Heuristic Information Acquisition and Restriction Rules for Decision Support

Abstract

The research question addressed by this study was: What information should be presented to or hidden from decision makers in order to facilitate high performance in decision tasks? Previous research on information search is limited because of its focus on analytic information acquisition methods; analytic because of the focus on maximizing expected utility; acquisition because of the focus on what information should be added or searched for. Implementing these methods requires reliable assessments of probabilities, cue weights, and cue values and does not provide suggestions on how to restrict or remove information. In this work, we present four heuristics, or simple rules, for acquiring and restricting information that only require an understanding of the distribution of known and unknown information (information imbalance and complete attribute pairs). The 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 that the rules were generally effective across all environments. For almost every combination of rule and strategy, the heuristic restriction rules were shown to be more likely to increase rather than decrease accuracy. In every combination, the heuristic acquisition rules were shown to increase accuracy more than acquiring information that did not adhere to the rules. Further statistical and mathematical analysis showed that rules are mediated by strategies' full information accuracy and estimates of missing information.

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