ABSTRACT:

While behavioral research has consistently documented the advantages of using decision aids, decision makers have been reluctant to rely on them, generally with unfavorable results. We offer a model of aided decisions that incorporates both the level of reliance on the decision aid and the agreement of decisions with the decision aid’s advice. In the model, the level of reliance is an attribute of the decision strategy adopted by the aided decision maker, while the extent of the decision maker’s agreement with the decision aid is observed by comparing the decisions with the aid’s advice. The model also includes a classification scheme for factors that affect decision strategy. The categories in this classification scheme are decision aid features, decision-maker characteristics, decision task characteristics and factors affecting the evaluation of decision strategies. In an experiment testing the sensitivity of decision aid reliance, neither the extent of algorithm revelation (revealed or hidden) nor decision framing (positive or negative) affected the level of reliance on the decision aid. In a comparison of aided and unaided decision makers, the decision aid had greater influence in the harder tasks than in the easier tasks.