This paper extends the literature on the evolution of norms with anagent-based modelcapturing a phenomenon that has been essentially ignored, namely thatindividual thought – orcomputing – is often inversely related to the strength of a social norm.Once a norm isentrenched, we conform thoughtlessly. In this model, agents learn how tobehave (what normto adopt), but – under a strategy I term Best Reply to Adaptive SampleEvidence – they also learnhow much to think about how to behave. How much they are thinking affects howthey behave,which – given how others behave – affects how much they think. Inshort, there is feedbackbetween the social (inter-agent) and internal (intra-agent) dynamics. Inaddition, we generate thestylized facts regarding the spatio-temporal evolution of norms: localconformity, global diversity,and punctuated equilibria.
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Epstein, J.M. Learning to Be Thoughtless: Social Norms and Individual Computation. Computational Economics 18, 9–24 (2001). https://doi.org/10.1023/A:1013810410243
- agent-based computational economics
- evolution of norms