Bayesian decision-making models fail to accurately predict human choices in specific tasks
Multiple studies demonstrate that human choices frequently deviate from optimal Bayesian predictions due to cognitive constraints, probability distortions, and bounded rationality.
The claim states that Bayesian decision-making models fail to accurately predict human choices in specific tasks. Papers [0] and [3] provide direct empirical and theoretical support for this claim, showing systematic human departures from Bayesian optimality due to bounded rationality and cognitive constraints. Therefore, the claim is supported.
Zhang H, Ren X, Maloney LT. The bounded rationality of probability distortion.. 2020. https://doi.org/10.1073/pnas.1922401117
The study demonstrates that human probability distortions systematically deviate from normative statistical values in decision-making and relative frequency tasks, supporting alternative bounded models over standard Bayesian approaches.
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Prat-Carrabin A, Meyniel F, Azeredo da Silveira R. Resource-rational account of sequential effects in human prediction.. 2024. https://doi.org/10.7554/elife.81256
Research on sequential effects shows that human predictions depart from optimal Bayesian processes due to cognitive constraints and specific belief updating costs.
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