflict from complexity typically arises in systems with many engineering controls. Each additional control added to a system must not only defend against a specific hazard, but must also work consistently with the existing controls. Unintended and unpredictable interactions can arise in complex sets of engineering controls. Conflicts or unexpected interactions between individual controls can also sometimes directly cause engineering failure. Adding a new control might prevent existing controls or the system from working properly, so disabling the new (or an existing) control may be the only way the system can be operated. If users cannot operate the system while following the rules written by designers, or control systems built by designers,

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then the engineering controls or barriers become treated less seriously, even where some of them may be very important. This can also result in operators disabling existing safety systems (e.g. automated control elements or alarms) to allow the system to function. Similarly, the additional complexity of adding rules or control elements may prevent some existing engineering controls from working properly. That is, when adding another engineering barrier to prevent mistakes, existing engineering controls may be altered, making them more likely to fail. Finally, a system with a complex set of engineering controls can become inflexible and thus brittle. Engineering controls may be designed to substantially limit users’ actions. Systems designed by humans cannot account for all possible future situations. If there is then a disturbance or error that is not prevented by the engineering controls, the controls may prevent the operators or users from correcting the system. This means that while the engineering controls may be effective at preventing things from going wrong almost all the time, but when things do go wrong, the result is more likely to be catastrophic. Engineering controls accumulate in response to discovered hazards or experienced errors. Elaborate defences against external or internal disturbances, mechanical failures, potential human inputs into a system, or unintended interactions between control elements within the system increase complexity. This results in complacency, conflict in complexity, and inflexibility and brittleness. Ultimately, complex engineering control systems can themselves contribute to failure as a consequence. Psychology provides some useful insights for conceptualising system failures, and some practical approaches to reduce failure or its consequences. The modern psychological view of system failures is that: 1. Most humans (including operators, end-users, designers, and managers) want to avoid error, and are in many instances necessary to facilitate safe system operation. This is especially the case under uncertain or variable external conditions; 2. The “Swiss cheese” model of system failure provides a conceptual framework to communicate, investigate, and analyse failures of complex systems. Failures in these systems arise through a complex combination of different component failures, involving both active failures (human error by those interacting with the system) such as distraction, inattention, and latent failures (systemic features that predispose toward particular types of active error) such as time or resource pressures, or organisational culture; 3. At the human-system interface, human factors engineering identifies how systems can be designed for ease of human-system interaction, so that users or operators can do so safely with good performance; and 4. At a systems level, resilience engineering identifies how systems and organisations can be designed to optimise function in an uncertain and variable environment over the long-term.

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6.1 The “Swiss-cheese” model of accidents The Swiss cheese model of accidents (or “defence in depth”) is a conceptual framework for understanding system failure [56, 57] that is widely used in engineering safety and other fields. It provides a useful basis to communicate regarding failures, to investigate accidents, and to analyse the safety of systems [56]. It recognises that in most complex systems, there are many barriers to systems failure, such as engineering controls, human actions, system processes and procedures, monitoring, etc. There are a number of different conceptualisations of the Swiss cheese model. Figure 2.3 provides one that we believe is useful for understanding system failures in engineering contexts. In the Swiss cheese analogy, some hazards are prevented by barriers (solid cheese), and others pass through the holes (failure of a particular barrier). Any one barrier might defend against some hazards (e.g., setting a value out of safe range), but not against others (e.g., ignoring a hazard warning). A different barrier (slice of cheese with holes in a different place) might be designed to defend against ignoring hazard warnings, but not malicious software code. When failures do occur, it involves concurrent failure of many different barriers.

Harm or damage prevented by system controls Undesired event Causing performance loss, damage, or injury

Hazards or disturbances

System controls provide layers of protection Individual controls can sometimes fail

Figure 2.3: The Swiss-cheese model of accidents; system controls represent a range of barriers that can prevent system failure, including engineering controls, system processes, and actions of personnel; failure of individual barriers can be due to active errors (made when humans interact with a system), or due to latent errors (features of a system’s processes which predispose it toward certain types of errors (such as organisational culture, performance demands, design features, or management decisions).

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The naïve view of barriers to systems failure is that each barrier acts independently. In this view, with a sufficient accumulation of independent engineering controls, the likelihood of a known hazard causing an error becomes vanishingly small. For example, three controls each with an independent 10% chance of failure would, when combined, have only a 0.1% chance of failure from a known hazard, if all operate correctly. This understanding of system failure is misleading, and generally leads to an accumulation of system controls as barriers against hazards and disturbances. As we have seen, accumulation of system controls can lead to complacency, conflict from complexity, and inflexibility. A critical insight of the Swiss cheese model is that failures that arise do so because barriers are not necessarily independent, and that concurrence of failure can arise due to a range of factors. In any complex system, trade-offs are present between performance and safety; and systemic constraints such as time pressures, cost pressures, resource limitations, organisational culture, and management decisions can alter the concordance between barrier failures. The ability for systems to fail may be ‘baked-in’ by the choices of designers, including in choices of barriers to failure and limits on operator control. As a consequence, this model can be useful to understand the complexity of system failures. However some caution must be used in the application of the model. It is not descriptive or predictive, it should not be used to encourage the accumulation of unnecessary or unhelpful barriers and defences to failure, and it must be remembered that not all hazards and external disturbances can be predicted.

6.2 Human factors engineering The field of human factors engineering uses psychological insights to improve the safety of engineered systems. The insight of human factors engineering is that many errors that arise from interactions between humans and complex technological systems can be reduced or eliminated by design that acknowledges the human role in the technological system [58]. By doing so, systems can empower human operators and users to achieve better outcomes. The key overarching lesson is that engineered equipment, objects, systems, and machines must be designed with the end-user or operator in mind. Design in this way is likely to assist in reducing error, may enhance performance, and in the context of products, may provide market-differentiation compared to products that are not userfriendly. This insight is not limited to engineering, but also applies to business and organisational processes. The design of business procedures that are too complex for staff to follow can lead to errors or waste as readily as poorly designed engineering systems. One critical general implication from human factors engineering is the way in which humans interact with systems should be both considered at a conceptual level

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and evaluated in practice. Design elements that generate errors during human-system interactions cannot always be predicted in advance. Evaluating how end-users or operators use a human-system interface is important in identifying aspects that may cause error-generation. Human factors engineering is a broad field of practice, involving a range of different techniques to determine current design features that promote error, and how they might be improved. Some of the types of techniques that are used include: – Critical incident technique is used to identify elements of a human-system interface that are related to accidents or near-misses. The key idea is to ask users to identify events associated with an error, under circumstances where there are no negative repercussions for accidents or near-misses. This assists in determining features of a system that can or often do go wrong, so that they can then be addressed. – Task analysis is used to understand the requirements of a task, in terms of the steps that a human operator must take to produce a desired outcome. The outcome might be altering a system setting, or retrieving data about the current state of the system. This technique can allow designers to recognise procedures that are overly complex, and allow them to be simplified. Simplification of complex procedures reduces the number of points at which a procedure can result in errors, and generally as a consequence the number of failures that occur. Human factors engineering provides some insight into the types of design that are more likely to enable good human performance and reduce errors. These include for example, intuitively understandable system controls. Ensuring that the way in which humans interact at a human-machine interface (or human-system interface) is as simple and clearly understandable as possible assists in minimising error and maximising the ability of human operators to produce useful outcomes. Some specific examples of intuitive control design include: – Stimulus-response compatibility [59, 60], which involves designing a clear relationship between a control and the effect it has on a system. An example is the design of a car steering wheel that turns the car to the left when the steering wheel is turned to the left. If the car turned to the right, then the stimulus and response would not be aligned. – Clear labelling, with large letters, colours, or other features to facilitate easy understanding. In Australia, methylated spirits is clearly labelled with large text, widely understood symbols indicating that it is toxic and flammable, ‘poison’ written as moulded plastic in the structure of the bottle, and a bright dye unmistakeably colouring the fluid within the bottle. These design features are aimed at empowering the user with critical information (that it is toxic to drink), so that when they are making decisions, they are likely to make the correct decision regardless of whether they are distracted or tired.

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Emergency stop buttons are another example of this type of design. They are clearly labelled, and provide direct understanding of consequences on the system, and empower operators with flexibility in unexpected situations. Shape coding [61], in which system controls have a different shape depending on their function. This is another instance of clarity in labelling, but is a type of labelling that is less obvious but often useful to minimise errors of personnel operating in complex or distracting environments.

Human factors also suggests that the effects of an action by a human on a system should ideally be visible to the human in a clear and prompt manner (the feedback design principle) [58]. This assists the user in developing an understanding of the system (corresponding to the development of expertise). Providing users with variables which identify the current state of the system is extremely important, as it allows operators to monitor the current status of the system, identify any current issues, and understand the effect of any actions they take. On the other hand, humans have a limited ability to process information. Too much information consequently means that operators can be distracted by irrelevant stimuli, or be unable to properly conceptualise the current situation. A clear interface with information structured in a hierarchy is the best approach: a high level overview that can be expanded to access subsequently more and more detailed information. Control panels in the operations rooms of factories or processing plants meet with this ideal to varying degrees. One area where careful information provision is important is in relation to alarm and warning systems [62]. It is often a temptation during design to incorporate alarms (or fail-safes) for any deviation beyond the expected design conditions. The unfortunate consequence of this is that alarms can be included that are not necessary for safe operation. This can result in a situation where some alarms become routinely ignored because they are irrelevant, and operators may not distinguish between an alarm signalling a routine deviation vs. a critical safety hazard. This issue is common in industrial environments, medical settings (e.g. hospitals) [63], and in computer systems. For example, consider how quickly many computer users click through error messages (or confirmation security prompts) without reading them. Any alarm or warning (as well as fail-safes or interlocks) should be carefully considered in terms of the end-user, including whether less intrusive approaches could be appropriate. For example, a warning light might be preferable to an alarm when the related disturbance is not urgent. Human-oriented design applies beyond simply the features of the machine- or system-side of a human-system interface, and can include the human-human interactions within a system. For example, team resource management is a set of training techniques aimed at improving team leadership and team interactions in time-critical situations, used in aviation [64, 65], health-care [66], other high-risk settings

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[67]. Leadership failure in teams can be an important factor in poor performance or system failure. Common features are lack of clear goal setting where important issues aren’t addressed, or authoritarian leadership where team members can’t raise concerns or issues. The solution that has been effective in many situations is practical training in team-work, specifically in how to formally delegate tasks, clearly identify team goals, and communicate individual intentions. This is generally coupled with practical experience in e.g. simulation. Some examples of relevant sub-skills are: – Closed-loop communication, in which the elements of a task being assigned are communicated by both the task assigner and assignee (potentially with clarification). For example: “Lower the landing gear when we reach 2000 feet” might be responded to with “OK, I will monitor our altitude and lower the landing gear once we reach 2000 feet.” In an engineering context, good scoping and relationship management processes have similar elements, in which back and forth communication is used to ensure that the work of an engineering team is closely aligned with the desires of the management or client. – Clearly defined, and pre-determined roles and responsibilities, which assist in ensuring that critical actions are completed. If responsibilities are not clear, it is easy for people to believe that particular tasks are others’ responsibility. If nobody recognises a task is their responsibility, it may never be done. In time-critical or emergency situations, this is very important. A common example of this in practice is designated fire wardens who have specific responsibilities (such as checking all personnel have evacuated) in an emergency. Finally, human factors engineering can also be applied to informational and procedural elements of complex systems. Consider cognitive aids, which ideally should be designed to support users and operators in their work, rather than constrain or hinder their actions. Systemic processes such as standardised operating procedures, checklists, and algorithmic guidance documents are common in many organisations and professions. These cognitive aids can be useful to operators as they can assist in guiding reasoning or reducing mental effort (such as remembering very large numbers of necessary steps). Human factors provides an insight that these are not necessarily a solution to sources of risk, but must be appropriately designed for the process and end-user. For example: – Checklists provide a reminder to operators or users of the various issues that should be considered before, during, or after a process. Processes which otherwise might be prone to error due to human inattention, forgetfulness, or distraction can be regularised to best use human capabilities. This type of intervention doesn’t necessarily control or dictate actions, but ensures that potential causes for concern are thought about. In this way, users are assisted in identifying when things might be going wrong, even when these are not necessarily foreseen in advance. Checklists can involve technical elements, but might also involve social

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components. For example, pre-flight checklists in air travel include checking that the pitot tube covers have been removed (a technical step to ensure that air speed can be accurately measured once in flight), but also pre-flight conversations between flight crew (a social step that addresses roles and responsibilities). The inclusion of social elements can be critical to preventing serious errors, as they clarify that it is appropriate to raise concerns or identify mistakes. However, checklists should be designed with ease of use by the operator in mind [68]. Checklists are available to consider the implementation of human factors principles in engineering contexts [69]. Guidance documents (based on algorithmic or other decision-making), when well-designed, can provide an alternative to inflexible systems. Automated systems that override the user, or rules that constrain their action can reduce the flexibility of a system to respond to the unexpected. That flexibility can be preserved by providing guidance instead of rules. For example, decision-making guides, or automated software might provide suggestions for a course of action, but also provide information about the key assumptions or limitations of that advice. This type of approach allows operators to use the guidance in most situations in which it will be useful, but to recognise out-of-the-ordinary situations in which human judgement should override.

6.3 Resilience engineering Resilience is the ability for systems, organisations, groups, or individuals to dynamically adapt to changing circumstances, including those for which they are not explicitly designed. In this context, changing circumstances may mean external or internal disturbances, disruptions, or variations in the behaviour of the system. Resilience engineering is concerned with understanding the ways in which systems and organisations can be designed or managed to improve or promote resilience, recognising that: – Most systems operate in a state of dynamic tension between performance pressures (cost, time, efficiency, output), and maintaining successful operations without failure [70, 71]. This tension is frequently referred to as the “efficiency-thoroughness trade-off” [72]. Systems that are successful in the long-run maintain the dynamic equilibrium between these demands. They are sufficiently efficient to be competitive, and sufficiently thorough to minimise serious failures or their consequences [70, 73]. – The performance of complex systems is variable due to external variability and the complexity of the system itself [74]. A principal idea of resilience engineering is that success and failure are emergent phenomena that arise from the same underlying processes [74].

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Regular and recognised system disturbances can be accounted for in design. However, system designers cannot anticipate all the disturbances that might be encountered. System failure due to irregular or unanticipated disturbances can only be prevented or mitigated by the flexibility of the system to adapt. There is a trade-off between (i) controls, processes, and procedures that maximise performance under typical operations while minimising risk from known, regular disturbances, and (ii) flexibility of a system to adapt to rare or unexpected perturbations [75]. Humans are the crucial element in maintaining successful system operation despite variable conditions, and particularly in unanticipated circumstances [11, 70, 73]. Humans also work to maintain the practical balance between efficiency and thoroughness.

The safest option in many circumstances is to avoid doing an activity entirely [76]. Resilience engineering is focussed on how we might accomplish activities that have valuable outcomes but are nonetheless risky (e.g. driving vehicles, aviation, many engineering activities, and emergency services), while staying within a safe operating envelope [77]. As a consequence, resilience engineering is concerned with both the technical elements which allow systems to maintain safe operation despite hazards and variability, and with the organisational processes that can promote resilience by successfully balancing efficiency and thoroughness. Resilience engineering provides insights into the overall system-wide design-objectives to maintain performance and safety of complex systems under conditions of risky variability [76]. How can resilience be achieved in practice? An important general consideration for engineering systems is to optimise with uncertainty in mind. Good performance over a wide variety of conditions or scenarios, including unanticipated variability, is preferable to better performance but a very narrow window of operation. At a more detailed organisational level, four capabilities of resilient organisations have been identified: responding, monitoring, anticipating, and learning [78]. Responding consists of a readiness to manage disturbances when they arise. In practice this means having relevant resources available. Resources may mean manpower, knowledge, and materials, which may be expensive. Adequate capacity to respond is consequently a managerial as well as technical problem. There is an efficiency-thoroughness trade-off, with long-term survival requiring the right balance between day-to-day performance and reserve capacity. Monitoring consists of an ability to detect disturbances to the system, which requires indicators of current performance and leading indicators of future performance changes. Good indicators of current operation may be available for some technical systems but in more complex and socio-technical systems, these indicators may be difficult to identify. Advance warning of failure is often not possible, even with a good selection of indicators, and caution should be used in revising indicators in response to failure.

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Anticipating consists of a capability to identify changes to the context within which a system operates, and the consequent changes in threats, disturbances, and performance requirements. Potential future changes should be considered beyond the near future, and in terms of more complex non-linear potential combinations than in typical risk assessment [78]. This requires sufficient capacity for imagination of the future [78, 79], which may be difficult to realise in practice. Learning consists of the capacity to recognise both failures and successes of the system, and to adapt the system in response. A key distinction between resilient learning and a reactive response to failure is the recognition that both failure and success are results of the same underlying process [78]. Revising a process that reduces failure but also reduces typical performance is often inadvisable. Changes to a system should be made with both performance and safety in mind; consider for example: ‘Will tightly coupling system processes to boost performance unacceptably increase risks?’ or ‘will adding additional but onerous engineering controls or safety barriers unacceptably reduce output and efficiency?’ Likewise, failing to adopt practices that have been useful is a wasted opportunity. Resilience engineering in practice depends on the characteristics of the system. Particularly in many engineering cases, the ability to incorporate sufficient margin, tolerance, or buffering capacity to manage unexpected events can be reasonably clear. A simple example of resilience is fuelling a commercial aircraft so that the aircraft can carry out the scheduled flight, and has sufficient fuel to divert to an alternative airport in case of need [80]. An example of recognising resilience in practice would be how an organisation treats an employee who stopped or slowed production due to safety or failure concerns. Most organisations would be expected to reward this behaviour if the concerns were borne out, but may discipline or censure the employee if no underlying issues were identified. A resilient organisation would not be expected to punish the employee, despite the costs of lost production, as they would recognise the value in a positive safety culture that fosters employees’ willingness to speak up about safety or failure concerns [81]. A more complex example of resilience in practice is the NASA Apollo moon landing program [82]. This program was to oversee the safe operation of space craft in a risky and challenging environment that involved considerable uncertainty and uncontrollable variability (such as in characteristics of the moon landing site). There were a number of crucial features in this resilient technological system. Hardware components needed to be able to withstand a range of possible conditions; the project team needed ‘requisite imagining’ capacity to envisage the different circumstances that could occur. Contingency capacity beyond that necessary for the basic mission was needed to allow for flexibility in unexpected situations (such as the excess oxygen capacity in the lunar module, crucial to the survival of astronauts in Apollo 13). Also key was facilitation of human capabilities within the team. Substantial training was necessary to ensure that the people were able to carry out appropriate

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corrective actions whenever something went wrong. Finally a culture of willing and free-flowing communication was vital, so that people would be willing to speak up about concerns, and that managers would be willing to listen. These features were present in the later Apollo program missions, and despite crises (such as the oxygen tank explosion on Apollo 13, and lightning strikes during launch of Apollo 12), the astronauts were brought home safely. Westrum argues that success in this context required a “technological maestro” as project director, who could both set technical strategy at a high level of abstraction, and understand and manage critical technical details [82].

6.4 Implications for practice Whenever accidents or near misses are encountered in professional or industrial environments, the most common reflexive response is to act to prevent the same mistake from happening again. This might include disciplining or firing staff involved, or adding additional safety elements or controls to remove the hazard or reduce its ability to cause harm. The main implication from the psychological understanding of safety is to approach such changes more cautiously. Before additional safety mechanisms are added, some effort should be taken to consider organisational culture, working environment, and existing safety mechanisms, to understand whether more comprehensive changes are needed. The ideal implementation of these approaches would result in an environment that empowers system operators to control the behaviour of the system, including ensuring that the system controls are straightforward to understand and use. It also means ensuring that operators are well-trained, understand how the system operates, and are provided with high quality information about the current state of the system. The cultural environment should also be structured to support the system operators, both in ensuring necessary resources for contingency, training, and longterm adaptive changes; and in recognising the human role in responding to unanticipated events. Resilience engineering offers a suite of tools to evaluate and improve organisational processes and engineering practice to create safer and more productive systems [83].

7 Study questions – –

According to psychological theory, what makes for effective leaders? Investigate a large-scale failure in an engineering project. What are the human factors that contributed to this failure? How might these have been ameliorated?

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A common view is that “automation reduces the potential for human error”. Discuss the validity of this view. A common view is that safety is the absence of accidents. What are the problems with approaching safety from that perspective? Under what conditions are the intuitions of professionals worthy of trust? Under what conditions would the judgements of professed experts be treated with scepticism, and why?

Further reading This chapter introduced key elements of the science of psychology relevant to professional engineering. This chapter should have provided an introduction to some of the key concepts, and some insight into how they might be used in the practice of engineering. Below is a list of resources which allow exploration of the concepts of this chapter in greater depth. Ariely, D. (2008) Predictably irrational: the hidden forces that shape our decisions. Harper: New York. An engaging overview of regularities in our heuristics and biases from an behavioural economic (i.e., psychological) perspective. Kahneman, D. (2011) Thinking fast & slow. Allen Lane: London. Overview of modes of thinking and common heuristics and biases. Kahneman, D. and Klein, G. (2009) Conditions for intuitive expertise: a failure to disagree. The American Psychologist, 64(6):515–26. Adversarial collaboration on the use and limits of expertise between Klein (pro-System I) and Kahneman (anti-System I). Tetlock, P, and Gardner, D. (2015) Superforecasting: the art and science of prediction. Crown Publishing: New York. Addresses the nature of expert prediction, the importance of good feedback, and the opportunity and pitfalls of working in teams Nisbett, R.E. (2015). Mindware: tools for smart thinking. Pengi – Richard E Nisbett. Farrar, Straus and Giroux: New York. An introduction to judgement, decision-making, and improving thinking and behaviour through psychological science. Haslam. S.A. (2004). Psychology in organisations: the social identity approach. SAGE Publications: London. Detailed introduction to social identity approach and how to build better teams in organisations Haslam, S.A., Reicher, S.D., and Platow, M.J. (2011). The new psychology of leadership: identity, influence and power. Psychology Press: New York. Detailed review of the history of leadership research, management science, and leadership in context Vicente, K. (2003). The human factor: revolutionizing the way we live with technology. Routledge: New York. Accessible, compelling introduction to the importance of designing technology for humans. Hollnagel, E., Woods, D.D., and Leveson, N.G. (2006). Resilience engineering: concepts and precepts. Ashgate Publishing: Aldershot. Detailed discussion of key elements in resilient engineering. Pariès, J., Woods, D. D.,Wreathall, J., Hollnagel, E. (2010). Resilience Engineering in Practice: A Guidebook. Ashgate Publishing: Aldershot.

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Provides a discussion of applied examples of resilience engineering, and includes a detailed prescription for components of resilient systems and an analytical method for evaluating system resilience. Safetydifferently (www.safetydifferently.com). Blog with multiple contributors working in fields of human factors and resilience engineering.

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Brian Head

Socio-political analysis

Understanding the context for designing and implementing complex projects and development programs Abstract: This chapter argues that social science should be seen as central for understanding how technologies can be developed, applied and mobilised to solve problems at every level. This perspective applies to a range of scales – from local projects in isolated locations through to addressing the global goals of sustainable development. Key concepts: Social context of technical projects; social values, stakeholders, and participation; capability, corruption and good governance; policies, regulations and incentives; democratic and authoritarian decision-making; evaluating effectiveness; forecasting and planning for future needs. Key ideas: 1. Social science is about understanding human behaviour and organisational practices in all areas of society. It is fundamental to understanding organisational structure and function; policies, regulations, institutions, and governance; project finance and management; and collaborations and partnerships. 2. Humanity faces major challenges, enumerated within this chapter in terms of sustainability, development, and engineering grand challenges. Engineering is critical to developing solutions to these challenges, and social science is vital to ensuring the solutions are effective and successful. 3. Project success depends on understanding the social context. Project management approaches devised in Washington or London will not ‘work’ in other cultural contexts. Project planning and management should incorporate attention to key social dimensions, such as scale, culture, sources of investment, compensation & rewards, accountability frameworks, or regulation and governance. 4. Partners are often required, but they are not always reliable; and government officials in some countries are open to changing the rules for inappropriate purposes. 5. Project design and management which takes seriously the “social licence to operate” will generally be more successful than a reliance on legal contracts alone. 6. The cooperation of local stakeholders is just as important in international projects as in local projects. Ignoring the local people can be counter-productive, leading to costly delays or disruptions, due to e.g. legal challenges or labour disputes. 7. Countries are variable in the structure of their political systems. Many countries operate very differently to democratic systems, with more authoritarian countries also sharing a passion for technical knowledge but less respect for citizens’ rights https://doi.org/10.1515/9783110535129-003

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and stakeholder perspectives. Authoritarian countries are usually characterised by absence of open elections; absence of party competition; selective rule of law and limited civil rights; supressed public opinion; programs, laws, and regulations decided by government interpretation of citizens’ needs and to suit dominant political and economic interests. 8. Countries are variable in terms of reliability of the legal and commercial frameworks, and presence of corruption and bribery. International frameworks covering corruption and bribery have been strengthened, and international treaties and national legislation require that Australian/USA/UK/Canada corporations involved in overseas projects are held to the same standards of accountability applicable within Australia/USA/UK/Canada. 9. Forecasting the future is risky and no techniques guarantee success. The effectiveness of bureaucratic planning depends on the knowledge and capabilities of planners. An alternative is to acknowledge uncertainty and engage stakeholders in planning; such consultation can produce better outcomes due to better information on needs and support for outcomes.

1 Introduction and purpose Social science is about human behaviour and organisational practices. Social science is therefore fundamental for understanding how businesses and universities are organised; how research and innovation are facilitated; how field projects are financed and managed; how policies and regulations operate in different countries and in different industry contexts; how good leadership can make a difference between success and failure; how good project management is essential for delivering quality results, on time and on budget; how consultation is vital for building support for new projects and programs; and how well-managed collaborations and partnerships achieve more than individual organisations can achieve.

2 Understanding the contribution of social science Understanding the problems of improving innovation, promoting sustainability, and protecting ecological assets requires deep technical knowledge, drawn from the engineering and bio-physical sciences. But while these STEM sciences are vital, they are not enough for achieving outcomes. The social, economic and political sciences are also essential, because every attempt to address a socio-ecological problem (e.g. water quality, sanitation systems, energy-efficient buildings) requires plans and recommendations about how to address the problems. These recommendations are always based on assumptions about human behaviour and human choices. The

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options for action have to be interpreted in the context of the diversity of socio-economic needs and interests, which need to be mapped and assessed. These in turn shape how appropriate policies and programs are to be selected and financed. “Social sciences” here include: economics, sociology, law and regulation, organisational management, psychology, political science, history, and cultural studies. They are distinctively different from the STEM sciences because: – they focus on human behaviour, which is highly variable rather than uniform or predictable; – they emphasise local and national differences in institutional and cultural contexts; – they emphasise that human actions are shaped by perceptions, meanings, values, loyalties, and interests; – they note the importance of differences in power and resources; and – they note the importance of collaboration and collective action (not just individual actions and decisions). The essential role of the social sciences in addressing real-world problems can be seen in the examples of sustainability, global change, engineering grand challenges, and international development.

3 Tackling real-world problems The search for sustainability is driven by concerns to solve real-world problems [1], rather than simply a ‘pure’ scientific desire for understanding of the physical and bio-social worlds. Sustainability science is inherently linked to promoting sustainability goals, rather than claiming to be detached or neutral [2]. Thus, in considering patterns, trends and causal relationships in socio-ecological systems, the ultimate concern is with developing more effective strategies and interventions. The desire to translate research findings into effective action underlies the motivation of sustainability-research communities around the globe. The ‘sustainability science’ approach draws on a range of disciplines, to address the significant interactive relationships between social and natural systems and their effect on how we can address the challenges of long-term sustainability, namely: meeting the needs of present and future generations while substantially reducing poverty and conserving the planet’s life support systems … sustainability science is a different kind of science that is primarily use-inspired, as are agricultural and health sciences, with significant fundamental and applied knowledge components, and commitment to moving such knowledge into societal action [3].

To tackle the grand challenges of sustainability, a broad coalition of expertise is needed. Margaret Palmer [4] urges the importance of researchers seeking to interact

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with decision-makers in order to identify shared perspectives on key science problems, and then for social and bio-physical scientists to collaborate in developing and addressing these issues. This type of science, she continues, can be called ‘actionable’ science because: it has the potential to inform decisions (in government, business, and the household), to improve the design or implementation of public policies, or to influence public- or private-sector strategies, planning and behaviors that affect the environment [4].

4 Human dimensions of global change The relevance of the social sciences to issues of socio-ecological sustainability has been clear for many decades. For example, in 1989 the US National Research Council established a committee on the ‘human dimensions of global change’. Its report mapped out a major research program and called for joint action and mutual understanding across the social and environmental sciences. The social sciences have special relevance for understanding many drivers of global change that impact on ecological health (e.g. population, economic growth, technological change); for understanding the interaction between local, regional and national scales; and for understanding the range of institutional and attitudinal responses to environmental risks [5]. This chapter emphasises the relevance of social science for understanding organisational and policy aspects of projects and programs. A partnership is needed between the social sciences and the STEM sciences in order to tackle both local and global issues. As suggested in Figure 3.1, engineers and bio-physical scientists need a broader socio-political understanding to ensure that their solutions and project ideas are designed and implemented in the most effective ways for local conditions.

Nature Culture & Society Economic activities

Technical projects & research Figure 3.1: Engineering skills in context.

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Case study: responding to “water scarcity” in Australia 2001–09 1.

What kind of a problem was this? (Identify the nature and extent of the problem in various local contexts)

2.

Causality (What are the underlying reasons for the problem?)

3.

Responsibilities for solving the problem – If the causes and remedies are seen to lie with individuals and groups, the approach might focus on information for making better choices in solving their own problems. – If the causes and remedies are seen to be systemic [e.g. stemming from natural resource deficits, or from unequal economic and political power], the approach may require collective action through government investment or regulatory changes (e.g. to protect scarce resources). Choosing and implementing solutions

3.

– What options are technically feasible in the local context? – Who pays and who benefits? – What options are affordable and cost-effective? – What options have unintended impacts (or externalities)? – What options have community support? 4.

Example: Decisions for Southeast Queensland/Brisbane 2006–07: – A deepening crisis in water security, owing to a long drought – Massive new infrastructure spending (regional pipelines, desalination plant, advanced wastewater treatment plant (recycled water), new dam) – Major campaign to voluntarily reduce consumption

5 Grand challenges in engineering Humanity has always sought to overcome barriers and challenges, and create opportunities that improve life in our own regions. With input from people around the world, an international group of leading technological thinkers were asked to identify the Grand Challenges for Engineering in the 21st Century. Their conclusions are detailed online [6]. According to this group, we now tend to take for granted many of the great engineering achievements of the twentieth century. “Technology allows an abundant supply of food and safe drinking water for much of the world. We rely on electricity for many of our daily activities. We can travel the globe with relative ease, and bring goods and services wherever they are needed. Growing computer and communications technologies are opening up vast stores of knowledge and entertainment.” Nevertheless, there are certainly many more great challenges and opportunities to be realised. While some seem clear, many others are emergent areas of inquiry and enterprise, and many more are yet to be imagined. The 14 Grand Challenges for Engineering in the 21st Century were identified as:

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Make Solar Energy Economical Provide Energy from Fusion Develop Carbon Sequestration Methods Manage the Nitrogen Cycle Provide Access to Clean Water Restore and Improve Urban Infrastructure Advance Health Informatics Engineer Better Medicines Reverse-Engineer the Brain Prevent Nuclear Terror Secure Cyberspace Enhance Virtual Reality Advance Personalized Learning Engineer the Tools of Scientific Discovery

It is noteworthy that these engineering-focused challenges would all require considerable collaboration across a range of knowledge areas, both bio-physical and socio-economic, as well as practical implementation issues of leadership, consultation, project management and financial skills.

6 The UN millennium development goals The same might be said of the great challenges of sustainable development for the 21st century as outlined by the United Nations. In the first phase for 2001–2015 the UN agreed on eight goals, known as the Millennium Development Goals (or MDGs). These eight MDGs became the focus for a considerable number of foreign aid programs. These were financed from several sources: the United Nations agencies for health and development, various non-government organisations (NGOs) dedicated to improving conditions in developing countries, and the foreign aid programs of the richer countries of Europe, America and Oceania. In other words, these broad internationally-agreed goals became the strategic context influencing and legitimating major initiatives within the ‘development’ programs of many organisations internationally. Without this high-level focus, financial support would not have been mobilised for hundreds of major projects every year. A large proportion of these projects required engineering skills – e.g. projects for economic development to eradicate poverty, projects for environmental sustainability in resource development and agriculture (Figure 3.2).

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1 ERADICATE EXTREME POVERTY AND HUNGER

5 IMPROVE MATERNAL HEALTH

2 ACHIEVE UNIVERSAL PRIMARY EDUCATION

3 PROMOTE GENDER EQUALITY AND EMPOWER WOMEN

6 COMBAT HIV/AIDS, MALARIA AND OTHER DISEASES

4 REDUCE CHILD MORTALITY

7 ENSURE ENVIRONMENTAL SUSTAINABILITY

 85

8 GLOBAL PARTNERSHIP FOR DEVELOPMENT

Figure 3.2: Millennium Development Goals 2001–2015.

7 The UN sustainable development goals Progress toward the eight MDGs was evaluated periodically, and a new package of goals was agreed by the United Nations for the following period of 15 years. These were known as the Sustainable Development Goals, with an operating span of 2015–2030. Having made some modest progress through the previous MDG process, the new package was even more ambitious: 17 goals covering a vast range of activities (Figure 3.3).

Figure 3.3: Sustainable Development Goals 2015–2030 [7].

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These expanded goals for the years to 2030 cover all aspects of industry, infrastructure, economic growth and jobs, food and agriculture, fisheries, forestry, energy production and distribution, water quality and distribution, affordable technologies for poor and remote locations, education, healthcare, and over-arching issues such as combating climate change and ensuring peace and justice. Importantly, the goals are also supported by ‘partnerships’ between many organisations to undertake joint activities [8]. It is clear that engineers will be contributing to many of these global goals through their work in specific projects around the world. In some cases this will be through big commercial project contracts – awarded through a competitive tender process – to build new facilities and to design affordable new solutions for longstanding problems that can be made more widely accessible in poorer countries. While much of the work will be project-based, and will be financed commercially through external funding, some of the opportunities for professionals to assist poorer countries will be created through NGOs and associations that mobilise the skills of young professionals, such as ‘Engineers Without Borders’ and ‘Doctors Without Borders’. These groups provide opportunities for young graduates to gain experience by working on real projects that make a difference for people in poor or remote locations. For example, Engineers Without Borders (EWB) [9] works with community partners in Australia and Asia  to facilitate meaningful and lasting change. Its philosophy is based on a community-centred approach, using engineering knowledge and resources to bridge self-identified gaps in access to community health, wellbeing and opportunity. EWB projects focus on sustained engagement to build strong relationships and best practice models, strategically deploying people to achieve long-term impact through knowledge sharing and sector building. Their international projects broadly align with the UN goals of enhancing access to the services needed for people to lead a life of opportunity, free from poverty. In Australia, there is also a program focusing on Indigenous communities, to improve their access to engineering skills, design and construction services and employment opportunities.

8 Understanding the social context of project design and implementation In summary, it is important to situate (or ‘frame’) the opportunities for engineering within these wider contexts. Engineering is not just about applying technical knowledge in specific projects; it is also about working with various stakeholders and local community leaders in order to accomplish social goals. Project work generally requires a solid understanding of the contexts or situations in which the work is being undertaken; an ability to work with local stakeholders relevant to each project; and

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an ability to collaborate with other professionals and experts from a range of disciplines such as law, management, finance, and community development. The notion of ‘context’ is very important (see Figure 3.4). In the first instance it is about time and place. A solution or a technique that has worked in one time and place may not be appropriate elsewhere. Countries, and even regions within countries, can change over time in terms of how their key priorities are defined and ranked, their capacities to deal with the problems, and the types of solutions that will be acceptable to local stakeholders both rich and poor. Some of the project management approaches devised in Washington or London will not ‘work’ in the cultural context of developing countries in Asia, Africa and South America, or indeed in the remote Indigenous communities of central Australia. Some of the infrastructure solutions that worked in the 1950s (e.g. large dams) might no longer be appropriate for the 2020s. Moreover, the nature of the legal and regulatory systems that govern local projects will be very different in developing countries compared with the rich liberal-democratic countries. The rule of law – its fairness, predictability, efficiency, timeliness – may be very different in authoritarian countries where bribery and corruption are normal. To take a concrete example, the planning and management of a major project, under current conditions, might require attention to a number of key dimensions. 1. Scale – micro/macro; local/regional, etc. 2. Culture – traditional/commercial; linguistic and ethnic identities; religious differences, etc. 3. Sources of investment – foreign aid, private investors, local governments, NGOs, philanthropy, etc.

NRM systems and built environment Community norms and priorities Laws, regulation and public authority

Business practices and investment projects

Figure 3.4: Context matters – the interaction of socio-historical institutions and practices.

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4. Compensation and rewards for resource development – who benefits? who is impacted? 5. Accountability frameworks – under formal contracts, and informal local arrangements. 6. Regulation and governance frameworks – clarity and consistency of legal requirements; corruption demands by local brokers, officials and decision-makers. Working with local stakeholders is a well-known element of professional ‘good practice’ in our own home-country projects. However, when working overseas on projects in unfamiliar countries, gaining the co-operation of local stakeholders is just as important. To illustrate how negative outcomes can emerge, consider a common situation with a major project in a developing country, where local people consider that their economic interests and cultural values have been over-ridden – e.g. by powerful elites within their own government together with foreign corporations that manage the development project. Ignoring the local people can be counter-productive, leading to costly delays or disruption including legal challenges, labour disputes and blockades, and compensation claims [10]. This issue has become widely discussed in terms of the advantages of having a “social licence to operate.” [11, 12] This accord can be built on informal arrangements with local stakeholders, including local employment guarantees and full consultation. Major development projects may require a process to build trust and mutual benefit: Changing societal expectations have influenced the way industries involved in the development or extraction of natural resources conduct their operations around the world. Increasingly, communities are demanding more involvement in decision-making around such operations, have expectations of receiving a greater share of the benefits from these operations, and require assurances that the industries involved are appropriately regulated. The combination of increasing pressures on industry performance and the associated societal acceptance of such operations has been described as the ‘social licence to operate’. In many ways, the social licence reflects the evolving nature of the relationships between industries and their communities and other stakeholders. Originally used to describe the social acceptability of mining operations, the term has since been applied to explore the broad acceptance that communities and other stakeholders (may) provide to the activities of the forest, agriculture and energy sectors. This article presents a critical review of the emergence of the concept in industry practice over the last two decades. Recent applied research to measure and model the social licence is also examined to demonstrate how the roles of trust, fairness and governance may underpin the development of more sustainable, trust-based relationships between industry and society [12].

9 Political and regulatory contexts Both democratic and authoritarian governments greatly value the technical knowledge of the STEM sciences because they are seen as helping to boost economic development and solve practical problems. Russia, China, Singapore and Indonesia may

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be very different in their political institutions, but all share a passion for technical knowledge. Governments interested in modernisation tend to have strong respect for science/technology as a basis for economic development (and for military strength). There is much less support in authoritarian countries for the social and political sciences (which tend to highlight respect for citizens’ rights and stakeholder perspectives, and the value of evidence-based decision-making). Moreover, authoritarian countries have been less supportive of protecting environmental values [13], because environmental protection is seen as a constraint on rapid economic growth. Richer democratic countries are more inclined to support the ‘triple-bottom-line’ approach to sustainable development, i.e. simultaneously pursuing economic, social and environmental objectives and benefits. Democratic countries ideally are characterised by the following features: – Electoral democracy, multiple parties, and citizens have right to vote; – Rule of law applies to all, with independent courts; – Needs and desires of citizens are regularly expressed and canvassed, community engagement processes are common; – Effectiveness of programs are regularly evaluated and improved; and – Taken together, these are key elements of ‘good governance’. On the other hand, authoritarian countries are usually characterised by the following: – Absence of free/open elections, absence of party competition and civil rights; – Rule of law applies selectively, elite interests are protected; – Needs of citizens are interpreted by government, public opinion is suppressed; and – Programs are decided and modified to suit dominant political and economic interests. The Economist provides a yearly league table of 167 countries ranked by their democratic characteristics [14]. Table 3.1 summarises four clusters of countries (Figure 3.5). Authoritarian countries are often strong supporters of technical innovation and engineering excellence [16]. But this does not mean they are therefore good at forecasting the needs of the population, either at a macro level for the whole society or at a local level for each specific region. The government’s capacity and commitment to identify and meet the needs of the population is a challenge for all countries, but it is even more difficult in countries where freedom of expression Table 3.1: Democracy Index 2016, by regime type. Source: Economist Intelligence Unit [14]. Regime type

No. of countries

% of countries

% of world population

Flawed democracies

27

34

45

Hybrid regimes

40

24

18

Authoritarian regimes

51

31

33

Full democracies

19

11

5

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Full democracy Authoritarian Regime

Flawed Democracy No Data

Hybrid Regime

Figure 3.5: The Economist Intelligence Unit, Democracy Index Map for 2016 (lighter shades represent more democratic countries) [15].

is highly constrained, and social needs are not well known or expressed. Authoritarian countries are sometimes successful in generating economic growth, but often at the expense of other values including social equity, human rights, and legal channels to redress maladministration. Of course, democracies themselves are also imperfect; they often have troubles arising from failure to achieve desired outcomes, such as the economic goals and social benefits promised by their political leaders [17].

10 Business risks In some countries, the reliability of the legal and commercial frameworks is uncertain, if not very risky. In so-called ‘failed states’ where the leadership is corrupt, and where the legal and administrative capacity to monitor standards and enforce agreements is weak, major projects can be subject to ‘sovereign risk’. This basically means that the previously established agreements and understandings might be undermined and contracts might not be honoured. This situation could occur either because the relevant laws are abruptly changed, or because the regime itself is radically changed through political turmoil or military force. The international frameworks covering corruption and bribery have been strengthened in recent years. In the past it was common for foreign firms to

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excuse themselves from high standards by claiming that side-payments were the ‘accepted’ way to conduct business and overcome bureaucratic delays in many countries. However, international treaties, backed by national legislation in many countries, now require that Australian corporations must conduct themselves in overseas projects by the same standards of integrity and accountability that would be applicable in Australia. Project workers overseas who feel pressured to make illicit payments to expedite a project are certainly placed in a very difficult position. In that situation, project staff should be advised to escalate the issues to ensure that the highest levels of management within their firm are taking full responsibility for negotiating an appropriate outcome, in a transparent manner that is fully documented. Globally, the extent of perceived corruption remains high, although variable across the countries surveyed by Transparency International in their annual corruption perception index. For example in 2015: “The scale of the issue is huge. Sixty-eight per cent of countries worldwide have a serious corruption problem. Half of the G20 are among them.” The rank order of countries in the survey is available online (Figure 3.6) [18]. This survey also provides indirect insight into whether it is easy for foreigners to establish and maintain a viable business in various countries. A related business issue in overseas operations is the relative ease of participating in a joint venture. In some countries, direct foreign investment (where foreigners directly own the business assets) is virtually impossible, but alternative options are encouraged such as the provision of specialised technical services through contracts.

Figure 3.6: Corruption perception index map for 2016; lighter shades represent countries ranked as having less corruption [18].

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11 Evidence-based planning? What are the most effective ways to identify the future infrastructure and services needs for a population? Consider alternative ways in which an investment program to meet future socio-economic needs could be devised. The traditional approach over the last century was to rely on bureaucratic planning. This was typical of most countries in the 1940s to 1970s, but centralised planning has continued to persist in authoritarian countries up to the present. The effectiveness of this approach depends on the capability of central planners to understand recent trends and predict future requirements in a changing world. Political objectives tend to be just as important as economic objectives for central planners. The alternative approach is to acknowledge uncertainties and to respect the preferences of citizens, by involving stakeholders regularly in gathering information and surveying opinions. The quality of data available for analysing social needs is important, and consultative processes enrich the data base through evidence about stakeholder experiences and their viewpoints about future priorities. In principle, consultation can usually produce better outcomes because (a) it produces better information on needs and capacities, and because (b) it generates better support for processes and outcomes. Forecasting the future is always risky and there are no techniques that guarantee accuracy [19]. However, there are advantages in working with feedback processes that are repeated regularly. For example, iterative consultation with a large panel of experts through Delphi techniques can be especially valuable as a complement to public opinion polls. For a summary of the Delphi method used for canvassing expert opinions, there are resources available online [20].

12 In conclusion: key messages This chapter has argued that understanding the social, economic, political, legal and environmental context is crucial for business success. This applies both for a small consulting engineering company and for a multinational resources corporation, both of which need to understand the risks and opportunities posed by their changing social and technical context. The social sciences contribute important insights and perspectives that complement and contextualise those of the technological sciences. Achieving sustainability is an inter-disciplinary challenge for us all. Successful implementation of innovative field projects relies on the organisational and leadership insights provided through the social sciences. Understanding the context of business success is not just about ‘technology plus finance’. It is also important to understand: – human behaviour, which is highly variable; – local and national differences in institutional and cultural patterns;

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– – –

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the importance of human perceptions, meanings, values, loyalties, and interests; the importance of differences in power and resources; and the opportunities for leveraging change through collaboration and collective action (not just individual actions and decisions).

13 Study questions –

–

– –

Both democratic and authoritarian governments greatly value technical knowledge (as a basis for national development). Does a commitment to technology mean that governments can identify and meet social needs? The capacity of business to deliver projects offshore is partly dependent on the legal and political operating environment. What are the risks for project managers in a country where the legal and political environment is uncertain or unstable? Does stakeholder participation generally improve the outcomes of major projects? What are the most effective ways to identify the future infrastructure and services needs of a population? Consider the special needs of remote locations.

Further reading The following resources provide a good starting point for further reading on sociology and political science. Coates, V., et al (2001). ‘On the Future of Technological Forecasting’. Technological Forecasting and Social Change, 67 (1), 1–17. This paper draws attention to the economic drivers of technological change; inventions and innovations occur in a complex economic and technical context. New tools for forecasting future trends are emerging and are important for positioning businesses for future growth. Davis, R. and Franks, D.M. (2014). “Costs of Company-Community Conflict in the Extractive Sector.” Corporate Social Responsibility Initiative Report No. 66. Cambridge, MA: Harvard Kennedy School. https://www.hks.harvard.edu/m-rcbg/CSRI/research/Costs%20of%20Conflict_Davis%20%20 Franks.pdf This paper provides case study examples and financial data for measuring the importance of company-community harmony. Disputes cost money, therefore it is worthwhile to invest in processes to build community understanding, support and benefit. Economist Intelligence Unit (2017). Democracy Index. https://www.eiu.com/topic/democracy-index http://www.economist.com/media/pdf/DEMOCRACY_INDEX_2007_v3.pdf Reports on the metrics of the democratic and authoritarian features of 167 countries. Franks, D.M, Davis, R., Bebbington, A., Ali, S.H., Kemp, D. and Scurrah, M. (2014). ‘Conflict translates environmental and social risk into business costs’. Proceedings of the National Academy of Sciences, 111 (21), 7576–7581. doi: 10.1073/pnas.1405135111

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A study by University of Queensland scholars, based on international cases demonstrating that mapping and managing social and environmental risks are just as important as mapping and managing financial and legal risks in major projects. Fukuyama, F. (2015). ‘Why is Democracy Performing So Poorly?’ Journal of Democracy, 26 (1), 11–20. This paper argues that modern liberal democracies combine three basic institutions: the state organisations, the rule of law, and democratic accountability processes. Democracies have been struggling in recent decades with their capacity to deliver good outcomes for citizens, i.e. to achieve good performance in service delivery and economic growth. A few authoritarian regimes have shown an increasing capacity for efficient performance. Head, B.W. (2014). ‘Managing urban water crises: adaptive policy responses to drought and flood in Southeast Queensland’, Ecology & Society, 19 (2): 33. http://www.ecologyandosciety.org/vol19/iss2/art33/ This paper outlines the background to the water scarcity crisis in the Brisbane region, and the range of policy responses and risk management strategies that emerged. These are contrasted with risk management of the subsequent severe floods. Kates, R.W., Clark, W., Corell, R. (and 20 others) (2001). Sustainability science. Science, 292 (5517), 641–642. Kates, R.W. (2011). What kind of a science is sustainability science? Proceedings of the National Academy of Science 108 (49), 19449–19450. These two papers by Kates make a case for why the sustainability sciences are important, action-oriented, and inherently inter-disciplinary. Lacey, J. (2013). ‘Can you legislate a social licence to operate?’ The Conversation, 27 February. http:// theconversation.com/can-you-legislate-a-social-licence-to-operate-10948 Moffat, K., Lacey, J., Zhang, A. and Leipold, S. (2015). ‘The social licence to operate: a critical review’. Forestry (online 22 November): doi:10.1093/forestry/cpv044 These two papers by Lacey and colleagues describe the meaning of the term ‘social licence to operate’ and how this differs from formal or law-based licences to operate. The argument is that community engagement and understanding are important ingredients for business success. OECD (2008). OECD Innovation Reviews – China. Summary available at: http://www.oecd.org/sti/ inno/41270116.pdf Palmer, M.A. (2012). Socioenvironmental sustainability and actionable science. BioScience 62 (1), 5–6. Similar to the argument by Kates, argues that a problem-solving approach is paramount for achieving advances in inter-disciplinary sustainability science and practice. Quah, J. (2013). ‘Ensuring good governance in Singapore: Is this experience transferable to other Asian countries?’ International Journal of Public Sector Administration, 26 (5), 401–420. This paper provides an explanation for the Singapore success story which has combined economic growth and social protection with ‘clean government’ and a strong stance against crime and corruption. The author argues that the leadership and policy factors responsible for success in Singapore are very difficult to ‘transfer’ to other countries. Sayer, A. (2011). Why things matter to people: social science, values and ethical life. Cambridge University Press, Cambridge, UK. A serious and reflective book about how our perceptions and values provide the social lens through which we see the world and formulate our goals and action plans. Science is not ‘neutral’, according to this perspective, and technology needs social guidance. Stern, P., Young, O. and Druckman, D. (eds) (1992). Global environmental change: Understanding the human dimension. National Academy Press, Washington DC. This substantial and prestigious collection of essays from 1992 emphasises the inherent interactions between the natural and the social worlds, the importance of well-informed decision-making, and the need for better collaboration across the social/ natural sciences.

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Wang, Z. and Tan, E. (2013) ‘The Conundrum of Authoritarian Resiliency: Hybrid Regimes and Non-Democratic Regimes in East Asia’. http://www.asianbarometer.org/newenglish/publications/workingpapers/no.65.pdf This paper provides a useful overview discussion of China, Vietnam, Singapore, and Malaysia. Two of these are one-party authoritarian systems, while the two others have elections but the state is dominated by a particular party. The discussion raises questions about the factors that might contribute to civic support or ‘legitimacy’ for the regime. Wang, M., Webber, M., Finlayson, B. and Barnett, J. (2008). ‘Rural Industries and Water Pollution in China’, Journal of Environmental Management 86, 648–659. This paper explores the gap between having environmental protection laws and being able to effectively monitor the sources of pollution and enforce compliance with the law.

References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20]

Kates RW, Clark W, Corell R, Hall JM, Jaeger CC, Lowe I, et al. Sustainability science. Science 2001;292(5517):641–2. Sayer A. Why things matter to people: social science, values and ethical life. Cambridge: Cambridge University Press, 2011. Kates RW. What kind of a science is sustainability science? Proc Natl Acad Sci 2011;108(49):19449–50. Palmer MA. Socioenvironmental sustainability and actionable science. BioScience 2012;62(1):5–6. Stern P, Young O, Druckman D, editors. Global environmental change: understanding the human dimension. Washington, DC: National Academy Press, 1992. National Academy of Engineering. Grand challenges for engineering, 2008. United Nations Development Programme. Sustainable development goals. United Nations. Sustainable development goals: post-2015 development agenda. Engineers Without Borders Australia. Available at: http://www.ewb.org.au Franks DM, Davis R, Bebbington A, Ali SH, Kemp D, Scurrah M. Conflict translates environmental and social risk into business costs. Proc Natl Acad Sci 2014;111(21):7576–81. Lacey J. Can you legislate a social licence to operate? Conversation 2013;27 February. Available at: http://www.theconversation.com/can-you-legislate-a-social-licence-to-operate-10948 Moffat K, Lacey J, Zhang A, Leipold S. The social licence to operate: a critical review. Forestry 2016;89(5):477–88. Wang M, Webber M, Finlayson B, Barnett J. Rural industries and water pollution in China. J Environ Manage 2008;86:648–59. Economist Intelligence Unit. Democracy index 2016: revenge of the “deplorables”. London: The Economist, 2017. Economist Intelligence Unit. Democracy index 2015: democracy in an age of anxiety. London: The Economist, 2015. Wang Z, Tan E. The conundrum of authoritarian resiliency: hybrid regimes and non-democratic regimes in East Asia. Taiwan J Democracy 2013;9(1):199. Fukuyama F. Why is democracy performing so poorly? J Democracy 2015;26(1):11–20. Transparency International. Corruption perceptions index 2016, 2017. Coates V, Farooque M, Klavans R, Lapid K, Linstone HA, Pistorius C, et al. On the future of technological forecasting. Technol Forecasting and Social Change 2001;67(1):1–17. Wikipedia. Delphi method.

Peter Knights

Engineering economics Abstract: This chapter provides an introduction to engineering economics by the consideration of the financial evaluation of engineering projects, and an outline of the principal means available to raise capital for such projects. This chapter introduces the law of supply and demand, and describes the factors that affect demand as well as price equilibrium. It also includes a brief analysis of market structure and barriers to entry for firms. It then introduces financial evaluation techniques for engineering projects, including the application of concepts such as: discount rates; net present value (NPV); internal rate of return (IRR) and ways of improving project value. Lastly, the chapter discusses debt and equity financing of projects, as well as measures of productivity. Key concepts: Supply and demand; price equilibrium; market structure; cash flow; payback period; return on investment; time value of money; discount rates; present value; internal rate of return; multi-attribute decision making; cost-benefit-risk evaluation; debt and equity financing; productivity. Key ideas: 1. Freely traded goods in competitive markets tend to be priced at the price equilibrium at which supply equals demand. 2. Market structure, including factors such as individual companies’ market power, competition between companies, or barriers to entry can also affect prices, and may allow them to be negotiated at different values than would be determined from supply and demand alone. 3. The net present value of a project is an evaluation in today’s dollars of all of the cash flows of the project over the life of the project. The project should only be considered if the net present value is positive. 4. The capital of a company is from debt (e.g. borrowing from a bank) and equity (i.e. issuing shares on the stock market). 5. The cost of equity is the amount of the dividends (to be paid) divided by the cost of the shares when issued. 6. Capital productivity is the ratio of output value (i.e. total product sales) over capital employed (this latter is measured as the difference between company asset value and liabilities) (and multiplied by 100). Labour productivity is similarly the ratio of output value divided by cost of labour (multiplied by 100). Note that these measures of productivity depend on the price obtained for the products, i.e. for mining the commodity prices.

https://doi.org/10.1515/9783110535129-004

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1 The determinants of price The potential profitability of a project, product, or production run, depends on the cost of inputs and the possible sales price of outputs. Consequently, these prices, and how they may change with time must be understood to make financial decisions. The price of goods and services depends primarily on their supply and demand. It also depends on the characteristics of the market within which they are traded, in terms of the size and number of buyers and sellers, their relative negotiating power, and barriers to entry of new firms. Understanding these principles assists in making business decisions.

1.1 The law of demand The law of demand is that the lower the prices of a good or service, the larger the quantity of the good or services demanded per unit of time, all other factors remaining the same. Conversely, the higher the price of the good or service, the smaller the quantity of that good or service will be per unit time. As an example, consider the demand for motor vehicles as shown in Table 4.1 (example courtesy of Maxwell) [1]. Figure 4.1 illustrates the corresponding demand curve for motor vehicles. As the price is decreased, demand levels increase. The manner in which demand varies in response to price change is known as the price elasticity of demand (or simply price elasticity) of the good or service. A low price elasticity means that demand is relatively insensitive to changes in price. Oil is a commodity with a low price elasticity in the short term (as people often do not have an easy option other than to pay higher prices for petrol to fuel their car), although in the long-run car-owners may substitute other options such as living closer to work or shops, or buying a more fuel-efficient car. Emergency health care for a very ill child is an example of a service with very low price elasticity. A high price elasticity means that small changes in price lead to large changes in demand. Goods with many different substitutes tend to have high price elasticity, because a small increase in price drives consumers to alternatives. Table 4.1: A demand schedule for motor cars (adapted from Maxwell) [1]. Price (x $1000) 15 25 35 45 55 65 75

Quantity demanded (x 1000) 500 450 400 350 300 250 200

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$80,000 $70,000 $60,000

Price ($)

$50,000 $40,000 $30,000 $20,000 $10,000 $0

0

100,000

200,000

300,000 Supply

400,000

500,000

600,000

Figure 4.1: Demand curve for motor cars (adapted from Maxwell) [1].

1.2 Factors affecting demand What causes changes in demand? Tilton [2] identifies the major determinants of demand for minerals (and we can extend this to goods or services) as: – Its own price; – Consumer incomes; – The prices of substitutes and complements; – Consumer preferences and expectations; and – Government activities. Figure 4.2 shows the effect of changes in supply. If the supply Q suddenly increases to Q1, then, all other factors remaining equal, the price will fall from P to P1. If the supply Q were to decrease to Q2, we could expect the price to increase from P to P2. The absolute value of the gradient (incline) of the graph is an inverse measure of the price elasticity. A steep, vertical, line indicates low sensitivity of demand to prices and hence price inelasticity. A horizontal, flat, curve indicates high price elasticity. If consumer incomes increase, as they did during the minerals investment boom in Australia from 2002–2012, then, coupled with lower exchange rates, more disposable income leads to higher consumption of goods and services. Conversely, a retraction in real wage growth will lead to consumers being more careful with their expenditures and lower consumption patterns. A good is a substitute of another good or service to the extent that it can satisfy similar needs or desires. For example, public transport is a substitute to owning a

 Peter Knights

Price

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Price Increase Price Decrease

P2 P P1

Q2

Q

Q1

Quantity

Figure 4.2: Movements along the demand curve [1].

motor car, particularly in big cities. If as Figure 4.3 shows, the price of public transport decreases (or availability of public transport increases such as the inauguration of a new rapid transport line), demand for purchasing motor vehicles will fall, at least amongst the affected residents. Conversely, if public transport prices were to rise significantly, it will encourage residents to purchase motor cars, thereby increasing demand. A good is a complement to another good or service to the extent that is used jointly with it. For example, parking spaces in a big city are complementary goods to motor cars. So is petrol. When the price of a complementary good increases, the demand for the good in question will decrease. The converse to this rule is also true. As we become more aware of the impact of fossil fuels on climate change, we are seeing a change in consumer preferences away from diesel and petrol motors cars to electric vehicles. Such changes in consumer preferences will also shift demand curves with reduced demand leading to the manufacture of fewer diesel and petrol motor vehicles. This in turn will move the demand curve from D to D2 (see Figure 4.3). Consumer expectations vary between optimism and pessimism. A stagnant economy can lead to concerns about job security, which will hold consumers back from spending. Alternatively, a growing economy encourages people to take out loans and invest, encouraging consumption of goods and services. The last factor affecting demand is government regulation. The imposition of a carbon tax, for example, will incentivise electric vehicles, providing they can be reliably powered from renewable sources. This in turn is likely to see reduced demand for petrol and diesel powered motor cars. Conflict can also drive the price of goods and services through the imposition of economic sanctions such as those imposed by the United Nations on Russia for the 2012 annexing of Crimea. Such sanctions are designed to decrease the availability of goods or services within a particular country. This leads to an increase in

Price

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D2

D

 101

D1 Price of public transport increases

Price of public transport decreases

Quantity Figure 4.3: Demand curve for motors vehicles. Changes in the availability of substitutes (e.g. public transport) can affect the demand curve [1].

price, and consequently increases the pressure on the government from the population who are not happy because of the scarcity or high prices of the goods or services.

1.3 The law of supply The law of supply states that suppliers of final goods will supply larger quantities of goods at higher prices than lower prices, where ‘price’ is the amount received by a vendor for the concept of sale of a product or service. Cost, on the other hand, reflects the value of materials and labour required to manufacture a product or effectively deliver a service. As an example, consider the following cumulative cost of production of motor vehicles (Table 4.2). Figure 4.4 shows a supply curve for new motor cars. The curve graphs the cost of production against the cumulative production. Thus there are 400,000 cars available at a cost of less than or equal to $40,000. Table 4.2: A supply schedule for motor cars (adapted from Maxwell) [1]. Price (× $1000) 15 20 25 30 40 55 75

Quantity demanded (×1000) 200 250 300 350 400 450 500

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$80,000 $70,000 $60,000

Price ($)

$50,000 $40,000 $30,000 $20,000 $10,000 $0

0

100,000

200,000

300,000 Demand

400,000

500,000

600,000

Figure 4.4: A supply curve for new motor cars (adapted from Maxwell) [1].

Factors that shift the position and gradient of the supply curve include (Maxwell, 2007): – The prices of major inputs (such as labour, steel or energy); – Exchange rates; – Technology changes; – Supplier restrictions and the number of suppliers; – Suppliers’ expectations; and – Government restrictions. For example, a sudden jump in wage prices would be expected to increase manufacturing costs, shifting the entire curve upwards. Unfavourable exchange rates can also impact on international prices received for goods. Higher exchange rates mean that imported products become relatively cheaper, leaving domestic manufacturers to struggle. High exchange rates stoked by the mining boom from 2002 to 2012 contributed to the demise of the automotive manufacturing industry in Australia. This phenomenon is known as Dutch disease, a term that was introduced in 1977 by The Economist magazine to describe the decline in the manufacturing sector in The Netherlands due to Shell’s discovery of the Groningen natural gas field [3]. Income flowing into the Dutch economy as a result of oil exports led to an appreciation of the Dutch Guilder, which greatly increased the cost of Dutch exports to foreign buyers. New technology can change the cost of producing goods. For example, increased automation of production lines is making many vehicles available at a cheaper price. Supplier restrictions include strikes, or natural disasters that restrict output in certain

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parts of the world. Generally this translates into higher prices. Supplier expectations affect supply in much the same way that consumer expectations are impacted. Tough economic times discourage investment in new capacity. Lastly, changes in government regulation can restrict supply. For example, the decision to ban vehicles from sale without catalytic convertors, led to restrictions on some higher powered engines being sold.

1.4 Price equilibrium Price equilibrium describes the price at which supply and demand are equal. It is a relatively simple matter to combine the supply and demand curves on the same diagram, to graphically identify the price equilibrium. Figure 4.5 shows where the demand curve cuts the supply curve. The intersection determines the equilibrium price ($38,000) as well as the equilibrium quantity of motor cars (380,000 vehicles). If, demand for motor vehicles suddenly increases (for example, as a result of petrol prices becoming much cheaper), prices will rise because of increased demand. This trend will continue until new production capacity increases supply (stretching the supply curve to the right) and a lower equilibrium price will result. Conversely, if demand should suddenly decrease due to concern around job security and the economy, the equilibrium price will correspondingly reduce. $80,000 $70,000 $60,000

Price ($)

$50,000 $40,000 $30,000 $20,000 $10,000 $0

0

100,000

200,000

300,000 Demand

400,000

500,000

600,000

Figure 4.5: Market equilibrium for the motor car example (open symbols = supply, filled symbols = demand).

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Thus, freely traded goods in competitive markets tend to be priced at this price equilibrium. Consequently, a conceptual understanding of supply and demand allows one to understand the pricing behaviour of a good or a service.

1.5 Market structure Normally, if a company tries to sell a good or service at a price above the market equilibrium price, buyers purchase the good or service from their competitors. However, if a company is responsible for a large proportion of the total production of a good or service, other companies may not be able to meet the total demand, and some buyers may need to purchase the good or service at a higher price. This can result in a company profiting from shifting their selling price above the market equilibrium. The measure of a company’s ability to do so is termed market power. A simple measure of market power is called the concentration ratio. This is the percentage production share of the largest companies. Terms such as C3 (the share of production of the three largest companies) and C10 (the share of the top ten) are used. For example, despite efforts by new entrants during the boom decade of 2002 – 2012, the production of iron ore remains highly concentrated. The world’s largest companies in 2015 were Vale, responsible for 17.2% of world production, BHP Billiton (13.6%) and Rio Tinto (13.1%). The C3 concentration of the three largest players increased from 36.4% in 2013 to 43.9% in 2015 [4]. This concentration of market share leads to an increased power of negotiation for the C3 companies. In a tight market where demand greatly exceeds supply, it is not easy for steelmakers to source iron ore from alternative producers and therefore high prices can be demanded by the C3 players. This situation accurately reflects the latter years of the mining boom 2008–2013. This market concentration is reflected in Figure 4.6 which shows global CIF (Cost including Freight) for iron ore supply. The dominant position of Australia and Brazil can be appreciated. The shape of the curve is effectively the supply curve for world iron ore. Current iron ore prices are around $90 per tonne free on board (FOB) a vessel. This means that the demand curve crosses the supply curve well within the red zone corresponding to Chinese supply. This explains why prices can be quite volatile as sudden increases or decreases in demand will result in large price movement.

1.6 Barriers to entry When faced with a tight market (which can be very profitable for established companies), why do additional players not seek to enter the market? The answer to this question is that they do, but new market entry companies often face barriers with respect to access to quality resources (such as skilled personnel or ore reserves), access to capital, and regulatory or intellectual property hurdles. In the case of the Australian iron ore boom, many companies entered the market by re-opening old

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Cost (including freight) to China, 2014 (US$/t)

140 Brazil

120

Australia China

100

Other Countries

80 60 40 20 0

0

200

400 600 800 1000 1200 Cumulative Annual Production (Mt)

1400

1600

Figure 4.6: Global iron production costs including freight in 2014 by country [5]. Colours indicate Brazil (blue), Australia (orange), Other countries (grey) and China (red).

mines or developing second tier resources. They faced many difficulties, not the least being access to railways and port infrastructure in order to export their product. One company, Fortescue Mining, decided to develop its own railway and port in the Pilbara region of Australia. However, the nature of the reserves meant that mining costs were often higher for these companies than for the C3 companies who owned the mining leases for the best quality resources. As result, when iron ore prices inevitably declined from 2013–16, many of the newer entrants suffered, some to the point of bankruptcy. Government regulation can also lead to barriers to entry (and to subsequent exit or relocation). An example of a regulatory barrier is the three mines policy implemented by the Hawke-Keating government in Australia as a compromise to satisfy political sentiment regarding uranium mining in Australia. For many years, just three mines have been permitted to produce and export uranium. One of these mines, Olympic Dam, just happens to be the largest producer of uranium in the world! Whilst this might be a cosy arrangement for the producers in that they face little competition locally, it is not advantageous for other companies (and arguably the nation) as it discourages exploration for new deposits in order to replenish Australia’s inventory of uranium resources. Taxation regulations, in particular provision for tax holidays or accelerated depreciation, can also encourage or discourage companies from locating productive facilities in certain countries. In recent years many of the large automotive manufacturers have announced closures in Australia whilst simultaneously

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investing in modern manufacturing plants in Asia, especially Thailand. Economists cite the comparatively small market for vehicles in Australia and high wage costs. The fact that the new manufacturing plants are almost entirely roboticised suggests that labour costs have little to do with decisions to relocate. A better explanation is that comparative taxation policy and write down provisions for invested capital strongly influenced such decisions! Supply and demand allow for an understanding of what drives an equilibrium price in a competitive market. Deviation of price from the equilibrium price can be understood in terms of market structure and the market’s barriers to entry. This also provides a starting point to understand why companies within a particular market behave as they do. For example, a company with dominant market power in a market with high barriers to entry may be aggressive in negotiating low prices from suppliers or demanding high prices from consumers (consider e.g. leading software companies’ pricing). In contrast, a dominant company in a market with minimal entry barriers is less able to profit from their dominant position as large profits would simply encourage the entry of other competitors.

2 Project management and cash flow 2.1 Cash flow Physical assets are purchased to perform a useful function. In the commercial world, this usually implies a productive function that will result in commercial revenue. In the world of defence, this function can be a deterrent, or value protection, which is more difficult to quantify in dollar terms. Thus, investment in a physical asset can be viewed as similar to executing an engineering project in which there is a period of investment followed by a period of revenue resulting from the project being put into execution. Figure 4.7 illustrates the cash flow resulting from a typical engineering project. Note that: Cash flow (Ci) = income – costs Income results from the sale of products or services, multiplied by unit sales price. Thus: Income = production volume × unit price Bulk mineral companies follow a strategy of increasing volume of sales versus a Swiss watch company that produce relatively few watches per year but invest millions on dollars per year in advertising to differentiate their products and increase unit sales prices.

Engineering economics 

0

1

2

C3

C4

C5

C6

C7

C8

C9

3

4

5

6

7

8

9

Ramp-up Phase C0

C1

Investment Phase

10

 107

years

Rated Capacity

C2

C10

Disposal

Figure 4.7: Cash flow diagram for a typical engineering project.

Operating costs include the following: – Labour (direct and indirect); – Materials (major and minor components, tools and consumables); – Energy (electrical, diesel); – Services (internal and external); and – General & Administrative. As Figure 4.7 illustrates, a typical engineering project can be visualised as consisting of: (i) an investment phase; (ii) a ramp-up phase followed by; (iii) operating at rated capacity and finally; (iv) closure, disposal and/or rehabilitation. Cash flow is negative during the investment phase. This is usually associated with EPCM (engineering, procurement, construction and management) of a new project. As the investment phase tails off, the mine (or production facility) will be commissioned and teething problems sorted out. From a cash flow point of view it is desirable that the ramp up to rated capacity is as fast as possible. At the end of a project’s life an additional investment is required in order to cover the closure, disposal and rehabilitation costs. Large closure costs can be avoided toward the end of a project if closure planning has been adopted all during the life of the project. Returning to Figure 4.7, cash flow in each year, i, is designated by Ci. Note that year 0 represents the current time (now). In this case, cash flow is negative during the first three years (the arrows point down) followed by a period of gradually increasing positive cash flow as the project capacity ramps up and then a few years of strong positive cash flow as the project produces at full capacity. The cash flow in year 10 reflects the inclusion of a ‘salvage value’ for the sale or disposal of the asset.

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2.2 Break even period The break-even point for a project is defined as the period of time in which the initial investment is paid off. Looking at the diagram in Figure 4.7, suppose that (C0 + C1 + C2) = (C3 + C4). The break-even period for the project, therefore, is 4 years.

2.3 Return on investment Return on Investment (ROI) is simply the ratio of profit (or loss) expressed in terms of the investment. Thus, in our project example, profit is the sum of C3 to C10 inclusive. The total investment into the project was C0 + C1 + C2. The ROI is simply the ratio of these two values. Note that we have used undiscounted values of cash flow to calculate this ratio.

2.4 Time value of money The value of money does not remain static over time. If you have one dollar and you spend it today, it will buy more than the case where you decide to postpone any purchase for a period of one year. This is because money can be invested in productive uses, such that it will provide additional value over time. Uninvested money thus loses relative value. The rate of this depreciation can be characterised by measures of inflation such as the consumer price index (CPI). For this reason, most of us invest our savings in banks that provide earnings as a result of interest or in other assets such as real estate that are capable of growing in value.

2.5 Discount rate Companies frequently borrow money from lenders or raise equity via the share market in order to invest in projects. Future cash flow must not only produce cash to cover the CPI rate, but must also cover the weighted average cost of capital (WACC). This results in a project hurdle rate that must be satisfied for the investment, also known as the discount factor, δ. The discount factor also reflects other aspects of risk associated with the investment. For example, a mining project in a developed country such as Australia might have a discount factor of 10% whereas a project in  West Africa might be subject to 14 to 15% to reflect political and geographical risk.

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The way that companies establish the discount rates for projects is interesting. A survey of chief financial officers in Canadian companies was conducted in 2000 that established that: – 33% of companies use the weighted average cost of capital; – 33% use the London interbank official rate (Libor); and – 33% use other mechanisms, such as adopting the long term bank deposit interest rates.

2.6 Discount factor Looking at Figure 4.8, $1 revenue in one year’s time (a future value) is equivalent to a present value of: 1 × $1 1 + δ   in year zero (that is to say, now). The factor: 1 1 + δ is known as the discount factor for year one. If the revenue were to occur at the end of year 2, the discount factor is: 1 2 (1 + δ) This rule can be generalized to establish that the discount factor for year n is equal to: 1 n (1 + δ)

C0

0

1 1+δ

Time (years)

Figure 4.8: Discount rate applied to $1 over 1 year.

$1

1

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FV = $1000

Present Year 0

Future Year 1 0 t years 1 PV = ? One year period

Figure 4.9: Practical example of present (P) and future (F) values.

2.7  Present value Figure 4.9 shows an example. The present value, P, of the future cash flow F evaluated at a discount rate of 10% over 1 year can be written as: (P/F, i%, n) = (P, $1000, 10%, 1) =

1  × $1,000 = $909 1 + 0.10  

2.8 Net present value Table 4.3 depicts a simple project cash flow whereby $1.5 million is invested in new plant capacity. It generates $800k of income in year 1, with costs of $500k, ramping up to $2 million in income in years 3 to 5 at a cost of $1.25 million per year. At the end of year 5 the plant is sold as second hand for a salvage value of $250k. Table 4.3: Sample project cash flow (all values in $) Year

0

1

Investment −1,500,000 Sales income 800,000 Costs −500,000 Salvage value Cash flow −1,500,000 300,000 Discount factor (10%) 1 0.909 Present value −1,500,000 272,200 NPV 840,400

2

3

4

5

1,200,000 2,000,000 2,000,000 2,000,000 −750,000 −1,250,000 −1,250,000 −1,250,000 250,000 450,000 750,000 750,000 1,000,000 0.826 0.751 0.683 0.621 371,700 563,250 512,250 621,000

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The present value of the cash flow for each year can be calculated by multiplying the cash flow Ci by the discount factor. A 10% discount rate has been used in these calculations. The Net Present Value (NPV) is then calculated as the sum of these discounted cash flows. Thus: NPV = C0 + 

C1 C2 C3 C4 C5 + 2 + 3+ 4 + 5 = (1 + δ) (1 + δ) (1 + δ) (1 + δ) (1 + δ)

n i =0

Ci i (1 + δ)

where n = 5 years. NPV provides a measure of the profitability of a project. If the NPV is negative, the project should not be considered. It should be stressed that NPV is not the only measure used to evaluate and screen project options. Other important factors include the magnitude of upfront investment (does the company have the capacity to raise this amount of capital?), payback period and internal rate of return (see section 2.9). Some projects promise high NPVs, however the payback period (evaluated when the cumulative undiscounted cash flow first becomes positive) might be extended, meaning that the company will need other sources of revenue in the interim to satisfy the demands of investors and shareholders.

2.9 Internal rate of return The internal rate of return (IRR) is a measure of the percentage return on investment that a project provides. It is calculated by considering the discount rate, δ, as a variable in the NPV equation, and then finding the value of δ that gives a NPV equal to zero. Thus:

0 = NPV= C0+

C1 C2 C3 C4 C5 + 2 + 3 + 4 + 5 = (1 + δ) (1 + δ) (1 + δ) (1 + δ) (1 + δ)

n i =0

Ci i (1 + δ)

For the example provided in Table 4.3, using the Excel function @IRR, the IRR can be determined to be equal to 26 %.

2.10 Annuities An annuity is a continuous cash flow of equal magnitude that is financially equivalent to either a present or future value. Loan repayments on a car loan or home mortgage can be considered a type of annuity. The debtor enters into a contract to pay back the principal (value of the loan) via equal weekly or fortnightly

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payments over a set period of time. The Excel function “@PRN” is useful for calculating annuities.

2.11 Equivalent annual cost The equivalent annual cost (EAC) is a stream of payments of constant magnitude equivalent to a Present Value. For the purchase of a physical asset, involving an initial purchase price, a salvage value and operating costs, we calculate this in the following manner: 1. First we calculate the Net Present Value equivalent to the Purchase price less the salvage value of the asset. 2. Next we calculate an annuity equivalent to the NPV. This is called the EAC of ownership of the asset. 3. We then calculate the NPV of operating disbursements, and 4. Next we calculate the EAC of these disbursements. This is called the EAC of operating the asset. 5. Adding these two equivalent annuity costs together gives the Total EAC. Figure 4.10 shows an example of such a calculation. In this case, we have calculated the EAC/km to own and operate a 4WD work vehicle. The minimum EAC/km occurs in Year 3. This is because maintenance expenses increase significantly following Year 4. This type of analysis can be used to optimise vehicle fleet operation (or the ownership 0.80 0.70

EAC/km ($/km)

0.60 0.50 0.40 0.30 0.20

EAC/km Ownership Cost EAC/km Operating Cost

0.10 0.00

Total EAC/km 0

2

4

6 Year

8

10

Figure 4.10: Equivalent annual cost (EAC) per km of ownership of a 4WD work vehicle.

12

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 113

of a private car). In the above example, the 4WD vehicles should only be owned for 3 years.

2.12 Ways of improving project NPV There are essentially four means to increase project NPV: – Increase cash flow by increasing sales, – Accelerating (bringing forward) sales by investing in additional capacity where it is warranted, – Reducing project investment costs, or – Postponing costs for renewal of capital (Figure 4.11). Accelerating the ramp-up phase of a project and bringing forward sales, (see dashed lines in Fig 4.11), improves the NPV of a project. By embedding maintenance in the design, planning and purchasing phase of a project (e.g. defining maintenance strategies, resources, spares and materials), we have greater assurance of trouble-free start-up.

2.13 Sensitivity analysis Once a spreadsheet has been established to model a project, key assumptions can be tested to determine to which variables the project is most sensitive. These variables might include, for instance, metal prices, interest rates, and cost-escalation

0

1

2

C3

C4

C5

C6

C7

C8

C9

3

4

5

6

7

8

9

Ramp-up Phase C0

C1

10

Rated Capacity

C2

Investment Phase Figure 4.11: Ways to improve project cash flow and NPV.

C10 Disposal

years

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0.7 0.6 0.5

NPV (MMUS$)

0.4 0.3 0.2 0.1 0 –0.1 –0.2 –0.3 –0.4

40

50

60

70 80 Price of copper (cUS$/lb)

90

100

110

Figure 4.12: Project net present value (in units of Million US$) of a mining project as a function of the price of copper (in units of US cents per pound (lb)).

estimates for labour and materials. Figure 4.12 shows the sensitivity of the NPV of a copper mining project to variation in copper prices as transacted on the London Metal Exchange.

2.14 Multi-attribute decision making Projects are seldom determined solely on economic criteria. Usually there are multiple attributes to consider. The current energy predicament facing Eastern Australia is a good case study. Australia needs an affordable, reliable and sustainable (low emissions) electricity supply. Thus, there are three key decision variables. As we transition to a low emissions electricity grid and consider the alternatives available to replace ageing coal-fired generators, each attribute needs to be considered with equal weighting. One way of managing this is to draw up a matrix in which the design alternatives are listed vertically, and the decision variables are listed horizontally. A qualitative or quantitative value is assigned in each cell of the matrix that reflects the degree to which the design alternative satisfies the objectives. Sometimes a simple go/no go variable can be included, reflecting a project ‘gate’. In some cases, non-equal weightings are applied to the decision variables to reflect their relative importance.

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Table 4.4: Decision matrix of three options based on affordability, reliability and sustainability. Affordability

Reliability

Sustainability

Option 1

8

9

No

Option 2

7

5

Yes

Option 3

5

7

Yes

An example of such a decision matrix is provided in Table 4.4, where three options have been numerically evaluated as regards affordability and reliability, and evaluated as regards sustainability by a yes/no criteria. In this example option 1 is ruled out because of lack of sustainability, despite the good scores on affordability and reliability. The decision between options 2 and 3 is then made depending on the relative importance of affordability versus reliability.

2.15 Cost-benefit-risk evaluation A classic means of evaluating project alternatives is to prepare a table that lists the costs, benefits and associated risks of each project alternative. This table should start with the ‘do nothing’ alternative, i.e. what are the costs and risks of doing nothing and allowing the current situation to prevail? For example, consider a project to install instrument assisted landing equipment on a country runway. The do-nothing alternative means that aircraft cannot land at night or during other times of poor visibility. For the Royal Flying Doctor Service this could mean the difference between saving a patient’s life, and incurring a fatality. Is the local council prepared to accept this risk? When considering engineering projects, two elements of risk need to be considered; technical risk and implementation risk. Technical risk deals with risk associated with the technical solution proposed by a project. Is proven technology being proposed or does the proposed solution employ technology that is novel and has previously only been demonstrated in a limited range of applications? Implementation risk considers the degree of difficulty that the project may incur with acceptance by stakeholder groups. For example, a nuclear power station is a feasible technological solution to Australia’s energy problems. However, the implementation risks associated with building and operating a commercial nuclear power  station in Australia are considerable, and would probably deter many manufacturers.

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3 Financing projects Just as value can be created, it can also be destroyed through inattention to key risks and resulting liabilities. Projects ‘add’ or ‘protect’ value in three ways: – Value creation (projects that enhance production volume or quality) – Value protection (capital work to reduce risk and resulting liabilities) – Asset valuation (e.g. valuing a building, land or mineral reserve) Value creation (or value added) is often measured in terms of increased NPV. However, a number of other financial measures can be useful. The first of these is Economic Value Added (EVA), defined as the net cash flow after taxes less the cost of the firm’s capital (Capital employed × WACC) [7]. Economic Value Added differs from ‘Value Added’ in that it includes the effects of taxation and depreciation. The second is Return on Capital Employed (ROCE), defined as the net profit after taxes divided by the capital employed. This is a measure of the efficiency with which capital is used to generate value.

3.1 Debt and equity financing Figure 4.13 illustrates these metrics in practice. Value added is defined as cash flow less the capital employed multiplied by the weighted average cost of capital (WACC). A firm has two options for raising capital. These are: debt or equity financing. Debt involves approaching a bank or financial institution to apply for a business investment loan. Let’s suppose that the interest rate on the resultant loan is 6%. Thus the debt rate is 6%.

Value created

=

Free Cash Flow

‒

Capital employed

×

Weighted Average Cost of Capital (WACC)

1. Income

2. Production costs 3. Working capital 4. Sustaining CAPEX

5. Fixed assets

Responsibility of Operations

Figure 4.13: Elements of a firm’s value creation [6].

Cost of debt Cost of equity

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Equity financing involves issuing shares on the stock market, often via an Initial Public Offering (IPO, sometimes also referred to as an initial purchase offer). Servicing these shares is done via annual dividends. The dividend, or equity rate is the dividend amount divided by IPO share value. The equity rate is determined by the company, by the amount of the dividends paid by the company. The company determines the amount paid in dividends to be such that the equity rate is less that the debt rate that the company can obtain at its bank. In our example above, let’s suppose that the equity rate for the firm is 4%. In addition to dividend payments, investors hope to make money by speculating on the capital value of their shares as they hopefully appreciate over time. Companies are prohibited from financing a venture 100% from equity. Usually, stock market investors will demand a reasonable ratio of debt/equity. This means that the firm has to raise part of its project capital from a bank or other financial institution, which means that the project has satisfied due diligence by that financial institution. This provides investors greater confidence of assured risks when purchasing shares. Returning to our example, suppose that the firm raises 50% of its project finance via debt with the remaining 50% is financed by equity. The resulting Weighted Average Cost of Capital (WACC) is equal to 0.5 x 6% + 0.5 x 4% = 5%. If the company has $1 billion of fixed plant assets, it is paying $50 million per year in costs for this capital.

3.2 Opportunity cost Companies (and individuals) with capital to invest are often faced with multiple alternatives that compete for the investment capital. If we suppose that a company has $1 million to invest, then the simplest option would be to invest in government bonds at a nominal rate, say 3%. Bonds are regarded as a stable, low risk investment as central banks rarely default on loans. By not investing in bonds, the company is forgoing a potential income of 0.03 x $1 million = $30,000 per year. This is known as the ‘opportunity cost’ of an investment. Of course, there are multiple opportunities for investment, each associated with different levels of risk. Standard practice, therefore, is to measure the opportunity cost as the product of the investment capital and the discount rate.

3.3 Stock market performance indicators Investors trade stocks on the principal bourses (stock markets) of the world. Stocks are purchased with the intention of making money from two mechanisms: (i) annual dividend returns as part of the profits of a company are shared with investors and (ii) capital growth in shares as investor demand increases over time.

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The rate at which shares increase on average in price is measured by performance indicators such as the Dow Jones Industrial Average, Standard and Poor’s 500 index, or the NASDAQ composite index. The Dow Jones Industrial Average is a weighted index that includes the stocks of the 30 largest companies in the United States (Investopedia, 2017). The S&P 500 Index is a larger, more diverse index made up of 500 of the most widely traded stocks in the U.S. It represents about 80% of the total value of US stock markets [8]. The NASDAQ is a market exchange on which technology stocks are traded. It includes both large and small firms. The ‘yield’ of individual shares is measured by a Price to Earnings ratios (PE ratio). This is the measure of the current purchase price of the share divided by the last declared dividend payment. Shares that have a high PE ratio effectively provide lower rates of dividend returns. This is typical of many ‘blue chip’ companies. These are companies that are well-established. Investors and large pension funds purchase blue chip stocks because they are low risk investments that provide a secure source of income. Over time, one can expect to make money as a result of the capital growth of such shares. Stocks having low PE ratios provide a higher dividend return, however, these stocks are often subject to greater price volatility and can represent greater risk to investors.

3.4 Measuring productivity Productivity is a measure of how efficiently a company (or country) turns factor inputs into useful outputs. Labour productivity is a measure of how efficiently labour is used to produce products and services. This includes a company’s own labour as well as contractor labour sources. Multi-factor productivity includes labour, capital, land, materials and energy inputs. It is also known as ‘total factor productivity’. It is calculated by converting the output and input into dollar terms. In recent years there has been much discussion regarding the decline in productivity experienced in the mining industry in Australia. Figure 4.14 illustrates the trend in labour, capital and multifactor productivity in the mining sector over the last few decades. Labour productivity is measured as the ratio of industry output (total product sales) and the cost of labour inputs (and multiplied by 100). Capital productivity is the ratio of output value over capital employed (this latter is measured as the difference between company asset value and liabilities) (and multiplied by 100). Multifactor productivity is the ratio of output value over the combined dollar value of labour and capital inputs (and multiplied by 100). Note that these measures of productivity depend on the price obtained for the products, i.e. for mining the commodity prices. It is interesting to note that both labour and capital productivity declined markedly over the period of the mining investment boom from 2002 to 2012. There are a number of factors that help explain this trend, including deeper, aging surface mines, necessitating the removal of more waste material in order to access ore, as well as

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205 185

Index Value

165 145 125 105 85 1990

1995

Capital productivity

2000

Year

2005

Labour productivity

2010

2015

Multifactor productivity

Figure 4.14: Indexes of Productivity in Mining [9].

declining head grades, which means that more material must be mined just to maintain production levels. In addition, there was a contribution from the massive injection of new capital (to open up new projects) with little immediate increase in output (that takes years to occur). This helps to explain the decrease in capital productivity and the simultaneous decrease in labour productivity, as there was also a massive increase in labour for the creation of new mines, but these had not increased production by a corresponding amount. Note that since 2012, the industry has experienced a turn-around with regards to labour productivity as a relentless focus on efficiency has reversed to the long run trend. This reflects a decrease in workforce numbers (from producing mines) as well as a decrease in the workforce constructing new mines. These measures of productivity are useful economic indicators at the level of an entire industry or country. These measures of productivity are somewhat abstract because they incorporate a number of underlying relationships. Nevertheless, they are useful as a point of comparison or benchmark and are of interest to politicians, economists and chief financial officers (CFOs). They do provide a comparison of capital and labour. For an individual project or enterprise, different metrics may be more useful.

3.5 Solow’s law Robert Solow is an American economist and Nobel Prize winner who developed a model in 1956 to describe economic development. The model was also developed inde-

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pendently in 1956 by Trevor Swann, and so is more accurately known as the SolowSwann model. Essentially, the model links GDP growth to three factors; labour (L), capital (K) and knowledge, or technological growth, (A). The Solow model holds that knowledge is a means of upskilling and augmenting the effectiveness of labour. Thus the model for growth is: GDP = Ka x (AL)1-a where a is the elasticity of output according to capital investment (0 < a < 1). Note also that K, L and A are functions of time. This model converges asymptotically to a steady state. The model is used principally as a policy development tool, and has both short and long term implications. In the short term, growth is determined by moving to a new steady state that can be achieved through change in capital investment, labour force growth or depreciation [10]. In the long term, growth is only achievable through education and technological progress, to increase the factor A.

4 Glossary Annuity: A series of equal cash flows over a fixed time period. CAPEX: An abbreviation of ‘Capital Expenditure’ Cash Flow: A sequence of profit or loss outlays resulting from a project Complement: A good or service that is correlated to another. For example, car parking fees will likely increase if motor vehicle sales increase. Cost: The value of materials and labour required to manufacture a product or deliver a service. CPI rate: Consumer Price Index, simply known as inflation. The rate at which a standard basket of goods and services increases in price over time. Demand: The quantity of that good or services that people are willing to purchase. Demand curve: A relationship that shows how much people are willing to pay for a good or a service as a function of its relative scarcity or abundance. Discount factor: The factor that need to be applies to a cash flow in year n in order to discount it to the present year. Discount rate: A project hurdle rate that must be satisfied for the investment Discounted cash flow: Application of the discount factors to a sequence of cash flows. Disposal: Sale or retirement of physical plant. Equivalent annual cost: A stream of payments of constant magnitude equivalent to a Present Value.

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Equivalent annual cost – operating: The portion of equivalent annual cost related to operating costs. Equivalent annual cost – ownership: The portion of equivalent annual cost related to ownership costs. Free cash flow: Income less operating costs (this does not include the costs of capital, nor interest nor depreciation). Free on board: The cost of supplying bulk minerals including all costs from the mine until the mineral is loaded onto a ship. Internal rate of return: A measure of the percentage return on investment that a project provides. Investment phase: A period in which cash flow is negative due to the design, planning and construction of an engineering project. Libor: London interbank offered rate: the rate that London banks charge other banks to loan money. Market concentration: A measure of the percentage production share of the largest companies. Net present value: The cumulative sum of the Present value of all cash flows over the life of a project. OPEX: An abbreviation of ‘Operating Expenditure’ Present value: The value of a cash outlay in year n discounted to the present year. Price: The amount received by a vendor for the concept of sale of a product or service. Price elasticity: How price varies according to demand. High elasticity means prices vary dramatically whereas low elasticity means prices are more or less constant with demand. Ramp up phase: The time that it takes a new plant to reach rated capacity once it is commissioned. Rated capacity: The desired capacity at which a plant should produce. Also called ‘name plate’ capacity. Revenue: Income (resulting from sales) Salvage value: The residual, or scrap, value of an asset (usually equipment). Second tier resource: Typically a resource that requires high costs to mine so that the resource is only economical if high prices are possible for the mined ore Sensitivity analysis: An analysis to determine how robust is the NPV of an engineering project in the case of key assumptions changing. Substitute: An alternative good or service that performs a similar function (e.g. aluminium and copper both conduct electricity and heat) Supply: The quantity of a good or service available for sale. Supply curve: A relationship that shows the cumulative cost of producing a good or service. Total equivalent annual cost: The sum of the operating and ownership costs. Weighted cost of capital: The effective rate that a company pays to access capital. It includes a relative weighting of debt and equity finance.

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5 Study questions – – – –

Explain how price equilibrium occurs, and what happens if supply is subsequently increased. Explain how a company can use its market power. Calculate the net present value for the example in Table 4.3 using a 12% discount rate. Calculate the return on capital employed for the example in Table 4.3 assuming a company tax rate of (a) 20% and (b) 40%.

References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

Maxwell P. Lecture notes prepared for “MEA Mine Management” course, 2007. Tilton J. Economics of the mineral industries. In: Hartman HL, editor. SME mining engineering handbook. Littleton: Society for Mining, Metallurgy, and Exploration, 1992, 47–62. The Dutch Disease. The economist, 26 November 1977:82–3. Löf A, Ericsson M. Iron ore market report – 2016. Eng Min J 2016;5 March 2017. Reserve Bank of Australia. Statement on monetary policy. August 2014. Adams RG. How the cost culture of the mining industry destroys shareholder value. CRU World Copper Conference. 21 March 2002, Santiago, Chile. Harper D. EVA: pulling it all together: investopedia, 2017. Available at: https://www. investopedia.com/university/eva/eva4.asp. Accessed 22 August 2017. Schick K. An introduction to stock market indices: investopedia.9 April 9 2017. Available at: https://www.investopedia.com/articles/analyst/102501.asp. Accessed 9 April 2017. Australian Bureau of Statistics. Estimates of industry multifactor productivity Australia, series 5260.0.55.002 table 24, December 2015. Solow–Swan model: Wikipedia. Available at: http://en.wikipedia.org/wiki/Solow–Swan_ model. Accessed 24 Aug 2017.

John Quiggin

The economics of climate change Abstract: The problem of climate change is one of the most critical issues facing modern societies. Economics is crucial in assessing the costs and benefits of various proposed responses to climate change, and in designing policies that encourage the adoption of the most cost-effective responses. In particular, economic analysis is needed to assess the appropriate balance between adaptation and mitigation, between price-based policies and regulation, and between current costs and future benefits. Key concepts: Climate change; mitigation and adaptation; uncertainty; discounting; externalities; common property; taxation; regulation. Key ideas: 1. There has been a clear global warming trend since 1970, which prompted the formation of the Intergovernmental Panel on Climate Change (IPCC), tasked with providing the science of human-induced climate change. 2. There is variability between climate models because of the complexity of the climate. Different models use different simplifying assumptions, and different approaches to the modelling of the atmosphere of the earth. Nevertheless, all models indicate an increase in global temperature with an increase in atmospheric CO2. 3. Economists characterise the problem of climate change as an example of market failure. 4. Polluters avoid costs that are borne by society as a whole. These costs are called externalities. Pigou suggested that these externalities should be taxed, i.e. the tax should equal the cost caused by the pollution. 5. Property rights leads to the idea of tradable permits to emit a certain amount of CO2. 6. Regulation can be used is some cases of market failure, but works best in simple cases.

1 Introduction and scope Climate change is one of the biggest problems facing the world today, and also one of the most complex. The most prominent attempts to synthesise the evidence on the central issues are the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC), the most recent of which appeared in 2014 [1], consisting of thousands of pages of text, linking to many more thousands of journal articles, https://doi.org/10.1515/9783110535129-005

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books and reports. It is a challenging task to read the report as a whole, and clearly impossible for any one person to read and understand more than a tiny fraction of the associated literature. The combination of human activities and natural processes that produces climate change is a highly complicated process. A wide variety of interactions take place between socioeconomic, biological and atmospheric systems to produce outcomes that are subject to a great deal of uncertainty. Hence, it is natural to speak of a complex system. The analysis of the system requires perspectives from the natural sciences and social sciences, integrated with an understanding of the engineering and technological challenges of decarbonising the economy and adapting to the climate changes that are already inevitable. In any assessment of the problem of climate change and the appropriate policy responses, economics plays a crucial role, along with other social and natural sciences and applied disciplines, notably including engineering. Economics is crucial in assessing the costs and benefits of various proposed responses to climate change, and in designing policies that will encourage the adoption of the most cost-effective responses. Among the many questions that must be addressed are: – What are the likely consequences of climate change? – How should we deal with uncertainty about events decades into the future? and – What are the best policy responses? These and other questions will be addressed in this chapter.

2 Background The fact that increased concentrations of carbon dioxide (CO2) arising from the combustion of fossil fuels could reduce the radiation of heat from the earth’s atmosphere into space, and thereby increase equilibrium temperatures was first observed by the Swedish physicist Arrhenius in 1896, who expected the effects to be beneficial (perhaps a natural view for a resident of a country where cold was the main constraint on crop production). For much of the mid-20th century, warming due to greenhouse gas emissions was offset by a combination of natural fluctuations and the cooling effects of other pollutants, such as particulates. From the late 1970s onwards, however, a clear warming trend emerged, as shown in Figure 5.1, which reports the deviation between the global mean temperature (averaged over land and ocean-based measurements) and the average for the period 1951–80. The line is a 5-year average, which shows the trend more clearly. The error bars represent measurement uncertainty. By 1988, there was sufficient evidence of warming to prompt the foundation of the Intergovernmental Panel on Climate Change (IPCC) which was required to provide ‘the scientific, technical and socio-economic information relevant to understanding

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0.8

Temperature Anomaly (°C)

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Annual Mean 5-year Running…

0.2 0 –0.2 –0.4 –0.6 1880

1900

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1940 1960 Year

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Figure 5.1: Mean global temperature since 1880 (deviation from 1951-80 mean) (data from NASA [2]).

the scientific basis of risk of human-induced climate change, its potential impacts and options for adaptation and mitigation.’ The IPCC has produced five Assessment Reports, the most recent of which was completed in 2014. International agreement on climate change is sought through the United Nations Framework Convention on Climate Change (UNFCCC). At the most recent Conference of the Parties held in Paris in 2015, it was agreed to seek policies that would: Hold the increase in the global average temperature to well below 2 °C above pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5 °C above pre-industrial levels, recognizing that this would significantly reduce the risks and impacts of climate change;

and also Increase the ability to adapt to the adverse impacts of climate change.

The aim of mitigating the increase in global mean temperatures is pursued through policies to limit emissions of greenhouse gases, of which the most important is CO2. It is common to refer to a monotonic relationship between concentrations of greenhouse gases and equilibrium temperatures, such that a stable concentration of 450 parts per million (ppm) is associated with equilibrium warming of 2 °C. Since there is still considerable uncertainty about global climate sensitivity, it is more precise to say that the achievement of a given cumulative emissions target changes the probability distribution of the increase in global mean temperatures. This point is illustrated in Figure 5.2, which shows a range of estimates of the probability that equilibrium warning will exceed 2 °C. As shown in Figure 5.2,

Net Anthropogenic

1.95

2.58

400

GHG only

3.65

4.12

4.54

600

Climate Sensitivity PDFs (trunkated at 10K): Andronova & Schlesinger (2001) with sol&aer forcing Forest et al (2002) - expert priors Forest et al (2002) - uniform prior sensitivity Frame et al (2005) - uniform prior observables Frame et al (2005) - uniform prior sensitivity Gregory et al. (2002) Knutti et al. (2003) Knutti & Meehl (submited) Murphy et al. (2004) Piani et al. (submitted) Wigley & Raper (2001)

3.14

450 500 550 CO2 Equivalence Stabilization Level (ppm)

Present (2005) Forcing (best-estimate)

0% 350

10%

20%

30%

40%

50%

60%

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Probability of Staying below 2°C in Equilibrium

Figure 5.2: Probability of global temperature change greater than 2 °C for given stabilisation levels for CO2 equivalents (from IPCC WG II, 2007) [3, 4].

Probability of Exceeding 2°C Target in Equilibrium

Radiative Forcing Stabilization Level (W/m2) very unlikely unlikely medium likelihood likely very likely

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climate models differ in their estimates of the likely impact of stabilising atmospheric concentrations of CO2 and other greenhouse gases on equilibrium temperatures. The upper horizontal axis shows the energy forcing associated with a given stable concentration of greenhouse gases. The red arrows above the axis show current levels of forcing. The upper arrow represents the forcing due to greenhouse gases alone, while the lower arrow shows the effects of all human emissions into the atmosphere. Because particulate emissions have a net cooling effect, the lower arrow is shorter. The vertical axes show the likelihood of exceeding, or staying below, 2 °C of warming in equilibrium, expressed in numerical (left axis) and verbal (right axis) terms. The trajectories on the graph are derived from a range of climate models. There is a similar range of uncertainty surrounding the effects of global warming on human and natural systems. Figure 5.3, sometimes referred to as the ‘burning embers’ diagram illustrates this. The figure illustrates five different kinds of impacts of warming, with the severity represented by the intensity of the red shade. Categories I and II (risks to unique and threatened systems, and extreme climate events) show significant effects even for the modest warming (slightly less than 1 °C) that has already taken place. This is consistent with actual experience in Australia, which includes an increase in the severity of droughts and coral bleaching events in the Great Barrier Reef.

2100

Future Increase in Temperature (°C)

6 5

Risks to Many

Large Increase

4 3 2 1 0 –1

Risks to Some

I

Increase

II

Negative for Most Regions

Net Negative in All Metrics

Positive or Negative Market Impacts; Majority Negative of People for Some Adversely Regions Affected

III

IV

Higher

2075

2050

2025

Very Low

V

1990 Range of Future Scenarios and Uncertainties

Figure 5.3: Risks and impacts of climate change; I: Risks to unique and threatened systems; II: Frequency and severity of extreme climate events; III: Global distribution and balance of impacts; IV: Total economic and ecological impacts; V: Risk of irreversible large-scale and abrupt transitions [5, 6].

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This process is complicated by a mixture of lags and both positive and negative feedbacks of various kinds. Detailed discussion is beyond the scope of this chapter. However, two critical points are: – Lags mean that the equilibrium change in temperature will be reached some decades after atmospheric concentrations stabilise; and – Feedback effects mean that reducing global mean temperatures after they have reached a new high equilibrium will be very difficult. To sum up the discussion as a whole, higher CO2 atmospheric levels, particularly above 450 ppm, are likely to lead to a temperature increase above 2 °C, which will increase the probability of harms as illustrated in Figure 5.3.

3 Mitigation and adaptation Discussion of responses to climate change has focused on the options of mitigation and adaptation. Here ‘mitigation’ refers to actions designed to reduce and slow down global warming, most importantly by reducing net emissions of greenhouse gases. ‘Adaptation’ refers to actions taken in response to climate change, such as relocation of buildings in response to rising sea levels. Mitigation and adaptation have frequently been presented as polar alternatives, with some opponents of action to stabilise the global climate arguing that it would be more cost-effective to focus on adaptation. However, mitigation and adaptation are not exclusive alternatives, and will, in many cases, be strategic complements [7]. Even with action to stabilise atmospheric concentrations of CO2 at or near current levels, climate change will continue for some decades, and adaptation will therefore be necessary. Conversely, as will be shown in this paper, most adaptation strategies are feasible only if the rate and extent of climate change is limited by mitigation. Examples of adaptation include: – changes in urban planning to take account of rising sea levels; – changes in the design and management of irrigation systems [8]; and – migration away from areas made uninhabitable, or less habitable, by climate change.

4 Change, variability, uncertainty and complexity Climate is always variable, over many time scales. This makes the task of detecting a change in the global climate system difficult, and that of predicting future changes even more so. The problem of uncertainty in climate change policy takes many forms.

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4.1 Model uncertainty A large number of global climate models have been constructed by different groups of researchers. All such models share the same general form, consisting of a large system of differential equations designed to simulate long-term changes in atmospheric and ocean systems. These equations are converted to discrete form to derive a grid representing the entire global system at a resolution determined by limits on data and computational capacity. A summary of the modelling literature is provided in the IPCC 5th Assessment report [1]. There are a large number of choices that must be made in constructing such a model. These include choices of functional form for equations, specification of variables, and the details of the process of discretisation and estimation. Inevitably, different choices lead to different results. On the other hand, the requirement for consistency with the observed data and with fundamental physical principles constrains the extent to which model predictions can differ [9]. All climate models predict a substantial increase in global temperatures in response to a doubling of atmospheric CO2 concentrations.

4.2 Parameter uncertainty The parameters of any model are estimated with reference to the available data. Given a finite data set, parameters are inevitably estimated with error, and this error creates uncertainty with respect to predictions. The crucial parameter in a global climate model is climate sensitivity, that is, the sensitivity of equilibrium global temperature to a given change in ‘forcing’. Forcing is the heating effect derived from changes in the concentration of greenhouse gases or other sources. Sensitivity is conventionally measured as the equilibrium response of average global temperature to a doubling of the total forcing derived from greenhouse gases, measured in CO2-equivalent parts per million. This is a useful basis for discussion since continuation of ‘business as usual’ policies is likely to generate a doubling of CO2-equivalent concentrations from the pre-industrial level by around the middle of the present century. It is important to interpret climate sensitivity carefully. On the one hand, it is an equilibrium measure, so the estimated change in temperature will not take place immediately. On the other hand, under ‘business as usual’ policies, there is no reason to expect that CO2 concentrations will stabilise at twice the preindustrial level. A variety of estimates of climate sensitivity have been presented, some as point estimates, and some with a range of uncertainty.

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4.3 Impacts uncertainty Even assuming that future changes in temperature could be projected with certainty, there would be considerable uncertainty about the costs and benefits. The largest economic impacts of climate change are likely to be those affecting agriculture. Surveying the literature on this topic, Quiggin [10] notes: Analysis of the impact of climate change on agriculture raises yet more complexities. The effects of changes in temperature and climate will vary across different regions, so that climate change will be beneficial in some areas and harmful in others. It is necessary to take account of adaptation to climate change, and therefore to take account of both the pace of change and the impact of uncertainty on human behaviour. Finally, to reach an economic evaluation of the impact of climate change, it is necessary to aggregate changes taking place in different parts of the world, at different times ranging from the present to at least the middle of this century, and affecting different people, some of them not yet born.

Most importantly, the two largest contributors to plausible estimates of expected damage are also the hardest to evaluate, though for different reasons. These are: the damage to natural ecosystems likely to occur with any significant increase in global temperatures over the next century or more; and the possibility of a catastrophic outcome, arising from some combination of high climate sensitivity and unexpectedly large feedbacks, yielding temperature increases of 5 °C or more. There is little doubt that global climate change has already affected vulnerable ecosystems. These effects will become more severe, even at the relatively modest rates of warming that could be achieved with aggressive mitigation programs. The consequences of warming of 3 °C or more, virtually inevitable under ‘business as usual’ policies, are hard to predict, and equally hard to value.

4.4 Emissions uncertainty Perhaps the most important single source of uncertainty, in forecasting likely climatic conditions in the future, relates to future growth of, or reductions in, emissions in CO2 and other greenhouse gases. Some ‘business as usual’ projections imply continuing growth in emissions, broadly in line with growth in income [1]. By contrast, policy proposals currently under discussion call for reductions in emissions of 50 to 90 per cent, relative to current levels, by 2050 [11, 12]. The relationship between climate change and uncertainty about emissions is complicated by the fact that the policy choices that will help to determine future growth in emissions are themselves a response to projections of future climate change. For some purposes, such as planning for adaptation to climate change, the primary concern is to predict future climate change as accurately as possible, taking

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account of all relevant factors. From this perspective, the adoption or rejection of policies to reduce emissions is just one more factor to take into account. By contrast, in discussing climate change mitigation, we are comparing the outcomes of alternative courses of action. A simple equation helps to illustrate the uncertainties involved in projecting emissions of CO2 from energy generation, the most important single source of greenhouse gases. Emissions = Population × Output per person × Energy Intensity of Output × Emissions Intensity of Energy Hence the rate of change of emissions is equal to the sum of the rates of change of the variables on the right-hand side. Most ‘business as usual’ projections assume that: global population will stabilise at around 9 billion after 2050; output per person will grow at a rate of around 2 per cent per year; and energy intensity of output will decline as incomes rise, but that energy use per person will continue to increase. Projections of the emissions intensity of energy use in the absence of policy intervention vary widely, with some projections suggesting continued reliance on fossil fuels, most notably coal, while others suggest that exogenous technological innovations will lead to the displacement of coal by alternative energy sources.

4.5 Fabricated uncertainty Many of the sources of uncertainty described above are common to all forecasts and projections. However, the typical aim of policy analysis is to reduce uncertainty as far as possible, and thereby to permit the formulation of policy on the basis of the best available evidence. Unfortunately, many participants in the debate about climate change are not concerned to reduce uncertainty, but rather to increase it, with the objective of preventing or delaying policy responses to which they object, either on ideological grounds or because their economic interests will be harmed. The scientific literature on climate change is virtually unanimous regarding the validity of the mainstream model and those seeking to manufacture uncertainty (commonly self-described as ‘skeptics’) have not undertaken significant peer-reviewed research to justify opposing conclusions. Rather they have attacked climate scientists and science itself through a range of think tanks, ‘Astroturf’ organisations, and articles in the mass media, on blogs and through other media. In the Australian debate, the attempt to fabricate uncertainty with respect to the science of global warming has been documented by Hamilton [13] and Pearse [14].

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5 Discounting Action to mitigate climate change must be taken now, but the effects of mitigation will not be felt until decades or centuries into the future. Mitigation involves making a trade-off between present costs and future benefits. The crucial idea here is that of opportunity cost. To make such a trade-off it is necessary to specify a discount factor βt for each future date t, enabling the conversion of future benefits into present values. For example, suppose that at time t = 0 (the present) the discount factor for time t = 20 is β20 = 0.5. This means that a benefit of $1 received in 20 years’ time has a present value of $0.50. A typical project, such as a climate mitigation project, involves a cost C incurred in the present t = 0, and generates a stream of benefits vt at times t = 1, 2, … into the future. The present value of the benefits is PV = ∑ βtυt t

where PV is present value (at time t = 0); t is time period; βt is the discount factor; and vt is net return in period t. The most common approach to calculating the discount factor is exponential discounting, based on the use of a constant discount rate δ with βt = (1 + δ)−t So, the standard present value calculation becomes: PV = ∑ (1 + δ)−t υt t

where PV is present value (at time t = 0); t is time period; δ is the discount rate; and vt is net return in period t. The higher the discount rate δ, the less future benefits matter. A useful mnemonic is the ‘Rule of 72’ for annual discounting (or 70 for continuous discounting, reflecting the fact that ln 2 ≈ 0.70). Dividing 72 by the discount rate δ gives the number of years in the future by which a benefit of v will have a present value of v/2, that is, when discounting reduces the value of future benefits by half. As an example, suppose that the discount rate is 4 per cent. Then, following the Rule of 72, a sum of $1000 to be received 72/4 = 18 years in the future is worth $500 today. The same sum received 36 years in the future is worth $250 today. Turning this around, an investment of $250, invested at 4 per cent compound interest will be worth $500 in 18 years’ time and $1000 in 36 years’ time. As this example illustrates, discounting is an application of the principle of opportunity cost. The discount rate is normally positive. This reflects the fact that investments in climate mitigation could alternatively be directed to other long-lived projects that would yield positive rates of return into the future, and the closely related implica-

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tion that living standards will be higher in the future than they are now. However, evaluation of extreme adverse scenarios involving catastrophic climate change may involve negative discount rates, because, in these scenarios, living standards decline and investments yield negative net returns.

5.1 Discounting and expected utility theory Assuming that the combination of the expected utility model and inherent discounting (discussed below) captures all the issues under consideration, the riskless social discount rate, r, is determined by a simple formula r = δ + ηg where g (%) is the rate of growth of consumption per person, δ (%) is the inherent discount rate, and η (%) is the elasticity of the marginal utility of consumption, each discussed below. A similar, slightly more complex formula can be used to derive the rate of return for a risky asset, based on its correlation with aggregate consumption. Debate about the appropriate choice of discount rate was stimulated by the Stern Review [12], in which a real discount rate of 2.1 per cent was proposed. In assessing Stern’s choices, it is useful to consider each of the parameters, δ, η and g in turn. The inherent discount rate, δ, has been the subject of one of the longest running controversies in welfare economics. This controversy concerns the appropriateness of applying different weights to people in different generations, and, more generally, of discounting future utility, whoever receives it. Ramsey [15], whose work is the starting point for formal analysis of intertemporal choices, rejected inherent discounting as ethically unjustified, and this viewpoint is shared by most philosophical advocates of utilitarianism. On the other hand, a good deal of evidence suggests that individuals tend to discount their own future consumption. Before discussing inherent discounting, it is worth observing that standard expected utility theory suggests one reason for discounting future consumption; namely the possibility that we will not be around to enjoy it. As individuals, we face a typical annual mortality risk of around 1 per cent, and it makes sense to discount future utility by this amount. But, at least some of the time, people (most notably teenagers) discount the future by much more than this. For society as a whole, there is a comparable risk arising from the possibility of nuclear annihilation, a killer meteor and so on. The risk need not involve a total extinction of the species; it is sufficient that the disaster be great enough that ‘all bets are off’ in terms of calculations about the future. With this point addressed, there remains the question of whether we do, and whether we should, discount future utility. The evidence on individual behaviour is far from clear. Individuals may allocate resources between activities and follow inconsistent rules in different activities. For example, the same person may allocate money

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to an automatic saving scheme offering low or even negative real returns, while displaying hyperbolic discounting with respect to the remaining cash flow. Leaving such phenomena to one side, the evidence for high inherent rates of discount is not strong. The most obvious market measure to use in assessing intertemporal trade-offs is the real rate of interest on low-risk bonds (government or AAA corporate). This rate has generally been between one and two per cent and is currently around two per cent. Given that the rate of growth of average consumption per person is between one and two per cent, this is consistent with zero discounting (i.e. δ = 1) and η = 1. Even if individuals do display inherent discounting, that does not necessarily mean that this is appropriate as a basis for social decisions. Future individuals presumably will not share the view that utility in our time is inherently more valuable than utility in theirs. In fact, as individuals, introspection and casual observation suggest that we generally regret decisions made in the past on the basis of inherent discounting. Such decisions represent selfishness on the part of our past selves at the expense of our current selves, analogous to individual selfishness with respect to others. The case against inherent discounting is summarised by DeLong [16]: A δ of 3% per year is unconscionable – it means that somebody born in 1970 “counts” for twice as much as somebody born in 1995, who in turn “counts” for twice as much as somebody born in 2020.

A crucial point, often overlooked in discussions of intergenerational equity, is that members of different generations are alive at the same time. Any policy that discounts future utility must discriminate not merely against generations yet unborn but against the current younger generation [8]. However, as discussed above, it may be appropriate to include a small inherent discount factor to account for fundamental uncertainty about the future. It is possible that some future event will render all of our calculations irrelevant. This event might be a disaster (such as a nuclear war) or a windfall (such as the discovery of a costless method of removing CO2 from the atmosphere, thereby making it unnecessary to reduce emissions). Stern incorporates a pure discount rate of 0.1 per cent to allow for this possibility [12]. The parameter η, the elasticity of the marginal utility of consumption, represents the proportional rate at which the marginal utility of consumption is reduced as consumption increases. The choice of η is central to the debate over discounting. High values of η imply a high preference for current consumption, high aversion to risk, and large benefits from redistribution. Even economists familiar with the mathematical derivation of η often have problems understanding the implications of different choices of η, particularly when time, uncertainty and interpersonal redistribution interact. It may be useful to consider the most common single choice, η = 1, representing logarithmic utility. There is a natural way of interpreting logarithmic utility in the intertemporal context. With this specification (and ignoring inherent discounting as discussed below) one per cent of consumption now has the same value as one per cent of consumption at any time in the future. For example, a policy that reduced consumption

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(not the rate of growth of consumption!) by one percentage point from 2000 to 2050, relative to some baseline, then increased consumption by one per cent relative to the same baseline until 2100, would come out exactly neutral. The rate of growth of consumption, g, is derived from economic projections. Typical values of g are between 1.5 per cent and 2 per cent. Given δ close to zero %, η=1 % and g between 1.5 per cent and 2 per cent, the implied discount rate r must also be between 1.5 and 2 per cent. This is very close to the real rate of interest on government bonds, observed over long periods. To sum up, there is no justification for ‘inherent discounting’, that is, discounting future benefits simply because they are realised in the future. Nevertheless, it is generally appropriate to discount future benefits for two reasons. First, as consumption increases over time, the marginal value of additional increases declines. Second, any calculation of future benefits may be rendered irrelevant by unforeseen events. In relation to climate change, a discount rate of 1.5 to 2 per cent appears appropriate.

6 Costs and benefits of climate stabilisation 6.1 Costs of inaction The benefits of stabilising climate are best thought of in terms of the damage that would be incurred in the absence of such stabilisation. Unfortunately, the most difficult task in any economic analysis of climate change is the valuation of the damage likely to be incurred as a result of climate change, particularly under ‘business as usual’, or other scenarios where the eventual concentration of CO2 is well above the pre-industrial level. The range of uncertainty is huge. The possibility of catastrophic climate change, with warming of more than 6 °C, raises different difficulties. Estimation of the expected value of low-probability events is always problematic. Subtle characteristics of the distribution of temperature changes, such as kurtosis (the fourth moment of a distribution, which characterises ‘fat tails’), can result in the absence of any finite value for expected damage [17]. Quiggin shows that the top decile of the probability distribution for warming, commonly described as ‘very unlikely’, accounts for at least half of the expected damage associated with global warming in scenarios with substantial mitigation, and even more in the case of ‘business as usual’ [18]. This extreme uncertainty makes it impossible to specify an accurate estimate of the likely costs of warming. Quiggin estimates that the cost of allowing CO2 concentrations to stabilise at 550 ppm is negligible for low values of climate sensitivity but may be in excess of 50 per cent of income for high values [18]. Quiggin gives a mean estimate of 15 per cent relative to stabilisation at 450 ppm [18].

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6.2 Stabilisation costs Much discussion of stabilising the global climate reflects one of two errors. Some assume that the problem can be solved by ‘no regrets’/voluntary action. Others, going to the opposite extreme, assume that reducing or eliminating CO2 emissions implies the end of industrial civilisation as we know it. Many people are intuitively convinced that since everything we do uses energy, large reductions in energy use can only be achieved at the cost of large reductions in living standards. Economic analysis says the opposite. Typical estimates of the cost of such reductions are in the range 0.5–5 per cent of income. Such a reduction in income, by 2050, would not be noticeable against the background noise of macroeconomic and individual income fluctuations. On plausible projections, it would mean average income would double by 2051 or 2052 instead of by 2050. It’s easy to do a ‘back of the envelope’ check on these numbers. Suppose that a complete replacement of fossil fuels with renewable energy, including storage where necessary, and making no other changes, would double our energy cost. Since energy costs account for around 5 per cent of GDP, the extra cost would be 5 per cent, which is equal to the upper bound of the range 0.5 to 5 per cent. However, if energy costs were doubled, we would take advantage of all kinds of options for improved energy efficiency. So, the likely final cost would be less than 5 per cent, and perhaps as low as 0.5 per cent. To get an idea of the amounts we’re talking about, Australian national income is currently about $1.5 trillion a year, so 1 per cent is $15 billion a year. For the world as a whole, income is around $US50 trillion, so the corresponding figure is $US500 billion. What kinds of policies and events fit into this scale? – A typical recession reduces GDP by around 3 per cent relative to its trend value. – The amount promised by rich countries in development aid is 0.7 per cent of their income. – Chronic national budget problems such as those now facing Australia typically involve a gap between revenue and expenditure of 1–5 per cent of national income. One problem is that numbers of this kind can be made to look big or small using presentational devices. For example, $15 billion a year amounts to roughly $600 per person per year or about $1.80 per person per day, less than the price of a cup of coffee. This amount looks very small. On the other hand, for a family of four, taken over 30 years, the same sum amounts to more than $70 000, which sounds like a lot. The same tricks can be played with GDP effects. Trivially, if we incur a loss of income of 1 per cent of income every year for 50 years, the total loss is equal 50 per cent of income. But this is not a meaningful calculation since it compares a stock to a flow. It’s like comparing the total amount of water flowing into a dam over 50 years with the storage capacity of the dam.

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Most importantly, any plausible estimate of the cost of stabilising CO2 concentrations at 450 ppm is far below the average estimate cost of allowing concentrations to rise to 550 ppm, let alone the much higher values associated with a ‘business as usual’ policy.

7 How do economists think about the problem? The problem of climate change is an example of what economists call ‘market failure’, that is, a situation where the ordinary functioning of markets produces undesirable results. In fact, Stern described it as ‘the biggest market failure in history’ [12]. Problems of pollution and congestion have been central to the economic analysis of market failure for nearly a century, beginning with the work of Pigou [19]. Pigou’s work was focused on prices and, in particular, the difference between the price paid by polluters for the inputs used in the production (or consumption) of a good or service and the costs borne by society as a whole. For historical reasons, the costs avoided by polluters were referred to as ‘external costs’ or, more commonly, externalities. Pigou’s key idea was that a tax could be used to ‘internalise’ these costs. An alternative approach developed in different forms by Coase [20] and Ostrom [21] considered the problem in terms of the failure to define appropriate property rights. Coase focused on private property rights, while Ostrom developed an analysis of common property systems. In the case of problems such as climate change, both private and common property must be considered.

7.1 Externalities An action by a firm or individual is defined as having external effects if it directly affects either the productive capacity of other firms or the welfare of other individuals. The key idea is that the actions of one individual directly impinge on others, without any direct market interaction. Pollution is the classic example of an externality. Smoke from a factory, or waste dumped in a river, imposes a cost on society as a whole that is not reflected in the costs faced by the firm in question. Many different concepts of externality have been analysed. A useful distinction is that between point-source externalities, diffuse or nonpoint externalities, and congestion externalities. Climate change is an example of a diffuse or nonpoint externality, which arises when many firms or individuals contribute to an external effect on one or more others. Congestion externalities arise when members of a group generate negative externalities affecting each other. Climate change is also a congestion externality since

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everyone in an industrialised society is responsible, directly or indirectly, for greenhouse gas emissions, and everyone will feel the effects. A second crucial distinction is that between unilateral and reciprocal or congestion externalities. Unilateral externalities arise when the actions of one party generate externalities affecting another, but not vice versa. Climate change is a unilateral externality in at least two respects. First, emissions of greenhouse gases today will have effects persisting far into the future, so that the costs are borne more by later-born generations. Second, people in less developed countries have contributed relatively little to total emissions, but are likely to bear a good deal of the burden. Pigou proposed that taxes or subsidies could be used to internalise externalities, by equating the marginal private cost of externality-generating activities with the marginal social cost. Ideally, the tax would be set to an amount equal to the (marginal) difference between private and social opportunity costs; that is, equal to the marginal damage associated with pollution. This approach fits naturally with textbook microeconomics of supply and demand. In the context of climate change, this analysis leads to the idea of a carbon tax, which could be levied when fossil fuels are first extracted, when fossil fuels are burned to produce energy, or on the products produced using that energy. A carbon tax sets a price on CO2 emissions. Market decisions would then determine level of emissions. Ideally, the price of CO2 emissions would be equal to the marginal social damage caused by additional emissions. Energy use is pervasive and complex. There are many different ways to increase or reduce the use of fossil fuels. The imposition of a carbon price, through a tax or otherwise, penalises increased use of fossil fuels, and rewards reductions in use. Firms and households will tend to reduce emissions unless such emissions generate a benefit greater than their price.

7.2 Property rights Another way to think about problems like air pollution is to observe that, under our current legal system, no one ‘owns’ the atmosphere. Before the adoption of Clean Air laws, the atmosphere was an ‘open access’ resource, meaning that everyone could use it freely, emitting as much pollution as they chose. There are a number of ways to solve the problem of overused assets using property rights. In the context of climate change, the most important is the creation of tradeable emissions permits. The starting point is to determine the total volume of emissions consistent with stabilising global concentrations of CO2 at an acceptable level. The next step is to create a set of emissions permits, equal to the total allowable volume of emissions. The permits may be auctioned or given away, and can then be

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traded. Anyone who wants to burn fossil fuels must surrender a permit for the associated quantity of CO2. The demand for permits will determine a market price.

7.3 Regulation The simplest solution to problems of market failure, and sometimes the best, is regulation. Instead of dumping their waste in the nearest river or pouring smoke out of a chimney, owners of a factory may be required to capture the waste and dispose of it in a way that does not pose risks to the environment. In the context of climate change, regulation has been used successfully to promote energy efficiency in consumer goods such as motor vehicles and light bulbs. Legislative requirements to reduce the electricity used by light bulbs created a demand for more efficient bulbs, which brought forth a series of innovations. Compact fluorescent bulbs were developed first, but have now been replaced, in large part by light-emitting diodes (LEDs). LED lamps are more expensive than the incandescent bulbs they have replaced, but repay this cost because of their reduced electricity use and longer lifetime. Regulation works well in simple cases. However, in a problem as complex as energy use, there will be many options for reducing emissions that cannot be foreseen by regulators. Also, if regulation prohibits activities that are otherwise profitable, firms and households will look for ways to get around the regulations. Regulations work best when accompanied by price policies such as carbon taxes or emissions trading schemes.

8 Conclusion Climate change is a massive and complex problem, and its solutions will require inputs from many disciplines. In particular, we need climate science to understand the nature of the problem and the likely response of the global climate system to changes in emissions of greenhouse gases. Engineering solutions are needed both for the problems of mitigation, such as decarbonising energy systems and for problems of adaptation, such as responding to rising sea levels. Economics also plays a crucial role in promoting cost-effective and equitable ways of responding to the problem. A central theme of economic analysis is the need for a price on emissions of greenhouse gases. A variety of price mechanisms are available, including carbon taxes, emissions trading schemes and various intermediate options. Although it is impossible for anyone to grasp the problem in its entirety, it is important for specialists in one discipline to be aware of the perspectives of others. Engineers need to understand that the choice of solution will not be determined purely by technology but will depend on the operation of markets and institutions. Conversely economists need to be aware of the engineering constraints on the operation of the price mechanism.

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Most importantly, the urgency of the problem of climate change means that an awareness of the issue will be relevant in a great many contexts. We have only a few decades left to stabilise the global climate. Decisions made now will have effects, for good or ill, far into the future.

9 Study questions – – – – –

Compare and contrast the externality and common property approaches to modelling carbon dioxide emissions There is considerable uncertainty surrounding the rate and effects of climate change. Does this strengthen or weaken the case for early action? How are carbon prices determined under carbon taxes and tradeable emission permit schemes? How does the rate of discount affect the evaluation of alternative policies to address climate change? Compare adaptation and mitigation in the context of coastal infrastructure

10 Glossary Adaptation: Policies or actions adopted to manage the consequences of climate change Business as usual: Outcomes in the absence of specific policies to mitigate climate change Climate sensitivity: The equilibrium warming associated with a doubling of CO2-equivalent concentrations in the atmosphere CO2 equivalent concentration: The total concentration of greenhouse gases in the atmosphere, weighted by forcing relative to CO2 Discount factor: The value of additional income in the future expressed as a fraction of the value of additional income now Discount rate: An annual interest rate used to calculate the discount factor Elasticity: The proportional change in one variable x arising from a unit proportional change in a causal variable y: mathematically, the log derivative dlog(x)/ dlog(y) Expected utility model: The standard economic model of choice under uncertainty Externality: A divergence between marginal private cost and marginal social cost arising (for example) from pollution Inherent discounting: Discounting of future benefits, solely because they arise in the future Intergenerational equity: Fair treatment of earlier-born and later-born generations, for example through the requirement that consumption patterns be sustainable

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Intergovernmental Panel on Climate Change (IPCC): The intergovernmental body set up under the United Nations Framework Convention on Climate Change to report on scientific research on climate change Internalise externality: To impose a cost on polluters to make marginal private cost equal marginal social cost Greenhouse gas (GHG): A gas such as carbon dioxide (CO2) or methane (CH4) which reduces the capacity of the atmosphere to radiate heat outwards, and therefore contributes to global warming Marginal private cost: The cost to an individual producer (or consumer) of a unit of additional output (or consumption) Marginal social cost: The cost to society as a whole of a unit of additional output or consumption Mitigation: Policies aimed at slowing climate change by limiting net emissions of GHGs Radiative forcing: The contribution to global warming from a given mass of greenhouse gas Rate of growth of consumption per person: A measure of the increase (or decrease) in average living standards over time Real discount rate: A discount rate for money values, adjusted for inflation Riskless social discount rate: The discount rate applicable to social investments where risk can be disregarded. Under appropriate conditions, equal to the real rate of interest on government bonds Temperature anomaly: The difference between the observed mean global temperature at a given time and the long term average

Further reading The following provide a good starting point to develop further understanding of these topics. Bosello, F., Carraro, C. and de Cian, E. (2009), ‘An analysis of adaptation as a response to climate change’, Copenhagen Consensus Center., Frederiksberg, Denmark. Coase, R. (1960), ‘The problem of social cost’, Journal of Law and Economics, 3(1), 1–44. DeLong, B. (2006), ‘Partha Dasgupta makes a mistake in his critique of the Stern Review’, http:// delong.typepad.com/sdj/2006/11/partha_dasgaptu.html, Garnaut, R. (2008), Garnaut Climate Change Review: Final Report, 30 September, Commonwealth of Australia, Melbourne. Hamilton, C. (2007), Scorcher: The Dirty Politics of Climate Change, Black Inc, Carlton, Victoria. Intergovernmental Panel on Climate Change (IPCC) (2014), Fifth Assessment Report, IPCC, Geneva. Nordhaus, W. (2007), ‘A review of The Stern Review on the Economics of Climate Change’, Journal of Economic Literature, XLV (September), 686–702. Ostrom, E. (1990), Governing the Commons: The Evolution of Institutions for Collective Action, Cambridge University Press, Cambridge. Pearse, G. (2007), High and Dry: John Howard, Climate Change and the Selling of Australia’s Future, Viking / Penguin Group, Camberwell, Victoria. Pigou, A. (1920), The Economics of Welfare, Macmillan, London.

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Quiggin, J. (2008a), ‘Counting the cost of climate change at an agricultural level’, CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources, 2(092), 1–9. Quiggin, J. (2008b), ‘Stern and his critics on discounting and climate change’, Climatic Change, 89(3–4), 195–205. Quiggin, J. (2009), ‘Climate change and intergenerational equity’, pp. 67–81 in Climate Change and Social Justice, (Ed. Moss, J.) Melbourne University Press, Carlton, Victoria. Quiggin, J. (2016), ‘The importance of ‘extremely unlikely’ events: Tail risk and the costs of climate change’, Working paper, University of Queensland. Quiggin, J., Adamson, D., Chambers, S. and Schrobback, P. (2009), ‘Climate change, mitigation and adaptation: the case of the Murray–Darling Basin in Australia’, Risk and Sustainable Management Group Murray-Darling Basin Program Working Paper M09_3, University of Queensland, Brisbane. Ramsey, F. (1928), ‘A mathematical theory of savings’, Economic Journal, 38, 543–59. Stern, N. (2007), The Economics of Climate Change: The Stern Review, Cambridge University Press, Cambridge. Thaler, R. (1990), ‘Anomalies: saving, fungibility, and mental accounts’, Journal of Economic Perspectives, 4(1), 193–205. Thorpe, Alan J. (2005), ‘Climate Change Prediction: A challenging scientific problem’, Institute of Physics, London. Weitzman, M.L. (2014), ‘Fat tails and the social cost of carbon’, American Economic Review, 104(5), 544–46.

References [1] [2] [3]

[4] [5] [6]  [7] [8] [9] [10] [11]

Intergovernmental Panel on Climate Change (IPCC). Fifth assessment report. Geneva: IPCC, 2014. NASA. GISS surface temperature analysis: analysis graphs and plots. NASA, 2017. Intergovernmental Panel on Climate Change (IPCC). Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge: Cambridge University Press, 2007. EU Climate Change Expert Group ‘EG Science’. The 2°C target: background on impacts, emission pathways, mitigation options and costs, 2008. Intergovernmental Panel on Climate Change (IPCC). Summary for policymakers: impacts, adaptation, and vulnerability, 2001. Rohde RA. Risks and impacts of global warming. Available at: http://ete.cet.edu/gcc/?/ resourcecenter/slideshow/3/44. Accessed 9 January 2017. Bosello F, Carraro C, de Cian E. An analysis of adaptation as a response to climate change. Frederiksberg: Copenhagen Consensus Center, 2009. Quiggin J. Climate change and intergenerational equity. In: Moss J, editor. Climate change and social justice. Carlton: Melbourne University Press,2009:67–81. Thorpe AJ. Climate change prediction: a challenging scientific problem. London: Institute of Physics, 2005. Quiggin J. Counting the cost of climate change at an agricultural level. CAB Rev: Perspect Agric Vet Sci Nutr Nat Res 2008;2(092):1. Garnaut R. Garnaut climate change review: final report. Melbourne: Commonwealth of Australia, 2008.

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[12] Stern N. The economics of climate change: the Stern review. Cambridge: Cambridge University Press, 2007. [13] Hamilton C. Scorcher: the dirty politics of climate change. Carlton: Black Inc, 2007. [14] Pearse G. High and dry: John Howard, climate change and the selling of Australia’s future, . Camberwell: Viking/Penguin Group, 2007. [15] Ramsey F. A mathematical theory of savings. Econ J 1928;38(152):543–59. [16] DeLong B. Partha Dasgupta makes a mistake in his critique of the Stern Review, 2006. Available at: http://delong.typepad.com/sdj/2006/11/partha_dasgaptu.html. Accessed 9 January 2017. [17] Weitzman ML. Fat tails and the social cost of carbon. Am Econ Rev 2014;104(5):544–6. [18] Quiggin J. The importance of ‘extremely unlikely’ events: Tail risk and the costs of climate change. Australian Journal of Agricultural and Resource Economics. 2018;62(1):4–20. [19] Pigou A. The economics of welfare. London: Macmillan, 1920. [20] Coase R. The problem of social cost. J Law Econ 1960;3(1):1–44. [21] Ostrom E. Governing the commons: the evolution of institutions for collective action. Cambridge: Cambridge University Press, 1990.

Trevor Grigg

The leadership challenge for engineers Abstract: The practice of engineering is undergoing profound change due to an even greater need to consider societal, environmental, economic and political demands in the design, implementation and operation of engineering projects. The engineering profession needs to address these demands. When coupled with fact that it can no longer be assumed that the community is still clear on the purpose and the role of engineers, there is a real leadership challenge for engineers. The nature of this challenge is examined in this chapter. In this environment of challenge and change, the best advice for a young engineer at the beginning of his or her career is to recognize that success is best achieved through self-knowledge of one’s own strengths and values and how to optimize one’s own performance. Key concepts: The innovation imperative; engineering and innovation; the role of engineering; leadership; change management; leadership in engineering; advice to young engineers. Key ideas: 1. The challenge for current and future engineering leaders is to mediate the societal, environmental, economic, and political needs for any engineering project. 2. To be successful, engineering leaders have to confront issues of digital disruption, changing demographics, workplace cultural diversity, scarce resources and shifting industrial relations frameworks. 3. Engineers should see themselves as professionals who use technology as a means to market competitiveness and the cost effectiveness of their organisation. This requires an understanding of how to lead the identification, development and management of a firm’s technological capabilities. 4. To be effective, a leader needs to create a realistic, convincing and attractive depiction of where her business/team should strive to be in the future; that is, to create a vision. Then the leader must motivate and inspire those around her, coach and build a team to achieve the vision, manage the delivery of the vision and any required change effectively. 5. An effective paradigm for leadership for engineers centres on being articulate, sensitive to others and to listen to their opinions, effective in diverse teams, conversant with organizational governance, and actively involved in their own continuing professional development.

https://doi.org/10.1515/9783110535129-006

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1 Introduction The nature of leadership in professions has been put to severe tests in the last two decades. Faced with rapid societal change and an increasingly ambiguous global and international world, the exclusive knowledge claims put forward by professionals, supported by university training, no longer stand the test. In the case of the engineering profession, its initial purpose was a clear mandate to build, construct and change the environment in order to improve the quality of living of a society. The gap between the then present state and the ideal was glaringly evident. This gave engineers the basis for strong, concerted and immediate action. There was no apparent reason to reflect on either the need or the urgency of the task. Although the gap may still be evident and varies widely from country to country, it is no longer the sole basis for action. The action is now couched in a series of overlapping contexts, namely, societal, environmental, economic and political. This new scenario challenges engineers to revisit their sense of mission and purpose for their profession and for the community at large. It cannot be assumed that the community is still clear on the purpose and the role of engineers. This is the leadership challenge for engineers. The challenge will vary from country to country and it is important for engineers to recognize this in the conduct of their professional activities. The discussion in subsequent sections could be couched in any national setting. As the author is most familiar with the Australian context, it is used as an example of the form of analysis that engineers should undertake when scoping the leadership challenge in any country. This is important, as engineers, both experienced as well as newly qualified, are typically engaged in professional assignments in many countries other than their own.

2 The Australian context Australia is a middle-sized, developed but non-dominant, island nation. It is rich in natural resources, yet largely technology dependent. It has heavy reliance on mining, energy and agricultural products, but with growing tourism and personal services, which sets it apart from virtually all other Western economies Because Australia is not a major world economy, its domestic firms require additional effort to become dominant in their industries internationally and to cope with international competition. The market conditions, infrastructure and industry clusters necessary for a dominant international position, rarely exist in Australia. Not surprisingly, business activities aimed at the domestic market comprise a large part of the Australian economy. These kinds of businesses tend to have varying degrees of natural protection in their own home market, as they would if they were located in any domestic setting.

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Consequently, the road to internationalization for Australian business is likely to be not so much through exports but overseas ownership, franchises and joint ventures. Indeed, Australian business has become very skilled in the establishment of multi-domestics; that is, they are very good at setting up complete operations in other countries and in other cultures which are quite separate from their Australian based operations. The Australian context for business, and this includes engineering services, implies that the Australian business environment requires a somewhat different approach from both government and industry than elsewhere in the world. Australia has to find its own model for business and the role of government in that model. This requires leadership from the nation’s government, business organisations and professionals.

3 Future challenges for leaders and managers To assist us in our thinking with respect to the future demands that will be placed on Australian leaders and managers, businesses and professionals, it is useful to examine some of the macro-trends which challenge the status quo. What are the future leadership, management and organisational challenges going to be? Firstly, there will be an increasing trade-off between economic growth and the conservation of the resources of the planet, which leaders, managers and organisations will need to learn to accommodate and manage. Secondly, governments will likely adopt new roles in terms of their relationship with business in particular, adopting a facilitating rather than interventionist role which will demand more from business than previously has been the case. Thirdly, there will be a growing emphasis on quality of life and genuine demands by the workforce for more active participation in the way that organisations are run. Fourthly, there will be increasing public scrutiny and disenchantment with big business, governments and unions which will command the attention and time, to an ever increasing extent, of leaders and managers. (By inference, engineers, by virtue of their role, will find themselves increasingly involved in consultation with important stakeholders.) Finally, new production technologies will alter the face of organisations, encouraging smaller and more powerful work-centred work places. Leaders and managers will have to confront issues of digital disruption, changing demographics, scarce resources and shifting industrial relations frameworks, by having to increasingly turn their attention to managing for the future, and pushing themselves beyond their conventional boundaries. This list of critical trends is similar to the mega-trends identified by Naisbitt over 30 years ago [1]. He identified ten mega trends, including: – the movement from an industrial to an information society, – the movement from a national to a world economy,

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the movement from centralisation to decentralisation of power, the shifting of reliance on institutions and governments to more self-reliance, the movement from representative democracy to more participatory democracy in politics as well as the workplace, the giving up on dependence on tradditional hierarchical structures in favour of informal networks of contacts, and moving from a society which enjoyed a limited number of personal choices to a multi-optioned society.

That is, there has been more than plenty of warning of the leadership and management challenges ahead. To what extent has engineering and the engineering profession adjusted to these trends and responded to them in a forward looking rather than a reactive way in an environment where there has been global recognition of the importance of leadership and managerial skills for success in the international business environment, particularly in respect of leadership, interpersonal skills and communication skills. For success, managers have to be able to address the important political, economic and social factors and trends, including not only those which have been identified above, but also: – how to encourage quality in the workforce – how to manage the cultural diversity in the workforce – how to balance economic growth objectives with environmental concerns – how to maintain economic competitiveness in a global market place, and – how to develop strategies that meet the need of a wide range of stakeholders and constituencies.

4 The innovation imperative From the discussion above, it should be clear that innovation is an imperative for the on-going international competitiveness of Australian enterprises. As far back as 1993, Carnegie and Butlin argued that to implement successfully an innovation strategy in Australia, the strategy would need to be both about people as well as enterprises and not just about science and technology [2]. They concluded that employee relations was the key issue for Australia’s prosperity and the competitiveness of its enterprises. They stressed that sustained success with innovation is largely the product of consistent, concentrated effort by people in an enterprise around a number of important success factors, including: – sustained leadership; – focusing on customers; and, – building in systematic approaches to innovation.

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To put these factors together requires a very strong connection between employee relations, applied technical development and the quality of leadership and management.

5 Engineering and innovation Innovation should be an area in which engineering professionals exert significant influence. Are the talents of engineers being applied to energise an improvement in the performance of Australian enterprises? Unfortunately, the perception of executive management in many organisations is that technology and technology management are regarded as strategic issues that make money whereas engineering and engineering management are perceived as operational issues that cost money. Consequently, the ends have not always been linked with the means. While there is a lack of technical literacy among many executive managers, in that many of them tend to delegate key engineering decisions, unfortunately, engineers have tended to be socialised to their parent discipline which contributes to them often being held in low esteem by executive management. As the innovative capacity of an organisation is likely to be a key differentiating success factor, those organisations that focus on the development of an operating culture that facilitates the development, transfer and application of knowledge to improve the performance of plant equipment and systems, and enhance products and services, are likely to be more successful than firms which do not have this innovative capacity. Engineers should be able to provide the necessary knowledge, skills and competencies in this operational area of organisations. If innovation is so critical to the future competitiveness of a nation’s businesses then engineers should have a critical role to play in this, but this will require the fusion of corporate strategy with engineering management and technology. Engineers must see themselves as the merchants of market competitiveness, that is, as professionals who use technology as a means to market competitiveness and the cost effectiveness of their organisation. As the technological sophistication of processes and products is enhanced, this will require an ever increasing level of understanding of how to identify, develop and manage a firm’s technological capabilities. This should mean a much closer link between overall firm corporate strategy and technology for the creation of sustained competitive advantage and, in turn, an enhanced role for engineering managers in senior leadership and management positions. This requires engineers to address their socialisation to the engineering discipline through: – continuing education, professional development; – modification of their traditional career paths; and – modification of their attitudes and values with respect to cost orientation and avoidance of risk, and broadening their technical skills to include also organizational, business, managerial and leadership skills.

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If the success of business depends on innovation, then the important task is to motivate and integrate cross-functional work teams and for firms to become more effective in exploiting their engineering and technological capabilities. This requires engineering leaders who possess not only technical knowledge, but also systems and strategic knowledge and who also have a firm understanding of organisational and business practices. A broadening of the education of engineers both prior to the commencement of their careers and then subsequently continuing through their professional careers is required, in not only areas central to the practice of engineering but also in the societal context of that role. This is very critical if the profession is to contribute in an intelligent informed and respected way to not only the success of their organization but also to public policy discussion and debate.

6 The engineering role Engineers have a vital role in the introduction of new technologies in industry. Indeed, if one looks more broadly, the contribution that engineers can make to economic growth and international competitiveness include the following: – the management and conduct of research and development and design leading to new products and processes; – the design of new or improved production systems, including the appraisal, selection, acquisition, and implementation of technology; – the ongoing improvement in production systems and products; – facilitating technology related links with customers, suppliers and external sources of knowledge; – bringing engineering knowledge and experiences in roles such as marketing; and – establishing new technology based firms through entrepreneurship. The ability of engineers to contribute in this way will be enhanced by wider recognition in organisations of the importance of technology to the firm for its overall strategic positioning and the significance of innovation for the sustained success of the firm, both nationally and internationally. Engineers need to be effective in not only research and development but also in commercialising the results of that research and development, and managing the overall processes of innovation associated with that commercialisation.

7 Societal change and the practice of engineering “Engineering is not an end in itself… Professional engineers are employed to create and manage assets, products and services for economic and social purposes and to facilitate the conduct of commercial, community and government functions.” Lloyd, 1994 [3].

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“Future effective leadership and management in engineering requires enhanced awareness of the impact of social change upon the way engineers work. Future attention to leadership roles by engineers, as engineers, requires modification to the traditional model of engineering as a professional occupation.” Lloyd, 1994 [3].

The changed paradigm of engineering employment is reflected in the downsizing of organisations, the delayering of organisations, and the out-sourcing of non-core activities (which often include engineering design, construction and maintenance). The new employment paradigm is providing challenges, spurring innovation and enterprise, and creating increased opportunities for consulting engineering firms as well as sole engineering practitioners. New graduates are attractive to employers because of their knowledge of the latest technologies, but employers often comment that they still have to acquire the skills which make then useful. Skills such as: – the understanding of engineering as a process; – tolerance for ambiguity and uncertainty; – an ability to specify and monitor performance measures; – an ability to work effectively as part of a team; – the ability to solve problems without constant supervision and direction; – an understanding of customer service; – an ability to communicate with colleagues and customers; – an understanding of financial matters; and – an understanding of engineering in relation to the community.

8 What is leadership? “Leadership is the art of getting someone else to do something you want done because he/she wants to do it” Dwight D. Eisenhower.

Leadership involves: – influencing others; – having followers; – being visible when an innovative response is required; and – having a clear idea of what needs to be achieved and why and how. Successful leaders are able to: – express themselves fully; – know what they want and why they want it; – know how to communicate what they want to others, in order to gain their co-operation and support; and – know how to achieve their goals.

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To be effective, a leader: – creates a realistic, convincing and attractive depiction of where a business/a team/a country should strive to be in the future; that is, creates a vision. A vision provides direction, sets priorities and targets; – motivates and inspires those around her; – manages the delivery of the vision; – needs to be able to manage change effectively; and – coaches and builds a team to achieve the vision [4]. Common attributes of successful leaders include [5]: – physical vitality and stamina; – intelligence and action-oriented judgement; – eagerness to accept responsibility; – task competence; – skill in dealing with people; – need for achievement; – capacity to motivate people; – courage and resolution; – trustworthiness; – decisiveness; – self-confidence; – assertiveness; and – adaptability/flexibility.

9 Leadership and managing change New initiatives, technology improvements, and staying ahead of the competition all require ongoing change within an organisation. Kotter has researched and written extensively on the management of change [6]. He argues that to successfully manage change requires sound leadership. He identifies a series of steps essential to the success implementation of change, namely, to: – create a sense of urgency – through identifying threats, developing scenarios of the future, and examining opportunities; – form a powerful coalition – by building an influential team; – create a vision for change and a strategy to execute that vision; – communicate the vision; – remove obstacles; – create short term wins; – build on the change; and – anchor the changes in corporate culture.

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10 Leadership in engineering As mentioned in the Introduction to this chapter, the nature of leadership in the professions has been put to severe tests in the last two decades. Faced with rapid societal change and an increasingly ambiguous global and international world, the exclusive knowledge claims put forward by professionals, supported by university training, no longer stand the test. Leadership is first and foremost pervaded by a sense of fundamental purpose [7]. This sense of purpose is the bridge which connects the world of work with society at large. It is from this purpose that leaders derive their authority. All this suggests that the emerging leadership model is no longer an authoritarian one but one that is authoritative. There is a recognition of the leader as an expert, as someone with expert knowledge, but not necessarily as the person with all the right answers. The expert knowledge being referred to here is the ability to ask the right questions and to seek answers. One does not need to be an engineer to manage multi-skilled teams, but one does need to have the experience to know the right questions to ask of an accountant or a lawyer or another engineer. This is a leadership model where the leader knows how to read the context. Individuals who do well with context typically have exceptional listening skills. Can leaders be made? In my opinion, although some individuals are born with a natural flair for leadership, most of us learn and acquire leadership skills through work and social experiences, education, mentoring and reflection. So a new paradigm for leadership for engineers centres on being: – very articulate; – sensitive to others and to the whole community; – effective in diverse teams across many different organisations; – very conversant with organization governance; – actively involved in continuing professional development; and – being very much aware of national and international trends.

11 Advice for young engineers The best advice for a young engineer at the beginning of his or her career is to recognize that “success in the knowledge economy comes to those who know themselves – their strengths, their values, and how they best perform.” [8] That is, manage oneself and that includes managing one’s career. If you have ambition and drive, take heed of the discussion on leadership and leadership attributes outlined in this chapter, and have answers to the following questions posed by Drucker [8], then you will be positioning yourself for professional life of success and achievement.

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The key questions to have answered centre around self-knowledge, namely: what are my strengths; in what ways do I work best; what are my values or ethics; in what work environment do I best belong; and what and how can I best contribute to my organization?

12 Study questions –

– – – –

As systems become more complex, the likelihood of systems failures becomes ever greater. What should this mean for the desired traits and attributes of the leaders of our public and private organisations? “Leadership is the art of getting someone else to do something you want done because he wants to do it.” Is this an adequate definition of leadership? What is the importance of continuing professional development to an individual’s career? What is it that you admire in leaders you judge to be effective? How did they develop their leadership skills? What does it mean to be both a “professional” and a “leader” within the field of engineering?

Further reading Blanchard, K. and Johnson, S. (2015). The New One Minute Manager. Harper Collins Bossidy, L., Charan, R and Burck, C. (2008). Execution: The Discipline of Getting Things Done. Random House Carnegie, D. (1981). How to Win Friends and Influence People. Revised Edition. Simon and Schuster Covey, S.R. (1989) The Seven Habits of Highly Effective People. Free Press Kouzes, J.M. and Posner, B (2010). The Truth About Leadership. Jossey-Bass Harvard Business Review (2011). 10 Must Reads – On Leadership. Includes the following articles “What Makes a Leader?” by Daniel Goleman; “What Leaders Really Do” by John P. Kotter; “The Work of Leadership” by Ronald A. Heifetz and Donald L. Lurie; and “Why Should Anyone Be Led by You?” by Robert Goffee and Gareth Jones. Harvard Business Review (2011). 10 Must Reads – On Managing Yourself. Includes the article “Managing Oneself” by Peter F. Drucker.

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References [1]

Naisbitt J. Megatrends: ten new directions transforming our lives. New York: Warner Books, 1982. [2] Carnegie R, Butlin M. Managing the innovating enterprise. Melbourne: Business Council of Australia, 1993. [3] Lloyd BE. Lessons from history: the great engineering eras. National conference on engineering management. 10–12 April 1994 The Institute of Engineers Australia. [4] Manktelow J. What is leadership? Available at: https://www.mindtools.com/pages/article/ newLDR_41.htm. Accessed: 3 Sept 2015. [5] Gardner JW. On leadership. New York: The Free Press, 1989. [6] Kotter J. Leading change. Boston: Harvard Business School Press, 1996. [7] Senge PM. The fifth discipline: the art and practice of the learning organization. London: Doubleday/Currency, 1990. [8] Drucker PF. Managing oneself. Harvard Business Review. Jan 2005.

Concluding remarks This book has provided an overview of sociopolitics, psychology, economics, and leadership. The key ideas have been selected, and are described in ways that they can be practically put to use by engineers during their careers. The chapter on psychology explored the different modes of thinking; how heuristics and biases can affect cognition; how social identity affects teamwork, organisations, and leadership; and how psychology can inform the safety of engineered systems, including design with humans in mind (human factors engineering), and design for robust performance despite variability and uncertainty (resilience engineering). The chapter on sociopolitics provided a framework to think about the social context of engineering work, including consideration of stakeholders’ needs and to whom the benefits of a project accrue; evaluation of different expectations of local communities and cultures; and the variations in the political environment around the world, including the characteristics of institutions and governance, political systems, legal frameworks, and transparency and corruption. This chapter also provided an overview of critical global challenges and the role of engineering in addressing these challenges. The chapter on engineering economics provided a comprehensive overview of the fundamental considerations for engineering financial decisions. In particular, it explored how prices result from an interaction between supply, demand, and market characteristics such as market power or barriers to entry; the basis for project-level decision-making, including how uncertainty of features of the project setting (such as prices) can affect this; and provided an introduction to economics at the industry or national level, through a discussion of productivity. The chapter on economics of climate change provided the key economic issues that engineers must understand related to climate change. These concepts, such as the role of common property, taxes, and regulation, are crucial ideas for engineers, relevant to many other areas as well as climate change. Providing the final element of the framework of the human forces, the chapter on leadership brought together key themes from across the different human forces disciplines, to provide a clear overview of how leaders can use their position and knowledge to drive important changes within an organisation or the wider world. It provided an overview of what leadership is, the challenges faced by leaders in modern times, and particular issues for leadership within engineering. All of the knowledge provided in this book is selected to be helpful for engineers over the course of their professional careers. However, the situations to which most of this knowledge applies do not typically identify themselves easily. Many of the technical engineering problems we encounter in an engineering career are readily recognisable as circumstances to apply relevant theory or known analytical techniques. We can https://doi.org/10.1515/9783110535129-007

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easily become accustomed to problem solving which consists of a quick recognition of the type of problem that we have encountered, followed by the application of a relevant technique from our knowledge of engineering theory. The knowledge described in this book is not always as straightforward to recognise when and how it should be applied. As the knowledge is more generalised, it applies to many different situations, but more effort is required to determine how this can be used and applied. Consequently our recommendation to you is to take the time to think carefully, laterally, and strategically on situations that you encounter, and reflect on how the ideas in this book can be applied. An important additional consideration for any engineer is to be able to interface smoothly with the environment within which they work. Engineers already ‘speak the language’ of engineering, and understand how to communicate easily with others from technical backgrounds regarding tasks and projects. But an important challenge for any professional engineer is to be able to develop excellent communication with the others with which they interact: clients, regulators, government, and the general population. In part, this book is aimed at introducing both concepts and specific knowledge to assist with that (i.e. psychological ideas underpinning a lot of communication, and the terminology of sociopolitics, and economics). Furthermore, it is useful for career progression to learn to speak the language of managers and decision makers. A case is more persuasive and more easily won when it is presented in the language that is easily understood by the decision maker. Similarly, some important stakeholders may have backgrounds and expectations different to those that you hold. It may be nevertheless important to listen to what they are saying, and to understand their viewpoint. Engineers aren’t expected to know everything or have all the answers in every situation. But it is important to be able to ask the right questions, and to understand the answers. It is also important to know who to ask, which requires being able to distinguish between those who know what they are talking about, and those who don’t. These aspects of engineering are all about communication, and about knowledge of the language of the fields with which engineers interact.