Abstract
This article examines how people depart from optimality during multiple-goal pursuit. The authors operationalized optimality using dynamic programming, which is a mathematical model used to calculate expected value in multistage decisions. Drawing on prospect theory, they predicted that people are risk-averse when pursuing approach goals and are therefore more likely to prioritize the goal in the best position than the dynamic programming model suggests is optimal. The authors predicted that people are risk-seeking when pursuing avoidance goals and are therefore more likely to prioritize the goal in the worst position than is optimal. These predictions were supported by results from an experimental paradigm in which participants made a series of prioritization decisions while pursuing either 2 approach or 2 avoidance goals. This research demonstrates the usefulness of using decision-making theories and normative models to understand multiple-goal pursuit.
Highlights
This article examines how people depart from optimality during multiple-goal pursuit
The authors operationalized optimality using dynamic programming, which is a mathematical model used to calculate expected value in multistage decisions. They predicted that people are risk-averse when pursuing approach goals and are more likely to prioritize the goal in the best position than the dynamic programming model suggests is optimal
Using the normative model as a benchmark, we examine whether people are risk-averse when pursuing multiple approach goals, and risk-seeking when pursuing multiple avoidance goals
Summary
This article examines how people depart from optimality during multiple-goal pursuit. They predicted that people are risk-averse when pursuing approach goals and are more likely to prioritize the goal in the best position than the dynamic programming model suggests is optimal. The authors predicted that people are risk-seeking when pursuing avoidance goals and are more likely to prioritize the goal in the worst position than is optimal. Decision support systems based on models of optimal decision making have been implemented to help investors overcome biases to improve returns (e.g., Bhandari & Hassanein, 2012) To address this gap, we implement a normative model of decision making during multiple-goal pursuit. Normative models provide a standard for evaluating behavior, rather than predicting the behavior itself (Baron, 2004, 2012) They are ideal for examining the optimality of prioritization decisions. A discrete task is a useful starting point for examining departures from optimality during multiple-goal pursuit, because discrete models provide a useful approximation for continuous processes (Busemeyer & Townsend, 1992; Jagacinski & Flach, 2003), and evidence suggests that the same effects emerge regardless of whether one uses a discrete or continuous task (Brehmer, 1992)
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