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Naive Learning Through Probability Overmatching

Author

Listed:
  • Itai Arieli

    (Faculty of Industrial Engineering and Management, Technion–Israel Institute of Technology, 3200003 Haifa, Israel)

  • Yakov Babichenko

    (Faculty of Industrial Engineering and Management, Technion–Israel Institute of Technology, 3200003 Haifa, Israel)

  • Manuel Mueller-Frank

    (Department of Economics, IESE Business School, University of Navarra, 28023 Madrid, Spain)

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 overmatching 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

  • Itai Arieli & Yakov Babichenko & Manuel Mueller-Frank, 2022. "Naive Learning Through Probability Overmatching," Operations Research, INFORMS, vol. 70(6), pages 3420-3431, November.
    • Handle: RePEc:inm:oropre:v:70:y:2022:i:6:p:3420-3431
    DOI: 10.1287/opre.2021.2202
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