Bayesian collective learning emerges from heuristic social learning
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Krafft, P.M.; Shmueli, Erez; Griffiths, Thomas L.; Tenenbaum, Joshua B.; Pentland, Alex![Thumbnail](/bitstream/handle/1721.1/131067/Bayesian%20collective%20learning%20emerges%20from%20heuristic%20social%20learning.pdf.jpg?sequence=5&isAllowed=y)
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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.
Date issued
2021-07Publisher
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.
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