Learning in Games with Unstable Equilibria

Ed Hopkins, Josef Hofbauer and Michel Benaim

Edinburgh School of Economics Discussion Paper Series from Edinburgh School of Economics, University of Edinburgh

Abstract: We investigate games whose Nash equilibria are mixed and are unstable under fictitious play-like learning processes. We show that when players learn using weighted stochastic fictitious play and so place greater weight on more recent experience that the time average of play often converges in these "unstable" games, even while mixed strategies and beliefs continue to cycle. This time average is related to the best response cycle first identified by Shapley (1964). For many games, the time average is close enough to Nash equilibrium to create the appearance of convergence to equilibrium. We discuss how these theoretical results may help to explain data from recent experimental studies of price dispersion.

Keywords: games; learning; best response dynamics; stochastic fictitious play; mixed strategy equilibria; TASP (search for similar items in EconPapers)
JEL-codes: C72 C73 D84 (search for similar items in EconPapers)
Pages: 36
Date: 2005-07
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 (4)

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http://www.econ.ed.ac.uk/papers/id135_esedps.pdf

Related works:
Journal Article: Learning in games with unstable equilibria (2009)
Working Paper: Learning in Games with Unstable Equilibria (2006)
Working Paper: Learning in Games with Unstable Equilibria (2005)
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Persistent link: https://EconPapers.repec.org/RePEc:edn:esedps:135

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