Show simple item record

Learning and Beliefs in Non-Centralized Markets.

dc.contributor.authorTablante, Bartolome W.en_US
dc.date.accessioned2015-09-30T14:24:50Z
dc.date.availableNO_RESTRICTIONen_US
dc.date.available2015-09-30T14:24:50Z
dc.date.issued2015en_US
dc.date.submitted2015en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/113592
dc.description.abstractThis dissertation uses microeconomic theory to examine learning and beliefs in markets that are not centralized, in order to refine our knowledge of how information asymmetries affect market outcomes. I approach problems in this space from a game theoretic perspective, first building an appropriate market game to study the question of interest, then characterizing the equilibria of the game, and finally applying those characterizations to the initial problem. The dissertation consists of three distinct chapters. The first of these examines the conditions that affect learning and efficiency in a decentralized market. Past research addressing this issue has shown in several models that learning is difficult and asymmetric information is costly. In a steady state environment, the first chapter shows that both learning and efficiency are possible if and only if the good being traded is divisible. This chapter identifies the divisibility of trade as a significant friction in decentralized markets. The second chapter studies how beliefs regarding a market front-runner affect surplus in a fragmented market. A front-runner is an agent who has advance knowledge of market orders, gained from either an illegal or a technological advantage. So far, research involving front-runners has placed restrictions on the behavior of other agents, assuming that agents cannot change their strategies based on the presence of a front-runner. This work provides a framework to relax those restrictions in order to allow agents to best respond to a front-runner. The research then suggests that the impact of a front-runner on traders' strategies generally leads to a decrease in total surplus. The third chapter again contributes to the literature on learning and efficiency in a decentralized market. This chapter studies the conditions that are necessary for learning and long-run efficiency in a non-stationary environment. Past research has obtained long-run efficiency if trade is divisible and bargaining is flexible; this research shows that only divisible trade is necessary. Furthermore, while past research requires traders use complex strategies in order to learn, our research demonstrates that traders may use simple strategies, and will learn by participating in the market and observing the actions of their match partners.en_US
dc.language.isoen_USen_US
dc.subjectmicroeconomic theoryen_US
dc.subjectdecentralized marketsen_US
dc.titleLearning and Beliefs in Non-Centralized Markets.en_US
dc.typeThesisen_US
dc.description.thesisdegreenamePhDen_US
dc.description.thesisdegreedisciplineEconomicsen_US
dc.description.thesisdegreegrantorUniversity of Michigan, Horace H. Rackham School of Graduate Studiesen_US
dc.contributor.committeememberBorgers, Tilman M.en_US
dc.contributor.committeememberWellman, Michael P.en_US
dc.contributor.committeememberLauermann, Stephanen_US
dc.contributor.committeememberMiller, David A.en_US
dc.subject.hlbsecondlevelEconomicsen_US
dc.subject.hlbtoplevelBusiness and Economicsen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/113592/1/tablante_1.pdf
dc.owningcollnameDissertations and Theses (Ph.D. and Master's)


Files in this item

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.