Estimating the form of a decision maker's preference function and converging towards preferred solutions

Preference functions have been widely used to scalarize multiple objectives. Various forms such as linear, quasiconcave, or general monotone have been assumed. In this article, we consider a general family of functions that can take a variety of forms and has properties that allow for estimating the...

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Bibliographic Details
Published in:	IISE transactions Vol. 52; no. 6; pp. 651 - 664
Main Authors:	Karakaya, Gülşah, Köksalan, Murat
Format:	Journal Article
Language:	English
Published:	Taylor & Francis 02.06.2020
Subjects:	interactive algorithm Multiple objective programming preference function form
ISSN:	2472-5854, 2472-5862
Online Access:	Get full text
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Summary:	Preference functions have been widely used to scalarize multiple objectives. Various forms such as linear, quasiconcave, or general monotone have been assumed. In this article, we consider a general family of functions that can take a variety of forms and has properties that allow for estimating the form efficiently. We exploit these properties to estimate the form of the function and converge towards a preferred solution(s). We develop the theory and algorithms to efficiently estimate the parameters of the function that best represent a decision maker's preferences. This in turn facilitates fast convergence to preferred solutions. We demonstrate on a variety of experiments that the algorithms work well both in estimating the form of the preference function and converging to preferred solutions.
ISSN:	2472-5854 2472-5862
DOI:	10.1080/24725854.2019.1670373