@misc{discovery14516,
          series = {ELSE Working Papers},
          number = {213},
           title = {Learning in games with unstable equilibria},
            year = {2006},
           month = {June},
         address = {London, UK},
       publisher = {ESRC Centre for Economic Learning and Social Evolution},
             url = {http://else.econ.ucl.ac.uk/newweb/papers.php#2006},
          author = {Benaim, M. and Hofbauer, J. and Hopkins, E.},
        abstract = {We propose a new concept for the analysis of games, the TASP, which gives
a precise prediction about non-equilibrium play in 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, the time average of play often
converges in these "unstable" games, even while mixed strategies and beliefs continue
to cycle. This time average, the TASP, is related to the best response
cycle first identified by Shapley (1964). Though conceptually distinct from Nash
equilibrium, for many games the TASP is close enough to Nash 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 = {JEL classification: C72, C73, D83. Games, learning, best response dynamics, stochastic fictitious play,
mixed strategy equilibria, TASP}
}