Belief Bias Identification

Pedro Gonzalez-Fernandez

Papers from arXiv.org

Abstract: This paper proposes a unified theoretical model to identify and test a comprehensive set of probabilistic updating biases within a single framework. The model achieves separate identification by focusing on the updating of belief distributions, rather than point beliefs alone. Estimating the model in a laboratory experiment reveals significant individual heterogeneity, with the hot-hand fallacy emerging as the most common bias and a key driver of biased inference. Accounting for additional distortions reveals associations across bias families and reduces the apparent prevalence of overinference and preference-based biases. At the population level, most biases average out, but base-rate neglect remains present.

Date: 2024-04, Revised 2026-10
New Economics Papers: this item is included in nep-dcm, nep-ecm and nep-exp
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