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Learning From Aggregated Opinion.

Kerem Oktar1, Tania Lombrozo1, Thomas L Griffiths1,2

  • 1Department of Psychology, Princeton University.

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|July 24, 2024
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Summary
This summary is machine-generated.

People often use aggregated opinion, like crowd wisdom, to make decisions. Research shows Bayesian models best predict how individuals integrate this social information with their own beliefs.

Keywords:
Bayesian inferencebeliefdisagreementheuristicsjudgmentopen dataopinionpreregistered

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Area of Science:

  • Cognitive Science
  • Social Psychology
  • Decision Science

Background:

  • Human cognition relies on leveraging information from others' opinions.
  • Previous research focused on learning from direct testimony.
  • Aggregated opinion is an increasingly important source of social information influencing judgments and decisions.

Purpose of the Study:

  • To investigate how individuals learn from aggregated opinion.
  • To compare the predictive accuracy of different computational models for social learning.
  • To determine if human judgments align with Bayesian principles when using aggregated opinion.

Main Methods:

  • Conducted three online experiments with 886 participants in the United States.
  • Compared human judgments against predictions from three computational models: a Bayesian model and two alternatives from epistemology and economics.
  • The Bayesian model represented a strategy for combining proportions with prior beliefs.

Main Results:

  • Participant judgments showed the strongest concordance with the predictions of the Bayesian model across all studies.
  • Some participants' judgments were better explained by alternative computational strategies.
  • Systematic inferences are drawn from aggregated opinion, often aligning with Bayesian solutions.

Conclusions:

  • Individuals systematically infer information from aggregated opinions.
  • Bayesian models provide a strong framework for understanding how people learn from aggregated social information.
  • Future research can build upon these findings to explore nuances in social learning strategies.