Catastrophe Theory was introduced in the 1960s by the renowned Fields Medal mathematician René Thom as a part of the general theory of local singularities. Since then it has found applications across many areas, including biology, economics, and chemical kinetics. By investigating the phenomena of bifurcation and chaos, Catastrophe Theory proved to
The aim of this book is the presentation of two new descriptive theories for experimental bargaining games and a comparison with other descriptive and normative theories. To obtain data it was necessary to develop two sets of computer programs for computer controlled ex periments. Moreover, data obtained by other researchers, which are available to us will be included in this study. The use of laboratory experiments in economics was introduced by THURSTONE [1931] in the field of utility theory. CHAMBERLIN [1948] was the first person to establish an expe rimental market for the purpose of testing a theory. The first experiment on characteristic function games was done by KALISH, MILNOR, NASH, and NERING [1954]. Today the use of experiments in controlled laboratory settings has become widespread. Earlier, economists went into the field to observe phenomena as the behavior of individuals, corporations and nations in action, then they formulated theories to explain what they saw. But unlike natural scientists, economists have not been able to test their theories under controlled conditions. Now experimental economists are able to replicate their results. Replication is very proble matic for field studies, because rarely the same conditions can be established again. Moreover, experimenters are able to test theories for situations described by simplified models which are not observable in the real world.
Interest in business cycles has had its 'ups and downs'. After a period of almost steady state growth and of economic tranquility, when the business cycle seemed to be obsolete, the turbulence of the 70s and 80s has contributedto a renewed interest in the topic. Important analytical and methodological innovations have also favored the present abundance of contributions. Four innovations are of particular importance: i. microfoundations ii. nonlinearities iii. stochastic variables iv. real aspects. Both Classical macroeconomics and new-Keynesian approaches seem to share these characteristics, which apply both to endogenous and exogenous explanations of the cycle. The distance separating the newer literature from its forebears seems vast. Previously, cycle theory was characterized by a macro approach and utilized nonlinearities either through piecewise 'linear models or with the aid of Classical theorems in the field of dynamic systems. To consider and to compare the old and the new literature on business cycles is one of the goals of this book. To narrow the distance separating them is another goal of this research. We do not try to bridge it, but rather to revisit the former tradition with new tools. Finally, a particular emphasis is put on the 'ceilings and floors' type of literature. One of us has written a D. Phil. thesis with Sir John Hicks, and both have worked with H. P. Minsky. Hicks, along with Goodwin, introdu. ced the concept of ceilings and floors into business cycle analysis, and Minsky made important contributions to the area.
Mathematics of Complexity and Dynamical Systems is an authoritative reference to the basic tools and concepts of complexity, systems theory, and dynamical systems from the perspective of pure and applied mathematics. Complex systems are systems that comprise many interacting parts with the ability to generate a new quality of collective behavior through self-organization, e.g. the spontaneous formation of temporal, spatial or functional structures. These systems are often characterized by extreme sensitivity to initial conditions as well as emergent behavior that are not readily predictable or even completely deterministic. The more than 100 entries in this wide-ranging, single source work provide a comprehensive explication of the theory and applications of mathematical complexity, covering ergodic theory, fractals and multifractals, dynamical systems, perturbation theory, solitons, systems and control theory, and related topics. Mathematics of Complexity and Dynamical Systems is an essential reference for all those interested in mathematical complexity, from undergraduate and graduate students up through professional researchers.
These Lecture Notes arose from discussions we had over a working paper written by the first author in fall 1987. We decided then to write a short paper about the basic structure of evolutionary stability and found ourselves ending up with a book manuscript. Parts of the material contained herein were presented in a seminar at the Department of Mathematics at the University of Vienna, as well as at a workshop on evolutionary game theory in Bielefeld. The final version of the manuscript has certainly benefitted from critical comments and suggestions by the participants of both the seminar and the workshop. Thanks are also due to S. Bomze-de Barba, R. Burger, G. Danninger, J. Hofbauer, R. Selten, K. Sigmund, G. Stiastny and F. Weising. The co-operation of W. Muller from Springer Verlag, Heidelberg, is gratefully acknowledged. Vienna, November 1988 Immanuel M. Bomze Benedikt M. Potscher III Contents 1. Introduction 1 2. Strategies and payoffs 5 2. 1. A general setting for evolutionary game theory 6 2. 2. Mixed strategies and population games 8 2. 3. Finite number of strategies . . . . . 13 2. 4. Infinitely many (pure) strategies 15 2. 5. Structured populations: asymmetric contests and multitype games 17 2. 6. Additional remarks . . . . . . . . . . . . . . . . . . . . . 21 3. Evolutionary stability 25 3. 1. Definition of evolutionary stability 25 3. 2. Evolutionary stability and solution concepts in classical game theory 30 3. 3. Conditions for evolutionary stability based on the normal cone 31 3. 4.
The investigation of special topics in systems dynamics -uncertain dynamic processes, viability theory, nonlinear dynamics in models for biomathematics, inverse problems in control systems theory-has become a major issue at the System and Decision Sciences Research Program of the International Insti tute for Applied Systems Analysis. The above topics actually reflect two different perspectives in the investigation of dynamic processes. The first, motivated by control theory, is concerned with the properties of dynamic systems that are stable under vari ations in the systems' parameters. This allows us to specify classes of dynamic systems for which it is possible to construct and control a whole "tube" of trajectories assigned to a system with uncertain parameters and to resolve some inverse problems of control theory within numerically stable solution schemes. The second perspective is to investigate generic properties of dynamic systems that are due to nonlinearity (as bifurcations theory, chaotic behavior, stability properties, and related problems in the qualitative theory of differential systems). Special stress is given to the applications of non linear dynamic systems theory to biomathematics and ecoloey.
Controlling the production in an industrial organisation is very complex. There are two different reasons for this complexity. On the one hand, complexity is due to the variety in range and in level of detail of the activities that playa role in such a control (think of manufacturing process development, capacity planning, coordinating the flow of material through the production process, releasing of workorders, and scheduling). On the other hand, the production process itself may be complex (many products, many stages, complex interrelationships between resources, and uncertainty in the availability of resources). To deal with the first cause for complexity, one creates different, but coordinated levels of control. At each of these levels a specific part of the control of the production process is accounted for (see Anthony [3]). To deal with the second cause for complexity, one groups manufacturing steps into so-called production units (see Bertrand [8]). Each production unit is responsible for a specific part of the production process. Of course, these production units have to be coordinated to ensure that the products are manufactured timely and efficiently. This activity will be referred to as material coordination (see Bertrand [8]).
This monograph deals with various classes of deterministic continuous time optimal control problems wh ich are defined over unbounded time intervala. For these problems, the performance criterion is described by an improper integral and it is possible that, when evaluated at a given admissible element, this criterion is unbounded. To cope with this divergence new optimality concepts; referred to here as "overtaking", "weakly overtaking", "agreeable plans", etc. ; have been proposed. The motivation for studying these problems arisee primarily from the economic and biological aciences where models of this nature arise quite naturally since no natural bound can be placed on the time horizon when one considers the evolution of the state of a given economy or species. The reeponsibility for the introduction of this interesting class of problems rests with the economiste who first studied them in the modeling of capital accumulation processes. Perhaps the earliest of these was F. Ramsey who, in his seminal work on a theory of saving in 1928, considered a dynamic optimization model defined on an infinite time horizon. Briefly, this problem can be described as a "Lagrange problem with unbounded time interval". The advent of modern control theory, particularly the formulation of the famoue Maximum Principle of Pontryagin, has had a considerable impact on the treatment of these models as well as optimization theory in general.
In production systems there are often capacity oriented performance objectives, like a desired total throughput, a desired average throughput time and average work in-process. Such performance objectives are expressed in "units of products" rather than in specific product types. This book presents a way of modeling and analyzing production systems so, that such capacity oriented performance criteria can be measured in a simple way. The model consists of three basic elements. 1. The product types in the system are aggregated. 2. The product flow is modeled as being continuous. 3. The machines in the model have a finite number of states. Each state has a phase-type sojourn distribution and an associated production speed. Transitions between the states are determined by an irreducible Markov transition matrix. In the book both the mathematical properties and the practical applicabilities of the model are investigated. The model is extensively analyzed for various layouts, like flow lines, assembly disassembly systems and networks where parallel machines share common buffers. Furthermore various ways of controlling the product flow in the model are investigated, such as Base Stock Control, Workload Control, control by finite buffers and control by the Reorder Point System. An approximation technique is developed for a quick estimation of performance measures like throughput and average work-in-process, for networks with layouts and control techniques like those above-mentioned.
These Proceedings report the scientific results of an International Workshop on Large-Scale Modelling and Interactive Decision Analysis organized Jointly by the System and Decision Sciences Program of the International Institute for Applied Systems Analysis (IIASA, located in Laxenburg, Austria), and the Institute for Informatics of the Academy of Sciences of the GDR (located in Berlin, GDR). The Workshop was held at a historically well-known place - the Wartburg Castl- near Eisenach (GDR). (Here Martin Luther translated the Bible into German.) More than fifty scientists representing thirteen countries participated. This Workshop is one of a series of meetings organizE!d by or In collaboration with IIASA about which two of the Lecture Notes In Economics and Mathematical Systems have already reported (Voi. 229 and Vol. 246). This time the aim of the meeting was to discuss methodological and practical problems associated with the modelling of large-scale systems and new approaches In interactive decision analysis based on advanced information processing systems.