People often speculate about why others make the choices they do. This paper investigates how such inferences are formed as a function of what is chosen. Specifically, when observers encounter someone else’s choice (e.g., of political candidate), they use the chosen option’s attribute values (e.g., a candidate’s specific stance on a policy issue) to infer the importance of that attribute (e.g., the policy issue) to the decision-maker. Consequently, when a chosen option has an attribute whose value is extreme (e.g., an extreme policy stance), observers infer—sometimes incorrectly—that this attribute disproportionately motivated the decision-maker’s choice. Seven studies demonstrate how observers use an attribute’s value to infer its weight—the value-weight heuristic—and identify the role of perceived diagnosticity: more extreme attribute values give observers the subjective sense that they know more about a decision-maker’s preferences, and in turn, increase the attribute’s perceived importance. The paper explores how this heuristic can produce erroneous inferences and influence broader beliefs about decision-makers.

I know why you voted for Trump: (over)inferring motives based on choice

Evangelidis, Ioannis
2019

Abstract

People often speculate about why others make the choices they do. This paper investigates how such inferences are formed as a function of what is chosen. Specifically, when observers encounter someone else’s choice (e.g., of political candidate), they use the chosen option’s attribute values (e.g., a candidate’s specific stance on a policy issue) to infer the importance of that attribute (e.g., the policy issue) to the decision-maker. Consequently, when a chosen option has an attribute whose value is extreme (e.g., an extreme policy stance), observers infer—sometimes incorrectly—that this attribute disproportionately motivated the decision-maker’s choice. Seven studies demonstrate how observers use an attribute’s value to infer its weight—the value-weight heuristic—and identify the role of perceived diagnosticity: more extreme attribute values give observers the subjective sense that they know more about a decision-maker’s preferences, and in turn, increase the attribute’s perceived importance. The paper explores how this heuristic can produce erroneous inferences and influence broader beliefs about decision-makers.
2019
2018
Barasz, Kate; Kim, Tami; Evangelidis, Ioannis
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11565/4013243
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