Overview
Agent-based modelling and simulation (ABMS) is a method that enables researchers to create a computer model and simulation of active entities and their behaviour and interaction with each other and their environment. These interacting, autonomous and adaptive agents can be individuals, households, groups, organisations, vehicles, equipment, products or cells, in social and evolutionary settings, for example. ABMS is a specific type of computer model and simulation: other types and a general discussion of modelling and simulation can be found in Chapter 7. There are different terms that are used to describe the same, or similar techniques, and these include agent-based computational modelling, agent-based modelling (ABM), agent-based simulation modelling, agent-based social simulation (ABSS) and agent-based simulation. It is important to note that models provide representations whereas simulations use models (or simulate the outcomes of models) for study and analysis: ABMS is a term that covers both.
ABMS enables researchers to build models of systems from the bottom up (micro to macro), with the aim of producing simulations in which patterns, structures and behaviours emerge from agent interaction. Models and simulations can be exploratory, descriptive and predictive: they can be used to provide insight into behaviour and decision-making, make predictions about future behaviour and trends or help to analyse, validate or explain data collected from other sources. Models and simulations can be relational, dynamic, responsive and adaptive. For example, agents:
- respond to the actions of others;
- respond to environmental stimuli;
- influence each other;
- learn from each other;
- learn from their experiences;
- adapt their behaviour as a result of other agentsâ behaviour;
- adapt their behaviour to suit their environment.
Researchers from a variety of disciplines and areas of study use ABMS including sociology and social psychology (Chattoe-Brown, 2014; Conte and Paolucci, 2014; Eberlen et al., 2017), geography (Millington and Wainwright, 2017), health and medicine (Auchincloss and Garcia, 2015), economics (Caiani et al., 2016), politics (Fieldhouse et al., 2016), the sports sciences (Lauren et al., 2013); the environmental sciences (Kerridge et al., 2001; Sun and Taplin, 2018) and the computer sciences (Abar et al., 2017). Examples of research projects that have used ABMS include a study that adapts principles of developmental biology and agent-based modelling for automated urban residential layout design (Sun and Taplin, 2018); research into pedestrian flow and movement (Kerridge et al., 2001); research into the interaction between the development of creative industries and urban spatial structure (Liu and Silva, 2018); research that helps to predict rates of burglary (Malleson et al., 2009); a study to model, simulate and test tactics in the sport of rugby union (Lauren et al., 2013); and research into voter turnout (Fieldhouse et al., 2016).
If you are interested in finding out more about ABMS, and using it for your research, a good reference to begin with is Silverman et al. (2018), which is an open access book that provides in-depth coverage of methodological issues and complexities associated with ABM and the social sciences. A useful reference for those working within, or studying, economics is Hamill and Gilbert (2016), which provides a practical introduction and history to ABM methods and techniques. Another is Caiani et al. (2016), which provides a practical guide and basic toolkit that highlights practical steps in model building and is aimed at undergraduates, postgraduates and lecturers in economics. A useful reference for those working within geography (and who are interested in mixed methods approaches) is Millington and Wainwright (2017: 68) who discuss âmixed qualitative-simulation methods that iterate back-and-forth between âthickâ (qualitative) and âthinâ (simulation) approaches and between the theory and data they produceâ. Auchincloss and Garcia (2015) provide a brief introduction to carrying out a simple agent-based model in the field of urban health research. Chapter 7 provides a useful overview of computer modelling and simulation and contains additional references and relevant questions for reflection. If you are interested in predictive modelling, more information can be found in Chapter 45.
Key texts
Abar, S., Theodoropoulos, G., Lemarinier, P. and OâHare, G. (2017) âAgent Based Modelling and Simulation Tools: A Review of the State-of-Art Softwareâ, Computer Science Review, 24, 13â33, May 2017, 10.1016/j.cosrev.2017.03.001.
Auchincloss, A. and Garcia, L. (2015) âBrief Introductory Guide to Agent-Based Modeling and an Illustration from Urban Health Researchâ, Cadernos De Saude Publica, 31(1), 65â78, 10.1590/0102-311X00051615.
Caiani, A., Russo, A., Palestrini, A. and Gallegati, M. (eds.) (2016) Economics with Heterogeneous Interacting Agents: A Practical Guide to Agent-Based Modeling. Cham: Springer.
Chattoe-Brown, E. (2013) âWhy Sociology Should Use Agent Based Modellingâ, Sociological Research Online, 18(3), 1â11, first published August 31, 2013, 10.5153/sro.3055.
Chattoe-Brown, E. (2014) âUsing Agent Based Modelling to Integrate Data on Attitude Changeâ, Sociological Research Online, 19(1), 1â16, first published March 5, 2014, 10.5153/sro.3315.
Conte, R. and Paolucci, M. (2014) âOn Agent-Based Modeling and Computational Social Scienceâ, Frontiers in Psychology, 5(668), first published July 14, 2014, 10.3389/fpsyg.2014.00668.
Eberlen, J., Scholz, G. and Gagliolo, M. (2017) âSimulate This! An Introduction to Agent-Based Models and Their Power to Improve Your Research Practiceâ, International Review of Social Psychology, 30(1), 149â160. 10.5334/irsp.115.
Fieldhouse, E., Lessard-Phillips, L. and Edmonds, B. (2016) âCascade or Echo Chamber? A Complex Agent-Based Simulation of Voter Turnoutâ, Party Politics, 22(2), 241â256, first published October 4, 2015, .
Hamill, L. and Gilbert, N. (2016) Agent-Based Modelling in Economics. Chichester: John Wiley & Sons Ltd.
Jackson, J., Rand, D., Lewis, K., Norton, M. and Gray, K. (2017) âAgent-Based Modeling: A Guide for Social Psychologistsâ, Social Psychological and Personality Science, 8(4), 387â395, first published March 13, 2017, 10.1177/1948550617691100.
Kerridge, J., Hine, J. and Wigan, M. (2001) âAgent-Based Modelling of Pedestrian Movements: The Questions that Need to Be Asked and Answeredâ, Environment and Planning B: Urban Analytics and City Science, 28(3), 327â341, first published June 1, 2001, 10.1068/b2696.
Lauren, M., Quarrie, K. and Galligan, D. (2013) âInsights from the Application of an Agent-Based Computer Simulation as a Coaching Tool for Top-Level Rugby Unionâ, International Journal of Sports Science & Coaching...
