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Gaussian Learning-Without-Recall in a dynamic social network

Author(s): Wang, C; Chazelle, Bernard

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Abstract: We analyze the dynamics of the Learning-Without-Recall model with Gaussian priors in a dynamic social network. Agents seeking to learn the state of the world, the 'truth', exchange signals about their current beliefs across a changing network and update them accordingly. The agents are assumed memoryless and rational, meaning that they Bayes-update their beliefs based on current states and signals, with no other information from the past. The other assumption is that each agent hears a noisy signal from the truth at a frequency bounded away from zero. Under these conditions, we show that the system reaches truthful consensus almost surely with a convergence rate that is polynomial in expectation. Somewhat paradoxically, high out degree can slow down the learning process. The lower-bound assumption on the truth-hearing frequency is necessary: even infinitely frequent access to the truth offers no guarantee of truthful consensus in the limit.
Publication Date: 3-Jul-2017
Electronic Publication Date: 3-Jul-2017
Citation: Wang, C, Chazelle, B. (2017). Gaussian Learning-Without-Recall in a dynamic social network. 5109 - 5114. doi:10.23919/ACC.2017.7963747
DOI: doi:10.23919/ACC.2017.7963747
Pages: 5109 - 5114
Type of Material: Conference Article
Journal/Proceeding Title: Proceedings of the American Control Conference
Version: Author's manuscript

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