Discussion papers

CPM-2019-214 - 9 May 2019

Published: Using agent-based simulation to inform policy – what could possibly go wrong?

A paper by Bruce Edmonds and Lia ní Aodha, based on the invited talk give at MABS 2018 (a version of which is to be published in the MABS 2018 collection). It looks at the potential dangers of mixing complex modelling and policy, and suggests a positive way forward. Very relevant to the debates about COVID19 and modelling happening at the moment.

Bruce Edmonds and Lia ní Aodha

“Naturally, politicians will look for any information or argument they can find to advance their agendas-that is their job” [1, p. 83]

Abstract. Scientific modelling can make things worse, as in the case of the North Atlantic Cod Fisheries Collapse. Some of these failures have been attributed to the simplicity of the models used compared to what they are trying to model. Agent-Based Modelling (ABM) pushes the boundaries of what can be simulated, prompting many to assume that it can usefully inform policy, even in the face of complexity. That said, ABM also brings with it new difficulties and potential confusions. This paper surveys some of the pitfalls that can arise when ABM analysts try to do this. Researchers who claim (or imply) that ABM can reliably predict complex phenomena are criticised in particular. However, an alternative is suggested – that of using ABM for a kind of uncertainty analysis – identifying some of the possible ways a policy can go wrong (or indeed go right). A fisheries example is given. This alternative may widen, rather than narrow, the range of evidence and possibilities that are considered, which could enrich the policy-making process. We call this Reflexive Possibilistic Modelling.

Published as:

Edmonds, B. & Adoha, L. (2019) Using agent-based simulation to inform policy – what could possibly go wrong? In Davidson, P. & Verhargen, H. (Eds.) (2019). Multi-Agent-Based Simulation XIX, 19th International Workshop, MABS 2018, Stockholm, Sweden, July 14, 2018, Revised Selected Papers. Lecture Notes in AI, 11463, Springer, pp. 1-16. DOI: 10.1007/978-3-030-22270-3_1

A version available here: