A preference ranking model based on both mean-variance analysis and cumulative distribution function using simulation
type: Item
instance of: scholarly article
title: A preference ranking model based on both mean-variance analysis and cumulative distribution function using simulation
zbMATH Open document ID: 1169.90373
DOI: 10.1504/IJOR.2009.025199
author: Peng Shi
author: Jamal R. M. Ameen
author: R. J. Wiltshire
author: Khwazbeen Saida Fatah
published in: International Journal of Operational Research
publication date: 2009-09-13T00:00:00Z
review text: Summary: In decision-making problems under uncertainty, mean-variance analysis consistent with expected utility theory plays an important role in analysing preferences for different alternatives. In this paper, a new approach for mean-variance analysis based on cumulative distribution functions is proposed. Using simulation, a new algorithm is developed, which generates pairs of random variables to be representative for each pair of uncertain alternatives. The proposed model is concerned with financial investment for risk-averse investors with non-negative lotteries. Furthermore, the proposed technique in this paper can be applied to different distribution functions for lotteries or utility functions.
Mathematics Subject Classification ID: 90B50
zbMATH DE Number: 5603401
zbMATH Keywords: mean variance theory
zbMATH Keywords: expected utility theory
zbMATH Keywords: cumulative distribution function
zbMATH Keywords: simulation
zbMATH Keywords: preference ranking
zbMATH Keywords: modelling
zbMATH Keywords: decision making
zbMATH Keywords: uncertainty
zbMATH Keywords: financial investment
zbMATH Keywords: risk-averse investors
zbMATH Keywords: non-negative lotteries
zbMATH Keywords: risk aversion
MaRDI profile type: Publication
full work available at URL: ijor.2009.025199
OpenAlex ID: W2162398461
description: scientific article; zbMATH DE number 5603401