Weight approximations in multi-attribute decision models

There are a wide range of techniques on offer to decision makers choosing between options where each option exhibits a range of attributes. Many of these techniques involve eliciting weights to represent the relative importance of each attribute. This thesis offers a mathematical explanation for the...

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Main Author: Roberts, Ronald Gordon
Published: University of the West of England, Bristol 2004
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Online Access:http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.410068
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spelling ndltd-bl.uk-oai-ethos.bl.uk-4100682015-08-04T03:26:17ZWeight approximations in multi-attribute decision modelsRoberts, Ronald Gordon2004There are a wide range of techniques on offer to decision makers choosing between options where each option exhibits a range of attributes. Many of these techniques involve eliciting weights to represent the relative importance of each attribute. This thesis offers a mathematical explanation for the consistent differences in the distribution of weights experienced when a fixed sum method, Point Allocation (PA), and a fixed scale method, Direct Rating (DR), are used. Fixed scale and fixed sum simulations, sampling from the Uniform distribution, produce different weight profiles matching those found in practical applications. Formulae are found representing the distribution of weights produced by the simulations. These enable ranked weights to be calculated, which can be used as surrogates for 'true' weights. In particular, a second major aspect of the study concerns the discovery of a family of piecewise probability density functions to represent the distributions of ranked weights generated using the DR method. The means of the distributions are the Rank Order Distribution (ROD) surrogate weights. These are compared with the Rank Order Centroid (ROC) weights. As the number of attributes in a decision problem increases, the ROD weights approximate to the more easily calculated Rank Sum weights. An interactive survey is conducted, using students and the internet, to compare the PA and DR methods in terms of their ease of use, time taken, accuracy achieved, and user's confidence, in producing weights that represented a series of known "true" weights presented in graphical form. Statistical analysis found significant effects of method, time, accuracy achieved, and confidence of participants, favouring the DR approach. The methods are also compared as a means of obtaining the importance weights given by students to seven listed attributes of universities. A significant difference is found between the pattern of decision weights produced using the two methods confirming previously published studies.511.4University of the West of England, Bristolhttp://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.410068Electronic Thesis or Dissertation
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sources NDLTD
topic 511.4
spellingShingle 511.4
Roberts, Ronald Gordon
Weight approximations in multi-attribute decision models
description There are a wide range of techniques on offer to decision makers choosing between options where each option exhibits a range of attributes. Many of these techniques involve eliciting weights to represent the relative importance of each attribute. This thesis offers a mathematical explanation for the consistent differences in the distribution of weights experienced when a fixed sum method, Point Allocation (PA), and a fixed scale method, Direct Rating (DR), are used. Fixed scale and fixed sum simulations, sampling from the Uniform distribution, produce different weight profiles matching those found in practical applications. Formulae are found representing the distribution of weights produced by the simulations. These enable ranked weights to be calculated, which can be used as surrogates for 'true' weights. In particular, a second major aspect of the study concerns the discovery of a family of piecewise probability density functions to represent the distributions of ranked weights generated using the DR method. The means of the distributions are the Rank Order Distribution (ROD) surrogate weights. These are compared with the Rank Order Centroid (ROC) weights. As the number of attributes in a decision problem increases, the ROD weights approximate to the more easily calculated Rank Sum weights. An interactive survey is conducted, using students and the internet, to compare the PA and DR methods in terms of their ease of use, time taken, accuracy achieved, and user's confidence, in producing weights that represented a series of known "true" weights presented in graphical form. Statistical analysis found significant effects of method, time, accuracy achieved, and confidence of participants, favouring the DR approach. The methods are also compared as a means of obtaining the importance weights given by students to seven listed attributes of universities. A significant difference is found between the pattern of decision weights produced using the two methods confirming previously published studies.
author Roberts, Ronald Gordon
author_facet Roberts, Ronald Gordon
author_sort Roberts, Ronald Gordon
title Weight approximations in multi-attribute decision models
title_short Weight approximations in multi-attribute decision models
title_full Weight approximations in multi-attribute decision models
title_fullStr Weight approximations in multi-attribute decision models
title_full_unstemmed Weight approximations in multi-attribute decision models
title_sort weight approximations in multi-attribute decision models
publisher University of the West of England, Bristol
publishDate 2004
url http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.410068
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