Naive Learning Through Probability Overmatching

Itai Arieli, Yakov Babichenko, Manuel Mueller-Frank

Research output: Contribution to journalArticlepeer-review


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. Ourmain 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.

Original languageEnglish
Pages (from-to)3420-3431
Number of pages12
JournalOperations Research
Issue number6
StatePublished - 1 Nov 2022


  • DeGroot dynamics
  • agreement
  • naive learning
  • probability matching
  • social learning
  • social networks

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Management Science and Operations Research


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