Belief Bias Identification
Pedro Gonzalez-Fernandez

TL;DR
This paper introduces a unified model to identify various probabilistic belief updating biases, revealing individual heterogeneity and key biases like optimism, gambler's fallacy, and base-rate neglect through laboratory experiments.
Contribution
It provides a comprehensive theoretical framework and methodological toolkit for detecting and analyzing multiple belief updating biases within a single unified approach.
Findings
All tested biases are present and co-occur across individuals.
Motivated-belief biases and sequence-related biases are key drivers.
Base-rate neglect remains a persistent influence at the population level.
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…
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Taxonomy
TopicsBayesian Modeling and Causal Inference
MethodsSparse Evolutionary Training · Balanced Selection
