ASPIRATION-BASED REINFORCEMENT LEARNING IN REPEATED INTERACTION GAMES: AN OVERVIEW

Jonathan Bendor (), Dilip Mookherjee and Debraj Ray
Additional contact information
Jonathan Bendor: Graduate School of Business, Stanford University, 518 Memorial Way, Stanford, CA 94305-5015, USA

International Game Theory Review (IGTR), 2001, vol. 03, issue 02n03, 159-174

Abstract: In models of aspiration-based reinforcement learning, agents adapt by comparing payoffs achieved from actions chosen in the past with an aspiration level. Though such models are well-established in behavioural psychology, only recently have they begun to receive attention in game theory and its applications to economics and politics. This paper provides an informal overview of a range of such theories applied to repeated interaction games. We describe different models of aspiration formation: where (1) aspirations are fixed but required to be consistent with longrun average payoffs; (2) aspirations evolve based on past personal experience or of previous generations of players; and (3) aspirations are based on the experience of peers. Convergence to non-Nash outcomes may result in either of these formulations. Indeed, cooperative behaviour can emerge and survive in the long run, even though it may be a strictly dominated strategy in the stage game, and despite the myopic adaptation of stage game strategies. Differences between reinforcement learning and evolutionary game theory are also discussed.

JEL-codes: B4 C0 C6 C7 D5 D7 M2 (search for similar items in EconPapers)
Date: 2001
References: Add references at CitEc
Citations: View citations in EconPapers (25)

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219198901000348
Access to full text is restricted to subscribers

Related works:
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:wsi:igtrxx:v:03:y:2001:i:02n03:n:s0219198901000348

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219198901000348

Access Statistics for this article

International Game Theory Review (IGTR) is currently edited by David W K Yeung

More articles in International Game Theory Review (IGTR) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().