Two-group classification in business: an evaluation of parametric and non-parametric approaches

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The Impact of DSS Use and Information Load on Errors and Decision Quality

This paper uses a laboratory experiment to examine the effect of DSS use on the decision maker‘s error patterns and decision quality. The DSS used in our experiments is the widely used Expert Choice (EC) implementation of the Analytic Hierarchy Process. Perhaps surprisingly, our experiments do not provide general support for the often tacit assumption that the use of a DSS such as EC improves decision quality. Rather, we find that, whereas a DSS can help decision makers develop a better understanding of the essence of a decision problem and can reduce logical errors (especially if the information load is high), it is also susceptible to introducing accidental effects such as mechanical errors. In some cases, as in our study, the accidental errors may outweigh the benefits of using a DSS, leading to lower quality decisions

Analytic Hierarchy Process in Group Decision Making: Much Ado About Nothing

This paper examines the use of the Analytic Hierarchy Process (AHP) in individual and group decision making. Group AHP without individual AHP resulted in the exchange of the most common information while the combination of both group and individual AHP resulted in the least. The use of AHP in group decision making took longer, but did not result in better decisions. Subjects reported that they processed less information when using AHP and felt there was less credibility in the information discussed

On multiplicative priority rating methods for the AHP

Recently, several alternative variants to the original Analytic Hierarchy Process (AHP) have been proposed. Most of these sought to resolve some of the theoretical problems associated with the original AHP, which uses an additive preference aggregation. In this paper, we take a close look at the multiplicative ratings method, which has recently received growing attention. The interest in the multiplicative AHP (MAHP) is motivated by the fact that, in contrast with the original AHP, it precludes certain types of rank reversals as the composite priority ratings continue to follow a ratio scale, even after normalization. The purpose of this paper is threefold. First, we derive and discuss several interesting properties of the MAHP that have eluded attention in previous studies. Second, we argue that these properties of the MAHP are interesting not only for mathematical reasons but also on behavioral grounds. We show how the MAHP offers a more flexible preference modeling framework, while still preserving the ratio scale property, by relaxing the ‘‘constant returns to scale’’ assumption made in previous research. Third, we use simulation experiments to explore the extent to which the theoretical differences between the original AHP (additive AHP) play out computationally for various different types of preference structures, enabling us to assess whether the MAHP is merely an interesting theoretical construct, or can in fact make a substantial difference in terms of the rankings and ratings of the alternatives and rank reversals between the alternatives.info:eu-repo/semantics/publishedVersio