especially in breadth of application. In addition, ABMs are increasingly a focus of interdisciplinary collaboration, between social/behavioral scientists from different disciplines (e.g., sociology and geography), between social/behavioral science and natural science (e.g., environmental science), and between social/behavioral science and computer science. Depending on purpose, the rules central to agent-based models can be derived from theory, past empirical research, and/or conversations with local experts. Indeed, ABMs are increasingly used in community-based participatory research. Given these trends, the need for a generally accessible primer is even greater now than when the first edition was published in 2007. This second edition fully satisfies that need.
Barbara Entwisle
Series Editor
Preface
Agent-based modeling is a form of computational simulation. Although simulation as a research technique has had a very important part to play in the natural sciences for decades in disciplines from astronomy to biochemistry, it was relatively neglected in the social sciences. This may have been because a computational approach that respected the particular needs of the social sciences was lacking. However, in the early 1990s the value of agent-based modeling began to be realized, and, since then, the number of studies that have used agent-based modeling has grown rapidly (Hauke, Lorscheid, & Meyer, 2017).
Agent-based modeling is particularly suited to topics where understanding processes and their consequences is important. In essence, one creates a computer program in which the actors are represented by segments of program code, and then runs the program, observing what it does over the course of simulated time. There is a direct correspondence between the actors being modeled and the agents in the program, which makes the method intuitively appealing, especially to those brought up in a generation used to computer games. Nevertheless, agent-based modeling stands beside mathematical and statistical modeling in terms of its rigor. Like equation-based modeling, but unlike prose, agent-based models must be complete, consistent, and unambiguous if they are to be capable of being executed on a computer. On the other hand, unlike most mathematical models, agent-based models can include agents that are heterogeneous in their features and abilities, can model situations that are far from equilibrium, and can deal directly with the consequences of interaction between agents.
Because it is a new approach, there are few courses yet available to teach the skills of agent-based modeling, although the number is increasing, and there are few texts directed specifically at the interested social scientist. This short book introduces the subject, emphasizing the decisions that a social scientist needs to make when selecting agent-based modeling as an appropriate method, and offering some tips on how to proceed. It is aimed at practicing social scientists and graduate students. It has been used as the recommended reading on agent-based modeling for a graduate-level module or doctoral program in computational social science, and it is also suitable as background reading in postgraduate courses on advanced social research methods. It would be a good preparation for any of the textbooks that provide a more in-depth guide to agent-based modeling (e.g., Hamill & Gilbert, 2015; Heppenstall, Crooks, See, & Batty, 2012; O’Sullivan & Perry, 2013; Railsback & Grimm, 2012; Squazzoni, 2012; Wilensky & Rand, 2015).
A knowledge of and experience with computer programming in any language would be helpful but is not essential to understand the book.
The book concludes with a list of printed and Web resources, a glossary, and a reference section. (The glossary terms will appear in bold at first use in the text.) Because the field is growing so rapidly, it has been possible to mention only a few examples of current research and some textbooks that provide more detail on some topics. There is much more that could have been cited if there had been space. In particular, the book mentions only briefly two closely linked areas: network models and game theory models, both of which are covered in much more detail in other SAGE volumes such as Knoke and Yang (2008) and Fink, Gates, and Humes (1998).
A website to accompany the book at study.sagepub.com/researchmethods/qass/gilbert-agent-based-models-2e includes an annotated exemplar model using NetLogo.
Acknowledgments
This book is born of some 25 years of building agent-based models, both large and small, and in domains ranging from science policy to anthropology. What I know about agent-based modeling has benefited immeasurably from the advice and companionship of many, including Andrew Abbott, Petra Ahrweiler, David Anzola, Robert Axelrod, Rob Axtell, Pete Barbrook-Johnson, Riccardo Boero, François Bousquet, Cristiano Castelfranchi, Edmund Chattoe, Claudio Cioffi-Revilla, Rosaria Conte, Guillaume Deffuant, Bruce Edmonds, Gusz Eiben, Corinna Elsenbroich, Lynne Hamill, Samer Hassan, Wander Jager, David Lane, Scott Moss, Kavin Narasimhan, Gilbert Peffer, Alex Penn, Andreas Pyka, Juliette Rouchier, Mauricio Salgado, Stephan Schuster, Flaminio Squazzoni, Luc Steels, Klaus Troitzsch, Paul Vogt, and Lu Yang. I thank Riccardo Boero, Lars-Eric Cederman, Lynne Hamill, Luis R. Izquierdo, Ken Kahn, Tim Liao, Michael Macy, Lu Yang, and eight anonymous reviewers for their detailed and constructive comments on drafts of the manuscripts for the first and second editions.
SAGE Publishing would like to thank the following reviewers for their feedback on the revision:
Andrew Crooks, George Mason University
Sally Jackson, University of Illinois at Urbana-Champaign
James Nolan, University of Saskatchewan
Oleg Smirnov, Stony Brook University
Garry Sotnik, Portland State University
About the Author
Nigel Gilbert is professor of sociology at the University of Surrey, Guildford, England. He is the author or editor of 34 books and many academic papers, and was the founding editor of the Journal of Artificial Societies and Social Simulation. His current research focuses on the application of agent-based models to understanding social and economic phenomena, especially the emergence of norms, culture, and innovation. He obtained a doctorate in the sociology of scientific knowledge in 1974 from the University of Cambridge and has subsequently taught at the universities of York and Surrey in England. He is one of the pioneers in the field of social simulation and is past president of the European Social Simulation Association. He is a Fellow of the UK Academy of Social Sciences and of the Royal Academy of Engineering.
Конец ознакомительного фрагмента.
Текст предоставлен ООО «ЛитРес».
Прочитайте эту книгу целиком, купив полную легальную версию на ЛитРес.
Безопасно оплатить книгу можно банковской картой Visa, MasterCard, Maestro, со счета мобильного телефона, с платежного терминала, в салоне МТС или Связной, через PayPal, WebMoney, Яндекс.Деньги, QIWI Кошелек, бонусными картами или другим удобным Вам способом.
