Abstract
In many real-life situations, we need to make a decision. In many cases, we know the optimal decision in situations when we know the exact value of the corresponding quantity x. However, often, we do not know the exact value of this quantity, we only know the bounds on the value x – i.e., we know the interval containing x. In this case, we need to select a decision corresponding to some value from this interval. The selected value will, in general, be different from the actual (unknown) value of this quantity. As a result, the quality of our decision will be lower than in the perfect case when we know the value x. Which value should we select in this case? In this paper, we provide a decision-theory-based recommendation for this selection.
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Acknowledgments
This work was supported in part by the National Science Foundation grants 1623190 (A Model of Change for Preparing a New Generation for Professional Practice in Computer Science), HRD-1834620 and HRD-2034030 (CAHSI Includes), EAR-2225395, and by the AT &T Fellowship in Information Technology.
It was also supported by the program of the development of the Scientific-Educational Mathematical Center of Volga Federal District No. 075-02-2020-1478, and by a grant from the Hungarian National Research, Development and Innovation Office (NRDI).
The authors are thankful to all the participants of the 29th Joint UTEP/NMSU Workshop on Mathematics, Computer Science, and Computational Science (Las Cruces, New Mexico, USA, April 1, 2023) for valuable suggestions.
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Lopez, J.A., Kreinovich, V. (2023). Towards Decision Making Under Interval Uncertainty. In: Cohen, K., Ernest, N., Bede, B., Kreinovich, V. (eds) Fuzzy Information Processing 2023. NAFIPS 2023. Lecture Notes in Networks and Systems, vol 751. Springer, Cham. https://doi.org/10.1007/978-3-031-46778-3_33
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DOI: https://doi.org/10.1007/978-3-031-46778-3_33
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