Bayesian collective learning emerges from heuristic social learning

P. M. Krafft, Erez Shmueli, Thomas L. Griffiths, Joshua B. Tenenbaum, Alex “Sandy” Pentland

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


Researchers across cognitive science, economics, and evolutionary biology have studied the ubiquitous phenomenon of social learning—the use of information about other people's decisions to make your own. Decision-making with the benefit of the accumulated knowledge of a community can result in superior decisions compared to what people can achieve alone. However, groups of people face two coupled challenges in accumulating knowledge to make good decisions: (1) aggregating information and (2) addressing an informational public goods problem known as the exploration-exploitation dilemma. Here, we show how a Bayesian social sampling model can in principle simultaneously optimally aggregate information and nearly optimally solve the exploration-exploitation dilemma. The key idea we explore is that Bayesian rationality at the level of a population can be implemented through a more simplistic heuristic social learning mechanism at the individual level. This simple individual-level behavioral rule in the context of a group of decision-makers functions as a distributed algorithm that tracks a Bayesian posterior in population-level statistics. We test this model using a large-scale dataset from an online financial trading platform.

Original languageEnglish (US)
Article number104469
StatePublished - Jul 2021

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Developmental and Educational Psychology
  • Cognitive Neuroscience
  • Language and Linguistics
  • Linguistics and Language


  • Bayesian models
  • Big data
  • Collective intelligence
  • Exploration-exploitation dilemma
  • Social learning
  • Wisdom of crowds


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