Scaling Empirical Game-Theoretic Analysis.

Deep Blue Home

Show simple item record

dc.contributor.author Cassell, Ben-Alexander en_US
dc.date.accessioned 2015-01-30T20:10:16Z
dc.date.available NO_RESTRICTION en_US
dc.date.available 2015-01-30T20:10:16Z
dc.date.issued 2014 en_US
dc.date.submitted en_US
dc.identifier.uri http://hdl.handle.net/2027.42/110315
dc.description.abstract To analyze the incentive structure of strategic multi-agent interactions, such scenarios are often cast as games, where players optimize their payoffs by selecting a strategy in anticipation of the strategic decisions of other players. When our modeling needs are too complex to address analytically, empirical game models, game models in which observations of simulated play are used to estimate payoffs of agents, can be employed to facilitate game-theoretic analysis. This dissertation focuses on extending the capability of the empirical game-theoretic analysis (EGTA) framework for modeling and analyzing large games. My contributions are in three distinct areas: increasing the scale of game simulation through software infrastructure, improving performance of common analytic tasks by bringing them closer to the data, and reducing sampling requirements for statistically confident analysis through sequential sampling algorithms. With the advent of EGTAOnline, an experiment management system for distributed game simulation that I developed, EGTA practitioners no longer limit their studies to what can be conducted on a single computer. Over one billion payoff observations have been added to EGTAOnline's database to date, corresponding to hundreds of distinct experiments. To reduce the cost of analyzing this data, I explored conducting analysis in the database. I found that translating data to an in-memory object representation was a dominant cost for game-theoretic analysis software. By avoiding that cost, conducting analysis in the database improves performance. A further way to improve scalability is to ensure we only gather as much data as is necessary to support analysis. I developed algorithms that interweave sampling and evaluations of statistical confidence, improving on existing ad hoc sampling methods by providing a measure of statistical confidence for analysis and reducing the number of observations taken. In addition to these software and methodological contributions, I present two applications: a strategic analysis of selecting a wireless access point for your traffic, and an investigation of mapping an analytical pricing model to a large simulated stock market. en_US
dc.language.iso en_US en_US
dc.subject empirical game-theoretic analysis en_US
dc.subject scalability en_US
dc.subject distributed systems en_US
dc.subject sequential analysis en_US
dc.title Scaling Empirical Game-Theoretic Analysis. en_US
dc.type Thesis en_US
dc.description.thesisdegreename PhD en_US
dc.description.thesisdegreediscipline Computer Science and Engineering en_US
dc.description.thesisdegreegrantor University of Michigan, Horace H. Rackham School of Graduate Studies en_US
dc.contributor.committeemember Wellman, Michael P. en_US
dc.contributor.committeemember MacKie-Mason, Jeffrey en_US
dc.contributor.committeemember Laird, John E. en_US
dc.contributor.committeemember Teneketzis, Demosthenis en_US
dc.subject.hlbsecondlevel Economics en_US
dc.subject.hlbsecondlevel Computer Science en_US
dc.subject.hlbsecondlevel Statistics and Numeric Data en_US
dc.subject.hlbtoplevel Business and Economics en_US
dc.subject.hlbtoplevel Engineering en_US
dc.subject.hlbtoplevel Science en_US
dc.description.bitstreamurl http://deepblue.lib.umich.edu/bitstream/2027.42/110315/1/bcassell_1.pdf
dc.owningcollname Dissertations and Theses (Ph.D. and Master's)
 Show simple item record

This item appears in the following Collection(s)


Search Deep Blue

Browse by

My Account

Information

Coming Soon


MLibrary logo