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
Source: 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.
URI: http://hdl.handle.net/2067/1516
ISSN: 978-3-642-15975-6
Appears in Collections:DEIM - Archivio della produzione scientifica

Files in This Item:
File Description SizeFormat
R11.doc20.5 kBMicrosoft WordView/Open
Show full item record

Page view(s)

1
Last Week
0
Last month
0
checked on Oct 23, 2020

Download(s)

1
checked on Oct 23, 2020

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.