Bayesian collective learning emerges from heuristic social learning.
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 super...
| Publicado en: | Cognition Vol. 212 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Jul2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150386346&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150386346 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00100277 3H9 jtl: Cognition issn: 00100277 maglogo: N pubinfo: dt: Jul2021 vid: 212 pid: 1004 pub: Elsevier B.V. artinfo: ui: 150386346 150386346 NLM33770743 150386346 10.1016/j.cognition.2020.104469 NLM33770743 150386346 ppct: 1 formats: tig: atl: Bayesian collective learning emerges from heuristic social learning. aug: au: Krafft, P.M. Shmueli, Erez Griffiths, Thomas L. Tenenbaum, Joshua B. Pentland, Alex "Sandy" affil: Creative Computing Institute, University of Arts London, London, England, United Kingdom sug: subj: Probability Decision Making Human Learning 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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