Bayesian collective learning emerges from heuristic social learning
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Bayesian collective learning emerges from heuristic social learning.pdf
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1.79 MB
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Author(s)
Krafft, P.M.
Shmueli, Erez
Griffiths, Thomas L.
Tenenbaum, Joshua B.
Pentland, Alex
Date Issued
July 2021
Publisher
Cognition
Citation
Krafft, P. M., Shmueli, E., Griffiths, T. L., & Tenenbaum, J. B. (2021). Bayesian collective learning emerges from heuristic social learning. Cognition, 212, 104469.
Abstract
Researchers across cognitive science, economics, and evolutionary biology have studied the ubiquitous phe- nomenon 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 popu- lation 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.
MIT Department
MIT Connection Science (Research institute)
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