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This work explores sequential Bayesian binary hypothesis testing in the social learning setup under expertise diversity. We consider a two-agent (say advisor-learner) sequential binary hypothesis test where the learner infers the hypothesis based on the decision of the advisor, a prior private signal, and individual belief. In addition, the agents have varying expertise, in terms of the noise variance in the private signal. Under such a setting, we first investigate the behavior of optimal agent beliefs and observe that the nature of optimal agents could be inverted depending on expertise levels. We also discuss suboptimality of the Prelec reweighting function under diverse expertise. Next, we consider an advisor selection problem wherein the belief of the learner is fixed and the advisor is to be chosen for a given prior. We characterize the decision region for choosing such an advisor and argue that a learner with beliefs varying from the true prior often ends up selecting a suboptimal advisor.
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The effects of base rate neglect on sequential belief updating and real-world beliefs
Base-rate neglect is a pervasive bias in judgment that is conceptualized as underweighting of prior information and can have serious consequences in real-world scenarios. This bias is thought to reflect variability in inferential processes but empirical support for a cohesive theory of base-rate neglect with sufficient explanatory power to account for longer-term and real-world beliefs is lacking. A Bayesian formalization of base-rate neglect in the context of sequential belief updating predicts that belief trajectories should exhibit dynamic patterns of dependence on the order in which evidence is presented and its consistency with prior beliefs. To test this, we developed a novel ‘urn-and-beads’ task that systematically manipulated the order of colored bead sequences and elicited beliefs via an incentive-compatible procedure. Our results in two independent online studies confirmed the predictions of the sequential base-rate neglect model: people exhibited beliefs that are more influenced by recent evidence and by evidence inconsistent with prior beliefs. We further found support for a noisy-sampling inference model whereby base-rate neglect results from rational discounting of noisy internal representations of prior beliefs. Finally, we found that model-derived indices of base-rate neglect—including noisier prior representation—correlated with propensity for unusual beliefs outside the laboratory. Our work supports the relevance of Bayesian accounts of sequential base-rate neglect to real-world beliefs and hints at strategies to minimize deleterious consequences of this pervasive bias.
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- Award ID(s):
- 1949418
- PAR ID:
- 10518162
- Editor(s):
- Gershman, Samuel J
- Publisher / Repository:
- PLoS Computational Biology
- Date Published:
- Journal Name:
- PLOS Computational Biology
- Edition / Version:
- 1
- Volume:
- 18
- Issue:
- 12
- ISSN:
- 1553-7358
- Page Range / eLocation ID:
- e1010796
- Subject(s) / Keyword(s):
- computational psychiatry Bayesian models of cognition leaky memory urn-and-beads task
- Format(s):
- Medium: X Size: 3MB Other: pdf
- Size(s):
- 3MB
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1371/journal.pcbi.1010796
