Fast Convergence in Evolutionary Equilibrium Selection

H. Young and Gabriel E. Kreindler

No 569, Economics Series Working Papers from University of Oxford, Department of Economics

Abstract: Stochastic learning models provide sharp predictions about equilibrium selection when the noise level of the learning process is taken to zero. The difficulty is that, when the noise is extremely small, it can take an extremely long time for a large population to reach the stochastically stable equilibrium. An important exception arises when players interact locally in small close-knit groups; in this case convergence can be rapid for small noise and an arbitrarily large population. We show that a similar result holds when the population is fully mixed and there is no local interaction. Selection is sharp and convergence is fast when the noise level is 'fairly' small but not extremely small.

Keywords: Stochastic stability; Logit learning; Markov chain; Convergence time (search for similar items in EconPapers)
JEL-codes: C72 C73 (search for similar items in EconPapers)
Date: 2011-09-01
New Economics Papers: this item is included in nep-evo and nep-gth
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://ora.ox.ac.uk/objects/uuid:11f08314-75cf-4aa0-b9ea-43a800baf36d (text/html)

Related works:
Journal Article: Fast convergence in evolutionary equilibrium selection (2013)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oxf:wpaper:569

Access Statistics for this paper

More papers in Economics Series Working Papers from University of Oxford, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Anne Pouliquen ( this e-mail address is bad, please contact ).