Learning Hypothesis Testing and Nash Equilibrium

H. Young

Economics Working Paper Archive from The Johns Hopkins University,Department of Economics

Abstract: Although there exist learning processes for which the empirical distribution of play comes close to Nash equilibrium it is an open question whether the players themselves can learn to play equilibrium strategies without assuming that they have prior knowledge of their opponents' strategies and/or payoffs We exhibit a large class of statistical hypotheses testing procedures that solve this problem Consider a finite stage game G that is repeated infinitely often At each time the players have hypotheses about their opponents' repeated game strategies They frequently test their hypotheses against the opponents' recent actions When a hypotheses fails test a new one is adopted Play is almost rational in the sense that at each point of time the players' strategies are є -best replies to their beliefs We show that at least 1 - є of the time t these hypotheses testing strategies constitute an є-equilibrium of the repeated game from t on; in fact the strategies are close to being subgame perfect for long stretches of time Further all players for whom prediction matters ie whose best responses depend on the opponents' behavior learn to predict within є

Date: 2002-08
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.econ2.jhu.edu/REPEC/papers/WP474.pdf (application/pdf)

Related works:
Journal Article: Learning, hypothesis testing, and Nash equilibrium (2003)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jhu:papers:474

Access Statistics for this paper

More papers in Economics Working Paper Archive from The Johns Hopkins University,Department of Economics 3400 North Charles Street Baltimore, MD 21218. Contact information at EDIRC.
Bibliographic data for series maintained by Humphrey Muturi ().