Preference Disaggregation: Towards an Integrated Framework

La desagregació de preferències pretén capturar models de preferències mitjançant la descomposició de la informació obtinguda a partir de preferències indirectes que estan en forma d'eleccions holístiques o judicis. Des d'una perspectiva d'ajuda a la presa de decisions multi criteri, aquesta informació es pren com a punt de partida en un procés d'inferència que condueix a model de preferències basat en punts de vista, generalment conflictius, que conjuntament formen una base per a la decisió. L'estudi de les decisions humanes ha rebut una atenció creixent en els últims anys des de diverses disciplines, que inclouen des de les ciències del comportament (anàlisi de decisions, desagregació de preferències), la intel·ligència artificial (aprenentatge de preferències), fins a l'economia i el màrqueting (teoria de l'elecció). Les tres corrents, encara que originades per diferents filosofies, convergeixen ràpidament cap a una comprensió integral de les preferències, que és l'element bàsic per a les decisions i accions humanes. Aquesta tesi doctoral aprofundeix en aquesta àrea de recerca mitjançant la introducció d'un marc analític integrat que permet capturar les preferències d'una forma complexa a partir de l'observació d'opcions holístiques, decisions i judicis.La desagregación de preferencias pretende capturar modelos de preferencias mediante la descomposición de la información obtenida con preferencias indirectas que están en forma de elecciones holísticas o juicios. Desde una perspectiva de ayuda a la toma de decisiones multicriterio, dicha información se toma como punto de partida en un proceso de inferencia que conduce a modelo de preferencias basado en puntos de vista, generalmente conflictivos, que conjuntamente forman una base para la decisión. El estudio de las decisiones humanas ha recibido una atención creciente en los últimos años desde varias disciplinas, que incluyen desde las ciencias del comportamiento (análisis de decisiones, desagregación de preferencias), la inteligencia artificial (aprendizaje de preferencias), hasta la economía y el márqueting (teoría de la elección). Las tres corrientes, aunque originadas por diferentes filosofías, convergen rápidamente hacia una comprensión integral de las preferencias, que es el elemento básico para las decisiones y acciones humanas. Esta tesis doctoral profundiza en esta área de investigación mediante la introducción de un marco analítico integrado que permite capturar las preferencias de una forma compleja a partir de la observación de opciones holísticas, decisiones y juicios.Preference disaggregation aims at capturing preference models by decomposing indirect preference information that are in form of holistic choices or judgments. From a multiple criteria decision aiding perspective, such information is taken as input to an inference procedure that yields to a preference model based on all the, usually conflicting, points of view that together form a basis for the judgments. Studying human judgments and choices has received increasing attention in the last few years from several disciplines, including behavioral science (decision analysis, preference disaggregation), artificial intelligence (preference learning), and economics and marketing (choice modeling). The three streams, although originated from different philosophies, are converging rapidly to a comprehensive understanding of human preferences, that is the main element of decisions and actions. This doctoral dissertation sheds light on this phenomenon by introducing an integrated analytical framework that allows capturing preferences of a complex form by observing holistic choices, decisions, and judgments

Structuring portfolio selection criteria for interactive decision support

A trichotomic evaluation system for portfolio selection support is proposed through this paper. The methodology works in two phases: First, Arbitrage Pricing Theory (APT) is used to estimate portfolios’ expected return and to identify influence factors and risk origins. ELECTRE TRI method aggregates all the common risk criteria into a unique one, which is more understandable by real investors or portfolio managers. By this way each alternative portfolio is evaluated on three criteria only including return, residual risk and common risk. In the second phase, the MINORA multicriteria interactive system based on preference disaggregation is proposed to select attractive portfolios. The whole methodological framework is illustrated by an application to the French stock market.peer-reviewe

Π5 – Τεχνική έκθεση (βιβλιογραφική ανασκόπηση συνδυασμού μεθόδων τεχνητής νοημοσύνης και πολυκριτήριας ανάλυσης)

Η παρούσα βιβλιογραφική ανασκόπηση επικεντρώνεται στην ανάλυση της σχέσης ανάμεσα σε τεχνικές ΠΑΑ και μεθοδολογίες από το πεδίο της ΤΝ, καθώς και του τρόπου με τον οποίο η ευστάθεια αντιμετωπίζεται στα δύο πεδία. Η καταγραφή αυτή συμβάλει στον εντοπισμό συνεργειών που μπορούν να προκύψουν από την ανάπτυξη διαδικασιών που συνδυάζουν ιδέες, έννοιες και αρχές από τα πεδία της ΠΑΑ και της ΤΝ για την καλύτερη μελέτη της ευστάθειας σε προβλήματα λήψης αποφάσεων. H βιβλιογραφική ανασκόπηση πραγματοποιείται στα πλαίσια διαδικασιών ανάπτυξης μοντέλων αποφάσεων μέσω της αναλυτικής-συνθετικής προσέγγισης (preference disaggregation approach, Jacquet-Lagrèze & Siskos, 2001) της ΠΑΑ, η οποία όπως θα αναλυθεί έχει σημαντικά κοινά στοιχεία με τεχνικές από το χώρο της ΤΝ και ιδιαίτερα με μεθοδολογίες μηχανικής μάθησης (machine learning)

An application of multicriteria decision aid models in the prediction of open market share repurchases

This study presents the first attempt to develop classification models for the prediction of share repurchase announcements using multicriteria decision aid (MCDA) techniques. We use three samples consisting of 434 UK firms, 330 French firms, and 296 German firms, to develop country-specific models. The MCDA techniques that are applied for the development of the models are the UTilités Additives DIScriminantes (UTADIS) and the ELimination and Choice Expressing REality (ELECTRE) TRI. We adopt a 10-fold cross validation approach, a re-sampling technique that allows us to split the datasets in training and validation sub-samples. Thus, at the first stage of the analysis the aim is the development of a model capable of reproducing the classification of the firms considered in the training samples. Once this stage is completed, the model can be used for the classification of new firms not included in the training samples (i.e. validation stage). The results show that both MCDA models achieve quite satisfactory classification accuracies in the validation sample and they outperform both logistic regression and chance predictions. The developed models could provide the basis for a decision tool for various stakeholders such as managers, shareholders, and investment analysts

What Is a Decision Problem? Designing Alternatives

International audienceThis paper presents a general framework for the design of alternatives in decision problems. The paper addresses both the issue of how to design alternatives within "known decision spaces" and on how to perform the same action within "partially known or unknown decision spaces". The paper aims at providing archetypes for the design of algorithms supporting the generation of alternatives

Constructing a MUSA Model to Determine Priority Factor of a SERVPERF Model.

The study aimed to determine the priority factor among the factors in a SERVPERF Model. The SERVPERF Model explains the students’ satisfaction towards the quality of service provided by their hostel management. Priority factor is the factor that is considered important by the customers, but they are not satisfied with the service provided for that factor. A Multi-criteria Satisfaction Analysis (MUSA) Model is built based on ordinal regression with linear programming approach. However, study found that the MUSA Model built is not stable and could not interpret the data set used. This finding is consistent with the fact that MUSA Model does not always give out an interpretable results.. Keywords: Multi-criteria Satisfaction Analysis (MUSA), Modified SERVPERF Model, Priority Factor, Ordinal Regression, Linear Programming Approach