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Hierarchical inference as a source of human biases

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.

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Paul B. Sharp is supported by postdoctoral fellowship from the Fulbright Association (PS00318453). Eran Eldar is supported by NIH grants R01MH124092 and R01MH125564, ISF grant 1094/20 and US 1336 Israel BSF grant 2019801.

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Sharp, P.B., Fradkin, I. & Eldar, E. Hierarchical inference as a source of human biases. Cogn Affect Behav Neurosci (2022). https://doi.org/10.3758/s13415-022-01020-0

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Keywords

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