Some Pitfalls to Beware When Applying Models to Issues of Policy Relevance

  • Lia ní AodhaEmail author
  • Bruce Edmonds
Part of the Understanding Complex Systems book series (UCS)


This chapter looks at some of the ways things can go wrong when mathematical or computational models are applied to inform policy on important issues. It looks at some of the pitfalls in the model construction and development phase, including choosing assumptions, the effect of ‘theoretical spectacles’, oversimplified models, not understanding model limitations, and not testing a model enough. It then goes on to discuss the pitfalls that can occur when a model is applied to inform policy, including entrenched policies based on models with little or no evidential support and how models can narrow the evidential base considered. It also looks at confusions concerning model purpose and kinds of question they may answer, when models are used out of context, asking unreasonable things of models, when the uncertainties are too great, when models give a false sense of security, and when the focus should be on values rather than facts. This discussion is then illustrated with two examples, one economic and one from fisheries. It concludes that most of these problems stem from the interface between the modelling and policy worlds. It ends with some simple recommendations to reduce these mistakes.


Policy Pitfalls Mental model Abstraction Formal model Transparency Political process Assumptions Reality Theoretical spectacles Oversimplification Model limitations Entrenched models Evidential support Evil models Model purpose Prediction Context of development Context of application Uncertainty False security Austerity policies Newfoundland cod 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Centre for Policy ModellingManchester Metropolitan UniversityManchesterUK

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