Do players learn how to learn? : evidence from conctant sum games with varying number of actions

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buir.advisorHasker, Kevin
dc.contributor.authorSaraçgil, İhsan Erman
dc.date.accessioned2016-01-08T18:09:41Z
dc.date.available2016-01-08T18:09:41Z
dc.date.issued2009
dc.descriptionAnkara : The Department of Economics, Bilkent University, 2009.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2009.en_US
dc.descriptionIncludes bibliographical references leaves 52-54.en_US
dc.description.abstractThis thesis investigates the learning behaviour of individuals in strategic environments that have different complexity levels. A new experiment is conducted in which ascending or descending series of constant sum games are played by subjects and the experimental data including both stated beliefs and actual plays are used to estimate which learning model explains the subjects’ behaviour best within and across these games. Taking learning rules that model the opponent as a learning agent and heterogeneity of the population into consideration, the estimation results support that people switch learning rules across games and use different models in different games. This game-dependency is confirmed by both action, beliefs and the joint estimations. Although their likelihoods vary from game to game, best response to uniform beliefs and reinforcement learning are the most commonly used learning rules in the four games considered in the experiment, while fictitious play and iterations on that are rare instances observed only in estimation by stated beliefs. Despite the change across games, there is no significant link between complexity of the game and the cognitive hierarchy of learning models. Belief statements and best response behaviour also differ across games as we observepeople making smoother guesses in large action games and more dispersed beliefs statements in small action games. Inconsistency between actions and stated beliefs is stronger in large action games. The evidence strongly supports that learning and belief formation are both game-dependent.en_US
dc.description.provenanceMade available in DSpace on 2016-01-08T18:09:41Z (GMT). No. of bitstreams: 1 0003799.pdf: 720837 bytes, checksum: 33382b8185889ec3d3c0b977198fee14 (MD5)en
dc.description.statementofresponsibilitySaraçgil, İhsan Ermanen_US
dc.format.extentxi, 63 leaves, tables, graphsen_US
dc.identifier.urihttp://hdl.handle.net/11693/14859
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectReinforcement learningen_US
dc.subjectexperimental economicsen_US
dc.subjectconstant sum gamesen_US
dc.subjectelicited beliefs,en_US
dc.subjectiterated best responseen_US
dc.subjectfictitious playen_US
dc.subject.lccQ325.6 .S37 2009en_US
dc.subject.lcshReinforcement learning (Machine learning)en_US
dc.subject.lcshGame theory.en_US
dc.titleDo players learn how to learn? : evidence from conctant sum games with varying number of actionsen_US
dc.typeThesisen_US
thesis.degree.disciplineEconomics
thesis.degree.grantorBilkent University
thesis.degree.levelMaster's
thesis.degree.nameMA (Master of Arts)

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