Abstract
Among the most efficient characteristics of a probability distribution are its moments and, more generally, generalized moments. One of the most adequate numerical characteristics describing human behavior is expected utility. In both cases, the corresponding characteristic is the sum of results of applying appropriate nonlinear functions applied to individual inputs. In this paper, we provide a possible theoretical explanation of why such functions are efficient.
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Acknowledgements
This work was supported in part by the National Science Foundation grants:
\(\bullet \) 1623190 (A Model of Change for Preparing a New Generation for Professional Practice in Computer Science), and
\(\bullet \) HRD-1834620 and HRD-2034030 (CAHSI Includes).
It was also supported:
\(\bullet \) by the AT &T Fellowship in Information Technology,
\(\bullet \) by the program of the development of the Scientific-Educational Mathematical Center of Volga Federal District No. 075-02-2020-1478, and
\(\bullet \) by a grant from the Hungarian National Research, Development and Innovation Office (NRDI).
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Padilla, R.N., Kreinovich, V. (2023). Why Moments (and Generalized Moments) Are Used in Statistics and Why Expected Utility Is Used in Decision Making: A Possible Explanation. In: Ceberio, M., Kreinovich, V. (eds) Decision Making Under Uncertainty and Constraints. Studies in Systems, Decision and Control, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-031-16415-6_27
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DOI: https://doi.org/10.1007/978-3-031-16415-6_27
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