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Stephan E. Kolitz and Asuman Ozdaglar. Hung, Benjamin W. K. (Benjamin Wei Kit) Massachusetts Institute of Technology. Operations Research Center. Massachusetts Institute of Technology. Operations Research Center Sloan School of Management a-af--- 2011-02-23T14:27:25Z 2011-02-23T14:27:25Z 2010 2010 http://hdl.handle.net/1721.1/61193 701073072 Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2010. Cataloged from PDF version of thesis. Includes bibliographical references (p. 177-185). This work considers the nonlethal targeting assignment problem in counterinsurgency in Afghanistan, the problem of deciding on the people whom US forces should engage through outreach, negotiations, meetings, and other interactions in order to ultimately win the support of the population in their area of operations. We developed three models: 1) the Afghan COIN social influence model, to represent how attitudes of local leaders are affected by repeated interactions with other local leaders, insurgents, and counter-insurgents, 2) the network generation model, to arrive at a reasonable representation of a Pashtun district-level, opinion leader social network, and 3) the nonlethal targeting model, a nonlinear programming (NLP) optimization formulation that identifies the k US agent assignment strategy producing the greatest arithmetic mean of the expected long-term attitude of the population. We demonstrate in experiments the merits of the optimization model in nonlethal targeting, which performs significantly better than both doctrine-based and random methods of assignment in a large network. by Benjamin W. K. Hung. S.M. 185 p. eng Massachusetts Institute of Technology 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. http://dspace.mit.edu/handle/1721.1/7582 Operations Research Center. Optimization-based selection of influential agents in a rural Afghan social network Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="ca961381-a3ad-4ee5-af91-b1f76e78b596"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Optimization-based selection of influential agents in a rural Afghan social network</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2010</PublicationDate> <Authors> <Author> <DisplayName>Hung, Benjamin W. K. (Benjamin Wei Kit)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Operations Research Center.</Keyword> <Abstract>This work considers the nonlethal targeting assignment problem in counterinsurgency in Afghanistan, the problem of deciding on the people whom US forces should engage through outreach, negotiations, meetings, and other interactions in order to ultimately win the support of the population in their area of operations. We developed three models: 1) the Afghan COIN social influence model, to represent how attitudes of local leaders are affected by repeated interactions with other local leaders, insurgents, and counter-insurgents, 2) the network generation model, to arrive at a reasonable representation of a Pashtun district-level, opinion leader social network, and 3) the nonlethal targeting model, a nonlinear programming (NLP) optimization formulation that identifies the k US agent assignment strategy producing the greatest arithmetic mean of the expected long-term attitude of the population. We demonstrate in experiments the merits of the optimization model in nonlethal targeting, which performs significantly better than both doctrine-based and random methods of assignment in a large network.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>