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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2067/1516
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| 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 arbitrary precision. The approach used is a constructive one. Neural networks
are used for their approximation properties in order to get the result, and their
functional form provides both the von Neumann-Morgenstern representation and
the necessary change of variables over the set of lotteries.
...more |
| 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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