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A Reference-Dependent Regret Model for Deterministic Trade-off Studies

  • Author(s): Kujawski, Edouard
  • et al.
Abstract

Today's typical multi-criteria decision analysis is based on classical expected utility theory that assumes a mythical "Rational Individual" immune to psychological influences such as anticipated regret. It is therefore in conflict with rational individuals who trade-off some benefits and forgo the alternative with the highest total classical utility for a more balanced alternative in order to reduce their levels of anticipated regret. This paper focuses on decision making under certainty. It presents a reference-dependent regret model (RDRM) in which the level of regret that an individual experiences depends on the absolute values rather than the differences of the utilities of the chosen and forgone alternatives. The RDRM best choice may differ from the conventional linear additive utility model, the analytic hierarchy process, and the regret theory of Bell and Loomes and Sugden. Examples are presented that indicate that RDRM is the better predictive descriptor for decision making under certainty. RDRM satisfies transitivity of the alternatives under pairwise comparisons and models rank reversal consistent with observed reasonable choices under dynamic or distinct situations. Like regret theory, the RDRM utilities of all the alternatives under consideration are interrelated. For complex trade-off studies regret is incorporated as an element of a cost-utility-regret analysis that characterizes each alternative in terms of its monetary cost, an aggregate performance utility, and a regret value. This provides decision makers adequate information to compare the alternatives and depending on their values they may trade-off some performance and/or cost to avoid high levels of regret. The result is a well-balanced alternative often preferred by reasonable decision makers to the optimal choice of classical multi-attribute utility analysis. The model can readily be extended to incorporate rejoicing to suit decision makers who seek it. The approach is illustrated using a hypothetical but realistic aircraft selection problem.

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