Abstract
Predictions are usually based on what is called laws of nature: many times, we observe the same relation between the states at different moments of time, and we conclude that the same relation will occur in the future. The more times the relation repeats, the more confident we are that the same phenomenon will be repeated again. This is how Newton’s laws and other laws came into being. This is what is called inductive reasoning. However, there are other reasonable approaches. For example, assume that a person speeds and is not caught. This may be repeated two times, three times—but here, the more times this phenomenon is repeated, the more confident we become that next time, he/she will be caught. Let us call this anti-inductive reasoning. So which of the two approaches shall we use? This is an example of a question that we study in this paper.
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References
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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);
\(\bullet \) HRD-1834620 and HRD-2034030 (CAHSI Includes).
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.
The authors are thankful to all the participants of the International Seminar on Computational Intelligence ISCI’2021 (Tijuana, Mexico, August 17–19, 2021), especially to Oscar Castillo and Patricia Melin, for valuable discussions.
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Amador, L.O., Kreinovich, V. (2022). What is a Reasonable Way to Make Predictions?. In: Castillo, O., Melin, P. (eds) New Perspectives on Hybrid Intelligent System Design based on Fuzzy Logic, Neural Networks and Metaheuristics. Studies in Computational Intelligence, vol 1050. Springer, Cham. https://doi.org/10.1007/978-3-031-08266-5_30
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DOI: https://doi.org/10.1007/978-3-031-08266-5_30
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