Learning to Be Thoughtless: Social Norms and Individual Computation.
This paper extends the literature on the evolution of norms with an agent-based model capturing a phenomenon that has been essentially ignored, namely that individual thought--or computing--is often inversely related to the strength of a social norm. Once a norm is entrenched, we conform thoughtlessly. In this model, agents learn how to behave (what norm to adopt), but--under a strategy I term Best Reply to Adaptive Sample Evidence--they also learn how much to think about how to behave. How much they are thinking affects how they behave, which--given how others behave--affects how much they think. In short, there is feedback between the social (inter-agent) and internal (intra-agent) dynamics. In addition, we generate the stylized facts regarding the spatio-temporal evolution of norms: local conformity, global diversity, and punctuated equilibria. Copyright 2001 by Kluwer Academic Publishers
Year of publication: |
2001
|
---|---|
Authors: | Epstein, Joshua M |
Published in: |
Computational Economics. - Society for Computational Economics - SCE, ISSN 0927-7099. - Vol. 18.2001, 1, p. 9-24
|
Publisher: |
Society for Computational Economics - SCE |
Saved in:
freely available
Saved in favorites
Similar items by person
-
POLICY RESPONSE TO PANDEMIC INFLUENZA: THE VALUE OF COLLECTIVE ACTION
Bobashev, Georgiy, (2011)
- More ...