ArticleProceedings of the National Academy of Sciences of the United States of America2020
The bounded rationality of probability distortion.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- How does explicit reliability guide choices? Effect of trustworthiness on choice and metacognition.PLoS computational biology · 2026Article
- Human learning of probability distributions is biased toward moderate structural complexity.Nature communications · 2026Article
- Social hierarchy influences monkeys' risky decisions.Communications biology · 2026Article
- Information sampling and Bayesian belief formation in statistical judgment.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Economic Consequences of Numerical Adaptation.Psychological science · 2025Article
- Leveraging artificial intelligence to reduce diagnostic errors in emergency medicine: Challenges, opportunities, and future directions.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2025Review
- Cognitive biases as Bayesian probability weighting in context.Frontiers in psychology · 2025Article
- Information-Theoretic Measures of Metacognition: Bounds and Relation to Group Performance.Open mind : discoveries in cognitive science · 2025Article
- Dissecting Bayes: Using influence measures to test normative use of probability density information derived from a sample.PLoS computational biology · 2024Article
- Modelling dataset bias in machine-learned theories of economic decision-making.Nature human behaviour · 2024Article
- A unifying theory explains seemingly contradictory biases in perceptual estimation.Nature neuroscience · 2024Article
- Navigating the COVID-19 infodemic: the influence of metacognitive efficiency on health behaviours and policy attitudes.Royal Society open science · 2023Article
- Biases and Variability from Costly Bayesian Inference.Entropy (Basel, Switzerland) · 2021Article
- Individual differences in the perception of probability.PLoS computational biology · 2021Article
- The bounded rationality of probability distortion.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
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3 authors.
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Abstract
In decision making under risk (DMR) participants' choices are based on probability values systematically different from those that are objectively correct. Similar systematic distortions are found in tasks involving relative frequency judgments (JRF). These distortions limit performance in a wide variety of tasks and an evident question is, Why do we systematically fail in our use of probability and relative frequency information? We propose a bounded log-odds model (BLO) of probability and relative frequency distortion based on three assumptions: 1) log-odds: probability and relative frequency are mapped to an internal log-odds scale, 2) boundedness: the range of representations of probability and relative frequency are bounded and the bounds change dynamically with task, and 3) variance compensation: the mapping compensates in part for uncertainty in probability and relative frequency values. We compared human performance in both DMR and JRF tasks to the predictions of the BLO model as well as 11 alternative models, each missing one or more of the underlying BLO assumptions (factorial model comparison). The BLO model and its assumptions proved to be superior to any of the alternatives. In a separate analysis, we found that BLO accounts for individual participants' data better than any previous model in the DMR literature. We also found that, subject to the boundedness limitation, participants' choice of distortion approximately maximized the mutual information between objective task-relevant values and internal values, a form of bounded rationality.
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