Accuracy and Effort of Decision-Making Strategies With Incomplete Information: Implications for Decision Support System Design

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 two heuristic strategies, take-the-best and Tallying, alongside two analytic decision-making strategies, weighted-additive and equal-weighting, in scenarios with varying levels of total information, information imbalance, dispersion, and dominance. Correct decisions were determined by the option with the higher overall score from the weighted-additive model with full information. Effort was measured as counts of elementary information processes required by each strategy to make decisions. Multi-and one-way statistical analyses measured the effect of total information, information imbalance, dispersion, and dominance, on accuracy and effort required for each decision strategy. Three principle results were found: 1) context features matching naturalistic decision settings result in heuristic strategies being closest in accuracy to analytic strategies; 2) the variability in the distribution of the effort requirements of the heuristic strategies for each level of total information indicates that the effort requirements of heuristics may not always be as favorable as prior studies have shown; and 3) the tradeoff between information imbalance and total information suggests new insight for DSS design of restrictiveness and guidance for scenarios with incomplete information.