TY  - GEN
N2  - 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.
PB  - ESRC Centre for Economic Learning and Social Evolution
Y1  - 2006/06//
A1  - Benaim, M.
A1  - Hofbauer, J.
A1  - Hopkins, E.
T3  - ELSE Working Papers
CY  - London, UK
ID  - discovery14516
AV  - public
UR  - http://else.econ.ucl.ac.uk/newweb/papers.php#2006
TI  - Learning in games with unstable equilibria
KW  - JEL classification: C72
KW  -  C73
KW  -  D83. Games
KW  -  learning
KW  -  best response dynamics
KW  -  stochastic fictitious play
KW  - 
mixed strategy equilibria
KW  -  TASP
ER  -