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Using Agent-Based Modelling to Inform Policy – What Could Possibly Go Wrong?

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Multi-Agent-Based Simulation XIX (MABS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11463))

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. MultiAgent-Based Simulation (MABS) 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, MABS also brings with it new difficulties and potential confusions. This paper surveys some of the pitfalls that can arise when MABS analysts try to do this. Researchers who claim (or imply) that MABS can reliably predict are criticised in particular. However, an alternative is suggested – that of using MABS 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.

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

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Notes

  1. 1.

    Uncertainty is understood here as a situation where we don’t know what we don’t know [24, 52].

  2. 2.

    This does not mean it is impossible as [53] shows, or that we should not try to predict [54], but that it is only feasible in cases with limited set of outcomes and lots of data. There are no cases I know of where MABS have been predictively reliable.

  3. 3.

    Sometimes it is merely implied that prediction is possible using evasive or unclear language, e.g. meaning prediction of model results only, but allowing the stakeholder to think this means prediction of aspects of reality.

  4. 4.

    The risk vs. uncertainty distinction was originally made by Knight [55].

  5. 5.

    Some of these assumptions, it has been argued, are more tightly related to the demands of mathematics, and the capacities of quantitative models, than anything else [38]. Alongside this, historians have been astute in highlighting that part of the reason this approach to fisheries gained such traction was that it provided an approach that was in line with a number of political and economic objectives [4].

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Acknowledgements

The authors acknowledge funding from the EU’s Marie-Curie Horizon 2020 program as part of the Social Science Aspects of Fisheries for the 21st Century (SAF21) project, number 642080. We thank all those with whom we have had useful discussions on these subjects, including those at the University of Tromsø and at the MABS international workshop in Stockholm, July 2018.

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  1. Centre for Policy Modelling, Manchester Metropolitan University, Manchester, UK

    Bruce Edmonds & Lia ní Aodha

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  1. Bruce Edmonds
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Correspondence to Bruce Edmonds .

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  1. Malmö University, Malmö, Sweden

    Paul Davidsson

  2. Stockholm University, Kista, Sweden

    Harko Verhagen

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Edmonds, B., ní Aodha, L. (2019). Using Agent-Based Modelling to Inform Policy – What Could Possibly Go Wrong?. In: Davidsson, P., Verhagen, H. (eds) Multi-Agent-Based Simulation XIX. MABS 2018. Lecture Notes in Computer Science(), vol 11463. Springer, Cham. https://doi.org/10.1007/978-3-030-22270-3_1

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