On the measurement of preferences in the analytic hierarchy process

Salo, Ahti; Hämäläinen, Raimo P.

doi:10.1002/(sici)1099-1360(199711)6:6<309::aid-mcda163>3.3.co;2-u

Cited by 73 publications

(96 citation statements)

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“…The weighting of attributes in the AHP (Saaty, 1980(Saaty, , 1994Salo and Hämäläinen, 1997) is based on estimates of weight ratios similarly to SMART and SWING. The decision maker is asked to compare the importance of two attributes at a time: "Which one of these two attributes is more important, and how much more important?"…”

Section: Attribute Weightingmentioning

confidence: 99%

“…Variants of the AHP studied here are obtained by changing the evaluation scales (see e.g. Lootsma, 1993;Salo and Hämäläinen, 1997). In the AHP the weight ratios are asked for all pairs of attributes.…”

Section: Attribute Weightingmentioning

confidence: 99%

“…These observations can be explained to originate from the evaluation scale used in the AHP. Salo and Hämäläinen (1997) suggested new balanced scales derived from the idea of evenly dispersed weights. The experiment of showed that the balanced scale (see Exhibit 2) increases the accuracy of the results, decreases the spread of weights and inconsistency of the comparisons compared to the 1-to-9 scale.…”

Section: H6mentioning

confidence: 99%

“…We used a scale independent consistency measure, C.M. (see Salo and Hämäläinen, 1997;Salo, 1993) to study the inconsistencies of the statements. This index approximates the range of possible weights resulting from the preference statements.…”

Section: Inconsistencies In the Preference Statementsmentioning

confidence: 99%

“…In this experiment we use the original AHP with two different ways of questioning and with a new scale . We consider the AHP as a variant of multiattribute value theory following Salo and Hämäläinen (1997). Thus the weights derived with the AHP are expected to be comparable to weights obtained with the other methods.…”

mentioning

confidence: 99%

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On the convergence of multiattribute weighting methods

Pöyhönen¹,

Hämäläinen²

2001

European Journal of Operational Research

244

106

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Five multiattribute weighting methods were compared in an Internet experiment. This is the first experiment where the subjects created the alternatives and attributes themselves. Each subject used five methods to assess attribute weights, one version of the Analytic Hierarchy Process (AHP), DIRECT weighting, Simple Multiattribute Rating Technique (SMART), SWING weighting and TRADEOFF weighting. They can all be used following the principles of multiattribute value theory. Furthermore, each of them asks decision makers to give numerical estimates of weight ratios although the elicitation questions are different. In earlier studies, however, these methods have yielded different weights. Our results suggest that weights are different because the methods explicitly or implicitly lead the decision makers to choose their responses from a limited set of numbers. The other consequences are that the spread of weights and the inconsistency between the preference statements depend on the number of attributes that a decision maker considers simultaneously.

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Section: Attribute Weightingmentioning

confidence: 99%

Section: Attribute Weightingmentioning

confidence: 99%

Section: H6mentioning

confidence: 99%

Section: Inconsistencies In the Preference Statementsmentioning

confidence: 99%

mentioning

confidence: 99%

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On the convergence of multiattribute weighting methods

Pöyhönen¹,

Hämäläinen²

2001

European Journal of Operational Research

244

106

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A Comprehensive Literature Review of the Rank Reversal Phenomenon in the Analytic Hierarchy Process

Maleki

Zahir

2012

Multi Criteria Decision Anal

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During the last few decades, several multi‐criteria decision analysis methods have been proposed to help in selecting the best compromise alternatives. Among them, analytic hierarchy process (AHP) and its applications have attracted much attention from academics and practitioners. However, since the early 1980s, critics have raised questions regarding its proper use. One of them concerns the unacceptable changes in the ranks of the alternatives, called rank reversal, upon changing the structure of the decision. Several modifications were suggested to preserve ranks. In this paper, a classification scheme and a comprehensive literature review are presented in order to uncover, classify and interpret the current research on AHP methodologies and rank reversals. On the basis of the scheme, 61 scholarly papers from 18 journals are categorized into specific areas. The specific areas include the papers on the topics of adding/deleting alternatives and the papers published in adding/deleting criteria. The scholarly papers are also classified by (1) year of publication, (2) journal of publication, (3) authors' geographic location and (4) using the AHP in association with other methods. It is hoped that the paper can meet the needs of researchers and practitioners for convenient references of AHP methodologies and rank reversals and hence promote the future of rank reversal research. Copyright © 2012 John Wiley & Sons, Ltd.

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Variation of Preference Inconsistency When Applying Ratio and Interval Scale Pairwise Comparisons

Sironen

Leskinen

Kangas

et al. 2013

J. Multi-Crit. Decis. Anal.

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Several studies on numerical rating in discrete choice problems address the tendency of inconsistencies in decision makers' measured preferences. This is partly due to true inconsistencies in preferences or the decision makers' uncertainty on what he or she really wants. This uncertainty may be reflected in the elicited preferences in different ways depending on the questions asked and methods used in deriving the preferences for alternatives. Some part of the inconsistency is due to only having a discrete set of possible judgments. This study examined the variation of preference inconsistency when applying different pairwise preference elicitation techniques in a five-item discrete choice problem. The study data comprised preferences of five career alternatives elicited applying interval scale and numerically and verbally anchored ratio scale pairwise comparisons. Statistical regression technique was used to analyse the differences of inconsistencies between the tested methods. The resulting relative residual variances showed that the interval ratio scale comparison technique provided the greatest variation of inconsistencies between respondents, thus being the most sensitive to inconsistency in preferences. The numeric ratio scale comparison gave the most uniform preferences between the respondents. The verbal ratio scale comparison performed between the latter two when relative residual variances were considered. However, the verbal ratio scale comparison had weaker ability to differentiate the alternatives. The results indicated that the decision recommendation may not be sensitive to the selection between these preference elicitation methods in this kind of five-item discrete choice problem. The numeric ratio scale comparison technique seemed to be the most suitable method to reveal the decision makers' true preferences. However, to confirm this result, more studying will be needed, with an attention paid to users' comprehension and learning in the course of the experiment.

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On the measurement of preferences in the analytic hierarchy process

Cited by 73 publications

References 0 publications

On the convergence of multiattribute weighting methods

On the convergence of multiattribute weighting methods

A Comprehensive Literature Review of the Rank Reversal Phenomenon in the Analytic Hierarchy Process

Variation of Preference Inconsistency When Applying Ratio and Interval Scale Pairwise Comparisons

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