Marc Pirlot – författare

Formal decision and evaluation models are sets of explicit and well-defined rules to collect, assess, and process information in order to be able to make recommendations in decision and/or evaluation processes. They are so widespread that almost no one can pretend not to have used or suffered the consequences of one of them. Our earlier companion volume, Evaluation and Decision Models, heavily criticised formal models but also argued that they could be useful. On the other hand, Evaluation and Decision Models with Multiple Criteria is a guide aimed at helping the analyst to choose a model and use it consistently. We propose a sound analysis of techniques and our presentation can be extended to most decision and evaluation models as a "decision aiding methodology".This volume is intended for the enlightened practitioner, for anyone who uses decision or evaluation models---for research or for applications---and is willing to question his practice, to have a deeper understanding of what he does.

Semiorder is probably one of the most frequently ordered structures in science. It naturally appears in fields like psychometrics, economics, decision sciences, linguistics and archaeology. It explicitly takes into account the inevitable imprecisions of scientific instruments by allowing the replacement of precise numbers by intervals. The purpose of this book is to dissect this structure and to study its fundamental properties. The main subjects treated are the numerical representations of semiorders, the generalizations of the concept to valued relations, the aggregation of semiorders, and their basic role in a general theoretical framework for multicriteria decision-aid methods. This volume is intended for students and researchers in the fields of decision analysis, management science, operations research, discrete mathematics, classification, social choice theory, and order theory, as well as for practitioners in the design of decision tools.

The purpose of this text is to provide a critical thinking framework for all individuals utilizing decision and evaluation models, whether it be for research or applications. It is axiomatic that all evaluation and decision models suffer some limitations. There are situations where a decision model will not perform to expectations. This book argues that there is no best decision or evaluation model, but that decision-makers must understand the principles of formal evaluation and decision models and apply them critically. Hence, the book seeks to deepen our understanding of evaluation and decision models and encourage users of these models to think more analytically about them. The authors work in six different European universities. Their backgrounds are varied: mathematics, economics, engineering, law, and geology, and they teach in engineering, business, mathematics, computer science, and psychology in their universities.As a group, the authors have particular expertise in a variety of decision models that include preference modelling, fuzzy logic, aggregation techniques, social choice theory, artificial intelligence, problem structuring, measurement theory, operations research, and multiple criteria decision support. In addition to their decision analysis research, all the authors have been involved in a variety of high-impact applications which include software evaluation, location of a nuclear repository, the rehabilitation of a sewer network, and the location of high-voltage lines. It is this variety within the authorship that unifies this book into a systematic examination of how best formal decision models can be used. The monograph should prove a useful tool for researchers of decision analysis and decision-makers.

Formal decision and evaluation models are sets of explicit and well-defined rules to collect, assess, and process information in order to be able to make recommendations in decision and/or evaluation processes. They are so widespread that almost no one can pretend not to have used or suffered the consequences of one of them. Our earlier companion volume, Evaluation and Decision Models, heavily criticised formal models but also argued that they could be useful. On the other hand, Evaluation and Decision Models with Multiple Criteria is a guide aimed at helping the analyst to choose a model and use it consistently. We propose a sound analysis of techniques and our presentation can be extended to most decision and evaluation models as a "decision aiding methodology".This volume is intended for the enlightened practitioner, for anyone who uses decision or evaluation models---for research or for applications---and is willing to question his practice, to have a deeper understanding of what he does.

1. 1 Motivations Deciding is a very complex and difficult task. Some people even argue that our ability to make decisions in complex situations is the main feature that distinguishes us from animals (it is also common to say that laughing is the main difference). Nevertheless, when the task is too complex or the interests at stake are too important, it quite often happens that we do not know or we are not sure what to decide and, in many instances, we resort to a decision support technique: an informal one-we toss a coin, we ask an oracle, we visit an astrologer, we consult an expert, we think-or a formal one. Although informal decision support techniques can be of interest, in this book, we will focus on formal ones. Among the latter, we find some well-known decision support techniques: cost-benefit analysis, multiple criteria decision analysis, decision trees, . . . But there are many other ones, sometimes not presented as decision support techniques, that help making decisions. Let us cite but a few examples. • When the director of a school must decide whether a given student will pass or fail, he usually asks each teacher to assess the merits of the student by means of a grade. The director then sums the grades and compares the result to a threshold. • When a bank must decide whether a given client will obtain a credit or not, a technique, called credit scoring, is often used.

This book provides an overview of the main methods and results in the formal study of the human decision-making process, as defined in a relatively wide sense. A key aim of the approach contained here is to try to break down barriers between various disciplines encompassed by this field, including psychology, economics and computer science. All these approaches have contributed to progress in this very important and much-studied topic in the past, but none have proved sufficient so far to define a complete understanding of the highly complex processes and outcomes. This book provides the reader with state-of-the-art coverage of the field, essentially forming a roadmap to the field of decision analysis. The first part of the book is devoted to basic concepts and techniques for representing and solving decision problems, ranging from operational research to artificial intelligence. Later chapters provide an extensive overview of the decision-making process under conditions of risk and uncertainty. Finally, there are chapters covering various approaches to multi-criteria decision-making. Each chapter is written by experts in the topic concerned, and contains an extensive bibliography for further reading and reference.

This book constitutes the thoroughly refereed conference proceedings of the Third International Conference on Algorithmic Decision Theory, ADT 2013, held in November 2013 in Bruxelles, Belgium. The 33 revised full papers presented were carefully selected from more than 70 submissions, covering preferences in reasoning and decision making, uncertainty and robustness in decision making, multi-criteria decision analysis and optimization, collective decision making, learning and knowledge extraction for decision support.

This book showcases a large variety of multiple criteria decision applications (MCDAs), presenting them in a coherent framework provided by the methodology chapters and the comments accompanying each case study. The chapters describing MCDAs invite the reader to experiment with MCDA methods and perhaps develop new variants using data from these case studies or other cases they encounter, equipping them with a broader perception of real-world problems and how to overcome them with the help of MCDAs.

This book showcases a large variety of multiple criteria decision applications (MCDAs), presenting them in a coherent framework provided by the methodology chapters and the comments accompanying each case study. The chapters describing MCDAs invite the reader to experiment with MCDA methods and perhaps develop new variants using data from these case studies or other cases they encounter, equipping them with a broader perception of real-world problems and how to overcome them with the help of MCDAs.