To all who taught me, and to all who will. Over the past fifteen years the notions of efficiency and sustainability have, more than any others, influenced the academic and public discussion concerning the intertemporal allocation of resources, especially as regards the economics of growth and environment. This treatise formally develops and counterposes these notions by means of the construct of a trajectorial objective, which is here developed, along with its implications, as a natural advance upon the classical scalar objective. In the course of this study it becomes clear that efficiency and sustainability are by no means identical, given that efficiency, on the one hand, is the concept for avoiding wasteful behavior, and sustainability, on the other, is the concept for ensuring that certain critical aspiration levels, which usually reflect the wish for survival, are maintained. Nonetheless, contrary to what may be assumed, these two concepts do not generally yield mutually exclusive solutions; in fact, they can be combined to complement each other in the quest for unimprovable long-term solutions which sustain given and necessary aspiration levels. This treatise develops and analyzes dynamic decision models (DDM) with one trajectorial objective according to the methodology of multi criteria decision making (MCDM). Moreover, introducing the method of distance maximization crucially augments MCDM and proves to be invaluable for DDMs in the case of a nonexistent utopia trajectory as well as in the case of sustainability as objective.

It has become part of the conventional wisdom in the economics of education that subsidies to higher education have a regressive distributional effect. Given that relatively more children from wealthier families enroll in higher education, many economist assume that these subsidies to higher education have an unwanted distributional impact. This volume presents new empirical evidence for the cross-sectional point of view and provides an analytical framework for the longitudinal perspective. The present volume also analyzes the equity and efficiency effects of widely-discussed funding reforms and proposes a voluntary graduate tax.

Classical econometrics - which plunges its roots in economic theory with simultaneous equations models (SEM) as offshoots - and time series econometrics - which stems from economic data with vector autoregr- sive (VAR) models as offsprings - scour, like the Janus's facing heads, the flowing of economic variables so as to bring to the fore their autonomous and non-autonomous dynamics. It is up to the so-called final form of a dy namic SEM, on the one hand, and to the so-called representation theorems of (unit-root) VAR models, on the other, to provide informative closed form expressions for the trajectories, or time paths, of the economic vari ables of interest. Should we look at the issues just put forward from a mathematical standpoint, the emblematic models of both classical and time series econometrics would turn out to be difference equation systems with ad hoc characteristics, whose solutions are attained via a final form or a represen tation theorem approach. The final form solution - algebraic technicalities apart - arises in the wake of classical difference equation theory, display ing besides a transitory autonomous component, an exogenous one along with a stochastic nuisance term. This follows from a properly defined ma trix function inversion admitting a Taylor expansion in the lag operator be cause of the assumptions regarding the roots of a determinant equation pe culiar to SEM specifications.

Economic application of nonlinear dynamics, microscopic agent-based modelling, and the use of artificial intelligence techniques as learning devices of boundedly rational actors are among the most exciting interdisciplinary ventures of economic theory over the past decade. This volume provides us with a most fascinating series of examples on "complexity in action" exemplifying the scope and explanatory power of these innovative approaches.

Production planning problems containing special characteristics from process industries are addressed in this book. The main subject is the development of mathematical programming models that allow to model production plans which are not disrupted by discretization of time. However, discrete time models are used as a basis and are subsequently enhanced to include aspects of time continuity. Their integration is achieved by different building blocks which may be combined freely according to the specific planning situation at hand. The primary area of application of these kinds of models are process industries.

This book develops and analyzes dynamic decision models (DDM) with one trajectoral objective according to the methodology of multi-criteria decision making (MCDM). Moreover, DDMs which concomitantly pursue multiple objectives are analyzed, with special emphasis given to hybrid models with scalar and trajectorial objectives as well as models with multiple trajectorial objectives. Introducing the method of distance maximization crucially augments MCDM and proves to be invaluable for DDMs with nonexistent utopia trajectory or with sustainability as objective. The notions of efficiency and sustainability are formally developed and counterposed by means of the construct of trajectorial objective, which is presented here, along with its implications, as a natural advance upon the classical scalar objective.