Multiagent systems: learning, strategic behavior, cooperation, and network formation

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dc.citation.epage721en_US
dc.citation.spage699en_US
dc.contributor.authorTekin, Cemen_US
dc.contributor.authorZhang, S.en_US
dc.contributor.authorXu, J.en_US
dc.contributor.authorSchaar, M. van deren_US
dc.contributor.editorDjurić, P. M.
dc.contributor.editorRichard., C.
dc.date.accessioned2019-05-21T11:43:20Z
dc.date.available2019-05-21T11:43:20Z
dc.date.issued2018en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionChapter 26
dc.description.abstractMany applications ranging from crowdsourcing to recommender systems involve informationally decentralized agents repeatedly interacting with each other in order to reach their goals. These networked agents base their decisions on incomplete information, which they gather through interactions with their neighbors or through cooperation, which is often costly. This chapter presents a discussion on decentralized learning algorithms that enable the agents to achieve their goals through repeated interaction. First, we discuss cooperative online learning algorithms that help the agents to discover beneficial connections with each other and exploit these connections to maximize the reward. For this case, we explain the relation between the learning speed, network topology, and cooperation cost. Then, we focus on how informationally decentralized agents form cooperation networks through learning. We explain how learning features prominently in many real-world interactions, and greatly affects the evolution of social networks. Links that otherwise would not have formed may now appear, and a much greater variety of network configurations can be reached. We show that the impact of learning on efficiency and social welfare could be both positive or negative. We also demonstrate the use of the aforementioned methods in popularity prediction, recommender systems, expert selection, and multimedia content aggregation.en_US
dc.identifier.doi10.1016/B978-0-12-813677-5.00026-2en_US
dc.identifier.eisbn9780128136782
dc.identifier.isbn9780128136775
dc.identifier.urihttp://hdl.handle.net/11693/51453
dc.language.isoEnglishen_US
dc.publisherElsevieren_US
dc.relation.ispartofCooperative and graph signal processing : principles and applicationsen_US
dc.relation.isversionofhttps://doi.org/10.1016/B978-0-12-813677-5.00026-2en_US
dc.subjectContextual banditsen_US
dc.subjectInformational decentralizationen_US
dc.subjectNetwork formationen_US
dc.subjectRegreten_US
dc.subjectGlobal feedbacken_US
dc.subjectGroup feedbacken_US
dc.subjectOpinion dynamicsen_US
dc.titleMultiagent systems: learning, strategic behavior, cooperation, and network formationen_US
dc.typeBook Chapteren_US
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