This book develops an innovative system, in the form of an "app", that harnesses the power of the internet to predict which sorts of people will prefer which policy in ANY planning situation. It chronicles the accumulated research wisdom behind the system's reasoning, along with several less successful approaches to policy making that have been found wanting in the past - including the myth, usually peddled by strategic planners, that it is possible to find a "best" plan which optimally satisfies everybody. The book lays out an entirely new kind of Planning Support System (PSS). It will facilitate decision-making that is far more community-sensitive than previously, and it will drastically improve the performance of anyone who needs to plan within socially-sensitive contexts - which is all of us. A standout feature of the system is its commitment to "scientific rigour", as shown by its predicted plan scores always being graphically presented within error margins so that true statistical significance is instantly observable. Moreover, the probabilities that its predictions are correct are always shown - a refreshing change from most, if not all other Decision Support Systems (DSS) that simply expect users to accept their outputs on faith alone.

The book you hold in your hands is the outcome of the "ISCS 2013: Interdisciplinary Symposium on Complex Systems" held at the historical capital of Bohemia as a continuation of our series of symposia in the science of complex systems. Prague, one of the most beautiful European cities, has its own beautiful genius loci. Here, a great number of important discoveries were made and many important scientists spent fruitful and creative years to leave unforgettable traces. The perhaps most significant period was the time of Rudolf II who was a great supporter of the art and the science and attracted a great number of prominent minds to Prague. This trend would continue. Tycho Brahe, Niels Henrik Abel, Johannes Kepler, Bernard Bolzano, August Cauchy Christian Doppler, Ernst Mach, Albert Einstein and many others followed developing fundamental mathematical and physical theories or expanding them. Thus in the beginning of the 17th century, Kepler formulated here the first two of his three laws of planetary motion on the basis of Tycho Brahe's observations. In the 19th century, nowhere differentiable continuous functions (of a fractal character) were constructed here by Bolzano along with a treatise on infinite sets, titled "Paradoxes of Infinity" (1851). Weierstrass would later publish a similar function in 1872. In 1842, Doppler as a professor of mathematics at the Technical University of Prague here first lectured about a physical effect to bear his name later. And the epoch-making physicist Albert Einstein - while being a chaired professor of theoretical physics at the German University of Prague - arrived at the decisive steps of his later finished theory of general relativity during the years 1911-1912. In Prague, also many famous philosophers and writers accomplished their works; for instance, playwright arel ape coined the word "robot" in Prague ("robot" comes from the Czech word "robota" which means "forced labor").

Modeling and Simulation: Theory and Practice provides a comprehensive review of both methodologies and applications of simulation and modeling. The methodology section includes such topics as the philosophy of simulation, inverse problems in simulation, simulation model compilers, treatment of ill-defined systems, and a survey of simulation languages. The application section covers a wide range of topics, including applications to environmental management, biology and medicine, neural networks, collaborative visualization and intelligent interfaces. The book consists of 13 invited chapters written by former colleagues and students of Professor Karplus. Also included are several short 'reminiscences' describing Professor Karplus' impact on the professional careers of former colleagues and students who worked closely with him over the years.

The theory of finite fields, whose origins can be traced back to the works of Gauss and Galois, has played a part in various branches of mathematics, in recent years there has been a resurgence of interest in finite fields, and this is partly due to important applications in coding theory and cryptography. Applications of Finite Fields introduces some of these recent developments. This book focuses attention on some specific recent developments in the theory and applications of finite fields. While the topics selected are treated in some depth, Applications of Finite Fields does not attempt to be encyclopedic. Among the topics studied are different methods of representing the elements of a finite field (including normal bases and optimal normal bases), algorithms for factoring polynomials over finite fields, methods for constructing irreducible polynomials, the discrete logarithm problem and its implications to cryptography, the use of elliptic curves in constructing public key cryptosystems, and the uses of algebraic geometry in constructing good error-correcting codes. This book is developed from a seminar held at the University of Waterloo. The purpose of the seminar was to bridge the knowledge of the participants whose expertise and interests ranged from the purely theoretical to the applied. As a result, this book will be of interest to a wide range of students, researchers and practitioners in the disciplines of computer science, engineering and mathematics. Applications of Finite Fields is an excellent reference and may be used as a text for a course on the subject.

The biennial conferences of the Society for Underwater Technology have achieved an excellent reputation for the quality of their presentations, which cover topics of the most acute current interest, as well as those at the forefront of review and development. The 1994 conference on Subsea Control and Data Acquisition formed no exception, since it covers subjects at the cutting edge of modern technology. It is a matter of increasing concern that products are becoming overspecified, resulting in excessive costs and longer development schedules, while not conferring an equivalent benefit in reliability of the finished product. Subsea Control and Data Acquisition is vital reading for all subsea control system designers, manufacturers and operators, equipment consultants, application engineers, academics in the subsea engineering field, and all subsea engineers.

The underlying technologies enabling the realization of recent advances in areas like mobile and enterprise computing are artificial intelligence (AI), modeling and simulation, and software engineering. A disciplined, multifaceted, and unified approach to modeling and simulation is now essential in new frontiers, such as Simulation Based Acquisition. This volume is an edited survey of international scientists, academicians, and professionals who present their latest research findings in the various fields of AI; collaborative/distributed computing; and modeling, simulation, and their integration. Whereas some of these areas continue to seek answers to basic fundamental scientific inquiries, new questions have emerged only recently due to advances in computing infrastructures, technologies, and tools. The book¿s principal goal is to provide a unifying forum for developing postmodern, AI-based modeling and simulation environments and their utilization in both traditional and modern application domains. Features and topics: * Blends comprehensive, advanced modeling and simulation theories and methodologies in a presentation founded on formal, system-theoretic and AI-based approaches * Uses detailed, real-world examples to illustrate key concepts in systems theory, modeling, simulation, object orientation, and intelligent systems * Addresses a broad range of critical topics in the areas of modeling frameworks, distributed and high-performance object-oriented simulation approaches, as well as robotics, learning, multi-scale and multi-resolution models, and multi-agent systems * Includes new results pertaining to intelligent and agent-based modeling, the relationship between AI-based reasoning and Discrete-Event System Specification, and large-scale distributed modeling and simulation frameworks * Provides cross-disciplinary insight into how computer science, computer engineering, and systems engineering can collectively provide a rich set of theories and methods enabling contemporary modeling and simulation This state-of-the-art survey on collaborative/distributed modeling and simulation computing environments is an essential resource for the latest developments and tools in the field for all computer scientists, systems engineers, and software engineers. Professionals, practitioners, and graduate students will find this reference invaluable to their work involving computer simulation, distributed modeling, discrete-event systems, AI, and software engineering.

1. 1 Introduction This book is written in two major parts. The ?rst part includes the int- ductory chapters consisting of Chapters 1 through 6. In part two, Chapters 7-26, we present the applications. This book continues our research into simulating fuzzy systems. We started with investigating simulating discrete event fuzzy systems ([7],[13],[14]). These systems can usually be described as queuing networks. Items (transactions) arrive at various points in the s- tem and go into a queue waiting for service. The service stations, preceded by a queue, are connected forming a network of queues and service, until the transaction ?nally exits the system. Examples considered included - chine shops, emergency rooms, project networks, bus routes, etc. Analysis of all of these systems depends on parameters like arrival rates and service rates. These parameters are usually estimated from historical data. These estimators are generally point estimators. The point estimators are put into the model to compute system descriptors like mean time an item spends in the system, or the expected number of transactions leaving the system per unit time. We argued that these point estimators contain uncertainty not shown in the calculations. Our estimators of these parameters become fuzzy numbers, constructed by placing a set of con?dence intervals one on top of another. Using fuzzy number parameters in the model makes it into a fuzzy system. The system descriptors we want (time in system, number leaving per unit time) will be fuzzy numbers.

Driven by the request for increased productivity, flexibility, and competitiveness, modern civilization increasingly has created high-performance discrete event dynamic systems (DEDSs). These systems exhibit concurrent, sequential, competitive activities among their components. They are often complex and large in scale, and necessarily flexible and thus highly capital-intensive. Examples of systems are manufacturing systems, communication networks, traffic and logistic systems, and military command and control systems. Modeling and performance evaluation play a vital role in the design and operation of such high-performance DEDSs and thus have received widespread attention from researchers over the past two decades. One methodology resulting from this effort is based on timed Petri nets and related graphical and mathematical tools. The popularity that Petri nets have been gaining in modeling of DEDSs is due to their powerful representational ability of concurrency and synchronization; however these properties of DEDSs cannot be expressed easily in traditional formalisms developed for analysis of classical' systems with sequential behaviors. This book introduces the theories and applications of timed Petri nets systematically. Moreover, it also presents many practical applications in addition to theoretical developments, together with the latest research results and industrial applications of timed Petri nets. Timed Petri Nets: Theory and Application is intended for use by researchers and practitioners in the area of Discrete Event Dynamic Systems.

organized around health and human development, environment and sustainability, and communities and social change Includes agent-based modeling, system dynamics, and network analysis Indroductory framing essays for each section

Infinite dimensional systems is now an established area of research. Given the recent trend in systems theory and in applications towards a synthesis of time- and frequency-domain methods, there is a need for an introductory text which treats both state-space and frequency-domain aspects in an integrated fashion. The authors' primary aim is to write an introductory textbook for a course on infinite dimensional linear systems. An important consideration by the authors is that their book should be accessible to graduate engineers and mathematicians with a minimal background in functional analysis. Consequently, all the mathematical background is summarized in an extensive appendix. For the majority of students, this would be their only acquaintance with infinite dimensional systems.

We are confronted with problems in virtually all of our major systems. Einstein said that "we cannot solve our problems on the same level of thinking we were on when we created them." We believe a fundamental fault underlying all these problems is the way we look at them. Our traditional method has been to analyze the system as if it were a machine to find the faulty elements and to fix them. We have done this with ailing ecosystems long enough to know that it doesn't work well. If it doesn't work here because of the complexity of the system how can we expect it to work on us or other equally, if not more, complex living systems? We are not machines. We can adapt and create in novel ways. In The Boids and the Bees we, and other living systems, are seen as the complex and adaptive systems that we (they) are, which leads to a perceptual revolution: - We are fighting a war with bacteria that we can't win. Seeing bacteria as adapting agents allows us to see how they adapt and opens other doors to end the war; - Patients that are informed and empowered can lead our health care system to focus on prevention and health rather than illness and profits; - Learning has been analyzed in the laboratory. Now we use the results of this analysis to teach our children; they become the lab-rats in the classroom. Seeing them as adaptive agents is the first step in correcting this dehumanizing error. How we adapt today will determine our tomorrows; and they can be optimized.

In June of 2002, over 500 professors, students and researchers met in Boston, Massachusetts for the Fourth International Conference on Complex Systems. The attendees represented a remarkably diverse collection of fields: biology, ecology, physics, engineering, computer science, economics, psychology and sociology, The goal of the conference was to encourage cross-fertilization between the many disciplines represented and to deepen understanding of the properties common to all complex systems.

This volume contains 43 papers selected from the more than 200 presented at the conference. Topics include: cellular automata, neurology, evolution, computer science, network dynamics, and urban planning.

About NECSI: For over 10 years, The New England Complex Systems Institute (NECSI) has been instrumental in the development of complex systems science and its applications. NECSI conducts research, education, knowledge dissemination, and community development around the world for the promotion of the study of complex systems and its application for the betterment of society. NECSI hosts the International Conference on Complex Systems and publishes the NECSI Book Series in conjunction with Springer Publishers.

ALI MINAI is an Affiliate of the New England Complex Systems Institute and an Associate Professor in the Department of Electrical and Computer Engineering and Computer Science at the University of Cincinnati.

YANEER BAR-YAM is President and founder of the New England Complex Systems Institute. He is the author of Dynamics of Complex Systems and Making Things Work: Solving Complex Problems in a Complex World.

Experimental Econophysics describes the method of controlled human experiments, which is developed by physicists to study some problems in economics or finance, namely, stylized facts, fluctuation phenomena, herd behavior, contrarian behavior, hedge behavior, cooperation, business cycles, partial information, risk management, and stock prediction. Experimental econophysics together with empirical econophysics are two branches of the field of econophysics. The latter one has been extensively discussed in the existing books, while the former one has been seldom touched. In this book, the author will focus on the branch of experimental econophysics. Empirical econophysics is based on the analysis of data in real markets by using some statistical tools borrowed from traditional statistical physics. Differently, inspired by the role of controlled experiments and system modelling (for computer simulations and/or analytical theory) in developing modern physics, experimental econophysics specially relies on controlled human experiments in the laboratory (producing data for analysis) together with agent-based modelling (for computer simulations and/or analytical theory), with an aim at revealing the general cause-effect relationship between specific parameters and emergent properties of real economic/financial markets. This book covers the basic concepts, experimental methods, modelling approaches, and latest progress in the field of experimental econophysics.

Intelligent technical systems, which combine mechanical, electrical and software engineering with control engineering and advanced mathematics, go far beyond the state of the art in mechatronics and open up fascinating perspectives. Among these systems are so-called self-optimizing systems, which are able to adapt their behavior autonomously and flexibly to changing operating conditions. Self-optimizing systems create high value for example in terms of energy and resource efficiency as well as reliability.

The Collaborative Research Center 614 "Self-optimizing Concepts and Structures in Mechanical Engineering" pursued the long-term aim to open up the active paradigm of self-optimization for mechanical engineering and to enable others to develop self-optimizing systems.

This book is directed to researchers and practitioners alike. It provides a design methodology for the development of self-optimizing systems consisting of a reference process, methods, and tools. The reference process is divided into two phases the domain-spanning conceptual design and the domain-specific design and development. For the conceptual design a holistic approach is provided. Domain-specific methods and tools developed especially for the design and development of self-optimizing systems are described and illustrated by application examples. This book will enable the reader to identify the potential for self-optimization and to develop self-optimizing systems independently."

The book covers nonlinear physical problems and mathematical modeling, including molecular biology, genetics, neurosciences, artificial intelligence with classical problems in mechanics and astronomy and physics. The chapters present nonlinear mathematical modeling in life science and physics through nonlinear differential equations, nonlinear discrete equations and hybrid equations. Such modeling can be effectively applied to the wide spectrum of nonlinear physical problems, including the KAM (Kolmogorov-Arnold-Moser (KAM)) theory, singular differential equations, impulsive dichotomous linear systems, analytical bifurcation trees of periodic motions, and almost or pseudo- almost periodic solutions in nonlinear dynamical systems.

This book develops applications of novel generalizations of fuzzy information measures in the field of pattern recognition, medical diagnosis, multi-criteria and multi-attribute decision making and suitability in linguistic variables. The focus of this presentation lies on introducing consistently strong and efficient generalizations of information and information-theoretic divergence measures in fuzzy and intuitionistic fuzzy environment covering different practical examples. The target audience comprises primarily researchers and practitioners in the involved fields but the book may also be beneficial for graduate students.

The systems movement is made up of many systems societies as well as of disciplinary researchers and researches, explicitly or implicitly focusing on the subject of systemics, officially introduced in the scientific community fifty years ago. Many researches in different fields have been and continue to be sources of new ideas and challenges for the systems community. To this regard, a very important topic is the one of EMERGENCE. Between the goals for the actual and future systems scientists there is certainly the definition of a general theory of emergence and the building of a general model of it. The Italian Systems Society, Associazione Italiana per la Ricerca sui Sistemi (AIRS), decided to devote its Second National Conference to this subject. Because AIRS is organized under the form of a network of researchers, institutions, scholars, professionals, and teachers, its research activity has an impact at different levels and in different ways. Thus the topic of emergence was not only the focus of this conference but it is actually the main subject of many AIRS activities.

This volume contains the lectures presented at the workshop on "Advances in Mathematical Systems Theory," held on the island of Borkum, Germany (April 20-23, 1999). The book will be of interest to graduate students and researchers interested in control theory and mathematical systems theory, who will find in-depth analysis and presentations from diverse perspectives interacting in this lively area. The editors are proud to dedicate this volume to Diederich Hinrichsen on the occasion of his 60th birthday in acknowl- edgment of his major contributions to linear systems theory and control theory and his long-term achievements in establishing mathematical sys- tems theory in Germany. We all owe much to him as a teacher, colleague, and friend. The editors thank the Graduiertenkolleg "Komplexe Dynamische Sys- teme" at the University of Bremen as well as the European "Nonlinear Control Network" for providing financial support that enabled this work- shop. Augsburg, Germany Fritz Colonius Wiirzburg, Germany Uwe Helmke Kaiserslautern, Germany Dieter Pratzel-Wolters Bremen, Germany Fabian Wirth Introduction The workshop "Advances in Mathematical Systems Theory" took place in honor of Diederich Hinrichsen on the occasion of his 60th birthday. The following chapters are based on invited lectures and cover a wide range of topics in linear and nonlinear systems theory including parameteriza- tion problems, behaviors of linear systems and convolutional codes, as well as complementarity systems and hybrid systems.

This book reveals how open innovation utilizes the developing circle of business models to establish new ones that define a unique link between technology and markets, focusing on how to develop and maintain successful business models. It draws readers into the philosophy and economic effects of open innovation from the outset.It presents four different developing circle business models for customers in the role of consumers, entrepreneurs, social entrepreneurs and engineers respectively, enabling each group to develop, utilize and enlarge creative business models, and even switch business models.In addition to these four circles, it takes a systemic approach to describe the relationship between technology and markets. From this relationship an open innovation strategy towards entrepreneurship can be adopted. From Open Innovation to a Creative Developing-Circle Business Model is an essential resource for start-up entrepreneurs, as well as for students of technology management, strategy and open innovation.

"Intelligent Control" considers non-traditional modelling and control approaches to nonlinear systems. Fuzzy logic, neural networks and evolutionary computing techniques are the main tools used. The book presents a modular switching fuzzy logic controller where a PD-type fuzzy controller is executed first followed by a PI-type fuzzy controller thus improving the performance of the controller compared with a PID-type fuzzy controller.The advantage of the switching-type fuzzy controller is that it uses one rule-base thus minimises the rule-base during execution. A single rule-base is developed by merging the membership functions for change of error of the PD-type controller and sum of error of the PI-type controller. Membership functions are then optimized using evolutionary algorithms. Since the two fuzzy controllers were executed in series, necessary further tuning of the differential and integral scaling factors of the controller is then performed. Neural-network-based tuning for the scaling parameters of the fuzzy controller is then described and finally an evolutionary algorithm is applied to the neurally-tuned-fuzzy controller in which the sigmoidal function shape of the neural network is determined.

The important issue of stability is addressed and the text demonstrates empirically that the developed controller was stable within the operating range. The text concludes with ideas for future research to show the reader the potential for further study in this area.

"Intelligent Control "will be of interest to researchers from engineering and computer science backgrounds working in the intelligent and adaptive control."

One criterion for classifying books is whether they are written for a single pur pose or for multiple purposes. This book belongs to the category of multipurpose books, but one of its roles is predominant-it is primarily a textbook. As such, it can be used for a variety ofcourses at the first-year graduate or upper-division undergraduate level. A common characteristic of these courses is that they cover fundamental systems concepts, major categories of systems problems, and some selected methods for dealing with these problems at a rather general level. A unique feature of the book is that the concepts, problems, and methods are introduced in the context of an architectural formulation of an expert system referred to as the general systems problem solver or aSPS-whose aim is to provide users ofall kinds with computer-based systems knowledge and methodo logy. Theasps architecture, which is developed throughout the book, facilitates a framework that is conducive to acoherent, comprehensive, and pragmaticcoverage ofsystems fundamentals-concepts, problems, and methods. A course that covers systems fundamentals is now offered not only in sys tems science, information science, or systems engineering programs, but in many programs in other disciplines as well. Although the level ofcoverage for systems science or engineering students is surely different from that used for students in other disciplines, this book is designed to serve both of these needs."

Most machine learning research has been concerned with the development of systems that implememnt one type of inference within a single representational paradigm. Such systems, which can be called monostrategy learning systems, include those for empirical induction of decision trees or rules, explanation-based generalization, neural net learning from examples, genetic algorithm-based learning, and others. Monostrategy learning systems can be very effective and useful if learning problems to which they are applied are sufficiently narrowly defined. Many real-world applications, however, pose learning problems that go beyond the capability of monostrategy learning methods. In view of this, recent years have witnessed a growing interest in developing multistrategy systems, which integrate two or more inference types and/or paradigms within one learning system. Such multistrategy systems take advantage of the complementarity of different inference types or representational mechanisms. Therefore, they have a potential to be more versatile and more powerful than monostrategy systems. On the other hand, due to their greater complexity, their development is significantly more difficult and represents a new great challenge to the machine learning community. Multistrategy Learning contains contributions characteristic of the current research in this area.

Whether costs are to be reduced, profits to be maximized, or scarce resources to be used wisely, optimization methods are available to guide decision making. In online optimization the main issue is incomplete data, and the scientific challenge: How well can an online algorithm perform? Can one guarantee solution quality, even without knowing all data in advance? In real-time optimization there is an additional requirement, decisions have to be computed very fast in relation to the time frame of the instance we consider. Online and real-time optimization problems occur in all branches of optimization. These areas have developed their own techniques but they are addressing the same issues: quality, stability, and robustness of the solutions. To fertilize this emerging topic of optimization theory and to foster cooperation between the different branches of optimization, the Deutsche Forschungsgemeinschaft (DFG) has supported a Priority Programme "Online Optimization of Large Systems".