Abstract
Analyzing how people actually make decisions, the Nobelist Daniel Kahneman and his co-author Amos Tversky found out that instead of maximizing the expected gain, people maximize a weighted gain, with weights determined by the corresponding probabilities. The corresponding empirical weights can be explained qualitatively, but quantitatively, these weights remain largely unexplained. In this paper, we show that with a surprisingly high accuracy, these weights can be explained by fuzzy logic ideas.
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Acknowledgment
This work was supported in part by the National Science Foundation grants HRD-0734825 and HRD-1242122 (CyberShARE Center of Excellence) and DUE-0926721.
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© 2016 Springer International Publishing Switzerland
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Lorkowski, J., Kreinovich, V. (2016). Fuzzy Logic Ideas Can Help in Explaining Kahneman and Tversky’s Empirical Decision Weights. In: Zadeh, L., Abbasov, A., Yager, R., Shahbazova, S., Reformat, M. (eds) Recent Developments and New Direction in Soft-Computing Foundations and Applications. Studies in Fuzziness and Soft Computing, vol 342. Springer, Cham. https://doi.org/10.1007/978-3-319-32229-2_7
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DOI: https://doi.org/10.1007/978-3-319-32229-2_7
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