Researchers release v5 of a unified theoretical model paper for identifying belief biases.
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Pedro Gonzalez-Fernandez released paper v5, proposing a unified theoretical model that can identify and test a full set of probability updating biases within a single framework, with a core focus on updating belief distributions rather than merely point beliefs. Estimates from laboratory experiments show that the hot-hand fallacy is the most common bias and a key driver of biased reasoning; most biases cancel out at the population level, but base-rate neglect persists.
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- arXiv q-finIdentifying Belief Biases
This study proposes a unified theoretical model that can identify and test a full set of probability updating biases within a single framework, focusing on belief distribution updates rather than point beliefs. Estimates from laboratory experiments show that the hot-hand fallacy is the most common bias and a key driver of biased reasoning. Most biases cancel out at the aggregate level, but base-rate neglect persists.
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