SIMSOC: What is your favourite ABM paper, and why?

On 20th October 2025, I asked the SIMSOC email list what is your favourite ABM paper, and why?. There were some great responses, which I’ve summarised here (with links to the original messages in case you’d like more detail).

Edmonds, B., Le Page, C., Bithell, M., Chattoe-Brown, E., Grimm, V., Meyer, R., Montañola-Sales, C., Ormerod, P., Root, H., & Squazzoni, F. (2019). Different Modelling Purposes. JASSS, 22(3), 6.

It provides a great overview of the different purposes ABMs can serve, and how we can justify their use depending on those purposes. This paper really changed my view of ABMs and made me feel I was standing on much more solid ground when applying them. It’s also a great starting point for anyone new to the field, as it helps discover related papers connected to the various uses of ABMs. (Vittorio Nespeca).

Bonabeau, E. (2002) Agent-Based Modeling: Methods and Techniques for Simulating Human Systems. Proceedings of the National Academy of Sciences of the United States of America, 99, 7280-7287. https://www.pnas.org/doi/10.1073/pnas.082080899.

Provides a holistic overview of the topic, explaining ABM as both a conceptual mindset and a practical implementation approach. The work clarifies that ABM is fundamentally about microscopic, agent-level modelling rather than macroscopic or aggregate approaches. Rather than presenting abstract theory, it offers concrete case studies across flows, markets, organisations, and diffusion processes. It also explains emergent phenomena, showing how simple individual rules generate counterintuitive collective behaviour. (Peer-Olaf Siebers). (Also one of my favouries).

Farmer (2025) Quantitative agent-based models: a promising alternative for macroeconomics. Oxford Review of Economic Policy, graf027. https://doi.org/10.1093/oxrep/graf027

It has been highly updated and is strongly assertive. (Pietro Terna).

Axtell, R.L., Farmer, J.D., 2025. Agent-Based Modeling in Economics and Finance: Past, Present, and Future. Journal of Economic Literature 63, 197–287. https://doi.org/10.1257/jel.20221319

Emphasises economics and finance, but has much broader scope and appeal. It gives a very thorough (91 page) overview of ABM research. They cover all the basics and put ABMs in a broader scientific, methodological context. I really think anyone serious about ABMs cannot afford to have missed this article. Also see Farmer’s recent book Making Sense of Chaos which is plainly the best advert of ABMs I have read. Ever. (Fjalar de Haan).

Jason Thompson also offered this fantastic list from a workshop that took place in Leiden earlier in the year:

• Templeton, A., Xie, H., Gwynne, S., Hunt, A., Thompson, P. and Köster, G., 2024. Agent-based models of social behaviour and communication in evacuations: A systematic review. Safety Science, 176, p.106520.

• Advising governments about science is essential but difficult. So train people to do it. https://www.nature.com/articles/d41586-024-03910-4

• Brouwers, L. and Verhagen, H., 2003. Applying the Consumat model to flood management policies. In Agent-based simulations.

• Giardini, F. and Vilone, D., 2021. Opinion dynamics and collective risk perception: An agent-based model of institutional and media communication about disasters. JASSS - The Journal of Artificial Societies and Social Simulation, 24(1), p.4.

• Oliver, K., Boaz, A. Transforming evidence for policy and practice: creating space for new conversations. Palgrave Communications 5, 60 (2019). https://doi.org/10.1057/s41599-019-0266-1

• Hammond, R.A. and Barkin, S., 2024. Making evidence go further: Advancing synergy between agent-based modeling and randomized control trials. Proceedings of the National Academy of Sciences, 121(21), p.e2314993121.

• Laurila-Pant, M., Pihlajamäki, M., Lanki, A. and Lehikoinen, A., 2023. A protocol for analysing the role of shared situational awareness and decision-making in cooperative disaster simulations. International Journal of Disaster Risk Reduction, 86, p.103544.

• Lammers, W. Supporting policy with scientific evidence. 2024. https://knowledge4policy.ec.europa.eu/blog/evidence-policy-makers-matter-timing-uncertainty_en

• Bifulco, L., Centemeri, L. and Mozzana, C., 2022. For preparedness as transformation. Sociologica.

• Padgham, L., Nagel, K., Singh, D. and Chen, Q., 2014. Integrating BDI agents into a MATSim simulation. In ECAI 2014 (pp. 681–686). IOS Press.

• UKRI MRC Epidemiology Unit. Integrated Transport and Health Modelling Tool. https://www.mrc-epid.cam.ac.uk/research/research-areas/public-health-modelling/ithim/

• Dilo, A. and Zlatanova, S., 2008. Spatiotemporal data modeling for disaster management in the Netherlands. Information Systems for Crisis Response and Management, pp.517–528.

• Tucker, J. and Lorig, F., 2024. Agent-based social simulations for health crises response: utilising the everyday digital health perspective. Frontiers in Public Health, 11, p.1337151.

• Box, G.E., 1976. Science and statistics. Journal of the American Statistical Association, 71(356), pp.791–799.

• Waldherr, A. and Wijermans, N., 2013. Communicating social simulation models to sceptical minds. Journal of Artificial Societies and Social Simulation, 16(4), p.13.

• Dietvorst, B.J., Simmons, J.P. and Massey, C., 2018. Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Management Science, 64(3), pp.1155–1170.

• Carter, W. and Wynne, K.T. Integrating artificial intelligence into team decision‐making: Toward a theory of AI–human team effectiveness. European Management Review.

• UK Government Office for Science and Council for Science and Technology: Computational Modelling: Technological Futures. https://assets.publishing.service.gov.uk/media/5a8c1a6fe5274a1277ce76c4/computational-modelling-blackett-review.pdf

• SNC Lavalin and Atkins. NTM Future Development Quality Report: Department For Transport. 2019. https://assets.publishing.service.gov.uk/media/616004b9d3bf7f56080b1a50/ntm-future-development-quality-report.pdf