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Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/1516

Title: Neural Networks for Non-independent Lotteries
Authors: Rotundo, Giulia
Keywords: lotteries; neural networks; von Neumann-Morgenstern
Issue Date: 2010
Publisher: Springer
Citation: 11. G. Rotundo, “Neural Networks for Non-independent Lotteries”. In: Springer series “Studies in Fuzziness and Soft Computing” (R.R. Kacprzyk, J. Ed.), “Preferences and Decisions” Greco, S., Marques Pereira, R.A., Squillante, M., Yager, R.R., Kacprzyk, J. (Eds.), , Vol. 257, pp. 369-375 (2010).
Abstract: The von Neuman-Morgenstern utility functions play a relevant role in the set of utility functions. This paper shows the density of the set von Neuman- Morgenstern utility functions on the set of utility utility function that can represent arbitrarily well a given continuous but not independent preference relation over monetary lotteries. The main result is that without independence it is possible to approximate utility functions over monetary lotteries by von Neuman-Morgenstern ones with a
URI: http://hdl.handle.net/2067/1516
ISSN: 978-3-642-15975-6
Appears in Collections:DEIM - Archivio della produzione scientifica

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