To predict human choice, consider the context

Ori Plonsky; Ido Erev

doi:10.1016/j.tics.2021.07.007

To predict human choice, consider the context

Ori Plonsky, Ido Erev

Data and Decision Sciences

Research output: Contribution to journal › Short survey › peer-review

Abstract

Choice prediction competitions suggest that popular models of choice, including prospect theory, have low predictive accuracy. Peterson et al. show the key problem lies in assuming each alternative is evaluated in isolation, independently of the context. This observation demonstrates how a focus on predictions can promote understanding of cognitive processes.

Original language	English
Pages (from-to)	819-820
Number of pages	2
Journal	Trends in Cognitive Sciences
Volume	25
Issue number	10
DOIs	https://doi.org/10.1016/j.tics.2021.07.007
State	Published - Oct 2021

Keywords

BEAST
decisions under risk
decisions under uncertainty
machine learning
reliance on small samples

ASJC Scopus subject areas

Neuropsychology and Physiological Psychology
Experimental and Cognitive Psychology
Cognitive Neuroscience

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10.1016/j.tics.2021.07.007

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title = "To predict human choice, consider the context",

abstract = "Choice prediction competitions suggest that popular models of choice, including prospect theory, have low predictive accuracy. Peterson et al. show the key problem lies in assuming each alternative is evaluated in isolation, independently of the context. This observation demonstrates how a focus on predictions can promote understanding of cognitive processes.",

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author = "Ori Plonsky and Ido Erev",

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year = "2021",

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doi = "10.1016/j.tics.2021.07.007",

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KW - decisions under risk

KW - decisions under uncertainty

KW - machine learning

KW - reliance on small samples

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JO - Trends in Cognitive Sciences

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To predict human choice, consider the context

Abstract

Keywords

ASJC Scopus subject areas

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