Show simple item record

dc.contributor.authorRhim, Joong Bumen_US
dc.contributor.authorGoyal, Vivek K.en_US
dc.date.accessioned2021-06-25T14:46:13Z
dc.date.available2021-06-25T14:46:13Z
dc.date.issued2014-12-01
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000344988500018&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=6e74115fe3da270499c3d65c9b17d654
dc.identifier.citationJoong Bum Rhim, Vivek K Goyal. 2014. "Distributed Hypothesis Testing With Social Learning and Symmetric Fusion." IEEE TRANSACTIONS ON SIGNAL PROCESSING, Volume 62, Issue 23, pp. 6298 - 6308 (11). https://doi.org/10.1109/TSP.2014.2362885
dc.identifier.issn1053-587X
dc.identifier.issn1941-0476
dc.identifier.urihttps://hdl.handle.net/2144/42715
dc.description.abstractWe study the utility of social learning in a distributed detection model with agents sharing the same goal: a collective decision that optimizes an agreed upon criterion. We show that social learning is helpful in some cases but is provably futile (and thus essentially a distraction) in other cases. Specifically, we consider Bayesian binary hypothesis testing performed by a distributed detection and fusion system, where all decision-making agents have binary votes that carry equal weight. Decision-making agents in the team sequentially make local decisions based on their own private signals and all precedent local decisions. It is shown that the optimal decision rule is not affected by precedent local decisions when all agents observe conditionally independent and identically distributed private signals. Perfect Bayesian reasoning will cancel out all effects of social learning. When the agents observe private signals with different signal-to-noise ratios, social learning is again futile if the team decision is only approved by unanimity. Otherwise, social learning can strictly improve the team performance. Furthermore, the order in which agents make their decisions affects the team decision.en_US
dc.description.sponsorshipThis work was supported by the National Science Foundation under Grant 1101147. This paper was presented in part in the Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Vancouver, BC, Canada, May 2013. (1101147 - National Science Foundation)en_US
dc.format.extentp. 6298 - 6308en_US
dc.languageEnglish
dc.language.isoen_US
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_US
dc.relation.ispartofIEEE TRANSACTIONS ON SIGNAL PROCESSING
dc.subjectScience & technologyen_US
dc.subjectTechnologyen_US
dc.subjectEngineering, electrical & electronicen_US
dc.subjectEngineeringen_US
dc.subjectBayesian hypothesis testingen_US
dc.subjectDecision fusionen_US
dc.subjectDistributed detectionen_US
dc.subjectSequential decision makingen_US
dc.subjectSocial learningen_US
dc.subjectNetworksen_US
dc.subjectBayesian hypothesis testingen_US
dc.subjectNetworking & telecommunicationsen_US
dc.titleDistributed hypothesis testing with social learning and symmetric fusionen_US
dc.typeArticleen_US
dc.description.versionAccepted manuscripten_US
dc.identifier.doi10.1109/TSP.2014.2362885
pubs.elements-sourceweb-of-scienceen_US
pubs.notesEmbargo: No embargoen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Engineeringen_US
pubs.organisational-groupBoston University, College of Engineering, Department of Electrical & Computer Engineeringen_US
pubs.publication-statusPublisheden_US
dc.identifier.orcid0000-0001-8471-7049 (Goyal, Vivek K)
dc.identifier.mycv37664


This item appears in the following Collection(s)

Show simple item record