In This Article Agent-Based Modeling

  • Introduction
  • General Overviews and Textbooks
  • Reviews and Reference Works
  • The Methodology of Agent-Based Modeling
  • Uses of Agent-Based Modeling
  • Analyzing Agent-Based Models
  • Cognitive and Environmental Elements of Agent-Based Models
  • Interactional and Social Elements of Agent-Based Models
  • Data and Agent-Based Models
  • Institutions of Agent-Based Modeling
  • Challenges to Agent-Based Modeling

Sociology Agent-Based Modeling
by
Edmund Chattoe-Brown
  • LAST MODIFIED: 24 May 2017
  • DOI: 10.1093/obo/9780199756384-0196

Introduction

Agent-Based Modeling is a research method that represents theories of social behavior as computer programs of a particular kind, rather than narratives (as ethnography does) or equations (as regression models do). Like existing research methods in sociology (both qualitative and quantitative) it can be applied throughout the discipline and offers advantages for certain research questions. In particular, the approach is referred to as agent-based because the computer program unambiguously represents interactions between heterogeneous social actors while also explicitly determining their aggregate simulated consequences. This distinguishes Agent-Based Modeling from existing quantitative approaches in sociology where the relationship between aggregate associations and individual agency is often unclear. It also distinguishes the method from existing qualitative approaches that, while investigating individuals and their interactions, have no systematic techniques for establishing their aggregate consequences. Given this capability, the methodology of Agent-Based Modeling has a distinctive logic. Agent-Based Models are calibrated using data on individual behavior (for example, using ethnography or laboratory experiments) and then the computer program generates simulated aggregate data. This can then be compared with equivalent real data for validation. It is the independence of these two activities that provides Agent-Based Modeling with its distinctive claim to explanatory power. The explicitly represented link between individual and aggregate respects the complexity of social systems, the phenomenon in which individuals and their simple interactions may produce surprisingly counter-intuitive aggregate outcomes. Agent-Based Models are thus particularly suitable for investigating sociological issues involving heterogeneous actors, diverse cognitive processes, and social systems mediated by entities operating between the level of the individual and the aggregate (like schools and churches). One aim of this bibliography is to strike a balance between technical aspects of the method (available programming languages, calibration, and validation) and important or distinctive applications in diverse areas of sociological interest. Another is to stress the importance of the distinctive methodology in maintaining (as with existing methods) the scientific quality of research.

General Overviews and Textbooks

As a relatively newly established field, the number of overviews and textbooks is not overwhelming. Epstein and Axtell 1996 is the earliest book-length introduction but one can only consider it as a textbook with difficulty. For a number of years, Gilbert and Troitzsch 2005 was the sole textbook available (though they also dealt with social science simulation more broadly). Gilbert 2008 has now produced an excellent short introduction dealing specifically with Agent-Based Modeling. More recently there has been a significant increase in the publication rate somewhat ramified by the range of different programming languages now available for Agent-Based Modeling. There is something of a tradeoff between books organized around particular programming languages (which tend to emphasize technical matters and choose examples to suit) and those organized around particular domains or disciplines (which tend to emphasize substantive issues). Railsback and Grimm 2012 and Wilensky and Rand 2015, both based on the very accessible programming language NetLogo, fall into the former class while Squazzoni 2012 (dealing with core sociological topics like cooperation) and Miller and Page 2007 (with their emphasis on the social science implications of complexity) fall into the latter.

  • Epstein, Joshua, and Robert Axtell. 1996. Growing artificial societies: Social science from the bottom up. Washington, DC: Brookings Institution.

    E-mail Citation »

    Organized around examples of basic social processes in the imaginary Sugarscape, this book has limitations despite its importance. The program code was not originally available (though now re-implemented by others) and the examples sometimes emphasize individualistic simplicity rather than social plausibility.

  • Gilbert, Nigel. 2008. Agent-based models. Thousand Oaks, CA: SAGE.

    DOI: 10.4135/9781412983259E-mail Citation »

    Extremely concise and based around a sociologically imaginative example of subjective group definition and membership.

  • Gilbert, Nigel, and Klaus Troitzsch. 2005. Simulation for the social scientist. 2d ed. Maidenhead, UK: Open Univ. Press.

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    This is still a very useful and accessible introduction to Agent-Based Modeling. Although some chapters cover different simulation approaches, others (like that implementing evolutionary approaches to social change) retain wider relevance to sociology.

  • Miller, John, and Scott Page. 2007. Complex adaptive systems: An introduction to computational models of social life. Princeton, NJ: Princeton Univ. Press.

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    The strength of this book is its focus on the relevance of complexity and emergence for social science understanding. The examples are somewhat more stylized and tend to impart inspiration rather than technical skills.

  • Railsback, Steven, and Volker Grimm. 2012. Agent-based and individual-based modeling: A practical introduction. Princeton, NJ: Princeton Univ. Press.

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    This book teaches NetLogo programming and ABM methodology very effectively through staged examples but only some of these will be relevant to sociologists.

  • Squazzoni, Flaminio. 2012. Agent-based computational sociology. Chichester, UK: Wiley.

    DOI: 10.1002/9781119954200E-mail Citation »

    A good all round introduction, particularly useful for its specific sociological focus, its unusual use of experimental data, and extensive reference and resource sections.

  • Wilensky, Uri, and William Rand. 2015. An introduction to agent-based modeling: Modeling natural, social, and engineered complex systems with NetLogo. Cambridge, MA: MIT.

    E-mail Citation »

    Written by developers of the programming language, this is a technically excellent textbook but for sociologists quite a few examples are less immediately relevant.

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