Bayesian collective learning emerges from heuristic social learning
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 su...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Cognition
2021
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| Online Access: | https://hdl.handle.net/1721.1/131067 |
| _version_ | 1824458338741518336 |
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| author | Krafft, P.M. Shmueli, Erez Griffiths, Thomas L. Tenenbaum, Joshua B. Pentland, Alex |
| author2 | MIT Connection Science (Research institute) |
| author_facet | MIT Connection Science (Research institute) Krafft, P.M. Shmueli, Erez Griffiths, Thomas L. Tenenbaum, Joshua B. Pentland, Alex |
| author_sort | Krafft, P.M. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T15:12:21Z |
| format | Article |
| id | mit-1721.1/131067 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2025-02-19T04:24:18Z |
| publishDate | 2021 |
| publisher | Cognition |
| record_format | dspace |
| spelling | mit-1721.1/1310672025-02-06T18:53:04Z Bayesian collective learning emerges from heuristic social learning Krafft, P.M. Shmueli, Erez Griffiths, Thomas L. Tenenbaum, Joshua B. Pentland, Alex MIT Connection Science (Research institute) 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. 2021-07-08T18:10:56Z 2021-07-08T18:10:56Z 2021-07 Article https://hdl.handle.net/1721.1/131067 Krafft, P. M., Shmueli, E., Griffiths, T. L., & Tenenbaum, J. B. (2021). Bayesian collective learning emerges from heuristic social learning. Cognition, 212, 104469. en_US Attribution-NonCommercial-ShareAlike 3.0 United States http://creativecommons.org/licenses/by-nc-sa/3.0/us/ application/pdf Cognition |
| spellingShingle | Krafft, P.M. Shmueli, Erez Griffiths, Thomas L. Tenenbaum, Joshua B. Pentland, Alex Bayesian collective learning emerges from heuristic social learning |
| title | Bayesian collective learning emerges from heuristic social learning |
| title_full | Bayesian collective learning emerges from heuristic social learning |
| title_fullStr | Bayesian collective learning emerges from heuristic social learning |
| title_full_unstemmed | Bayesian collective learning emerges from heuristic social learning |
| title_short | Bayesian collective learning emerges from heuristic social learning |
| title_sort | bayesian collective learning emerges from heuristic social learning |
| url | https://hdl.handle.net/1721.1/131067 |
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