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

Abstract

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
JournalCognition
Volume212
DOIs
StatePublished - Jul 2021

All Science Journal Classification (ASJC) codes

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

Keywords

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

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