Bounded rationality and learning in dynamic programming environments

Simple item page
buir.advisorBaşçı, Erdem
dc.contributor.authorErdem, Mahmut
dc.date.accessioned2016-01-08T18:09:33Z
dc.date.available2016-01-08T18:09:33Z
dc.date.issued2001
dc.descriptionAnkara : Department of Economics and the Institute of Economics and Social Sciences of Bilkent University, 2001.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2001.en_US
dc.descriptionIncludes bibliographical references.en_US
dc.description.abstractThe purpose of this thesis is to explain “excess sensitivity” puzzle observed in consumption behavior an alternative way. By deviating from full optimization axiom, in a dynamic extension of Arthur’s stochastic decision model, it was observed that a tendency of excess consumption following temporary income shock prevails. Another main technical contribution achieved in this thesis is in modelling behavior and learning in intertemporal decision problems. In particular, an extension of Arthur’s type of behavior to dynamic situations and comparison of the corresponding values with those of Bellman’s dynamic programming solution is achieved. Moreover it was shown by using stochastic approximation theory that classifier systems learning ends up at the ‘strength’ values corresponding to the Arthur’s value function.en_US
dc.description.provenanceMade available in DSpace on 2016-01-08T18:09:33Z (GMT). No. of bitstreams: 1 0001612.pdf: 473247 bytes, checksum: 87bcedab9941f7ee7a46a33b0088142e (MD5)en
dc.description.statementofresponsibilityErdem, Mahmuten_US
dc.format.extent26 leaves, graphicsen_US
dc.identifier.urihttp://hdl.handle.net/11693/14852
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDynamic programmingen_US
dc.subjectconsumptionen_US
dc.subjectexcess sensitivity puzzleen_US
dc.subjectstochastic approximation theoryen_US
dc.subjectclassifier systems learningen_US
dc.subjectvalue functionen_US
dc.subject.lccHB801 .E73 2001en_US
dc.subject.lcshConsumption (Economics)--Mathematical models.en_US
dc.subject.lcshDynamic programming.en_US
dc.subject.lcshDemand function (Economic theory).en_US
dc.subject.lcshStochastic control theory--Mathematical models.en_US
dc.subject.lcshEquilibrium (Economics).en_US
dc.subject.lcshStatistics and dynamics (Social sciences).en_US
dc.titleBounded rationality and learning in dynamic programming environmentsen_US
dc.typeThesisen_US
thesis.degree.disciplineEconomics
thesis.degree.grantorBilkent University
thesis.degree.levelMaster's
thesis.degree.nameMA (Master of Arts)

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
0001612.pdf
Size:
462.16 KB
Format:
Adobe Portable Document Format
Download

Collections

Graduate School of Economics and Social Sciences