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Learning in Games with Unstable Equilibria

  • Michel Benaim
  • Josef Hofbauer
  • Ed Hopkins

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.

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Paper provided by UCLA Department of Economics in its series Levine's Bibliography with number 321307000000000547.

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Date of creation: 27 Oct 2006
Handle: RePEc:cla:levrem:321307000000000547
Contact details of provider: Web page: http://www.dklevine.com/

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