Embedding Decision Heuristics in Discrete Choice Models: A Review

Abstract

Contrary to the usual assumption of fixed, well-defined preferences, it is increasingly evident that individuals are likely to approach a choice task using rules and decision heuristics that are dependent on the choice environment. More specifically, heuristics that are defined by the local choice context, such as the gains or losses of an attribute value relative to the other attributes, seem to be consistently employed. Recent empirical findings also demonstrate that previous choices and previously encountered choice tasks shown to respondents can affect the current choice outcome, indicating a form of inter-dependence across choice sets. This paper is primarily focused on reviewing how heuristics have been modelled in stated choice data. The paper begins with a review of the heuristics that may be relevant for coping with choice task complexity and then proceeds to discuss some modelling approaches. Next, relational heuristics, such as prospect theory, random regret minimization and extremeness aversion (compromise effect) are discussed. These are heuristics which operate within the local choice set. Another major class of heuristics reviewed in this paper pertains to ordering effects and more generally on past outcomes and past attribute levels of the alternatives.