Information diffusion in networks with the Bayesian Peer Influence heuristic

Gilat Levy and Ronny Razin

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Repeated communication in networks is often considered to impose large information requirements on individuals, and for that reason, the literature has resorted to use heuristics, such as DeGroot's, to compute how individuals update beliefs. In this paper we propose a new heuristic which we term the Bayesian Peer Influence (BPI) heuristic. The BPI accords with Bayesian updating for all (conditionally) independent information structures. More generally, the BPI can be used to analyze the effects of correlation neglect on communication in networks. We analyze the evolution of beliefs and show that the limit is a simple extension of the BPI and parameters of the network structure. We also show that consensus in society might change dynamically, and that beliefs might become polarized. These results contrast with those obtained in papers that have used the DeGroot heuristic

JEL-codes: J1 (search for similar items in EconPapers)
Pages: 9 pages
Date: 2018-03-23
New Economics Papers: this item is included in nep-mic, nep-ore and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

Published in Games and Economic Behavior, 23, March, 2018, 109, pp. 262-270. ISSN: 0899-8256

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