Skip to main content
Article thumbnail
Location of Repository

Learning Across Games ∗

By Friederike Mengel, David Easley and Ani Guerdjikova

Abstract

In this paper learning of decision makers that face many different games is studied. As learning separately for all games can be too costly (require too much reasoning resources) agents are assumed to partition the set of all games into analogy classes. Partitions of higher cardinality are more costly. A process of simultaneous learning of actions and partitions is presented and equilibrium partitions and action choices characterized. The model is able to explain deviations from subgame perfection that are sometimes observed in experiments even for vanishingly small reasoning costs. Furthermore it is shown that learning across games can stabiliz

Topics: Game Theory, Bounded Rationality, Learning, Analogies. Comments welcome
Year: 2007
OAI identifier: oai:CiteSeerX.psu:10.1.1.352.4501
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.