Book Description
The leading authority on system dynamics explains this approach to organizational problem solving, emphasizing simulation models to understand issues such as fluctuating sales, market growth and stagnation, the reliability of forecasts and the rationality of business decision-making. The CD includes modeling software from Vensim, ithink, and PowerSim.
Customer Reviews:
Excelent book.......2007-10-17
This book is really impressive. Is an eye opener. Must read for Industrial Engineering Students, must have for professors and great addition for a professional looking for new ideas.
One of the best SD books with connection to practical work.......2007-10-06
"Business Dynamics" is a great book leading the newcomer -as myself- into the field of SD and the experienced system dynamicist can use it as a knowledge pool.
Even though the book is rather expensinve -and heavy alike- it covers great wisdom of John Sterman (he is by the way a scholar of the founder of the field, Jay W. Forrester) and is more than worthwhile buying if you are strongly interested in the field.
I was lucky to meet John and Jay this summer during a specific SD workshop at MIT and the yearly System Dynamics Society Conference and could chat with both of them (they are both very practicably using SD with a strong academic background). Learning and getting more experienced in System Dynamics and the use for daily problem solving is a dynamic and evolving process of wisdom with lots of feedback ("Business Dynamics" can help a lot in getting deeper insights.
Best regards
Ralf
Excellent.......2007-08-29
Excellent guide to systems thinking, clear examples, clear thinking and very interesting conclusions reached. highly recommended
buen libro.......2007-02-22
como parte de la materia lo llevo, me salio mas barato que en mexico y me es util para mi carrera
Amazing.......2007-01-12
The definitive book on Business dynamics !
It may look dificult to follow, but it isn`t really easy to read and follow !
The cd brings good examples.
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Geometrical Dynamics of Complex Systems: A Unified Modelling Approach to Physics, Control, Biomechanics, Neurodynamics and Psycho-Socio-Economical Dynamics ... AND INTELLIGENT SYSTEMS ENGINEERING)
Ivancevic V.G
Manufacturer: Springer
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ASIN: 1402045441 |
Book Description
This volume presents a comprehensive introduction into rigorous geometrical dynamics of complex systems of various natures. By "complex systems", in this book are meant high-dimensional nonlinear systems, which can be (but not necessarily are) adaptive. This monograph proposes a unified geometrical approach to dynamics of complex systems of various kinds: engineering, physical, biophysical, psychophysical, sociophysical, econophysical, etc. As their names suggest, all these multi-input multi-output (MIMO) systems have something in common: the underlying physics. Using sophisticated machinery composed of differential geometry, topology and path integrals, this book proposes a unified approach to complex dynamics – of predictive power much greater than the currently popular "soft-science" approach to complex systems. The main objective of this book is to show that high-dimensional nonlinear systems in "real life" can be modeled and analyzed using rigorous mathematics, which enables their complete predictability and controllability, as if they were linear systems. The book has two chapters and an appendix. The first chapter develops the geometrical machinery in both an intuitive and rigorous manner. The second chapter applies this geometrical machinery to a number of examples of complex systems, including mechanical, physical, control, biomechanical, robotic, neurodynamical and psycho-social-economical systems. The appendix gives all the necessary background for comprehensive reading of this book.
Book Description
foreword by Hermann Haken
For the past twenty years Scott Kelso's research has focused on extending the physical concepts of self- organization and the mathematical tools of nonlinear dynamics to understand how human beings (and human brains) perceive, intend, learn, control, and coordinate complex behaviors. In this book Kelso proposes a new, general framework within which to connect brain, mind, and behavior.
Kelso's prescription for mental life breaks dramatically with the classical computational approach that is still the operative framework for many newer psychological and neurophysiological studies. His core thesis is that the creation and evolution of patterned behavior at all levels -- from neurons to mind -- is governed by the generic processes of self-organization. Both human brain and behavior are shown to exhibit features of pattern-forming dynamical systems, including multistability, abrupt phase transitions, crises, and intermittency.
Dynamic Patterns brings together different aspects of this approach to the study of human behavior, using simple experimental examples and illustrations to convey essential concepts, strategies, and methods, with a minimum of mathematics.
Kelso begins with a general account of dynamic pattern formation. He then takes up behavior, focusing initially on identifying pattern-forming instabilities in human sensorimotor coordination. Moving back and forth between theory and experiment, he establishes the notion that the same pattern-forming mechanisms apply regardless of the component parts involved (parts of the body, parts of the nervous system, parts of society) and the medium through which the parts are coupled. Finally, employing the latest techniques to observe spatiotemporal patterns of brain activity, Kelso shows that the human brain is fundamentally a pattern forming dynamical system, poised on the brink of instability. Self-organization thus underlies the cooperative action of neurons that produces human behavior in all its forms.
Customer Reviews:
foundation Material.......2000-08-20
What language would It use to describe itself? Can something like mathematics, music, language or art, which It created contain the essence to even describe itself? Do modern models describe It any better than earlier models? In the context of the times does Non-linear Dynamics attractors describe (or for that matter predict) any better than C.Jung's archetypes? As far as that goes, since they both describe scales of magnitudes, why is their so much similarity between so many of each others concepts?(enantiomorphism vs. entrainment). One would think the biological scientific community would really embrace the concepts in this book for the simple reason to get them off the hook with one of their most difficult areas. If the human brain and thus cognitive and conscious processes are a product of limit cycles and memory simply a function of a hystersis curve the entire process becomes a function of Newtonian physics in support of biology's basic premise. Self-determination and teleological arguments become academic. Mind boggling. (I think the human brain is highly overrated). While Kelso's book Dynamic Patterns doesn't answer these questions either, it is still a pretty good book (overlooking not having any equations on the first page). Already in it's third printing it doesn't need me to critique it. For computer modelers: Mathcad contains the necessary (logistics) for scalar mapping and iterated bifurcations to provide the background to create the models used in the book. (including Lorenz models, Duffings, ODE solvers). Dynamics: Numerical Explorations, (Springer) with related program even goes one better. Matlab's signal processing toolbox, (creates any signal p.48, check out the Hofstader sequence relation to Kelso's basic signal), wavelets toolbox (creates Farey sequence, most converging fraction sequences paint this same picture). Because these equations can be set up as state-space models the same model can be used Matlab's system identification, control, and neural net toolboxes (use feedforward instead of feedback). For biologicals: Most applicable non-linear equations are much better handled by differential equations. While a unit circle torus may work for pictures it is does not give unique solutions and thus calculable answers. Assuming the time constant is the same in each differential equation and then cascading the solutions into each other to build matrices that can then be used to build the phase plots easily solves this. Check out Spikes, Decisions, and Actions (Wilson) probably the best as far as Matlab add-ons go (real action figures basic formulas and models can be modified to see the effect of gain i.e., use of amphetamines or inhibitors SSRI). Although Koch's (Biophysics of Computation provides good background in stochastic processes in the Markovian sense and Tri-diagonal matrices) startneuro Simulink from Methods in Neuronal Modeling (MIT Press) is a lot more fun!(Gabrianni and Koch) In the same book is Rinzel and Ermentrout's original work on Oscillations. And last but not least the internet address for all these and the classic Neuron model by Hines is included.
Book Description
Why do women stabilize our societies? Why can we enjoy and understand Shakespeare? Why are fruitflies uniform? Why do omnivorous eating habits aid our survival? Why is Mona Lisa’s smile beautiful? – Is there any answer to these questions? This book shows that the statement: "weak links stabilize complex systems" holds the answers to all of the surprising questions above. The author (recipient of several distinguished science communication prizes) uses weak (low affinity, low probability) interactions as a thread to introduce a vast variety of networks from proteins to ecosystems. Many people, from Nobel Laureates to high-school students have helped to make the book understandable to all interested readers. This unique book and the ideas it develops will have a significant impact on many, seemingly diverse, fields of study.
Customer Reviews:
Primer on real-life networks with a theme .......2007-02-02
If you ever needed another good reason to value your grandmother even more, you'll find the answer in "Weak links".
Structurally, his book starts with an exposition on network theory and
terminology, then the application and discussion of these concepts to
real-life complex systems on many scales and applied to many domains (physical, natural, technological, social). His main point is, as the reviewer noted above, that 'weak' links (weak: additional/removal does not statistically affect the average of some metric) stabilize systems.
The book has thorough footnotes, one can delve as deep as one would like
into the professional papers. In addition, Csermely is an honest scholar - he shows his hands when there is mere speculation (you have to see the book's unique pictograms to appreciate the effects)
After pouring through several alternatives, I have adopted this book as a
textbook for my Science of Networks class (I'm CS fac at an elite US liberal arts school), and I recommend it to anyone without hesitation for a readable, and learned exposition.
I only have two or three caveats from a specialist's point of view: The
phenomenological discovery of power laws in complex systems is not unusual
and may not be evidence of any SF properties. Scale-free is an abused
term, and I wish the controversy about it were explained a bit more. Also, from a modelling point of view, I wish Doyle and Carlson's work on HOT systems were discussed in more depth.
But these are minor points, relatively speaking. This is a gem of a book:
erudite, humane, funny, accessible and thoroughly fascinating. On every
page, there are delights that lead down new intellectual paths.
Csermely did a great service to pedagogy and to this budding science with
this magisterial survey. Outstanding in its ease of access for intelligent
undergraduates and commendable for intellectual honesty - I wish more
books (textbooks and otherwise) were written this way.
Weak Links Stabilize Complex Systems.......2006-05-13
It is an intriguing concept.
Weak links, invisible in many networks, are critical to its stability. In this book, Peter Csermely shows that all networks, from the universe to molecules are governed by the same principles. Regardless of the system -- atoms, cells, companies, web pages or countries -- surprisingly, the weak links stabilize each.
Csermely, a professor at Semmelweis University in Budapest, a former Fogarty Fellow at Harvard University, is a molecular chaperones specialist. In 2003, he became fascinated by the concept of affinity -- a network's stabilizing components of must have weak links to the other components. These weak links act as hubs. Attack the hubs; disrupt the network.
Csermely demonstrates the concept hold true in field after field. The professor begins his study with a discussion of the Granovetter study of a job search and then proceeds to describe network dynamics. By chapter four, the reader is ready to be introduced to the concept of weak links as universal stabilizers. Then, the professor conducts a network tour ranging from macromolecules to the planet earth. Finally he ends with a discussion of weak links, stability landscapes and game theory.
Surprisingly, his book is understandable, even to non-academics. It is loaded with gems that can be applied to the reader's networks and relationships.
This is not a book I would have ever picked up on my own. Thankfully, Professor Csermely sent me an advanced copy. It is a unique book that takes a thorough look at an intriguing concept.
Customer Reviews:
Turtles for all.......2006-03-28
This book is a great guide to taking the somewhat difficult ideas explored in Mitchel Resnick's book "Turtles, Termites, and Traffic Jams" and implementing them with students. Adventures in modeling can be used alone but, for any teacher who wants to use this book in a computer class, it is helpful to read the other book first. The activities and challenges here are reasonable for students from college down though middle school. I know of one teacher who has even successfully used these activites with pre-teens.
Good book but not perfect..........2006-01-03
The book is good overall but I felt they need to use more carefully thought out examples and not try to push an 'agenda'.
It is as if the author(s) are trying to make an issue over creation vs evolution. In the very beginning chapter, they make an invalid example by comparing evolution vs creation to central control vs decentralized systems. This is a quote from the book, "This tendency to assume centralized control, which we call the centralized mindset, makes it difficult for people to understand the workings of many phenomena in the world. The recurrent questioning of evolutionary theories is another example: When people see complex living systems in the world, they assume that someone or something must have explicitly designed them; instead, these livings systems are the products of millions of incremental changes over time."
Great guide to modeling systems!.......2002-02-20
This is a great book for anyone interested in modeling dynamic systems. The authors provide wonderful background theory as a basis for building computer simulations. The simple step-by-step instructions guide you through the process of creating your own simulations using StarLogo. This book is an interesting, easily understandable beginner's manual and comes with all the software you need. It's a great way to teach yourself or other people how to program a simulation. I love the turtles!
Excellent guide to modeling systems!.......2002-02-18
This is a great book for people intereted in modeling systems behavior. The authors give an excellent background summary of the theory involved in StarLogo. The program given with the CD is easy to install and use. The book takes you step by step through thinking about and creating your own computer simulation in an easy to understand manner. There's lots of support for any technical difficulties you might have and good examples of what you can do with the program. I highly recommend it to everyone.
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The Logistic Map and the Route to Chaos: From the Beginnings to Modern Applications (Understanding Complex Systems)
Ausloos M.
Manufacturer: Springer
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ASIN: 3540283668 |
Book Description
Pierre-Francois Verhulst, with his seminal work using the logistic map to describe population growth and saturation, paved the way for the many applications of this tool in modern mathematics, physics, chemistry, biology, economics and sociology. Indeed nowadays the logistic map is considered a useful and paradigmatic showcase for the route leading to chaos. This volume gathers contributions from some of the leading specialists in the field to present a state-of-the art view of the many ramifications of the developments initiated by Verhulst over a century ago.
Book Description
The study of complex systems in a unified framework has become recognized in recent years as a new scientific discipline, the ultimate in the interdisciplinary fields. Breaking down the barriers between physics, chemistry, and biology and the so-called soft sciences of psychology, sociology, economics and anthropology, this text explores the universal physical and mathematical principles that govern the emergence of complex systems from simple components. Dynamics of Complex Systems is the first text describing the modern unified study of complex systems. It is designed for upper-undergraduate/beginning graduate level students, and covers a broad range of applications in a broad array of disciplines. A central goal of this text is to develop models and modeling techniques that are useful when applied to all complex systems. This is done by adopting both analytic tools, including statistical mechanics and stochastic dynamics, and computer simulation techniques, such as cellular automata and Monte Carlo. In four sets of paired, self-contained chapters, Yaneer Bar-Yam discusses complex systems in the context of neural networks, protein folding, living organisms, and finally, human civilization itself. He explores fundamental questions about the structure, dynamics, evolution, development and quantitative complexity that apply to all complex systems. In the first chapter, mathematical foundations such as iterative maps and chaos, probability theory and random walks, thermodynamics, information and computation theory, fractals and scaling, are reviewed to enable the text to be read by students and researchers with a variety of backgrounds.
Customer Reviews:
beautifully written and highly useful.......2007-07-16
This is a beautifully written and thought-provoking work that presents the field of complex systems in a unified manner. The writing is highly engaging and stimulating with a broad range of topics. The material is pitched at just the right level, focusing on the concepts without getting buried in unnecessary details, while avoiding superficiality. I highly recommend this excellent book.
The worst side of normal science.......2007-04-24
The book is a tour around the paradigms used by scientists in
Complex Systems. While normal science is about using and re-using the paradigms without much creativity or true aportation to knowledge or understanding, the situation is worse in complex systems, since, as an emerging area it has multiple (competing?) paradigms, to the point that it is not possible to define a "complex system" in a form that encompases all the paradigms. The book certainly does not solve this problem, yet the author acknowledges the difficulty present in saying what a complex system is.
Complex systems is not an area of research but a community of researchers united by their interests.
The book is then a compilation of the "how to" and the believes for each paradigm, several of them carry very little science and have left the idea of "refutation" burried under piles of meaningless papers. Not surprissingly, some authors claim that complex systems is a postmodern scienceComplexity and Postmodernism: Understanding Complex Systems. And truly, the complex systems of Bar-Yam are only possible after we have buried reason and have accepted that science has nothing to do with truth.
Too much for me, not a book I recommend to my students.
Perpetuates the usual myths.......2007-04-03
that information is the opposite of entropy which is a measure of disorder or uncertainty. However because this book is about complexity and not information per se, I will only briefly refer to his mistakes with the latter as I have explained them further in other reviews that are specifically on that topic.
Shannon's information rate from communications theory, R, is an entropy like formula but most critically it is a state function difference of the uncertainty reduction to a recognizer after a measurement. Entropy is not a proper measure of disorder or uncertainty; the 2nd law of entropy increase of the universe applied long before there were any observers. It is a measure of the dispersal of energy. Going back in time is not going back to perfect order, but quite the opposite. I have not seen proper definitions in any book but there are PhD level articles available on the internet with proper definitions such as the Principia Cybernetica Web and molecular biologist Dr Thomas Schneider's website. Biologist Richard Dawkins also has an accurate short article on the internet. Most physicists have the definitions wrong unfortunately and believe information evolved before life, which is false. (A recognizer is required, whether a ribosome or mind etc.) Instead a better definition of complexity than the present author offers would indicate that the universe has increased in complexity through gravitational clumping (among other things). By making the mistake then the physicists and present author believe maximum information is randomness or equilibrium. This is the definition of algorithmic complexity.
As the author adapts algorithmic theory to his complexity profile he arrives at formulas that are observer dependant: "the complexity profile [is] the length of the description [of] the error allowed [as] the description increases." This is of little or no practical use. Again the universe has grown in complexity (or at least in pockets or we wouldn't be here) without relying on the degree of focus of any observer. A crystal is highly ordered relative to say a human cell whose complexity is a result of a multitude of interactions of chemical agents and macro molecules. This is where his analysis falls silent, in fact wrong. He says (page 741) "short-range correlations decrease the microstate complexity..." Well that's because he has a flawed method of using statistical mechanics. There is likely no universal complexity algorithm. Consider that a single gene can yield up to thousands of different proteins. One should be wary however of any formula that treats correlations as reduced complexity! Again the crystal vs the cell!
However there are ways of measuring the critical biological requirement of interactions that in fact increase complexity, the opposite of equilibrium statistical mechanics, a flawed tool. For instance in a recent article at lanl.arXiv.org, authors Edwin Wang et. al. apply Pearson's correlation coefficient to show that "genes with higher cis-regulation complexity are more coordinately regulated by transcription factors at the transcriptional level and by micro RNA's at the post-transcriptional level. This is a potentially novel discovery of a mechanism for coordinated regulation of gene expression...We found a positive correlation between these two groups of transcriptional regulators... " Measures of correlations are key in studying biological complexity and are not based on an observer's focus ability.
For a layman's guide to the issue of correlations for life see Irun Cohen's book 'Tending Adam's Garden' (though it has no quantitative aspect).
Interesting but incomplete.......2006-10-14
That physical systems are complex has been acknowledged for centuries, but only in recent decades has the scientific community, especially physicists and biologists, directly confronted complexity. This book discusses complex systems from the dynamical systems perspective, and as such can be read by physicists, mathematicians, and mathematical biologists. Biologists in particular will find the discussion of `emergence' the most important one, especially systems biologists. Physicists and mathematicians who study dynamical systems tend to not be concerned with their origins, whether they are in biology or some other area. But physicists do concern themselves with the experimental relevance of dynamical systems, unlike mathematicians who are sorely concerned with their formal properties, and do not care at all if they can find expression in the real world. But it goes without saying that the theory of complex systems has found application in finance, genetic engineering, cryptography, network engineering, and many other areas. This book gives a good overview of the techniques used to study complex systems, and can be read by anyone with the necessary mathematical preparation, consisting of probability theory and elementary calculus.
Systems that are simple can become complex by only a slight alteration in their configuration. The gravitational three-body system in classical mechanics is a good example of this. The dynamics of two objects interacting gravitationally can be solved explicitly, but the system consisting of three bodies cannot. The complexity in these two cases is measured by the availability of solutions to the dynamics of the system. The author is very aware that more involved measures of complexity are needed and he gives examples of these in the book. Mathematical techniques from probability and statistics are of course used throughout the book to frame these measures more quantitatively. This reflects the author's stated strategy throughout the book, namely to describe the essential characteristics of a class of systems, and employ statistical techniques to find the properties and behaviors of these systems.
The concepts of emergence and complexity are fundamental to a study of complex systems, the author argues and early on in the book he clears up some of the confusions behind the use of these terms in the scientific literature. A `complex system' is one which is constructed from many components and whose behavior cannot be determined from the behavior of these components, i.e. the behavior of the system is `emergent.' The `complexity' of a system, on the other hand, is the amount of information needed to describe the system. This is a somewhat subtle definition, and quite a few proposals have been put forward in the literature for measuring complexity. The author settles on a familiar method, the `entropy' for measuring complexity, but with a warning to the reader that the calculation of the entropy is dependent on the particular length or time scale over which the system is observed. For extremely long time scales (of observation), one can get away with describing systems as always in equilibrium. In this case the entropy would be maximum but the system would not be viewed as being complex. For very short time scales (of observation) , the entropy of the system is very small but due to the ability to observe the microscopic dynamics of the system it would be viewed as highly complex.
These considerations lead the author to introduce the concept of a 'complexity profile' of a system, which he discusses at some length in the last pages of the book. The complexity profile is designed to study the the dependence of complexity on both length and time scales. The concept is dependent on the notion of a sequence of observers that are ordered according to their ability to distinguish microstates. The author calculates the complexity profile of the ideal gas and shows that the complexity of a microstate for this case is simply the entropy, but as the number of microstates with a given region increases, the complexity approaches zero. Other examples of the complexity profile are discussed, one being for observers that only measure the positions of particles and not the momentum. The author also studies the connection between the complexity profile and the predictability or chaotic behavior of the system, where chaotic systems are viewed as being ones where information from a particular scale can be transferred to a larger scale, as contrasted with dissipative systems where information on a large scale is transferred to a smaller scale. The author gives various arguments and calculations that illustrate the difference in complexity profiles between chaotic systems and those of conservative, nonchaotic systems. The discussion is fairly convincing but if the complexity profile is important in complex systems, its defintion and properties should have been included at the beginning of the book, and serve as a central theme behind the discussions throughout the entire book. As it stands the complexity profile comes across as a concept that is purely ancillary to the study of complex systems. It certainly does not appear to be indispensable in discussing irreversibility of physical systems, this problem still being the most pressing one in statistical mechanics and is still hotly debated at the present time.
How complicated are we?.......2005-04-04
This book is designed as a text to introduce graduate students in science to the concepts and methods in the ``science of complexity'' which comprises studies in mathematics, physics, chemistry, biology, computer science, sociology, psychology, economics, anthropology, and philosophy. Written from the perspectives of a physicist, definitions are informal; thus a concise definition of a complex system is not given. The concept of a complex system is introduced through examples, and informally described as having ``a large number of interacting parts'' although ``even a few interacting objects can behave in complex ways.'' More precisely, complexity is defined as ``the amount of information necessary to describe a system.'' Another key concept is the phenomenon of emergence which arises when ``the collective behavior [of a complex system] is not readily understood from the behavior of its parts.''
Dynamics of Complex Systems opens with a long chapter (278 pages) of ``introduction and preliminaries'' which surveys iterative maps; thermodynamics and statistical mechanics; activated processes (glasses); cellular automata; statistical fields; computer simulations; information theory; computation; and fractals, scaling and renormalization. It is suggested that this chapter can serve as the basis for a one-semester course. This introductory chapter is followed by eight chapters devoted two each to four different subjects: neural networks, protein folding, biological evolution, and human civilization. In each of these pairs of chapters, the first is more detailed and the second more general. Thus the first of the two chapters on neural networks describes neural network models (Hopfield's attactor models) whereas the second discusses the phenomenon of sleep and models of mind, with similar divisions of labor in the pairs of chapters on protein folding and on biological evolution. In the final chapter, it is noted that ``human civilization is more complex than we are as individuals.''
Alwyn Scott
http://personal.riverusers.com/~rover/
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Modeling Chemical Systems Using Cellular Automata
Lemont B. Kier ,
Paul G. Seybold , and
Chao-Kun Cheng
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ASIN: 1402036574 |
Product Description
Molecular Modeling using Cellular Automata provides a practical introduction to an exciting modeling paradigm for complex systems. The book first discusses the nature of scientific inquiry using models and simulations, and then describes the nature of cellular automata models. It then gives detailed descriptions, with examples and exercises, of how cellular automata models can be used in the study of a wide variety chemical, physical, and biochemical phenomena. Topics covered include models of water itself, solution phenomena, solution interactions with stationary systems, first- and second-order kinetic phenomena, enzyme kinetics, vapor-liquid equilibrium, and atomic and molecular excited-state kinetics. The student experiences these systems through hands-on examples and guided studies, and there is room for further original experimentation using the accompanying computer program (on CD Rom).
This book is the first of its kind: a textbook and a laboratory manual about cellular automata modeling of common systems in chemistry. It is not only a text, but includes a CD Rom which allows readily-assimilated, real-time experience with the methodology and practice of cellular automata simulations. The book is designed to be used as a text in undergraduate courses dealing with complex systems and/or as a computational supplement to laboratory courses taught at the undergraduate level.
The book includes:
- Compact descriptions of a large variety of physical and chemical phenomena
- An accompanying program (CD) for the study of these phenomena
- Illustrative examples of simulations, with exercises for further study
- An instructor's manual for use of the program
The book will be of great value in undergraduate courses in chemistry, physics, biology, applied mathematics, and bioinformatics, and as a supplement for laboratory courses in introductory chemistry, organic chemistry, physical chemistry, medicinal chemistry, chemical engineering and other courses dealing with statistical and dynamic systems. It allows the exploration of a wide range of dynamic phenomena, many of which are not normally accessible within conventional laboratory settings due to limitations of time, cost, and experimental equipment.
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Complex Systems
Manufacturer: Cambridge University Press
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ASIN: 0521462452 |
Book Description
This book explores the exciting new field of complexity. It features in-depth coverage of important theoretical areas, including fractals, chaos, nonlinear dynamics, artificial life, and self organization. It also provides overviews of complexity in several applied areas, including parallel computation, control systems, neural systems, and ecosystems. Contributors examine some of the properties that best characterize complex systems, including algorithmic richness, nonlinearity, and abundant interactions between components. In this way the book draws themes, especially the ideas of connectivity and natural computation, that reveal deep, underlying similarities among phenomena that have formerly been treated as completely distinct. Researchers in a wide array of fields, including ecology, neuroscience, computer science, and mathematics, will find this volume to be a fascinating collection of ideas.
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Dynamical Systems: Examples of Complex Behaviour (Universitext)
Jürgen Jost
Manufacturer: Springer
ProductGroup: Book
Binding: Paperback
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Accessories:
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Chaos and Fractals: New Frontiers of Science
-
Markov Random Field Modeling In Image Analysis (Computer Science Workbench)
-
Theoretical Aspects of Local Search (Monographs in Theoretical Computer Science. An EATCS Series)
ASIN: 3540229086 |
Book Description
This book presents a survey of the field of dynamical systems and its significance for research in complex systems and other fields, based on a careful analysis of specific important examples. It also explains the fundamental underlying mathematical concepts, with a particular focus on invariants of dynamical systems, including a systematic treatment of Morse-Conley theory. Entropy and related concepts in the topological, metric, measure theoretic and smooth settings and some connections with information theory are discussed, and cellular automata and random Boolean networks are presented as specific examples.
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