A preference ranking model based on both mean-variance analysis and cumulative distribution function using simulation

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Fatah, Khwazbeen, Shi, Peng, Ameen, Jamal and Wiltshire, Ronald (2009) A preference ranking model based on both mean-variance analysis and cumulative distribution function using simulation. International Journal of Operational Research, 5 (3). pp. 311-327. ISSN 1745-7645

Abstract

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 applies to different distribution functions for lotteries or utility functions.

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Item type	Article
URI	https://vuir.vu.edu.au/id/eprint/4284
DOI	10.1504/IJOR.2009.025199
Official URL	http://dx.doi.org/10.1504/IJOR.2009.025199
Subjects	Historical > Faculty/School/Research Centre/Department > Institute for Logistics and Supply Chain Management (ILSCM) Historical > FOR Classification > 0906 Electrical and Electronic Engineering Historical > SEO Classification > 970109 Expanding Knowledge in Engineering
Keywords	ResPubID17673, mean variance theory, expected utility theory, cumulative distribution function, simulation, preference ranking, modelling, decision making, uncertainty, financial investment, risk-averse investors, non-negative lotteries, risk aversion
Citations in Scopus	3 - View on Scopus
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