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dc.contributor.authorSeo, Daewonen_US
dc.contributor.authorRaman, Ravi Kiranen_US
dc.contributor.authorRhim, Joong Bumen_US
dc.contributor.authorGoyal, Viveken_US
dc.contributor.authorVarshney, Lav Rajen_US
dc.date.accessioned2020-05-08T14:56:24Z
dc.date.available2020-05-08T14:56:24Z
dc.date.issued2018-11-23.
dc.identifier.citationDaewon Seo, Ravi Kiran Raman, Joong Bum Rhim, Vivek Goyal, Lav Raj Varshney. "Beliefs and Expertise in Sequential Decision Making." https://arxiv.org/abs/1812.04419v1
dc.identifier.urihttps://hdl.handle.net/2144/40702
dc.description.abstractThis work explores a sequential decision making problem with agents having diverse expertise and mismatched beliefs. We consider an N-agent sequential binary hypothesis test in which each agent sequentially makes a decision based not only on a private observation, but also on previous agents’ decisions. In addition, the agents have their own beliefs instead of the true prior, and have varying expertise in terms of the noise variance in the private signal. We focus on the risk of the last-acting agent, where precedent agents are selfish. Thus, we call this advisor(s)-advisee sequential decision making. We first derive the optimal decision rule by recursive belief update and conclude, counterintuitively, that beliefs deviating from the true prior could be optimal in this setting. The impact of diverse noise levels (which means diverse expertise levels) in the two-agent case is also considered and the analytical properties of the optimal belief curves are given. These curves, for certain cases, resemble probability weighting functions from cumulative prospect theory, and so we also discuss the choice of Prelec weighting functions as an approximation for the optimal beliefs, and the possible psychophysical optimality of human beliefs. Next, we consider an advisor selection problem where in the advisee of a certain belief chooses an advisor from a set of candidates with varying beliefs. We characterize the decision region for choosing such an advisor and argue that an advisee with beliefs varying from the true prior often ends up selecting a suboptimal advisor, indicating the need for a social planner. We close with a discussion on the implications of the study toward designing artificial intelligence systems for augmenting human intelligence.en_US
dc.description.urihttps://arxiv.org/abs/1812.04419
dc.language.isoen_US
dc.subjectSocial learningen_US
dc.subjectSequential binary hypothesis testen_US
dc.subjectCumulative prospect theoryen_US
dc.subjectAugmented intelligenceen_US
dc.titleBeliefs and expertise in sequential decision makingen_US
dc.typeArticleen_US
pubs.elements-sourcemanual-entryen_US
pubs.notesEmbargo: Not knownen_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-statusPublished onlineen_US
dc.date.online2018-11-23
dc.description.oaversionFirst author draft
dc.identifier.mycv455209


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