Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/102641
Title: Non-Bayesian social learning with observation reuse and soft switching
Authors: Md. Zulfiquar Ali Bhotto
Tay, Wee Peng
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Non-Bayesian Social Learning
Misinforming Agent
Issue Date: 2018
Source: Md. Zulfiquar Ali Bhotto., & Tay, W. P. (2018). Non-Bayesian social learning with observation reuse and soft switching. ACM Transactions on Sensor Networks, 14(2), 14-. doi:10.1145/3199513
Series/Report no.: ACM Transactions on Sensor Networks
Abstract: We propose a non-Bayesian social learning update rule for agents in a network, which minimizes the sum of the Kullback-Leibler divergence between the true distribution generating the agents’ local observations and the agents’ beliefs (parameterized by a hypothesis set), and a weighted varentropy-related term. The varentropy-related term allows us to control the rate of convergence of our update rule, which also reuses some of the most recent observations of each agent to speed up convergence. Under mild technical conditions, we show that the belief of each agent concentrates on the optimal hypothesis set, and we derive a bound for the convergence rate. Furthermore, to overcome the performance degradation due to misinforming agents, who use a corrupted likelihood functions in their belief updates, we propose to use multiple social networks that update their beliefs independently and a convex combination mechanism among the beliefs of all the networks. Simulations with applications to location identification and group recommendation demonstrate that our proposed methods offer improvements over two other current state-of-the art non-Bayesian social learning algorithms.
URI: https://hdl.handle.net/10356/102641
http://hdl.handle.net/10220/48151
ISSN: 1550-4859
DOI: 10.1145/3199513
Rights: © 2018 ACM. All rights reserved. This paper was published in ACM Transactions on Sensor Networks and is made available with permission of ACM.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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