Bipolar and bivariate models in multi-criteria decision analysis: descriptive and constructive approaches

Multi-criteria decision analysis studies decision problems in which the alternatives are evaluated on several dimensions or viewpoints. In the problems we consider in this paper, the scales used for assessing the alternatives with respect to a viewpoint are bipolar and univariate or unipolar and bivariate. In the former case, the scale is divided in two zones by a neutral point; a positive feeling is associated to the zone above the neutral point and a negative feeling to the zone below this point. On unipolar bivariate scales, an alternative can receive both a positive and a negative evaluation, reflecting contradictory feelings or stimuli. The paper discusses procedures and models that have been proposed to aggregate multi-criteria evaluations when the scale of each criterion is of one of the two types above. We present both a constructive and a descriptive view on this question; the descriptive approach is concerned with characterizations of models of preference, while the constructive approach aims at building preferences by questioning the decision maker. We show that these views are complementary.Multiple criteria, Decision analysis, Preference, Bipolarmodels, Choquet integral

Electre Methods: Main Features and Recent Developments

We present main characteristics of Electre family methods, designed for multiple criteria decision aiding. These methods use as a preference model an outranking relation in the set of actions - it is constructed in result of concordance and non-discordance tests involving a specific input preference information. After a brief description of the constructivist conception in which the Electre methods are inserted, we present the main features of these methods. We discuss such characteristic features as: the possibility of taking into account positive and negative reasons in the modeling of preferences, without any need for recoding the data; using of thresholds for taking into account the imperfect knowledge of data; the absence of systematic compensation between "gains" and "losses". The main weaknesses are also presented. Then, some aspects related to new developments are outlined. These are related to some new methodological developments, new procedures, axiomatic analysis, software tools, and several other aspects. The paper ends with conclusions

Parameter estimation of a land surface scheme using multicriteria methods

Attempts to create models of surface-atmosphere interactions with greater physical realism have resulted in land surface schemes (LSS) with large numbers of parameters. The hope has been that these parameters can be assigned typical values by inspecting the literature. The potential for using the various observational data sets that are now available to extract plot-scale estimates for the parameters of a complex LSS via advanced parameter estimation methods developed for hydrological models is explored in this paper. Results are reported for two case studies using data sets of typical quality but very different location and climatological regime (ARM-CART and Tucson). The traditional single-criterion methods were found to be of limited value. However, a multicriteria approach was found to be effective in constraining the parameter estimates into physically plausible ranges when observations on at least one appropriate heat flux and one properly selected state variable are available. Copyright 1999 by the American Geophysical Union

Decision analysis under uncertainity for sustainable development

Aplicat embargament des de la data de defensa fins el 31 de desembre de 2019Policy-making for sustainable development becomes more efficient when it is reliably backed by evidence-based decision analysis. Concretely, this is crucial in the planning of public services delivery. By translating "raw" data into information, decision analysis illuminates our judgment, and ultimately the policies we adopt. In the context of public services provision, decision analysis can support the prioritization of policy options and the monitoring of progress. However, most models are deterministic - that is, they do not consider the uncertainty in their evidence. These "incomplete" models, through their impact in policy decisions, can ultimately lead to an inefficient use of resources. The main barriers to a wider incorporation of uncertainty are: (i) the complexity of the approaches currently available, and (ii) the need to develop methods tailored to the specific decision problems faced in public services delivery. To overcome these limitations, this thesis intends to facilitate the incorporation of uncertainty in the evidence into decision analysis for sustainable development. We propose two methods. First, a non-compensatory multi-criteria prioritization under uncertainty model. Given multiple criteria and uncertain evidence, the model identifies the best policy option to improve service provision for sustainable development. The non-compensatory nature of our model makes it an attractive alternative to the widely used composite index approach. Second, a compositional trend analysis under uncertainty model to monitor service coverage. By considering the non-negativity and constant-sum constraints of the data, our model provides better estimates for measuring progress than standard statistical approaches. These two methods are validated in real case studies in the energy, water and health sectors. We apply our prioritization model to the context of strategic renewable energy planning, and the targeting of water, sanitation and hygiene services. Furthermore, we use our trend analysis model to the global monitoring of water and sanitation and child mortality. Our results emphasize the importance of considering and incorporating uncertainty in the evidence into decision analysis, particularly into prioritization and monitoring processes, both central to sustainable development practice.La formulación de políticas para el desarrollo sostenible es más eficiente cuando está respaldada por un análisis de decisiones basado en evidencia. Esto es especialmente crucial en la planificación de la prestación de servicios públicos. Al transformar los datos "brutos" en información, el análisis de decisiones ilumina nuestro juicio y, en última instancia, las políticas que adoptamos. En el contexto de la provisión de servicios públicos, el análisis de decisiones puede apoyar la priorización de las políticas públicas, así como el monitoreo del progreso. Sin embargo, la mayoría de los modelos son deterministas, es decir, no consideran la incertidumbre presente en la evidencia. Estos modelos "incompletos" pueden, a través de su impacto en las decisiones políticas, conducir a un uso ineficiente de los recursos. Las principales barreras para una incorporación más amplia de la incertidumbre son: (i) la complejidad de los enfoques actualmente disponibles, y (ii) la necesidad de desarrollar métodos adaptados a los problemas de decisión específicos a la planificación de los servicios públicos. Para superar estas limitaciones, esta tesis pretende facilitar la incorporación de la incertidumbre presente en la evidencia en el análisis de decisiones para el desarrollo sostenible. Proponemos dos métodos. Primero, un modelo de priorización multicriterio no compensatorio bajo incertidumbre. Dados múltiples criterios y evidencias con incertidumbre, el modelo identifica la mejor política para mejorar la provisión de servicios para el desarrollo sostenible. La naturaleza no compensatoria de nuestro modelo lo convierte en una alternativa atractiva al enfoque de índices compuestos ampliamente utilizado. Segundo, un modelo de análisis de tendencias composicionales bajo incertidumbre para monitorear la cobertura de los servicios. Al considerar las restricciones de no negatividad y de suma constante de los datos, nuestro modelo proporciona mejores estimadores para medir el progreso que los enfoques estadísticos estándar. Estos dos métodos se validan en casos de estudio reales en los sectores de energía, agua y salud. Aplicamos nuestro modelo de priorización al contexto de la planificación estratégica de energías renovables y de los servicios de agua, saneamiento e higiene. Además, utilizamos nuestro modelo de análisis de tendencias para el monitoreo global del accesso a agua y saneamiento, así como de la reducción de la mortalidad infantil. Nuestros resultados enfatizan la importancia de considerar e incorporar la incertidumbre de la evidencia en el análisis de decisiones, particularmente en los procesos de priorización y monitoreo, ambos centrales para la práctica del desarrollo sostenible.Postprint (published version

A review of application of multi-criteria decision making methods in construction

Construction is an area of study wherein making decisions adequately can mean the difference between success and failure. Moreover, most of the activities belonging to this sector involve taking into account a large number of conflicting aspects, which hinders their management as a whole. Multi-criteria decision making analysis arose to model complex problems like these. This paper reviews the application of 22 different methods belonging to this discipline in various areas of the construction industry clustered in 11 categories. The most significant methods are briefly discussed, pointing out their principal strengths and limitations. Furthermore, the data gathered while performing the paper are statistically analysed to identify different trends concerning the use of these techniques. The review shows their usefulness in characterizing very different decision making environments, highlighting the reliability acquired by the most pragmatic and widespread methods and the emergent tendency to use some of them in combination

A comparative study on decision-making methodology

Decision making (DM), the process of determining and selecting alternative decisions based on information and the preferences of decision makers (DMs), plays a significant role in our daily personal and professional lives. Many DM methods have been developed to assist DMs in their unique type of decision process. In this thesis, DM methods associated with two types of DM processes are studied: Decision-making under uncertainty (DMUU) and Multi-criteria decision making (MCDM). DMUU is making a decision when there are many unknowns or uncertainties about the kinds of states of nature (a complete description of the external factors) that could occur in the future to alter the outcome of a decision. DMUU has two subcategories: decision-making under strict uncertainty (DMUSU) and decision-making under risk (DMUR). Five classic DMUSU methods are Laplace’s insufficient reason principle, Wald’s Maximin, Savage’s Minimax regret, Hurwicz’s pessimism-optimism index criterion and Starr’s domain criterion. Furthermore, based on a review of the relation between a two-player game in game theory and DMUSU, Nash equilibrium is considered a method for approaching DMUSU as well. The well-known DMUR DM methods are expected monetary value, expected opportunity loss, most probable states of nature and expected utility. MCDM is a sub-discipline of operations research, where DMs evaluate multiple conflicting criteria in order to find a compromise solution subject to all the criteria. Numerous MCDM methods exist nowadays. The Analytic Hierarchy Process (AHP), the ELimination et Choix Traduisant la REalité (ELECTRE), the Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are the most employed of all the various MCDM methods. This PhD work focuses on presenting a comparative study of DM methods theoretically and evaluating the performance of different methods on a single decision problem. This contribution can guide DMs in gathering the relative objective and subjective information, structuring the decision problem and selecting the right DM method to make the decision that suits not only their subjective preferences, but also the objective facts. The case study used here is the selection of a sewer network construction plan. It is a representative and complex practical decision problem that requires the quality, life-cycle maintenance and performance of the selected sewer system to meet long-term planning for future climate changes and urban development. La prise de décision (DM), un processus de détermination et de sélection de décisions alternatives en fonction des informations et des préférences des décideurs (DM), apparaît largement dans notre vie personnelle et professionnelle quotidienne. Un grand nombre de méthodes DM ont été développées pour aider les DM dans leur type unique de processus de décision. Dans cette thèse, les méthodes DM associées à deux types de processus DM sont étudiées : la prise de décision sous incertitude (DMUU) et la prise de décision multicritère (MCDM). La DMUU doit prendre la décision lorsqu'il existe de nombreuses inconnues ou incertitudes sur le type d'états de la nature (une description complète des facteurs externes) qui pourraient se produire à l'avenir pour modifier le résultat d'une décision. La DMUU comprend deux sous-catégories : la prise de décision sous incertitude stricte (DMUSU) et la prise de décision sous risque (DMUR). Cinq méthodes classiques de DM pour DMUSU sont le principe de raison insuffisante de Laplace, le Waldimin Maximin, le regret Savage Minimax, le critère d'index pessimisme-optimisme de Hurwitz et le critère de domaine de Starr. En outre, l'examen de la relation entre un jeu à deux joueurs dans la théorie des jeux et l'équilibre DMUSU et Nash Equilibrium est également considéré comme l'une des méthodes pour résoudre le DMUSU. Les méthodes DM bien connues de DMUR sont la valeur monétaire attendue, la perte d'opportunité attendue, les états de nature les plus probables et l'utilité attendue. Le MCDM est une sous-discipline de la recherche opérationnelle, où les DM évaluent plusieurs critères conflictuels afin de trouver la solution compromise soumise à tous les critères. Un certain nombre de méthodes DM pour MCDM sont présentes de nos jours. Le processus de hiérarchie analytique (AHP), l'élimination et le choix traduisant la réalité (ELECTRE), les méthodes d'organisation du classement des préférences pour les évaluations d'enrichissement (PROMETHEE) et la technique de préférence par ordre de similitude et de solution idéale (TOPSIS) sont les plus choisies et utilisées des méthodes parmi toutes les différentes méthodes MCDM. Ce travail de thèse se concentre sur la présentation théorique d'une étude comparative des méthodes DM et l'évaluation des performances de différentes méthodes avec un problème de décision particulier. Cette contribution peut guider les DM à rassembler les informations relatives objectives et subjectives, à structurer le problème de décision et à sélectionner la bonne méthode de DM pour prendre la décision qui convient non seulement à leurs préférences subjectives, mais aussi aux faits objectifs. L'étude de cas utilisée ici est la sélection du plan de construction du réseau d'égouts. Il s'agit d'un problème de décision pratique représentatif et complexe qui nécessite la qualité, l'entretien du cycle de vie et les performances du réseau d'égouts sélectionné pour répondre à la planification à long terme des futurs changements climatiques et du développement urbain

An Outranking Approach for Gene Prioritization Using Multinetworks

High-throughput experimental techniques such as genome-wide association studies have been instrumental in the identification of disease-associated genes. These methods often produce large lists of disease candidate genes which are time-consuming and expensive to experimentally validate. Computational gene prioritization methods are required to identify relevant genes from a larger pool of candidates. Research has shown that the integration of diverse “omic” evidence can reduce the candidate-gene search space. In this paper we present a general framework that integrates “omic” data using a multinetwork approach and topological analysis to prioritize disease-candidate genes. Specifically, we propose a data integration method within a multicriteria decision analysis context using aggregation mechanisms based on decision rules identifying positive and negative criteria for judging gene-candidates ranks. The proposed multinetwork disease gene prioritization method is applied to the prioritization of disease genes in ovarian cancer progression. Using this approach we uncovered known ovarian cancer genes GSTA1, ERBB2, IL1A, MAGEB2, along with significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways ErbB signaling and pathways in cancer. Relatively high predictive performance (area under Receiver Operating Characteristic [ROC] curve 0.704) was observed when classifying epithelial ovarian high-grade serous carcinoma cancer early and late stage RNA-Seq expression profiles from individuals using 10-fold cross-validation