Decision Making with Incomplete Information Dataset

The data submitted has been generated through a simulation constructed by the authors to evaluate decision scenarios with varying levels of incomplete information using four different decision making strategies. The strategies are modeled as input-output programs which input the decision scenario and output a chosen decision option and the number of elementary information processes (EIPs) required to make the decision. The inclusion of EIPs allows effort and accuracy to be measured. The combination of studying incomplete information while measuring effort and accuracy enables this simulation to provide new insights into the reasons for the effectiveness of exemplar heuristics in scenarios with incomplete information --- whether those reasons are an efficient accuracy-effort trade-off or ecological rationality. To generate an effective sampling of the possible combinations of attribute scores and incomplete information, the attribute scores and total information availability were varied through a full factorial experimental design. Each decision attribute for each decision option (a total of eight decision attributes for each decision scenario) had two possible information availability levels (attribute score known, 1, or attribute score unknown, 0) and four possible attribute score levels (20, 40, 60, and 80). The result is a full factorial simulation with 2^8x4^8 (greater than 16 million) scenarios applied to each of the four decision strategies. Information availability and the attribute scores were inputs (independent variables) for the simulation whereas the remaining context features, effort, accuracy, and information bias were outputs of the simulation (dependent variables).This work was supported by the Office of Naval Research Command Decision Making Program (CDM) under Contract #: N00014-13-1-0083 and the National Science Foundation Graduate Research Fellowship.The following two publications are based on these data: Marc C. Canellas and Karen M. Feigh. Heuristic decision making with incomplete information: Conditions for ecological rationality. In IEEE International Conference of Systems Man and Cybernetics, San Diego, USA, October 2014. (Accepted).; and Marc C. Canellas, Karen M. Feigh, and Zarrin K. Chua. Accuracy and effort of decision making with incomplete information: Implications for DSS design. IEEE Transactions on Human-Machine Systems, 2014. (Submitted).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.United States. Office of Naval Researc