The representation of conditional relative importance between criteria

We study in this paper the notion of conditional relative importance in a quantitative framework. Bi-capacities are shown to be suitable to represent such a notion. We restrict ourself to the case when the relative importance of two criteria are conditional on a third being attractive or repulsive. We give two algorithms that enable to construct the neutral level from the preference relation.     

Using interdependent information on the relative importance of criteria in decision making

A multicriteria choice problem is considered. The Edgeworth-Pareto principle is established under the assumption that certain axioms hold true. Quantitative interdependent information on the relative importance of two groups of criteria is used to derive upper bounds for the unknown set of selected vectors.     

A careful look at the importance of criteria and weights

We investigate the connection between weights, scales, and the importance of criteria, when a linear value function is assumed to be a suitable representation of a decision maker’s preferences. Our considerations are based on a simple two-criteria experiment, where the participants were asked to indicate which of the criteria was more important, and to pairwise compare a number of alternatives. We use the participants’ pairwise choices to estimate the weights for the criteria in such a way that the linear value function explains the choices to the extent possible. More specifically, we study two research questions: (1) is it possible to find a general scaling principle that makes the rank order of the importance of criteria consistent with the rank order of the magnitudes of the weights, and (2) how good is a simple, direct method of asking the decision maker to “provide” weights for the criteria compared to our estimation procedure. Our results imply that there is reason to question two common beliefs, namely that the values of the weights would reflect the importance of criteria, and that people could reliably “provide” such weights without estimation.     

Relative importance of criteria in multiobjective programming: A cone-based approach

This article develops a convex polyhedral cone-based preference modeling framework for decision making with multiple criteria which extends the classical notion of Pareto optimality and accounts for relative importance of the criteria. The decision maker’s perception of the relative importance is quantified by an allowable tradeoffs between two objectives representing the maximum allowable amount of decay of a less important objective per one unit of improvement of a more important objective. Two cone-based models of relative importance are developed. In the first model, one criterion is designated as less important while all the others are more important. In the second model, more than one criterion may be classified as less important while all the others are considered more important. Complete algebraic characterization of the models is derived and the relationship between them and the classical Pareto preference is examined. Their relevance to decision making is discussed.     

A multiple criteria model for pricing alcoholic beverages

In this paper we discuss the problem of determining optimal price changes in an alcohol sales monopoly. In Finland, the pricing of alcoholic beverages is entrusted to a State Monopoly (Alko Ltd.). The price decisions are considered to be among the most important alcohol policy measures. The pricing problem is not only a profit maximization problem. Other relevant objectives include the restriction of sales with the intention of reducing harmful effects due to alcohol consumption, and the minimization of the impact of price increases on the consumer price index.A multiple criteria model for finding the most preferred solution for the problem of pricing alcoholic beverages is developed. The model includes three criteria: profit (max.), consumption of absolute alcohol (min.), and impact on the consumer price index (min.). Using logarithms of relative changes instead of absolute criterion values makes it possible to operate with a fully linear model.The model can be solved by using any existing multiple criteria decision method. Reflecting our own bias, we use a visual interactive goal programming method developed by Korhonen and Laakso [3] and further refined by Korhonen and Wallenius [4]. The method is implemented on an IBM/PC1 under the name VIG (Visual Interactive Goal Programming).     

Consistent weights for judgements matrices of the relative importance of alternatives

We prove that the only solution satisfying consistency axioms for the problem of retrieving weights from inconsistent judgements matrices whose entries are the relative importance ratios of alternatives is the geometric mean.     

A better measure of relative prediction accuracy for model selection and model estimation

Surveys show that the mean absolute percentage error (MAPE) is the most widely used measure of prediction accuracy in businesses and organizations. It is, however, biased: when used to select among competing prediction methods it systematically selects those whose predictions are too low. This has not been widely discussed and so is not generally known among practitioners. We explain why this happens. We investigate an alternative relative accuracy measure which avoids this bias: the log of the accuracy ratio, that is, log (prediction/actual). Relative accuracy is particularly relevant if the scatter in the data grows as the value of the variable grows (heteroscedasticity). We demonstrate using simulations that for heteroscedastic data (modelled by a multiplicative error factor) the proposed metric is far superior to MAPE for model selection. Another use for accuracy measures is in fitting parameters to prediction models. Minimum MAPE models do not predict a simple statistic and so theoretical analysis is limited. We prove that when the proposed metric is used instead, the resulting least squares regression model predicts the geometric mean. This important property allows its theoretical properties to be understood.     

A Comparative Result for Multiplicative Functions

In this paper, we compare the asymptotic behavior of _nx f(n) and _nx g(n) for multiplicative functions f and g, respectively, where |f| g. Our results extend relevant theorems by E. Wirsing and G. Hal@aacute;sz. The methods we use are elementary.     

A data model for algorithmic multiple criteria decision analysis

Various software tools implementing multiple criteria decision analysis (MCDA) methods have appeared over the last decades. Although MCDA methods share common features, most of the implementing software have been developed independently from scratch. Majority of the tools have a proprietary storage format and exchanging data among software is cumbersome. Common data exchange standard would be useful for an analyst wanting to apply different methods on the same problem. The Decision Deck project has proposed to build components implementing MCDA methods in a reusable and interchangeable manner. A key element in this scheme is the XMCDA standard, a proposal that aims to standardize an XML encoding of common structures appearing in MCDA models, such as criteria and performance evaluations. Although XMCDA allows to present most data structures for MCDA models, it almost completely lacks data integrity checks. In this paper we present a new comprehensive data model for MCDA problems, implemented as an XML schema. The data model includes types that are sufficient to represent multi-attribute value/utility models, ELECTRE III/TRI models, and their stochastic extensions, and AHP. We also discuss use of the data model in algorithmic MCDA.     

Group decision making in a multiple criteria environment: A case using the AHP in software selection

This paper reports the results of a case study where the analytic hierarchy process (AHP) technique was employed to support the selection of a multi-media authorizing system (MAS) in a group decision environment. Three MAS products were identified and ultimately ranked using the AHP. Six software engineers, who are technically competent and experienced, participated in our study. These engineers were trained to use the AHP and asked to apply this technique to select the most appropriate MAS product for adoption. A post-study survey and interview were conducted with all the engineers to collect further feedback on the use of the AHP, as compared to their frequently used Delphi technique, in supporting group decisions. The experiment results and survey findings indicated that the AHP is preferable to Delphi as the AHP helps group members center a discussion around objectives, rather than alternatives. We also found the AHP to be more conducive to consensus building in group decision settings.     

Reducing mosquito-borne disease outbreak size: The relative importance of contact and transmissibility in a network model

The complex biological and environmental factors involved in the transmission of mosquito-borne diseases in humans have made their control elusive in many instances. Conceptual models contribute to gain insight and help to reduce the risk of taking poor managerial decisions. The focus of this paper is to compare, using a contact network model, the impact that perturbation of the number infectious contacts and of transmissibility have on the size of an outbreak. We illustrate the analysis on a contact network parametrized with data that associates humans and the mosquito Culex quinquefasciatus, a vector for lymphatic filariasis. The model suggests that, if the values corresponding to transmissibility and number of infectious contacts is relatively large, variations in the size of an outbreak are significantly in favor of control measures to reduce infectious contacts.     

A comparison of the Saaty's AHP and modified AHP for right and left eigenvector inconsistency

Over the past two decades, Saaty's Analytic Hierarchy Process (AHP) has been developed to solve decision problems in various fields by prioritization of alternatives using eigenvectors and manipulations in matrix algebra. However, a fundamental problem called “Right and Left Eigenvector Inconsistency” has been observed which may yield inconsistent results using the right and the left eigenvectors. A new method known as the Modified AHP has been recently devised by H.A. Donegan, F.J. Dodd, T.B.M. McMaster, The Statistician 41 (1992) 295–302 who claimed that the inconsistency problem can be effectively reduced. This work is an attempt to compare the Saaty's AHP (SAHP) and the Modified AHP (MAHP) using 42 models comprising 294 reciprocal matrices. It was discovered that the Modified AHP is no better than the Saaty's AHP.     

AHP model for competitiveness analysis of selected countries

Competitiveness analysis of countries and regions stands in the foreground in recent years. Different methods as well as indicators are used to assess competitiveness, but no single procedure is considered to be the main one and it can hardly be stated, which of the measurement approaches is the most proper. The IMD World Competitiveness Online database represents one of such tools, containing the Overall Competitiveness ranking that evaluates 59 world countries by more than 300 individual indicators in 2012. The paper proposes an original Analytic Hierarchy Process (AHP) model for competitiveness ranking of selected countries. The model is based, due to the high number of indicators and countries, on absolute measurement and expert evaluation. The results given by the AHP model are compared to IMD competitiveness ranking. Differences of both results are analysed and discussed.     

A comparison of some criteria for states selection in the latent Markov model for longitudinal data

We compare different selection criteria to choose the number of latent states of a multivariate latent Markov model for longitudinal data. This model is based on an underlying Markov chain to represent the evolution of a latent characteristic of a group of individuals over time. Then, the response variables observed at different occasions are assumed to be conditionally independent given this chain. Maximum likelihood estimation of the model is carried out through an Expectation–Maximization algorithm based on forward–backward recursions which are well known in the hidden Markov literature for time series. The selection criteria we consider are based on penalized versions of the maximum log-likelihood or on the posterior probabilities of belonging to each latent state, that is, the conditional probability of the latent state given the observed data. Among the latter criteria, we propose an appropriate entropy measure tailored for the latent Markov models. We show the results of a Monte Carlo simulation study aimed at comparing the performance of the above states selection criteria on the basis of a wide set of model specifications.     

Approaches to determining the relative importance weights for cross-efficiency aggregation in data envelopment analysis

Cross-efficiency evaluation has been widely used for identifying the most efficient decision making unit (DMU) or ranking DMUs in data envelopment analysis (DEA). Most existing approaches for cross-efficiency evaluation are focused on how to determine input and output weights uniquely, but pay little attention to the aggregation process of cross-efficiencies and simply aggregate them equally without considering their relative importance. This paper focuses on aggregating cross-efficiencies by taking into consideration their relative importance and proposes three alternative approaches to determining the relative importance weights for cross-efficiency aggregation. Numerical examples are examined to show the importance and necessity of the use of relative importance weights for cross-efficiency aggregation and the most efficient DMU can be significantly affected by taking into consideration the relative importance weights of cross-efficiencies.     

A Class of Multiplicative Lattices

Czechoslovak Mathematical Journal - We study the multiplicative lattices L which satisfy the condition a = (a : (a : b))(a : b) for all a, b ∈ L. Call them sharp lattices. We prove that every...