Risk matters less when options are apples-to-oranges: The translate-and-accommodate model.
Year: 2026
Type: article
Abstract: Risk aversion for moderate-likelihood gains is perhaps the best-known stylized fact from decision research. Though studies documenting it have focused on decisions involving money, such behavior is presumed to prevail very generally, across diverse domains. We investigate the validity of this generalization by contrasting two types of decisions. In unimodal choices, outcomes are "apples-to-apples." Consider choosing between sure and uncertain monetary payoffs, or between sure receipt of a product and a chance at several units of it. In crossmodal choices, outcomes are "apples-to-oranges." Consider choosing between sure receipt of one product and uncertain receipt of a disparate product. We observe two patterns by which risk matters less crossmodally, contrary to straightforward generalizations. First, relative to unimodal preferences involving actuarially fair risky options, corresponding crossmodal preferences exhibit less risk aversion. Second, crossmodal preferences vary less across risk levels: As the likelihood and subjective value of a risky option's outcomes become increasingly unfavorable (favorable), people do not exhibit as much additional distaste (appetite) for it. These patterns of insensitivity engender an interaction: relative to unimodal settings, crossmodal settings yield less aversion to unfavorable and fair risk but more aversion to favorable risk. To explain this interaction, we present the translate-and-accommodate model, in which unimodal preferences follow standard accounts, but crossmodal preferences reflect processes of (a) deterministic translation and (b) risk accommodation. The translate-and-accommodate model also explains the uncertainty effect and related patterns of seemingly bizarre, dominated choices. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Source: Psychological Review
DOI: 10.1037/rev0000633
Language: English
Subfield: General Decision Sciences
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7170143108