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Learning From Aggregated Opinion
Kerem Oktar1, Tania Lombrozo1, Thomas L Griffiths1,2
1Department of Psychology, Princeton University.
Psychological Science
|July 24, 2024
Summary
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.
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.
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