Hierarchical inference as a source of human biases

Paul B. Sharp, Isaac Fradkin, Eran Eldar*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

The finding that human decision-making is systematically biased continues to have an immense impact on both research and policymaking. Prevailing views ascribe biases to limited computational resources, which require humans to resort to less costly resource-rational heuristics. Here, we propose that many biases in fact arise due to a computationally costly way of coping with uncertainty—namely, hierarchical inference—which by nature incorporates information that can seem irrelevant. We show how, in uncertain situations, Bayesian inference may avail of the environment’s hierarchical structure to reduce uncertainty at the cost of introducing bias. We illustrate how this account can explain a range of familiar biases, focusing in detail on the halo effect and on the neglect of base rates. In each case, we show how a hierarchical-inference account takes the characterization of a bias beyond phenomenological description by revealing the computations and assumptions it might reflect. Furthermore, we highlight new predictions entailed by our account concerning factors that could mitigate or exacerbate bias, some of which have already garnered empirical support. We conclude that a hierarchical inference account may inform scientists and policy makers with a richer understanding of the adaptive and maladaptive aspects of human decision-making.

Original languageAmerican English
Pages (from-to)476-490
Number of pages15
JournalCognitive, Affective and Behavioral Neuroscience
Volume23
Issue number3
DOIs
StatePublished - Jun 2023

Bibliographical note

Publisher Copyright:
© 2022, The Psychonomic Society, Inc.

Keywords

  • Computational model
  • Decision-making
  • Heuristics and biases
  • Hierarchical model
  • Inference

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