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: all tested biases are present and exhibit systematic co-occurrence patterns across individuals, with motivated-belief biases (optimism and pessimism) and sequence-related biases (gambler's and hot-hand fallacy) emerging as key drivers of biased inference. At the population level most biases average out, but base-rate neglect remains a persistent influence. This study contributes to the belief-updating literature by providing a methodological toolkit for researchers examining links between conflicting biases and connections between updating biases and other behavioral phenomena.

Date: 2024-04, Revised 2026-03
New Economics Papers: this item is included in nep-dcm, nep-ecm and nep-exp
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