ArticleProceedings of the National Academy of Sciences of the United States of America2021
Optimal utility and probability functions for agents with finite computational precision.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. 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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15 citing papers in PubMed.
- Context-dependent feature modulation shapes human decision policies in approach-avoidance conflicts.PLoS computational biology · 2026Article
- The geometry of efficient codes: How rate-distortion trade-offs distort the latent representations of generative models.PLoS computational biology · 2025Article
- Decrease in decision noise from adolescence into adulthood mediates an increase in more sophisticated choice behaviors and performance gain.PLoS biology · 2024Article
- Modelling dataset bias in machine-learned theories of economic decision-making.Nature human behaviour · 2024Article
- Grid-like entorhinal representation of an abstract value space during prospective decision making.Nature communications · 2024Article
- Sensory perception relies on fitness-maximizing codes.Nature human behaviour · 2023Article
- Adaptive tuning of human learning and choice variability to unexpected uncertainty.Science advances · 2023Article
- Article
- Control over sampling boosts numerical evidence processing in human decisions from experience.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
- EEG-representational geometries and psychometric distortions in approximate numerical judgment.PLoS computational biology · 2022Article
- Human value learning and representation reflect rational adaptation to task demands.Nature human behaviour · 2022Article
- Efficiently irrational: deciphering the riddle of human choice.Trends in cognitive sciences · 2022Review
- Asymmetric reinforcement learning facilitates human inference of transitive relations.Nature human behaviour · 2022Article
- Rational inattention in mice.Science advances · 2022Article
- A Normative Account of Confirmation Bias During Reinforcement Learning.Neural computation · 2022Article
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4 authors.
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Abstract
When making economic choices, such as those between goods or gambles, humans act as if their internal representation of the value and probability of a prospect is distorted away from its true value. These distortions give rise to decisions which apparently fail to maximize reward, and preferences that reverse without reason. Why would humans have evolved to encode value and probability in a distorted fashion, in the face of selective pressure for reward-maximizing choices? Here, we show that under the simple assumption that humans make decisions with finite computational precision--in other words, that decisions are irreducibly corrupted by noise--the distortions of value and probability displayed by humans are approximately optimal in that they maximize reward and minimize uncertainty. In two empirical studies, we manipulate factors that change the reward-maximizing form of distortion, and find that in each case, humans adapt optimally to the manipulation. This work suggests an answer to the longstanding question of why humans make "irrational" economic choices.
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