Descriptive Profiles for Sets of Alternatives in Multiple Criteria Decision Aid

International audienceIn the context of Multiple Criteria Decision Aid, a decision-maker may be faced at any time with the task of analyzing one or several sets of alternatives, irrespective of the decision he is about to make. As in this case the alternatives may express contrasting gains and losses on the criteria on which they are evaluated, and while the sets that are presented to the decision-maker may potentially be large, the task of analysing them becomes a difficult one. Therefore the need to reduce these sets to a more concise representation is very important. Classically, profiles that describe sets of alternatives may be found in the context of the sorting problem, however they are either given beforehand by the decision-maker or determined from a set of assignment examples. We would therefore like to extend such profiles, as well as propose new ones, in order to characterize any set of alternatives. For each of them, we present several approaches for extracting them, which we then compare with respect to their performance

A Multidimensional Framework for Financial-Economic Decisions

Most financial-economic decisions are made consciously, with a clear and constant drive to ???good???, ???better??? or even ???optimal??? decisions. Nevertheless, many decisions in practice do not earn these qualifications, despite the availability of financial economic theory, decision sciences and ample resources. We plea for the development of a multidimensional framework to support financial economic decision processes. Our aim is to achieve a better integration of available theory and decision technologies. We sketch (a) what the framework should look like, (b) what elements of the framework already exist and which not, and (c) how the MCDA community can co-operate in its development.decision making;finance;decision analysis;financial decisions;multiple criteria

Benchmarking in cluster analysis: A white paper

To achieve scientific progress in terms of building a cumulative body of knowledge, careful attention to benchmarking is of the utmost importance. This means that proposals of new methods of data pre-processing, new data-analytic techniques, and new methods of output post-processing, should be extensively and carefully compared with existing alternatives, and that existing methods should be subjected to neutral comparison studies. To date, benchmarking and recommendations for benchmarking have been frequently seen in the context of supervised learning. Unfortunately, there has been a dearth of guidelines for benchmarking in an unsupervised setting, with the area of clustering as an important subdomain. To address this problem, discussion is given to the theoretical conceptual underpinnings of benchmarking in the field of cluster analysis by means of simulated as well as empirical data. Subsequently, the practicalities of how to address benchmarking questions in clustering are dealt with, and foundational recommendations are made

Looking at the SDEWES Index from a Multi-Criterion Decision Analysis Perspective

The SDEWES Index is obtained by aggregating several numerical indicators related to sustainable development. In the context of Multi-Criterion Decision Analysis (MCDA) this index can be seen as the solution to the \u201cranking problematic\u201d for an underlying decisional problem. Accordingly, in this work we look at the SDEWES Index from an MCDA point of view. First, we consider some theoretical aspects, in particular the one usually referred to as \u201crank reversal\u201d. Then we consider some (classic as well as original) visual tools for decision aid, showing how they can be adapted and exploited

A Multidimensional Framework for Financial-Economic Decisions

Most financial-economic decisions are made consciously, with a clear and constant drive to "good", "better" or even "optimal" decisions. Nevertheless, many decisions in practice do not earn these qualifications, despite the availability of financial economic theory, decision sciences and ample resources. We plea for the development of a multidimensional framework to support financial economic decision processes. Our aim is to achieve a better integration of available theory and decision technologies. We sketch (a) what the framework should look like, (b) what elements of the framework already exist and which not, and (c) how the MCDA community can co-operate in its development.Most financial-economic decisions are made consciously, with a clear and constant drive to ???good???, ???better??? or even ???optimal??? decisions. Nevertheless, many decisions in practice do not earn these qualifications, despite the availability of financial economic theory, decision sciences and ample resources. We plea for the development of a multidimensional framework to support financial economic decision processes. Our aim is to achieve a better integration of available theory and decision technologies. We sketch (a) what the framework should look like, (b) what elements of the framework already exist and which not, and (c) how the MCDA community can co-operate in its development

Decision making study: methods and applications of evidential reasoning and judgment analysis

Decision making study has been the multi-disciplinary research involving operations researchers, management scientists, statisticians, mathematical psychologists and economists as well as others. This study aims to investigate the theory and methodology of decision making research and apply them to different contexts in real cases. The study has reviewed the literature of Multiple Criteria Decision Making (MCDM), Evidential Reasoning (ER) approach, Naturalistic Decision Making (NDM) movement, Social Judgment Theory (SJT), and Adaptive Toolbox (AT) program. On the basis of these literatures, two methods, Evidence-based Trade-Off (EBTO) and Judgment Analysis with Heuristic Modelling (JA-HM), have been proposed and developed to accomplish decision making problems under different conditions. In the EBTO method, we propose a novel framework to aid people s decision making under uncertainty and imprecise goal. Under the framework, the imprecise goal is objectively modelled through an analytical structure, and is independent of the task requirement; the task requirement is specified by the trade-off strategy among criteria of the analytical structure through an importance weighting process, and is subject to the requirement change of a particular decision making task; the evidence available, that could contribute to the evaluation of general performance of the decision alternatives, are formulated with belief structures which are capable of capturing various format of uncertainties that arise from the absence of data, incomplete information and subjective judgments. The EBTO method was further applied in a case study of Soldier system decision making. The application has demonstrated that EBTO, as a tool, is able to provide a holistic analysis regarding the requirements of Soldier missions, the physical conditions of Soldiers, and the capability of their equipment and weapon systems, which is critical in domain. By drawing the cross-disciplinary literature from NDM and AT, the JA-HM extended the traditional Judgment Analysis (JA) method, through a number of novel methodological procedures, to account for the unique features of decision making tasks under extreme time pressure and dynamic shifting situations. These novel methodological procedures include, the notion of decision point to deconstruct the dynamic shifting situations in a way that decision problem could be identified and formulated; the classification of routine and non-routine problems, and associated data alignment process to enable meaningful decision data analysis across different decision makers (DMs); the notion of composite cue to account for the DMs iterative process of information perception and comprehension in dynamic task environment; the application of computational models of heuristics to account for the time constraints and process dynamics of DMs decision making process; and the application of cross-validation process to enable the methodological principle of competitive testing of decision models. The JA-HM was further applied in a case study of fire emergency decision making. The application has been the first behavioural test of the validity of the computational models of heuristics, in predicting the DMs decision making during fire emergency response. It has also been the first behavioural test of the validity of the non-compensatory heuristics in predicting the DMs decisions on ranking task. The findings identified extend the literature of AT and NDM, and have implications for the fire emergency decision making