Paper on some of the things that can go wrong when modellers interact with Policy Actors

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

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 (Open access version available at: http://cfpm.org/discussionpapers/236)

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.

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