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A Generalized Approach to Belief Learning in Repeated Games

A Generalized Approach to Belief Learning in Repeated Games
A Generalized Approach to Belief Learning in Repeated Games
We propose a methodology that is generalizable to a broad class of repeated games in order to facilitate operability of belief-learning models with repeated-game strategies. The methodology consists of (1) a generalized repeated-game strategy space, (2) a mapping between histories and repeated-game beliefs, and (3) asynchronous updating of repeated-game strategies. We implement the proposed methodology by building on three proven action-learning models. Their predictions with repeated-game strategies are then validated with data from experiments with human subjects in four, symmetric games: Prisoner's Dilemma, Battle of the Sexes, Stag-Hunt, and Chicken. The models with repeated-game strategies approximate subjects' behavior substantially better than their respective models with action learning. Additionally, inferred rules of behavior in the experimental data overlap with the predicted rules of behavior.
adaptive models, belief learning, repeated-game strategies, finite automata, prisoner's dilemma, battle of the sexes, stag-hunt, chicken
0899-8256
178-203
Ioannou, Christos A.
753c2afb-918b-4576-ba47-da42502f37c9
Romero, Julian
1f0fc2ed-1110-47e0-b98b-c40b56043251
Ioannou, Christos A.
753c2afb-918b-4576-ba47-da42502f37c9
Romero, Julian
1f0fc2ed-1110-47e0-b98b-c40b56043251

Ioannou, Christos A. and Romero, Julian (2014) A Generalized Approach to Belief Learning in Repeated Games. Games and Economic Behavior, 87, 178-203. (doi:10.1016/j.geb.2014.05.007).

Record type: Article

Abstract

We propose a methodology that is generalizable to a broad class of repeated games in order to facilitate operability of belief-learning models with repeated-game strategies. The methodology consists of (1) a generalized repeated-game strategy space, (2) a mapping between histories and repeated-game beliefs, and (3) asynchronous updating of repeated-game strategies. We implement the proposed methodology by building on three proven action-learning models. Their predictions with repeated-game strategies are then validated with data from experiments with human subjects in four, symmetric games: Prisoner's Dilemma, Battle of the Sexes, Stag-Hunt, and Chicken. The models with repeated-game strategies approximate subjects' behavior substantially better than their respective models with action learning. Additionally, inferred rules of behavior in the experimental data overlap with the predicted rules of behavior.

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More information

e-pub ahead of print date: 24 May 2014
Published date: September 2014
Keywords: adaptive models, belief learning, repeated-game strategies, finite automata, prisoner's dilemma, battle of the sexes, stag-hunt, chicken
Organisations: Economics

Identifiers

Local EPrints ID: 336387
URI: http://eprints.soton.ac.uk/id/eprint/336387
ISSN: 0899-8256
PURE UUID: c941060b-195c-4014-8189-a45cce7a5295

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Date deposited: 04 Apr 2012 11:44
Last modified: 14 Mar 2024 10:42

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Contributors

Author: Christos A. Ioannou
Author: Julian Romero

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