Modelling enduring institutions: The complementarity of evolutionary and agent-based approaches

Powers, Simon T., Ekárt, Anikó and Lewis, Peter (2018). Modelling enduring institutions: The complementarity of evolutionary and agent-based approaches. Cognitive Systems Research, 52 , pp. 67-81.


Empirical work has shown that societies can sometimes avoid antisocial outcomes, such as the Tragedy of the Commons, by establishing institutional rules that govern their interactions. Moreover, groups are more likely to avoid antisocial outcomes when they design and enforce their own rules. But this raises the question: when will group members put effort into maintaining their institution so that it continues to provide socially beneficial outcomes? Ostrom derived a set of empirical principles that predict when institutions will endure, which have subsequently been formalised in agent-based models that are based on an executable description of the content of an individual’s behaviour. Here we show how these models can be complemented by evolutionary game theory, which focuses on the value or payoff of different behaviours, rather than on the mechanistic content of the behaviour. Using such a value-based model, we determine exactly when individuals will be incentivised to maintain their institution and enforce its rules, including the critical amount that a group must invest into incentivising agents to monitor rule compliance. We highlight the complementarity of content-based and value-based modelling approaches, and therefore provide a step towards unifying theoretical and empirical approaches to understanding enduring institutions and other social phenomena.

Publication DOI:
Divisions: Engineering & Applied Sciences > Computer Science
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Engineering & Applied Sciences
Additional Information: © 2018, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Uncontrolled Keywords: Institutions,Evolutionary game theory,Agent-based modelling
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Related URLs: https://www.sci ... 389041718301268 (Publisher URL)
Published Online Date: 2018-06-28
Published Date: 2018
Authors: Powers, Simon T.
Ekárt, Anikó ( 0000-0001-6967-5397)
Lewis, Peter ( 0000-0003-4271-8611)



Version: Accepted Version

Access Restriction: Restricted to Repository staff only until 28 December 2019.

License: Creative Commons Attribution Non-commercial No Derivatives

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