This book presents selected papers from an international workshop devoted tothe theory, techniques and tools of decision analysis and support. Major trends in the development of this field are stressed, such as the tendency to place the final user of a decision support system in the center of attention, or an emerging connection between tools and software environments for modeling and for decision support. The volume is acontinuation of the reports on earlier meetings which were published in the same series.

At a practical level, mathematical programming under multiple objectives has emerged as a powerful tool to assist in the process of searching for decisions which best satisfy a multitude of conflicting objectives, and there are a number of distinct methodologies for multicriteria decision-making problems that exist. These methodologies can be categorized in a variety of ways, such as form of model (e.g. linear, non-linear, stochastic), characteristics of the decision space (e.g. finite or infinite), or solution process (e.g. prior specification of preferences or interactive). Scientists from a variety of disciplines (mathematics, economics and psychology) have contributed to the development of the field of Multicriteria Decision Making (MCDM) (or Multicriteria Decision Analysis (MCDA), Multiattribute Decision Making (MADM), Multiobjective Decision Making (MODM), etc.) over the past 30 years, helping to establish MCDM as an important part of management science. MCDM has become a central component of studies in management science, economics and industrial engineering in many universities worldwide. Multicriteria Decision Making: Advances in MCDM Models, Algorithms, Theory and Applications aims to bring together `state-of-the-art' reviews and the most recent advances by leading experts on the fundamental theories, methodologies and applications of MCDM. This is aimed at graduate students and researchers in mathematics, economics, management and engineering, as well as at practicing management scientists who wish to better understand the principles of this new and fast developing field.

The book is dedicated to multi-objective methods in decision making. The first part which is devoted to theoretical aspects, covers a broad range of multi-objective methods such as multiple linear programming, vector optimisation, fuzzy goal programming, data envelopment analysis, game theory, and dynamic programming. The reader who is interested in practical applications, will find in the remaining parts a variety of approaches applied in numerous fields including production planning, logistics, marketing, and finance.

Decision makers in managerial and public organizations often encounter de cision problems under conflict or competition, because they select strategies independently or by mutual agreement and therefore their payoffs are then affected by the strategies of the other decision makers. Their interests do not always coincide and are at times even completely opposed. Competition or partial cooperation among decision makers should be considered as an essen tial part of the problem when we deal with the decision making problems in organizations which consist of decision makers with conflicting interests. Game theory has been dealing with such problems and its techniques have been used as powerful analytical tools in the resolution process of the decision problems. The publication of the great work by J. von Neumann and O. Morgen stern in 1944 attracted attention of many people and laid the foundation of game theory. We can see remarkable advances in the field of game theory for analysis of economic situations and a number of books in the field have been published in recent years. The aim of game theory is to specify the behavior of each player so as to optimize the interests of the player. It then recommends a set of solutions as strategies so that the actions chosen by each decision maker (player) lead to an outcome most profitable for himself or her self.

Combinatorial Engineering of Decomposable Systems presents a morphological approach to the combinatorial design/synthesis of decomposable systems. Applications involve the following: design (e.g., information systems; user's interfaces; educational courses); planning (e.g., problem-solving strategies; product life cycles; investment); metaheuristics for combinatorial optimization; information retrieval; etc.

An approach to the modeling of and the reasoning under uncertainty. The book develops the Dempster-Shafer Theory with regard to the reliability of reasoning with uncertain arguments. Of particular interest here is the development of a new synthesis and the integration of logic and probability theory. The reader benefits from a new approach to uncertainty modeling which extends classical probability theory.

One of the most fascinating debates of our times is the discussion over the merits and capabilities of market economies. Very often, one sees strong endorsements to the idea that markets provide an efficient way of allocating resources. Some years ago, opposite views on this issue used to be very popular (at least in Europe) and were held by similarly qualified people. In my opinion, the contribution of economics to this question can not be dismissed on the grounds that economics still in its infancy and that this question is a "practical" one (whatever this means). Economics started with similar naive ideas, two hundred years ago. In particular it has taken a long time to realize that competition does not work in such a smooth way as many classical writers thought it did, and that many facts can not be explained by the theory of perfectly competitive markets. This issue is explored at depth in the Introduction to this Lectures. In this sense the contribution of the Theory of industrial Organization has been to make a convincing case for the view that monopoly and oligopoly can persist in the long run in a world populated by rational agents. Despite of the fact that we are far from having a satisfactory theory of how markets work, progress has been immense, and we certainly understand why current theories are still not completely satisfactory.

This book was born out of a five-years research at Sonderforschungsbe reich 303 by the Deutsche Forschungsgemeinschaft (DFG) at Rheinische Friedrich-Wilhelms-Universitiit Bonn and was approved as my doctoral thesis by the Rechts-und Staatswissenschaftliche Fakultiit in December 1994. It was my former colleague Wolfgang Peters who had drawn my atten tion to overlapping-generations models and to problems of intergenerational efficiency and distribution. The subtle connection between the latter two has been fascinating me from the very beginning: redistribution of the results of free trade can become necessary from the point of view of efficiency, although no externalities hamper the development of an economy. In spite of being a matured part of economics, neoclassical growth theory had left many questions unsolved, some of them even unrecognized by a large part of our profession. I took up the challenge to contribute to the investigation of some of these thorny problems. One of these issues is the often quoted idea of the inter generational con tract. Although intergenerational transfers can improve intertemporal effi ciency, the design of pension schemes to achieve an improvement of well-being of some generations without hurting that of any other, is not an easy task in an economy with flexible prices. Quite frequently, only interest rate and growth rate are taken into account when deciding on whether a generation wins or looses.

This book contributes to the scientific field of optimal control theory applied to dynamic models of the firm. It discusses optimal investment, financing and production policies of the firm, that have to deal with a variety of aspects, such as financial constraints, start-up costs, business cycles, increasing returns to scale, production life cycles and experience curves. In contrast to many other publications on this subject, here, in combination with an analytical approach, the dynamic optimization problems are solved numerically with the aid of a powerful computer and specific programs for optimizing non-linear functions of a finite number of variables and non-linear constraints.

As large physical capital stock projects need long periods to be built, a time-to-build specification is incorporated in factor demand models. Time-to-build and adjustment costs dynamics are identified since by the first moving average dynamics, whereas by the latter autoregressive dynamics are induced. Empirical evidence for time-to-build is obtained from data from the Dutch construction industry and by the estimation result from the manufacturing industry of six OECD countries.