Robust Non-Bayesian Social Learning

29 Pages Posted: 30 May 2019 Last revised: 9 Aug 2019

See all articles by Itai Arieli

Itai Arieli

Technion-Israel Institute of Technology

Yakov Babichenko

Technion, Industrial Engineering and Managemenet

Segev Shlomov

Technion

Date Written: May 2, 2019

Abstract

We study a canonical setting of learning in networks where initially agents receive conditionally i.i.d. signals about a binary state. The distribution according to which signals are drawn is called an information structure. Agents repeatedly communicate beliefs with their neighbors and update their own belief. We focus on large vanishing-influence networks. Our goal is to design simple non-Bayesian learning heuristics that succeed to robustly learn the correct state for a wide class of initial information structures. We provide an exact characterization of the cases when it is possible. Our main contribution focuses on the positive side of this characterization by introducing the class of virtually additive heuristics. Such a heuristic is characterized by a single function that maps beliefs (elements of [0, 1]) onto the reals which are the virtual beliefs. In the initial period, an agent maps his belief to a virtual belief and in all subsequent periods agent simply sums up all virtual beliefs of his neighbors to obtain his new virtual belief. We show that whenever it is possible to robustly learn the correct state, it is possible to do so with a virtually additive heuristic. Finally, we show that our main positive result can be extended for the case where the agent’s initial information is not identically distributed. Moreover, this result remains true even if agents do not share a common prior.

Keywords: Learning in networks, Information aggregation, Social networks, Non-Bayesian learning

JEL Classification: C00

Suggested Citation

Arieli, Itai and Babichenko, Yakov and Shlomov, Segev, Robust Non-Bayesian Social Learning (May 2, 2019). Available at SSRN: https://ssrn.com/abstract=3381563 or http://dx.doi.org/10.2139/ssrn.3381563

Itai Arieli

Technion-Israel Institute of Technology ( email )

Technion City
Haifa 32000, Haifa 32000
Israel

Yakov Babichenko

Technion, Industrial Engineering and Managemenet ( email )

Hiafa, 3434113
Israel

Segev Shlomov (Contact Author)

Technion ( email )

Haifa 32000
Israel
0545575739 (Phone)

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