Núria Agell

Using consensus and distances between generalized multi-attribute linguistic assessments for group decision-making

This paper proposes a mathematical framework and methodology for group decision-making under multi-granular and multi-attribute linguistic assessments. It is based on distances between linguistic assessments and a degree of consensus. Distances in the space of qualitative assessments are defined from the geodesic distance in graph theory and the Minkowski distance. The degree of consensus is defined through the concept of entropy of a qualitatively-described system. Optimal assessments in terms of both proximity to all the expert opinions in the group and the degree of consensus are used to compare opinions and define a methodology to rank multi-attribute alternatives.

Measuring consensus in group decisions by means of qualitative reasoning

This paper presents a mathematical framework to assess the consensus found among different evaluators who use ordinal scales in group decision-making and evaluation processes. This framework is developed on the basis of the absolute order-of-magnitude qualitative model through the use of quantitative entropy. As such, we study the algebraic structure induced in the set of qualitative descriptions given by evaluators. Our results demonstrate that it is a weak partial semi-lattice structure that in some conditions takes the form of a distributive lattice. We then define the entropy of a qualitatively described system. This enables us, on the one hand, to measure the amount of information provided by each evaluator and, on the other hand, to consider a degree of consensus among the evaluation committee. This new approach is capable of managing situations where the assessment given by experts involves different levels of precision. In addition, when there is no consensus regarding the group decision, an automatic process assesses the effort required to achieve said consensus.

IDD: A Supervised Interval Distance-Based Method for Discretization

This article introduces a new method for supervised discretization based on interval distances by using a novel concept of neighbourhood in the targets space. The method proposed takes into consideration the order of the class attribute, when this exists, so that it can be used with ordinal discrete classes as well as continuous classes, in the case of regression problems. The method has proved to be very efficient in terms of accuracy and faster than the most commonly supervised discretization methods used in the literature. It is illustrated through several examples and a comparison with other standard discretization methods is performed for three public data sets by using two different learning tasks: a decision tree algorithm and SVM for regression.

Relative and absolute order-of-magnitude models unified

The aim of this paper is to analyze under which conditions Absolute Order-of-Magnitude and Relative Order-of-Magnitude models may be concordant and to determine the constraints which guarantee concordance. A graphical interpretation of the constraints is provided, bridging the absolute qualitative labels of two quantities into their corresponding relative relation(s), and conversely. The relative order of magnitude relations are then characterized in the absolute order-of-magnitude world.

Decision making under uncertainty using a qualitative TOPSIS method for selecting sustainable energy alternatives

Multi-criteria decision-making methods support decision makers in all stages of the decision-making process by providing useful data. However, criteria are not always certain as uncertainty is a feature of the real world. MCDM methods under uncertainty and fuzzy systems are accepted as suitable techniques in conflicting problems that cannot be represented by numerical values, in particular in energy analysis and planning. In this paper, a modified TOPSIS method for multi-criteria group decision-making with qualitative linguistic labels is proposed. This method addresses uncertainty considering different levels of precision. Each decision maker’s judgment on the performance of alternatives with respect to each criterion is expressed by qualitative linguistic labels. The new method takes into account linguistic data provided by the decision makers without any previous aggregation. Decision maker judgments are incorporated into the proposed method to generate a complete ranking of alternatives. An application in energy planning is presented as an illustrative case example in which energy policy alternatives are ranked. Seven energy alternatives under nine criteria were evaluated according to the opinion of three environmental and energy experts. The weights of the criteria are determined by fuzzy AHP, and the alternatives are ranked using qualitative TOPSIS. The proposed approach is compared with a modified fuzzy TOPSIS method, showing the advantages of the proposed approach when dealing with linguistic assessments to model uncertainty and imprecision. Although the new approach requires less cognitive effort to decision makers, it yields similar results.

Modeling group assessments by means of hesitant fuzzy linguistic term sets

Hesitant linguistic term sets have been introduced to capture the human way of reasoning using linguistic expressions involving different levels of precision. In this paper, a lattice structure is provided to the set of hesitant fuzzy linguistic term sets by means of the operations intersection and connected union. In addition, in a group decision making framework, hesitant fuzzy linguistic descriptions are defined to manage situations in which decision makers are assessing different alternatives by means of hesitant fuzzy linguistic term sets. Based on the introduced lattice structure, two distances between hesitant fuzzy linguistic descriptions are defined. These metric structures allow distances between decision makers to be computed. A centroid of the decision making group is proposed for each distance to model group representatives in the considered group decision making framework.

A Qualitative Reasoning Approach to Measure Consensus

This chapter introduces a mathematical framework on the basis of the absolute order-of-magnitude qualitative model. This framework allows to develop a methodology to assess the consensus found among different evaluators who use ordinal scales in group decision-making and evaluation processes. The concept of entropy is introduced in this context and the algebraic structure induced in the set of qualitative descriptions given by evaluators is studied. We prove that it is a weak partial semilattice structure that in some conditions takes the form of a distributive lattice. The definition of the entropy of a qualitatively-described system enables us, on one hand, to measure the amount of information provided by each evaluator and, on the other hand, to consider a degree of consensus among the evaluation committee. The methodology presented is able of managing situations where the assessment given by experts involves different levels of precision. In addition, when there is no consensus within the group decision, an automatic process measures the effort necessary to reach said consensus.

Understanding the impact of brand colour on brand image

A new preference disaggregation method is proposed.The method is able to handle non-monotonic preferences.The role of brand colour in brand image is studied by applying the method on a real dataset.Results show that colour plays an important role in almost all of the brand image attributes.Results show that colour hue has less impact on brand perception, compared to colour saturation and value. What is the role that colour plays in perception of a brand by customers? How can we explore the cognitive role that colour plays in determining brand perception? To answer these questions we propose a preference disaggregation method based on multi-criteria decision aid. We identify the criteria aggregation model that underlies the global preference of a brand with respect to each brand image attribute. The proposed method is inspired by the well-known UTASTAR algorithm, but unlike the original formulation, it represents preferences by means of non-monotonic value functions. The method is applied to a database of brands ranked on each brand image attribute. For each brand image attribute, non-monotonic marginal value functions from each component of the brand colour are obtained separately. These functions contain the fitness between each colour component and each brand image attribute, in an understandable manner.

Fuzzy decision-making and consensus

Francisco Chiclanaa, Nuria Agellb, Jian Wuc,a, Enrique Herrera-Viedmad Centre for Computational Intelligence, Faculty of Technology, De Montfort University, Leicester, UK ESADE Business School, Universitat Ramon Llull, Barcelona, Spain School of Economics and Management, Zhejiang Normal University, Jinhua, Zhejiang, China Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

Using AI Techniques in the Grocery Industry: Identifying the Customers Most Likely to Defect

The food retailing market has reached a mature stage where companies need to be competitive if they are to survive. Customers are ever more demanding and retailers need to design and introduce new ways of learning about their customers if they are to retain them (Leeflang & Van Raaij 1995 ). This article examines the efficiency of the LAMDA classifier (Learning Algorithm Machine for Data Analysis) (Aguado 1998 ; Aguado et al. 1999 ) in identifying customers behaviour; specifically examining which customers are most likely to defect when a new retailer appears on the scene. The study carried out in this project is based on data gathered from a Spanish grocery chain: Supermercats Pujol, SA – ‘Plus Fresc’, winner of the 1998 Global Electronic Marketing Award, www.plusfresc.es