DEC 30, 2016 - What information should be presented to or hidden from decision makers in order to facilitate high performance in decision tasks? In a recently accepted article to IEEE Transactions on Human-Machine Systems, "Heuristic Information Acquisition and Restriction for Decision Support," CEC researchers, Marc Canellas and Karen Feigh, contribute new rules for information acquisition and restriction which do not require reliable assessments of probabilities, cue weights, and cue values, as most normative, Bayesian methods do. These heuristic rules were tested on a range of analytic and heuristic decision strategies within two-option decision tasks across 15 real-world environments. Though the rules are transparent and easy to communicate (create a balance of information between options and within cues) and require little information to perform, the simulation results show the rules were generally effective across all environments. Over the next two months, Canellas will be conducting human-subjects studies to determine the effectiveness of the heuristic information acquisition rules for human decision making support.