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Finding optimal strategies for influencing social networks in two player games

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dc.contributor.advisor Itai Ashlagi. en_US Howard, Nicholas J. (Nicholas Jacob) en_US
dc.contributor.other Massachusetts Institute of Technology. Operations Research Center. en_US 2011-12-19T18:49:30Z 2011-12-19T18:49:30Z 2010 en_US 2011 en_US
dc.description Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, June 2011. en_US
dc.description Cataloged from PDF version of thesis. en_US
dc.description Includes bibliographical references (p. 141). en_US
dc.description.abstract This thesis considers the problem of optimally influencing social networks in Afghanistan as part of ongoing counterinsurgency efforts. The social network is analyzed using a discrete time agent based model. Each agent has a belief [-0.5,0.5] and interacts stochastically pairwise with their neighbors. The network converges to a set of equilibrium beliefs in expectation. A 2-player game is formulated in which the players control a set of stubborn agents whose beliefs never change, and who wield significant influence in the network. Each player chooses how to connect their stubborn agents to maximally influence the network. Two different payoff functions are defined, and the pure Nash equilibrium strategy profiles are found in a series of test networks. Finding equilibrium strategy profiles can be difficult for large networks due to exponential increases in the strategy space but a simulated annealing heuristic is used to rapidly find equilibria using best response dynamics. We demonstrate through experimentation that the games formulated admit pure Nash equilibrium strategy profiles and that best response dynamics can be used to find them. We also test a scenario based on the author's experience in Afghanistan to show how nonsymmetric equilibria can naturally emerge if each player weights the value of agents in the network differently. en_US
dc.description.statementofresponsibility by Nicholas J Howard. en_US
dc.format.extent 161 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri en_US
dc.subject Operations Research Center. en_US
dc.title Finding optimal strategies for influencing social networks in two player games en_US
dc.type Thesis en_US S.M. en_US
dc.contributor.department Massachusetts Institute of Technology. Operations Research Center. en_US
dc.identifier.oclc 767528499 en_US

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