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Practical Strategic Reasoning with Applications in Market Games.

dc.contributor.authorJordan, Patrick R.en_US
dc.date.accessioned2010-06-03T15:39:01Z
dc.date.availableNO_RESTRICTIONen_US
dc.date.available2010-06-03T15:39:01Z
dc.date.issued2010en_US
dc.date.submitteden_US
dc.identifier.urihttps://hdl.handle.net/2027.42/75848
dc.description.abstractStrategic reasoning is part of our everyday lives: we negotiate prices, bid in auctions, write contracts, and play games. We choose actions in these scenarios based on our preferences, and our beliefs about preferences of the other participants. Game theory provides a rich mathematical framework through which we can reason about the influence of these preferences. Clever abstractions allow us to predict the outcome of complex agent interactions, however, as the scenarios we model increase in complexity, the abstractions we use to enable classical game-theoretic analysis lose fidelity. In empirical game-theoretic analysis, we construct game models using empirical sources of knowledge—such as high-fidelity simulation. However, utilizing empirical knowledge introduces a host of different computational and statistical problems. I investigate five main research problems that focus on efficient selection, estimation, and analysis of empirical game models. I introduce a flexible modeling approach, where we may construct multiple game-theoretic models from the same set of observations. I propose a principled methodology for comparing empirical game models and a family of algorithms that select a model from a set of candidates. I develop algorithms for normal-form games that efficiently identify formations—sets of strategies that are closed under a (correlated) best-response correspondence. This aids in problems, such as finding Nash equilibria, that are key to analysis but hard to solve. I investigate policies for sequentially determining profiles to simulate, when constrained by a budget for simulation. Efficient policies allow modelers to analyze complex scenarios by evaluating a subset of the profiles. The policies I introduce outperform the existing policies in experiments. I establish a principled methodology for evaluating strategies given an empirical game model. I employ this methodology in two case studies of market scenarios: first, a case study in supply chain management from the perspective of a strategy designer; then, a case study in Internet ad auctions from the perspective of a mechanism designer. As part of the latter analysis, I develop an ad-auctions scenario that captures several key strategic issues in this domain for the first time.en_US
dc.format.extent9567676 bytes
dc.format.extent1373 bytes
dc.format.mimetypeapplication/octet-stream
dc.format.mimetypetext/plain
dc.language.isoen_USen_US
dc.subjectEmpirical Game Theoryen_US
dc.titlePractical Strategic Reasoning with Applications in Market Games.en_US
dc.typeThesisen_US
dc.description.thesisdegreenamePh.D.en_US
dc.description.thesisdegreedisciplineComputer Science & Engineeringen_US
dc.description.thesisdegreegrantorUniversity of Michigan, Horace H. Rackham School of Graduate Studiesen_US
dc.contributor.committeememberWellman, Michael P.en_US
dc.contributor.committeememberBaveja, Satinder Singhen_US
dc.contributor.committeememberMacKie-Mason, Jeffrey K.en_US
dc.contributor.committeememberParkes, David C.en_US
dc.contributor.committeememberSami, Rahulen_US
dc.subject.hlbsecondlevelComputer Scienceen_US
dc.subject.hlbtoplevelEngineeringen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/75848/1/prjordan_1.pdf
dc.owningcollnameDissertations and Theses (Ph.D. and Master's)


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