NDLI: Social Learning With Bayesian Agents and Random Decision Making

Content Provider	IEEE Xplore Digital Library
Author	Yunlong Wang Djuric, P.M.
Copyright Year	1991
Abstract	In this paper, we study the effect of randomness in the decision making of agents on social learning. In the addressed system, the agents make decisions sequentially about the true state of nature. Each agent observes a signal produced according to one of two hypotheses, which represents the state of nature. The signals of all the agents are generated independently from the same state. The agents also know the decisions of all the previous agents in the network. The randomness in this paper is modeled by a policy that amounts to random mapping of the beliefs of the agents to the action space. We propose that the agents learn from the decisions of the previous agents and update their beliefs by using the Bayesian theory. We define the concept of social belief about the truthfulness of the two hypotheses and provide results on the convergence of the social belief. We also prove that with the proposed random policy, information cascade can be avoided and asymptotic learning occurs. We apply the random policy to data models that represent the observations by a distribution belonging to the exponential family. We then provide performance and convergence analysis of the proposed method as well as simulation results that include comparisons with deterministic and hybrid policies.
Sponsorship	IEEE Signal Processing Society
Starting Page	3241
Ending Page	3250
Page Count	10
File Size	2175323
File Format	PDF
ISSN	1053587X
Volume Number	63
Issue Number	12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision making Bayes methods Convergence Learning systems Simulation Abstracts Data models information cascade Social learning Bayesian learning asymptotic learning random decision making
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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