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• W2788891325 abstract "Abstract Modern theories of decision making emphasize the reference-dependency of decision making under risk. In particular, people tend to be risk-averse for outcomes greater than their reference point, and risk-seeking for outcomes less than their reference point. A key question is where reference points come from. A common assumption is that reference points correspond to expectations about outcomes, but it is unclear whether people rely on a single global expectation, or multiple local expectations. If the latter, how do people determine which expectation to apply in a particular situation? We argue that people discover reference points using a form of Bayesian structure learning, which partitions outcomes into distinct contexts, each with its own reference point corresponding to the expected outcome in that context. Consistent with this theory, we show experimentally that dramatic change in the distribution of outcomes can induce the discovery of a new reference point, with systematic effects on risk preferences. By contrast, when changes are gradual, a single reference point is continuously updated." @default.
• W2788891325 created "2018-03-06" @default.
• W2788891325 creator A5000554107 @default.
• W2788891325 creator A5008264390 @default.
• W2788891325 date "2018-01-24" @default.
• W2788891325 modified "2023-09-23" @default.
• W2788891325 title "Reference-dependent preferences arise from structure learning" @default.
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• W2788891325 doi "https://doi.org/10.1101/252692" @default.
• W2788891325 hasPublicationYear "2018" @default.
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