Although Agent Based Models (hereafter ABM) are now regularly reported in sociology journals, explaining the approach, describing models and reporting results leaves little opportunity to examine wider implications of ABM for sociological practice. This article uses an established ABM (the Schelling model) for this. The first part argues that ABM integrates qualitative and quantitative data distinctively, provides novel tools for understanding social causes and offers a significantly different perspective on theory building. The second part shows how the emerging ABM methodology is compatible with existing sociological practice while undermining several criticisms of ABM perceived to limit its sociological relevance.

Agent-based modeling (ABM) is in many ways an outsider in social research methods, but I will here argue that it has an important role to play in combination with other methods and approaches. ABM can be used in ways that offer unique benefits to social science researchers, but due to the abstract nature of the gained insights, this requires the employment of various methodologies in combination with ABM. Mixed methods (MM) is an approach that has become quite popular, and I show that even if ABM naturally may seem like a natural ingredient in research where methods are mixed, there exists some peculiarities within MM that may make it less accommodating to ABM that one may assume.

2000

Abstract The use of computer simulation for building theoretical models in social science is introduced. It is proposed that agent-based models have potential as a “third way” of carrying out social science, in addition to argumentation and formalisation. With computer simulations, in contrast to other methods, it is possible to formalise complex theories about processes, carry out experiments and observe the occurrence of emergence.

2016

Computational agent-based models are entering the toolbox of quantitative sociologists. However, markedly contrasting views still exist as to its capacity to contribute to causally-oriented empiric ...

2011

Article history: Available online xxxx Keywords: Agent-based Modelling Model development Research process Modelling Cycle Methodology

2008

This paper considers some of the difficulties in establishing verification and validation of agent based models. Simulation based results limit the ability to verify and blur the boundary between verification and validation. We suggest that a clear description of the phenomena to be explained by the model and testing for the simplest possible agent rules of behaviour are key to the successful validation of ABMs and will provide the strongest base for enabling model comparison and acceptance. In particular, the empirical evidence that in general agents act intuitively rather than rationally is now strong. This implies that models which assign high levels of cognition to their agents require particularly strong justification if they are to be considered valid. “Where do correct ideas come from? Do they drop from the skies? No. Are they innate in the mind? No. They come from social practice, and from it alone; they come from three kinds of social practice, the struggle for production, ...

Following the aims of the Methodos Series perfectly, this 13th volume on agent-based models provides a general view of the problems raised by this approach and shows how these problems may be solved. These methods are derived from computer simulation studies used by mathematicians and physicists. They are now applied in many social disciplines such as artificial life (Alife), political sciences, evolutionary psychology, demography, and many others. Those who introduced them often took care not to consider each social science separately but to view them as a whole, incorporating a wide spectrum of social processes – demographic, economic, sociological, political, and so on. Rather than modelling specific data, this approach models theoretical ideas and is based on computer simulation. Its aim is to understand how the behaviour of biological, social, or more complex systems arises from the characteristics of the individuals or agents composing the said system.

Journal of Artificial Societies and Social Simulation, 2019

Review of:Eric Silverman (2018) Methodological Investigations in Agent-Based Modelling. Springer-Verlag: Berlin

Canadian Review of Sociology, 2020

This article considers the implications of an approach to computer simulation called Agent Based Modelling for Process-Oriented Analysis. It argues that many theoretical and methodological debates found in the latter field can be effectively advanced by the former. The argument is presented and then extended using a ubiquitous Agent-Based Model proposed to improve understanding of ethnic residential segregation. The argument has three strands. The first is that theoretical and methodological debates are unlikely to progress unless they can be "cashed out" empirically. The second is that Agent-Based Modelling (and its distinctive methodology) have capabilities to do this that existing research methods lack and, in fact, that Agent-Based Models are a natural way to represent “social process” as apparently conceived by Process-Oriented Analysis. The third is that possibilities exist for productive synthesis between Agent-Based Modelling and Process-Oriented Analysis with the former clarifying, instantiating and perhaps even testing notions of process developed by the latter.

Advances in Social Simulation. Springer, 2020

Agent-based models (ABMs) are sometimes associated with a commitment to methodological individualism (MI) (see, e.g.] In this paper, we explore this linkage. We argue both that ABMs need not, and sometimes should not, be seen as embodying the methodological individualist program. The discussion that follows is intended both for ABM modelers and philosophers of science. That means at times some discussions will either be obvious or of only indirect interest to readers. But in the spirit of trying to build useful bridges we look at ABM from a philosophy perspective. Why is the relation of MI to ABM of interest? The MI debate is one of the most long lasting debates in the philosophy of social science, and we provide an opportunity for MI modelers to step back a bit and reflect on how their work relates to that debate. However, the debate is not purely intellectual. MI matters to the extent that it encourages ABM modelers to work in certain ways or claim certain virtues for their models. We want to show that their models can meet good scientific standards without fitting MI standards. For those MI modelers not already inclined to be individualist, it is nonetheless still good to think explicitly about how the

AI

2003

In the last years a growing community of practitioners of agent based mod-elling1 has tried to rise the methodological and epistemological status of the tool. Since the relatively few years passed from the birth of this kind of tool, its innovative content and the differences inside ...

Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique, 2010

This note is a contribution to the advancement of social simulation as a method to model complex social behaviors with computational tools. We maintain that agent-based models would benefit enormously by the application of qualitative research (in-depth interviews, participant observations and extended case studies). We do this by providing a general analytical framework for empirically informed agent-based simulations and by assessing existing approaches to the instantiation and validation of models via qualitative data. We believe that this methodology would provide present-day agent-based models with a sound and proper insight as to the behavior of social agents-an insight that statistical data often fall short to provide at least at a micro level and for hidden and sensitive populations. On the other hand, simulations would provide qualitative researchers in sociology and anthropology (as well as to associated research fields) with valuable tools for: a) testing the consistency, and pushing the boundaries, of specific theoretical frameworks; b) replicating and generalizing results; c) providing a platform for cross-disciplinary validation of results. 1 While the roots of ABM can be traced back to the 1940s, the methodology has established itself with the surge in computational power of the 1990s and 2000s (Gilbert and Troitzsch 2005). In particular, ABM has been greatly encouraged by advances in simulation languages and editor software, such as SWARM, RePast, Ascape, NetLogo, and Mason. A multidisciplinary community of scholars has contributed to it, including not only social scientists but also psychologists, computer scientists, biologists and evolutionary theorists, and physicists. Wilensky and Rand (2007: 1.2) claim that "thousands of agent-based models have been published in the past few decades". Indeed growing attention by the social science community has propelled pioneering publications like the Journal of Artificial Societies and Social Simulation, Complexity, or Advances in Complex Systems to prominence. It has also inspired several special issues of more generalist journals (Social Sciences Computer Review 2002;

Oxford Bibliographies in Sociology, 2017

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).

This paper provides an overview on the impact of agent-based models in the social sciences. It focuses on the reasons why agest-based models are seen as important innovations in the recent decades. It is aimed to evaluate the impact of this innovation on various disciplines, such as economics, sociology, anthropology, and behavioural sciences. It discusses the advances it contributed to achieve and illustrates some comparatively new fields to which it gave rise. Finally, it emphasizes some research issues that need to be addressed in the future.

The article discusses agent-based simulation as a tool of sociological understanding. Based on an inferential account of understanding, it argues that computer simulations increase our explanatory understanding both by expanding our ability to make what-if inferences about social processes and by making these inferences more reliable. However, our ability to understand simulations limits our ability to understand real world phenomena through them. Thomas Schelling’s checkerboard model of ethnic segregation is used to demonstrate the important role played by abstract how-possibly models in the process of building a mechanistic understanding of social phenomena.

This Book Series is devoted to examining and solving the major methodological problems social sciences are facing. Take for example the gap between empirical and theoretical research, the explanatory power of models, the relevance of multilevel analysis, the weakness of cumulative knowledge, the role of ordinary knowledge in the research process, or the place which should be reserved to "time, change and history" when explaining social facts. These problems are well known and yet they are seldom treated in depth in scientific literature because of their general nature.

It is argued that the formation of disciplines in the social sciences-and sciences in general-is linked to ontological commitments. Thus the development of methodologies in the social sciences analyze and display specific dimensions of social reality. With the proliferation of problem-based social science and interdisciplinary knowledge spaces it is becoming increasingly important for researchers to understand the interrelationships between the structures studied by other disciplines and their own view on reality. In this paper we present a social science laboratory called Virtual Simulation and Analysis of Group Evolution (ViSAGE), which was developed to bridge methods and theories in computer science and social science to study the evolution of social groups. We describe the development of ViSAGE into a tool for traversing various levels and dimensions of sociality.

Physica A: Statistical Mechanics and its Applications, 2004

Abstract The question whether and how to use models in sociology is not a new one but it is still worth to discuss concerning the state of sociological research and current development of research on agent models in sociology and other disciplines. The aim of this paper is to discuss the most e!ective way of using models in sociology by taking into account essential features of social phenomena and the speci"c subject of sociological research. In order to do this I present main problems of research subject and methods in sociology. The article is written for a speci"c purpose, namely, to develop the cooperation between sociology and physics. It is an attempt to present the sociological approach to models for physicists who are not very much familiar with sociological perspective.

Advances in Complex Systems, 2008

Although in many social sciences there is a radical division between studies based on quantitative (e.g. statistical) and qualitative (e.g. ethnographic) methodologies and their associated epistemological commitments, agent-based simulation fits into neither camp, and should be capable of modelling both quantitative and qualitative data. Nevertheless, most agent-based models (ABMs) are founded on quantitative data. This paper explores some of the methodological and practical problems involved in basing an ABM on qualitative participant observation and proposes some advice for modelers.

Models, Simulations, and Representations, ed. by Humphreys and Imbert, 2011

Agent-​​based mod­el­ing is show­ing great promise in the social sci­ences. However, two mis­con­cep­tions about the rela­tion between social macro­prop­er­ties and micro­prop­er­ties afflict agent-based mod­els. These lead cur­rent mod­els to sys­tem­at­i­cally ignore fac­tors rel­e­vant to the prop­er­ties they intend to model, and to over­look a wide range of model designs. Cor­rect­ing for these brings painful trade-​​offs, but has the poten­tial to trans­form the util­ity of such models.

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