Abstract
In high-risk, time-pressure domains, the ability to get only salient information is of paramount importance. Missing or superfluous information in these domains can detract from a decision maker’s ability to make correct judgments. Consequently, decision support systems are being developed to facilitate expert decision-making by modifying the information presented to the operator. In this paper we introduce a method for presenting decision makers with the most environment-relevant information for a given decision task. This study explores a statistical method, Bayesian Optimal Experimental Design (OED), as a means of acquiring and restricting information to improve the probability of selecting the correct decision. We use probability gain theory to acquire the most useful piece of information to present to the decision maker, and we extend this to create a probability loss theory that restricts information that does not aid (probabilistically) or aids the least in the decision-making process.
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Only the three cues and the criterion used in the example are described here.
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Acknowledgements
This work was supported by the Office of Naval Research Command Decision-making Program under Contract N00014-13-1-0083. The results do not reflect the official position of this agency.
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Walsh, S.E., Sealy, W., Feigh, K.M. (2021). Optimal Experimental Design Methods for Acquiring and Restricting Information to Improve Decision Making. In: Ayaz, H., Asgher, U. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1201. Springer, Cham. https://doi.org/10.1007/978-3-030-51041-1_38
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DOI: https://doi.org/10.1007/978-3-030-51041-1_38
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