What can computer simulation contribute to the social sciences? Which of the many approaches to simulation would be best for my social science project? How do I design, carry out and analyse the results from a computer simulation? This is a practical textbook on the techniques of building computer simulations to assist understanding of social and economic issues and problems. Interest in social simulation has been growing rapidly worldwide as a result of increasingly powerful hardware and software and also a rising interest in the application of ideas of complexity, evolution, adaptation and chaos in the social sciences. This authoritative book details all the common approaches to social simulation, to provide social scientists with an appreciation of the literature and allow those with some programming skills to create their own simulations.New for this edition is a chapter on how to use simulation as a tool. A new chapter on multi-agent systems has also been added to support the fact that multi-agent modelling has become the preferred approach to simulation. Social scientists in a wide range of fields will find this book an essential tool for research, particularly in sociology, economics, anthropology, geography, organizational theory, political science, social policy, cognitive psychology and cognitive science. It will also appeal to computer scientists interested in distributed artificial intelligence, multi-agent systems and agent technologies.

Simulation for the Social Scientist

The emerging academic field focused on sustainability has been engaged in a rich and converging debate to define what key competencies are considered critical for graduating students to possess. For more than a decade, sustainability courses have been developed and taught in higher education, yet comprehensive academic programs in sustainability, on the undergraduate and graduate level, have emerged only over the last few years. Considering this recent institutional momentum, the time is seemingly right to synthesize the discussion about key competencies in sustainability in order to support these relatively young academic programs in shaping their profiles and achieving their ambitious missions. This article presents the results of a broad literature review. The review identifies the relevant literature on key competencies in sustainability; synthesizes the substantive contributions in a coherent framework of sustainability research and problem-solving competence; and addresses critical gaps in the conceptualization of key competencies in sustainability. Insights from this study lay the groundwork for institutional advancements in designing and revising academic programs; teaching and learning evaluations; as well as hiring and training faculty and staff.

http://reforma.fen.uchile.cl/Papers/Key%20Competences%20in%20Sustainability%20Framework%20-%20Wiek,%20Withycombe,%20Redman.pdf

Key competencies in sustainability: a reference framework for academic program development

■ Abstract Sociologists often model social processes as interactions among variables. We review an alternative approach that models social life as interactions among adaptive agents who influence one another in response to the influence they receive. These agent-based models (ABMs) show how simple and predictable local interactions can generate familiar but enigmatic global patterns, such as the diffusion of information, emergence of norms, coordination of conventions, or participation in collective action. Emergent social patterns can also appear unexpectedly and then just as dramatically transform or disappear, as happens in revolutions, market crashes, fads, and feeding frenzies. ABMs provide theoretical leverage where the global patterns of interest are more than the aggregation of individual attributes, but at the same time, the emergent pattern cannot be understood without a bottom up dynamical model of the microfoundations at the relational level. We begin with a brief historical sketch of the shift from “factors” to “actors” in computational sociology that shows how agent-based modeling differs fundamentally from earlier sociological uses of computer simulation. We then review recent contributions focused on the emergence of social structure and social order out of local interaction. Although sociology has lagged behind other social sciences in appreciating this new methodology, a distinctive sociological contribution is evident in the papers we review. First, theoretical interest focuses on dynamic social networks that shape and are shaped by agent interaction. Second, ABMs are used to perform virtual experiments that test macrosociological theories by manipulating structural factors like network topology, social stratification, or spatial mobility. We conclude our review with a series of recommendations for realizing the rich sociological potential of this approach.

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FROM FACTORS TO ACTORS: Computational Sociology and Agent-Based Modeling

Thoughtful leaders increasingly recognize that we are not only failing to solve the persistent problems we face, but are in fact causing them. System dynamics is designed to help avoid such policy resistance and identify high-leverage policies for sustained improvement. What does it take to be an effective systems thinker, and to teach system dynamics fruitfully? Understanding complex systems requires mastery of concepts such as feedback, stocks and flows, time delays, and nonlinearity. Research shows that these concepts are highly counterintuitive and poorly understood. It also shows how they can be taught and learned. Doing so requires the use of formal models and simulations to test our mental models and develop our intuition about complex systems. Yet, though essential, these concepts and tools are not sufficient. Becoming an effective systems thinker also requires the rigorous and disciplined use of scientific inquiry skills so that we can uncover our hidden assumptions and biases. It requires respect and empathy for others and other viewpoints. Most important, and most difficult to learn, systems thinking requires understanding that all models are wrong and humility about the limitations of our knowledge. Such humility is essential in creating an environment in which we can learn about the complex systems in which we are embedded and work effectively to create the world we truly desire. The paper is based on the talk the author delivered at the 2002 International System Dynamics Conference upon presentation of the Jay W. Forrester Award. Copyright  2002 John Wiley & Sons,

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All models are wrong: reflections on becoming a systems scientist†

In 1972, the Club of Rome's infamous report “The Limits to Growth” [Meadows, D.H., Meadows, D.L., Randers, J., Behrens_III, W. W. (1972). The Limits to Growth: A Report for the Club of Rome's Project on the Predicament of Mankind. Universe Books, New York] presented some challenging scenarios for global sustainability, based on a system dynamics computer model to simulate the interactions of five global economic subsystems, namely: population, food production, industrial production, pollution, and consumption of non-renewable natural resources. Contrary to popular belief, The Limits to Growth scenarios by the team of analysts from the Massachusetts Institute of Technology did not predict world collapse by the end of the 20th century. This paper focuses on a comparison of recently collated historical data for 1970–2000 with scenarios presented in the Limits to Growth. The analysis shows that 30 years of historical data compare favorably with key features of a business-as-usual scenario called the “standard run” scenario, which results in collapse of the global system midway through the 21st century. The data do not compare well with other scenarios involving comprehensive use of technology or stabilizing behaviour and policies. The results indicate the particular importance of understanding and controlling global pollution.

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A comparison of The Limits to Growth with 30 years of reality

PrefaceMost people acknowledge that the earth is finite. Yet the belief that continuednmaterial growth is possible and desirable remains an important influence on thenmajority of public and private decisions. Policy makers generally assume that growthnwill provide them tomorrow with the resources required to deal with todayrs problems.nPromises that the poor will receive a share of the additional goods and servicesncreated by growth are widely substituted for any real effort to change social values ornredistribute current income. Deficit spending, high interest rates, and the squanderingnof materials are all prevalent, and all are justified in part by the claim that morenincome, greater productivity, and increased resource reserves will inevitably be availablenin the future.Recently, however, concern about the consequences of population growth, increasednenvironmental pollution, and the depletion of fossil fuels has cast doubt uponnthe belief that continuous growth is either possible or a panacea. Yet it is difficult fornindividuals or institutions to recognize or respond to limits to growth, for the causesnand consequences of increased production and population extend over long timenperiods, are numerous, and lie interlinked within a system too complex to be encompassedneasily by the human mind. Before current social objectives and institutionalnforms can be intelligently reevaluated, new theoretical bases are required to assist innunderstanding the dynamics of growth in a finite world.In July 1970 the executive committee of The Club of Rome attended a seminarnpresented by members of the System Dynamics Group at the Massachusetts Institutenof Technology. The committee had come to determine whether the system analysisntechniques developed at M.I.T. by Professor Jay W. Forrester and his associatesncould provide new perspectives on the interlocking complex of costs and benefitsninherent in continued physical growth on a finite planet. Professor Forrester broughtnto The Club of Rome meeting a preliminary computer simulation model, callednWorld2, that specified important relationships among population, economic output,nand environmental constraints. At the meeting, plans were developed for a researchnprogram to test and extend Forresterrs initial theories. I directed the group of scientists and students involved in that effort, and worked with my associates to preparenthe material for three reports on our research. n n

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Roger F. Naill

Papers

Dynamics of Growth in a Finite World