Bayesian collective learning emerges from heuristic social learning

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

*Corresponding author for this work

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
Article number104469
JournalCognition
Volume212
DOIs
StatePublished - Jul 2021

Funding

FundersFunder number
National Science Foundation1122374
Defense Advanced Research Projects AgencyD17AC00004
Army Research LaboratoryW911NF-09-2-0053

    Keywords

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

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