TY - JOUR
T1 - Bayesian collective learning emerges from heuristic social learning
AU - Krafft, P. M.
AU - Shmueli, Erez
AU - Griffiths, Thomas L.
AU - Tenenbaum, Joshua B.
AU - Pentland, Alex “Sandy”
N1 - Funding Information:
Special thanks to Julia Zheng for contributing to an early version of this work, Wei Pan for discussion about the data, Nicolás Della Penna for advice on statistical tests, Yaniv Altshuler for providing the dataset, Guy Zyskind for assisting with data curation, and Jonathan Huggins for discussions about the mathematics of our model. This research was partially sponsored by the Army Research Laboratory Cooperative Agreement Number W911NF-09-2-0053 , the United States Defense Advanced Research Projects Agency (DARPA) Cooperative Agreement D17AC00004 , and a National Science Foundation Graduate Research Fellowship Grant No. 1122374 . Views and conclusions in this document are those of the authors and should not be interpreted as representing the policies, either expressed or implied, of the sponsors.
Publisher Copyright:
© 2020
PY - 2021/7
Y1 - 2021/7
N2 - 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.
AB - 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.
KW - Bayesian models
KW - Big data
KW - Collective intelligence
KW - Exploration-exploitation dilemma
KW - Social learning
KW - Wisdom of crowds
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U2 - 10.1016/j.cognition.2020.104469
DO - 10.1016/j.cognition.2020.104469
M3 - Article
C2 - 33770743
AN - SCOPUS:85102862005
SN - 0010-0277
VL - 212
JO - Cognition
JF - Cognition
M1 - 104469
ER -