Belief Bias Identification

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 connections between updating biases and other behavioral phenomena.