Agent-based Modelling for the Social Scientist

24-26 May 2023

Dr. Elisa Volpi, coordinatrice CUSO

Jennifer Badham (Durham University) and Corinna Elsenbroich (University of Glasgow)

The course has three ingredients:

• 1) Lectures: There will be traditional lecture style sessions. These will cover conceptualisation of modelling, i.e. what models might be used for or what you have to think about and specify, as well as practical aspects of agent-based modelling such as validation and experimentation.

• 2) Tutorial: The tutorials are programming sessions in which you will develop a particular model step by step. The sessions have the dual purpose of learning how to program in NetLogo, a specialist agent-based modelling language, and applying the most important concepts of agent-based modelling.

• 3) Discussion: Discussion is for you to explore aspects of agent-based modelling in groups by considering what model you might want to build and what you might need in order to build it.

All of the sessions are carefully integrated into each other to allow for a rounded learning experience. The tutorial is the heart of the course. Note that it is built in a linear way, meaning that it is important to attend every session. It is also built in such a way that it revisits relevant aspects of programming recursively – and it is important that no student works ahead by themselves as it will lead to confusion for the student and most likely hold up the whole class.

Session Description:

Modelling for Complex Systems

Introductory session covering general aspects of seeing society as a complex system, ways of modelling society and some classic agent-based models.

Using NetLogo and Simple Coding

First session where you get your hands dirty. The session covers what NetLogo is, how it works and how you can work with it. It also introduces you to the first steps of programming in NetLogo using a simple model.

Models, Simulation and the Modelling Process

This session provides a conceptual background to modelling. It covers conceptualisations of models and simulations as well as the process of model construction.

Model Uses

Why model? This session will cover different purposes for modelling and go through different specifications of models. It will in particular focus on the kinds of research questions that can (and can't) be answered with an agent-based model.

Verification, Validation, Data, Calibration

How do you know you have got a good model? This session will go through ways of assessing the quality of your model discussing internal consistency, its relationship to reality and ways to find out how good your model is.

Experimentation

This session will go through what needs to be done once the model has been built and checked. Models are not ends in themselves but means to answer research questions. Experiments are done to generate data to answer these questions.

The workshop will take place on three days (May 24 to May 26) from 9:00 to 16:30. For the exact schedule, please contact Elisa Volpi ([email protected]).

Genève (Graduate Institute)

Reading List 

Essential Reading

Gilbert, G. N. (2007) Agent-based models. London: SAGE. Available at: eu.alma.exlibrisgroup.com/view/action/uresolver.do age_service_id=3714069910002346&institutionId=2346&customerId=2345.This is a really good, comprehensive and short introduction to agent-based modelling. Highly recommended.

Railsback, S. F. and Grimm, V. (2012) Agent-based and individual-based modeling: a practical introduction. Princeton: Princeton University Press.
You do not have to read the whole book to begin with, but this will be your go-to place for specific questions.

Squazzoni, F., Jager, W. and Edmonds, B. (2014) 'Social Simulation in the Social Sciences', Social Science Computer Review, 32(3), pp. 279–294. doi: 10.1177/0894439313512975. A good overview of the field.

Chattoe-Brown, E. (2013) 'Why Sociology Should Use Agent Based Modelling', Sociological Research Online, 18(3), pp. 1–11. doi: 10.5153/sro.3055.
Good motivational article.

Recommended Reading

Agent-based models: understanding the economy from the bottom up | Bank of England (no date). Available at: www.bankofengland.co.uk/quarterly-bulletin/2016/q4/agent- based-models-understanding-the-economy-from-the-bottom-up.

Agar, M. 'Agents in Living Color: Towards Emic Agent-Based Models' (2005). JASSS. Available at: jasss.soc.surrey.ac.uk/8/1/4.html.

Badham, J. et al. (2018) 'Developing agent-based models of complex health behaviour', Health & Place, 54, pp. 170–177. doi: 10.1016/j.healthplace.2018.08.022.

Elsenbroich, C. and Badham, Jennifer (2016) 'The Extortion Relationship: A Computational Analysis', Journal of Artificial Societies and Social Simulation, 19(4). Available at: jasss.soc.surrey.ac.uk/19/4/8.html.

Elsenbroich, C., & Badham, J. (2022). Negotiating a Future that is not like the Past. International Journal of Social Research Methodology

Gilbert, G. N. and Troitzsch, K. G. (2005) Simulation for the social scientist. 2nd ed. Maidenhead: Open University Press. Available at: ebookcentral.proquest.com/lib/surrey/detail.action.

Natarajan, S., Padget, J. and Elliott, L. (2011) 'Modelling UK domestic energy and carbon emissions: an agent-based approach', Energy and Buildings, 43(10), pp. 2602–2612. doi: 10.1016/j.enbuild.2011.05.013.

Johnson, J., Nowak, A., Omerod, P., Roswell, B., Zhang, Y. (2017) Non-Equilibrium Social Science and Policy. Springer. Available at: link.springer.com/book/10.1007%2F978-3- 319-42424-8.

Axelrod, Robert (1986), An Evolutionary Approach to Norms, American Political Science Review, Vol. 80, pp. 1095-1111

Gilbert, N., Hawksworth, J. C. and Swinney, P. A. (2008) An agent-based model of the UK housing market. Technical report, CRESS University of Surrey.

Granovetter, Mark (1978), Threshold Models of Collective Behavior, American Sociological Review, Vol. 83, pp. 1420-1442.

Sakoda, J. M. (1971) The checkerboard model of social interaction. Journal of Mathematical Sociology, 1(1):119– 132.

Schelling, T. (1971) Dynamic models of segregation. Journal of Mathematical Sociology, 1:143–186.

Background Reading

Epstein, J.
(1999) Agent-Based Computational Models And Generative Social Science, Complexity, vol. 4, no. 5.

Squazzoni, F. (2010) The impact of agent-based models in the social sciences after 15 years of incursions. History of Economic Ideas, XVIII(2).

Jager, W. (2017) 'Enhancing the Realism of Simulation (EROS): On Implementing and Developing Psychological Theory in Social Simulation', Journal of Artificial Societies and Social Simulation, 20(3). Available at: jasss.soc.surrey.ac.uk/20/3/14.html.

Macy, M. W. and Willer, R. (2002) 'From Factors to Factors: Computational Sociology and Agent-Based Modeling', Annual Review of Sociology, 28(1), pp. 143–166. doi: 10.1146/annurev.soc.28.110601.141117.

Squazzoni, F.(2010) 'THE IMPACT OF AGENT-BASED MODELS IN THE SOCIAL SCIENCES AFTER 15 YEARS OF INCURSIONS', History of Economic Ideas. Accademia Editoriale, 18(2), pp. 197– 233. Available at: www.jstor.org/stable/23723517.

Wilensky, U. and Rand, W. (2015) An introduction to agent-based modeling: modeling natural, social, and engineered complex systems with NetLogo. Cambridge, Massachusetts: MIT Press. Available at: ebookcentral.proquest.com/lib/surrey/detail.action

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