Decision Making with Incomplete Information Dataset

Abstract

Decision makers are often required to make decisions with incomplete information. In order to design decision support systems (DSSs) utilizing restrictiveness and guidance to assist decision makers in these situations, it is essential to understand how certain decision making strategies are affected by incomplete information. This paper presents the results of a simulation measuring the accuracy and effort of take-the-best (TTB) and Tallying alongside two normative-rational decision making strategies, weighted-additive (WADD) and equal-weighting (EW) in scenarios with varying levels of total information, information imbalance, dispersion, and dominance. The results show there is significant variability in the effort requirements of heuristic strategies which may diminish the arguments for effort-accuracy trade-offs. Additionally, heuristic strategies were shown to be closest in accuracy to normative-rational strategies when context features matched dynamic decision settings. Ultimately, methods for restrictiveness and guidance based on trade-offs between total information and information imbalance were shown to enable reductions in total information that actually increased the accuracy of heuristics.