This volume is the final result of the research project ''Micro growth model", that was sponsored by the Central Research Pool of Tilburg University, the Netherlands. Apart from the University Council for this important financial support, I owe Prof. Dr. Fiet Verheyen very much for the way in which he introduced me into scientific circles and for the way in which he supervised and stimulated my work. Dr. Jan de Jong and Peter Janssen C. E. , Technical University of Eindhoven, piloted me safely through the mathe matics of optimal control theory and removed some technical barriers. Their help was indispensable for the success of this project. I would also like to mention the kind support of Prof. Dr. Jack Kleijnen, who gave me many valuable hints on how to present the results of this project. In this way I was able to contact with several resear chers inside and outside the Netherlands. Most grateful I am to Prof. Dr. Charles Tapiero, Jerusalem University, who commented on important parts of this book in a constructive way and who suggested many subjects for further research. Also Mr. Geert Jan vsn Schijndel, Tilburg University, should be mentioned here, because he closely read the work and I appreciated his remarks and corrections very much. Many collea gues have contributed to the results of this research project in a direct or indirect way. Especially I should like to mention my contacts with Prof. Dr.

This book explores the econometric implications of a policy-maker's learning. In a simple discrete model the policymaker maximizes the discounted sum of tax revenues subject to a constraint which involves an unknown parameter specifying the occurrence of Bayesian learning. An optimal balance needs to be found between maximizing current payoff and generating information which enhances the efficiency of maximization in subsequent periods. Learning is demonstrated to affect the exogeneity status of policy variables in small samples and to have implications analogous to the phenomenon in the focus of the Lucas critique. Active learning is proven to be a distinct cause of time inconsistency of optimal plane. It is argued that learning cannot be dismissed as a merely transitory source of these phenomena. A simulation exercise supplies quantitative evidence. Finally, the data generated are used to perform empirical exogeneity tests. Results show that the effect of Bayesian learning is empirically detectable. Learning is generally accepted as the driving force towards a rational expectations equilibrium with perfect information. In this context, the result on the loss of exogeneity of policy variables is an out-of-equilibrium econometric implication of the rational expectations hypothesis. This book is the first to explore the effect of an informational feedback on exogeneity.

A Silverman game is a two-person zero-sum game defined in terms of two sets S I and S II of positive numbers, and two parameters, the threshold T > 1 and the penalty v > 0. Players I and II independently choose numbers from S I and S II, respectively. The higher number wins 1, unless it is at least T times as large as the other, in which case it loses v. Equal numbers tie. Such a game might be used to model various bidding or spending situations in which within some bounds the higher bidder or bigger spender wins, but loses if it is overdone. Such situations may include spending on armaments, advertising spending or sealed bids in an auction. Previous work has dealt mainly with special cases. In this work recent progress for arbitrary discrete sets S I and S II is presented. Under quite general conditions, these games reduce to finite matrix games. A large class of games are completely determined by the diagonal of the matrix, and it is shown how the great majority of these appear to have unique optimal strategies. The work is accessible to all who are familiar with basic noncooperative game theory.

The title of this book seems to indicate that the volume is dedicated to a very specialized and narrow area, i. e. , to the relationship between a very special type of optimization and mathematical programming. The contrary is however true. Optimization is certainly a very old and classical area which is of high concern to many disciplines. Engineering as well as management, politics as well as medicine, artificial intelligence as well as operations research, and many other fields are in one way or another concerned with optimization of designs, decisions, structures, procedures, or information processes. It is therefore not surprising that optimization has not grown in a homogeneous way in one discipline either. Traditionally, there was a distinct difference between optimization in engineering, optimization in management, and optimization as it was treated in mathematical sciences. However, for the last decades all these fields have to an increasing degree interacted and contributed to the area of optimization or decision making. In some respects, new disciplines such as artificial intelligence, descriptive decision theory, or modern operations research have facilitated, or even made possible the interaction between the different classical disciplines because they provided bridges and links between areas which had been developing and applied quite independently before. The development of optimiiation over the last decades can best be appreciated when looking at the traditional model of optimization. For a well-structured, Le.

This book presents an econometric modeling approach for analysing macroeconomic disequilibria, focusing on the market for goods and labor and the spillovers between these markets transmitted through firms' decisions in the production sphere. The macroeconomic markets are treated as heterogeneous aggregates, consisting of a multitute of micro markets on which demand/supply ratios differ. Disequilibrium models have been under attack because they neglect that inventories enable firms to smooth production over the cycle, but the author argues that buffer stocks (output inventories, unfilled orders) should be accounted for within the disequilibrium framework, giving rise to a dynamic modification rather than a fundamental invalidation of rationing and spillover effects. The model developed in this book combines traditional Keynesian-type analysis with supply-side considerations and at the same time allows for micro-level imbalance. The resulting econometric structure is inherently nonlinear, reflecting that the response of economic activity to demand-side and supply-side factors varies over the cycle, depending on the aggregate mix of regimes. The model is estimated with quarterly data for Switzerland. Various simulation experiments clearly demonstrate the potential of this type of model for empirical business cycle analysis and policy discussions.

It is probably fair to say that there does not exist a unique and generally accepted not ion of justice. Even if one confines oneself to economic justice one can still get many answers to the question of how justice - henceforth we shall use the term "distributive justice" should be defined and characterized. This may be disappointing for the outside observer but one can also view this as achallenge, at least as long as one thinks that distributive justice is an issue economics should be concerned with. Many problems of distributive justice can be described as follows: There is a fixed collection of well-defined objects (usually commodi ties) which are quantitatively measurable and perfectly divisible. This collection is to be divided up among a certain number of individuals, the members of society, let 's say. According to which principles or rules should this distribution be carried out? Should people be rewarded according to their ability or according to their needs? Should the distribution be such that nobody envies the bundle of any other member of society? Should the collection of entities be distributed in a way that yields the greatest benefi.