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

  • Bruce EdmondsEmail author
  • Lia ní Aodha
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Notes

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Centre for Policy ModellingManchester Metropolitan UniversityManchesterUK

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