Bayesian social learning with decision making in multiple rounds

Yunlong Wang, Lingqing Gan, Petar M. Djurić

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we consider the problem of social learning in a network of agents where the agents make decisions repeatedly on two hypotheses. At every time slot, each agent in the system obtains sequentially a private observation that is independently generated under one of the hypotheses, and makes a decision on choosing the true hypothesis. The private belief of each agent is the posterior of one of the hypotheses conditioned on its private observations and the latest decision of the other agents. This private belief is used for the agent's decision making. We present a Bayesian learning scheme for the agents that exploits information from the other decisions. We show that an information loop in decisions can be avoided in the studied system. We demonstrate the performance of the method by computer simulations.

Original languageEnglish (US)
Title of host publication2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages257-260
Number of pages4
ISBN (Electronic)9781479919635
DOIs
StatePublished - Jan 1 2015
Event6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015 - Cancun, Mexico
Duration: Dec 13 2015Dec 16 2015

Publication series

Name2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015

Other

Other6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
CountryMexico
CityCancun
Period12/13/1512/16/15

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