An important problem in descriptive and prescriptive research in decision making is to identify “regions of rationality,” i.e., the areas for which heuristics are and are not effective. To map the contours of such regions, we derive probabilities that heuristics identify the best of m alternatives (m ≥ 2) characterized by k attributes or cues (k ≥ 1). The heuristics include a single variable (lexicographic), variations of elimination-by-aspects, equal weighting, hybrids of the preceding, and models exploiting dominance. We use 20 simulated and 4 empirical data sets for illustration. We further provide an overview by regressing heuristic performance on factors characterizing environments. Overall, “sensible” heuristics generally yield similar choices in many environments. However, selection of the appropriate heuristic can be important in some regions (e.g., if there is low intercorrelation among attributes or cues). Because our work assumes a hit-or-miss decision criterion, we conclude by outlining extensions for exploring the effects of different loss functions.
['Robin M. Hogarth', 'Natalia Karelaia']
Regions of Rationality: Maps for Bounded Agents