eprintid: 14516
rev_number: 33
eprint_status: archive
userid: 600
dir: disk0/00/01/45/16
datestamp: 2009-07-29 15:09:25
lastmod: 2015-07-23 09:36:19
status_changed: 2009-07-29 15:09:25
type: working_paper
metadata_visibility: show
creators_name: Benaim, M.
creators_name: Hofbauer, J.
creators_name: Hopkins, E.
creators_id: 
creators_id: JHOFB46
creators_id: 
title: Learning in games with unstable equilibria
ispublished: pub
subjects: 13200
subjects: 10700
divisions: F59
keywords: JEL classification: C72, C73, D83. Games, learning, best response dynamics, stochastic fictitious play,
mixed strategy equilibria, TASP
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.
date: 2006-06
publisher: ESRC Centre for Economic Learning and Social Evolution
official_url: http://else.econ.ucl.ac.uk/newweb/papers.php#2006
vfaculties: VMPS
oa_status: green
language: eng
primo: open
primo_central: open_green
lyricists_name: Hofbauer, J
lyricists_id: JHOFB46
full_text_status: public
series: ELSE Working Papers
number: 213
place_of_pub: London, UK
citation:        Benaim, M.;    Hofbauer, J.;    Hopkins, E.;      (2006)    Learning in games with unstable equilibria.                    (ELSE Working Papers  213). ESRC Centre for Economic Learning and Social Evolution: London, UK.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/14516/1/14516.pdf