eCite Digital Repository

Hurwiczα expected utility to rank p-approximated generalized lotteries of I Type with partially quantified uncertainty

Citation

Nikolova, N and Mednikarov, B and Dimitrakiev, D and Tenekedjiev, K, Hurwiczα expected utility to rank p-approximated generalized lotteries of I Type with partially quantified uncertainty, International Conference - Automatics and Informatics '18 - Proceedings, 04-06 October 2018, Sofia, Bulgaria, pp. 103-106. ISSN 1313-1850 (2018) [Refereed Conference Paper]

Copyright Statement

Copyright 2018 John Atanasoff Society of Automatics and Informatics

Official URL: https://www.fmi.uni-sofia.bg/sites/default/files/d...

Abstract

We discuss how to rank one-dimensional fuzzy-rational generalized lotteries of I type lotteries with continuous real set of prizes, where uncertainty is partially quantifies by p-ribbon distribution functions (CDFs). The p-ribbon CDFs are interpolated on interval quantile estimates of the decision maker. We utilize the Hurwiczα criterion under strict uncertainty to approximate the p-ribbon distribution functions into classical ones. That allows us to transform p-fuzzy rational generalized lotteries of I type into classical ones and use Hurwiczα expected utility principle to rank those. Since the Hurwiczα method weighs the extreme pessimism and extreme optimism, we need to first approximate the p-ribbon distribution functions using the Wald and maximax criteria under strict uncertainty (representing the extreme pessimism and extreme optimism of the fuzzy rational decision maker) before we use the Hurwiczα criterion. A numerical example demonstrates the procedures.

Item Details

Item Type:Refereed Conference Paper
Keywords:ribbon distributions, interval estimates, lotteries, strict uncertainty, expected utility
Research Division:Psychology and Cognitive Sciences
Research Group:Cognitive Sciences
Research Field:Decision Making
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Information and Computing Sciences
UTAS Author:Nikolova, N (Professor Nataliya Nikolova)
UTAS Author:Tenekedjiev, K (Professor Kiril Tenekedjiev)
ID Code:128690
Year Published:2018
Deposited By:Maritime and Logistics Management
Deposited On:2018-10-06
Last Modified:2019-03-25
Downloads:0

Repository Staff Only: item control page