Naive Learning Through Probability Over-Matching

27 Pages Posted: 11 Mar 2019 Last revised: 13 Aug 2020

See all articles by Itai Arieli

Itai Arieli

Technion-Israel Institute of Technology

Yakov Babichenko

Technion, Industrial Engineering and Managemenet

Manuel Mueller-Frank

University of Navarra, IESE Business School

Date Written: February 19, 2019

Abstract

We analyze boundedly rational updating in a repeated interaction network model with binary actions and binary states. Agents form beliefs according to discretized DeGroot updating and apply a decision rule that assigns a (mixed) action to each belief. We first show that under weak assumptions random decision rules are sufficient to achieve agreement in finite time in any strongly connected network. Our main result establishes that naive learning can be achieved in any large strongly connected network. That is, if beliefs satisfy a high level of inertia, then there exist corresponding decision rules coinciding with probability over-matching such that the eventual agreement action matches the true state, with a probability converging to one as the network size goes to infinity.

Suggested Citation

Arieli, Itai and Babichenko, Yakov and Mueller-Frank, Manuel, Naive Learning Through Probability Over-Matching (February 19, 2019). Available at SSRN: https://ssrn.com/abstract=3338015 or http://dx.doi.org/10.2139/ssrn.3338015

Itai Arieli (Contact Author)

Technion-Israel Institute of Technology ( email )

Technion City
Haifa 32000, Haifa 32000
Israel

Yakov Babichenko

Technion, Industrial Engineering and Managemenet ( email )

Hiafa, 3434113
Israel

Manuel Mueller-Frank

University of Navarra, IESE Business School ( email )

Avenida Pearson 21
Barcelona, 08034
Spain

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