5 comments about Dynamics Of Complex Systems (Studies in Nonlinearity).

1. 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/



2. 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.



3. 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).



4. 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.



5. 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.

5 comments about Complexity and Postmodernism: Understanding Complex Systems.

1. The book combines elements of different philosophies: post-modernism, structuralism, and deconstruction. It is a meeting of vague philosophical generalizations and scientific terminology (e.g., neural networks), and as such, it muddles things instead of making them clear. The hope being that, if things look complex and muddled, people will consider the book profound.

   I have to say that stylistically the book is fairly well written, yet this is not something one would read for entertainment. Bottom line: this is an attempt at some sort of philsophical synthesis which, in reality, is an intellectual dead end.




2. I read this book primarily through an interest in the philosophy of language. Of particular relevance in this respect is the emphasis on a characterisation of complexity as being opposed to traditional notions of representation. Cilliers draws parallels between the philosophy of Saussure and Derrida and scientific developments in distributed representation, particularly with respect to connectionist approaches as implemented in neural networks. Cilliers argues that a classical representational theory of language that posits syntax as an instantiation of semantics does not sufficiently allow for the complexity evident in language, but rather that meaning is constituted by the dynamic relationships between both the components of language and the environment in which it is embedded. Cilliers explicitly rejects rule-based symbol systems as being adequete for modelling language, referring to recent scientific research using neural networks to simulate language learning indicating that "though rules may be useful to describe linguistic phenomena, explicit rules need not be employed when language is acquired or when it is used" (p. 32). In Chapter 4 (pp. 48-57), Cilliers considers the Chinese Room Gedankenexperiment from the perspective of his thesis. He suggests that the debate has unquestionably assumed that the formal model of language represented by the argument is correct, that is, that a rule-book such as the one supposed is even possible. Cilliers suggests that this assumes certain features of language: that a formal grammar for a natural language can be constructed and represented in a lookup table; that there is a clean split between syntax and semantics; and that language represents rather than constitutes meaning (p. 53).

   The overall picture of language that Cilliers develops has important parallels with the views of Wittgenstein, though, somewhat surprisingly, Wittgenstein is never explicitly mentioned (except with regard to his family concepts). Firstly, meaning is construed as occuring through dynamic processes (use) rather than static representations (the conception that Wittgenstein's private language argument criticises). Secondly, the idea that there is some fact of the matter (whether inside or outside human agents) that determines meaning is explicitly rejected. Finally, a straightforward split between syntax and semantics is denied (a distinction that the sceptical interpretation of Wittgenstein, offered by Kripke, takes advantage of).

   In summary, I would recommend this book to anyone interested in making connections between dynamic systems theory and philosophy of mind or language -- Cilliers proves an effective communicator in both of the fields he wishes to connect.




3. Frankly, I'm astonished by some of the favorable reviews this book has received. First of all, I still haven't figured out if this really is a book or if it's a collection of essays, due to the amount of repetition of content between chapters.

   Cilliers attempts to demonstrate the mutual relevance of complexity science (CS) and postmodern philosophy, but his knowledge of CS and thermodynamics seems to go no deeper than what he's read on the dustjackets of pop-sci books. The number of claims he makes that are either blatantly false or not necessarily true are outnumbered only by the number of uninsightful comments and statements that appear to have been gleaned directly from more technical sources. Here are a few to make one's skin crawl:

   On p. 6, as an example of a non-linear relationship: "money can receive compounded interest". In fact, this is a classic *linear* relationship (so common it's often used as an introductory problem the first day of a course in linear differential equations). The equation representing it is simply: dM/dt = n*M, where M is the amount of money in an account, and n is the interest rate. The solution is Mo * e^(nt), where Mo is the initial amount of money in the account and 'e' represents 'exponential'. (Simply because compounded interest generates an exponential curve over time does not make the relationship non-linear; the underlying equation is linear.)

   On p. 4: "Any analysis of a complex system that ignores the dimension of time is incomplete, or at most a synchronic snapshot of a diachronic process." This is completely false - One of the very purposes of 'phase space' analysis is to *completely* represent a system without considering time. The elliptical relationship between velocity and momentum in a simple harmonic oscillator is a common example that many might remember from high school physics.

   On p. 8: "In classical mechanics, time was reversible, and therefore not part of the equation. In thermodynamics time plays a vital role." This quote still makes me tear at my hair. The *exact opposite* is true: almost every equation in classical mechanics (projectile motion, harmonic oscillation, planetary motion) explicitly involve time as a dimension, while, because thermodynamics is only concerned with initial and final (equilibrium) states, few thermo equations do so.

   On p. 3, Cilliers says: "The grains of sand on a beach do not interest us as a complex system." but includes later in the book a quote from complexity scientist Per Bak, who has achieved his fame specifically for the study of the 'self-organized criticality' of sand grains.

   And this is just the first few pages! The list goes on and on: He repeatedly confuses the thermodynamic concepts of 'closed' and 'isolated' systems; He seems to think that 'non-linear' equations are all somehow phenomenally complex and unsolvable and that the phrase 'non-linear' is therefore a synonym for being non-reductionist, non-rational, and, in short, 'postmodern'. (In doing so, he falls into many of the traps Alan Sokal identified in Fashionable Nonsense.)

   I think that the basic concept behind the book could have been interesting, but due to Cilliers elementary-level grasp of half the subject matter with which he deals, the statement Cilliers himself makes on p. 133 (in reference to a recent book by Rouse) applies equally well to this text: "For me, reading this book was about as pleasant as it would be to eat it."




4. ... in spite of the appearance of the reviews associated with this work and the work itself, there is a valid connection between postmodernism and (let me be patient!) complexity.

   First of all, about terminology... isn't complexity theory a branch of computer science that deals with execution time as a metric of algorithms? I think the reviewers here want to refer to complex systems theory. Wasn't connectionism a fad which was piled on top of a catchily-conceived name for artificial neural networks .... which were the popularization of more serious works of people like Papert, Minsky, Grossberg...and doesn't the reviewer who pretends to know something about physical science understand what "irreversibility" is and that, indeed, classical mechanics is indeed reversible? J. Willard Gibbs would roll over in his grave if he could read the reviews on this page...

   IF you are seriously trying to find out what this stuff is about, start out by getting Lars Skyttner's book on General Systems Theory. Use it as a guidebook. Then, if you want to understand the evolution of the ideas, read the opening sections of Kant's Critique of Pure Reason. After that, read Saussure and Piaget on structuralism and read Terence Hawkes' book, "Structuralism and Semiotics" After that, try to get at least a rudimentary understanding of the work of the process philosophers...Bergson, Peirce, James and, of course, "Process and Reality" by Whitehead. At this point, you should seriously consider getting at least a passing familiarity with the work of Karl Marx with the goal of understanding what was really bothering him - and of seeing that Marx's ideas are important in ways that he probably never even thought about.

   At that point, if you are one of many for whom there is a schism between the culture of liberal arts and the culture of mathematics and science, you should, at this point read a few of the popular works of Richard Feynman - perhaps, "The Character of Physical Law" or the opening lecture of Volume I of "The Feynman Lectures on Physics". Compare what Feynman has to say about science to what Piaget has to say about structures and - hopefully, by now you are beginning to realize that mathematics is a liberal art - and that the so-called liberal arts are sometimes excuses for people who don't want to be very careful in their thinking....(not always, mind you) - go and read Sunny Auyang's wonderful books, "Foundations of Complex Systems Theories" and "How is Quantum Field Theory Possible?"

   By this point, you should feel somewhat secure in addressing "Postmodernism" and being able to distinguish what is there because people want to sound "cool" for their friends, and what is valid and sometimes deeply disturbing for the evolution of humanity.




5. Cilliers does an excellent job to bring together difficult concepts. One or two reviewers come up with issues around time and thermodynamics: let's just say that entropy (central to the second law)is a temporal concept and leave it at that. I found his work something to go back to regularly, well referenced and an example of how to fuse philosophy and science in a rather rigorous manner. We have moved ahead in neural network technology and thinking since 1998, but I still use the concepts of Cilliers to validate thinking around the modern concepts of non-linear estimation and pattern matching. I believe that this is a very good book to use as part of core reading material if you come from a scientific or a social sciences background and need to work on real world problems at that uncomfortable cross-over point between the two.
   
   Why not 5 stars? This is 2006 and we have internalised many of the ideas that Cilliers had to expose and defend in 1998. So Paul, what about a new work that brings in your ideas on "slowness" and bounding of systems?

1 comments about Physiological Control Systems: Analysis, Simulation, and Estimation (IEEE Press Series on Biomedical Engineering).

1. I have been using this text for a class in Physiological Control Systems, but have been largely disappointed. One of my disappointing experiences is on p. 170-1, where Khoo shows how to get an RLC model transfer function out of MATLAB's ss(). Since the MATLAB documentation on ss() is skimpy, this is a place where Khoo could have added value, illuminating what the A, B, C, and D matrices represent to ss(), but Khoo simply brushes past the opportunity. Khoo also discusses bifurcation in the logistic map, but if you look for 'logistic' in the index, you won't find it. Khoo mentions Fitzhugh-Nagumo and Hodgkin-Huxley within the context of his section on Bonhoeffer-van der Pol, but those four authors are not in the index (Bonhoeffer and van der Pol are). I admit to not having made a comprehensive study of the MATLAB examples, but I downloaded his code for sensitivity analysis (sensanl.m and two supporting .m files) mentioned in section 7.3.2, and consider the code to be poorly written. If I didn't have Dorf & Bishop's "Modern Control Systems, 9th Edition" to fall back on, I would have been in dire straights getting anything beyond a cursory reading out of Khoo's text. In short, this book should command a price in the $50 to $60 range, not the stellar $110-120 its priced at. Dorf & Bishop is priced about the same and delivers three times the value that Khoo does. Every chapter where I made an effort to get to the bottom of some discussion, I found Khoo's exposition wanting. The index is exasperatingly useless. There are only two entries under 'H', one under 'K', one under 'W', etc. That's alarming for a book with 307 pages.

4 comments about Global Positioning Systems, Inertial Navigation, and Integration.

1. Of course the experience of the authors is great but none of it you can use either in your study or research. It is impossible to learn about Kalman filtering from this book as well as to improve your knowledge in this area. Book is intended for professionals in this area but professionals will not need it. Many intersting facts but no detailed information. For example one can know that the square root filtering method came from James Potter but there is nothing more about this method from this book. Otherwise you have to study sources of the software (included). Book is written as advertizing of author's skills and nothing else. Too much about nothing ...



2. Here are some quotes from a review of this book in GPS World, July 2001, pp 46-47, by Dr. John Angus, a consultant and researcher in the area of GPS-aided navigation systems, AND professor at Claremont Graduate University, Claremont, CA. Dr. Angus' credentials qualify him to review this book.

   "Noteworthy is the comprehensiveness of the material on GPS, Kalman filtering and Kalman filter engineering, and the appendix on coordinate transforms...An instructor could easily develop a one-semester course on basic GPS or a full year course on GPS and inertial navigation, each of them "glued" together by the Kalman filter and enlivened by computer experiments with the MATLAB code provided."

   "The writing...tends to be concise and the mathematics is kept to the minimum necessary to expose the theory and methods of filtering, GPS, and INS."

   "...effectively addresses most of the basic engineering and performance issues relating to GPS/INS."

   "...recommended for personal and professional libraries."

   This is an application-oriented book, which as such, does not include detailed mathematical derivations. It does provide Kalman filter algorithms (on floppy and in text), but if one needs the theory of Kalman filtering behind these, one needs to use a Kalman filtering text, such as Kalman Filtering Theory & Practice Using MATLAB (Second Edition), Wiley 2001, by Grewal and Andrews. The latter book gives all of the methods in square root filtering algorithms and derivations and more. If the "Asian Reviewer" is most interested in Kalman filtering, he/she would be better advised to buy a book on Kalman filtering.




3. Here are some quotes from a review of this book in GPS World, July 2001, pp 46-47, by Dr. John Angus, a consultant and researcher in the area of GPS-aided navigation systems, AND professor at Claremont Graduate University, Claremont, CA. Dr. Angus' credentials qualify him to review this book.

   "Noteworthy is the comprehensiveness of the material on GPS, Kalman filtering and Kalman filter engineering, and the appendix on coordinate transforms...An instructor could easily develop a one-semester course on basic GPS or a full year course on GPS and inertial navigation, each of them "glued" together by the Kalman filter and enlivened by computer experiments with the MATLAB code provided."

   "The writing...tends to be concise and the mathematics is kept to the minimum necessary to expose the theory and methods of filtering, GPS, and INS."

   "...effectively addresses most of the basic engineering and performance issues relating to GPS/INS."

   "...recommended for personal and professional libraries."

   This is an application-oriented book, which as such, does not include detailed mathematical derivations. It does provide Kalman filter algorithms (on floppy and in text), but if one needs the theory of Kalman filtering behind these, one needs to use a Kalman filtering text, such as Kalman Filtering Theory & Practice Using MATLAB (Second Edition), Wiley 2001, by Grewal and Andrews. The latter book gives all of the methods in square root filtering algorithms and derivations and more. If the "Asian Reviewer" is most interested in Kalman filtering, he/she would be better advised to buy a book on Kalman filtering.




4. One of the students in my lecture came up to me and said he had studied this book all summer to learn about GPS. He found it to be very helpful to him and he said it was the best source of information on GPS he had come across.

4 comments about Introduction to Dynamic Systems: Theory, Models, and Applications.

1. I studied this book for two semesters as a doctoral student, and consider it the best mathematical textbook ever written. Luenberger writes concisely and with great clarity and elegance. His notation is crisp and easy to follow. The book begins with basic concepts of matrix algebra and dynamic equations, and then builds step-by-step to encompass an enormously broad set of applications. The examples are drawn from all over the map, and are great fun to explore. This is a truly mind-expanding text.

   Thomas P. Lyon, Associate Professor, Business Economics and Public Policy, Indiana University




2. The economic application examples are interesting for engineering students.



3. This introduction to dynamic systems is presented with an algebraic formalism which makes things clear and concise. All concepts are explained intuitively as well as formally, having in mind the objective of making things clear. Few books exhibit such a good approach and other reviewers are right when they emphasize the highly pedagogical quality of Luenberger's books ! This is no overstatement.

   The advantage of using this algebraic formulation lies in the simplicity as well as the understandability of the state-space approach, which is best explained in those terms. Most books assume that everyone knows what a state space is without explicitly showing what it is really about. This book just uses the reverse assumption, in that you're not asssumed to know everything before getting into it.

   Only some basic knowledge in algebra (undergraduate-level) is required but even without experience in algebraic formalism, it is possible to go through the content thanks to the important number of examples and the intuitive explanations.

   A must-read !




4. This "2001 edition" (according to Amazon) was actually copyrighted in 1979. While the book remains very useful, prospective consumers may wish to consider a good used copy in order to save some money.
   
   Although the publication date is misleading, at least Wiley correctly prints the original copyright date within its "new" texts (there are other examples of this practice). Springer-Verlag performs still greater magic. In "Linear and Nonlinear Programming", also by Luenberger, Springer decided to print the copyright date as 2003 instead of 1984, thus giving the impression that the text contains an up-to-date treatment of the subject matter. If I were buying a copy of "Green Eggs and Ham", I would not care about an incorrect copyright date. However, where technical subjects are concerned, this practice borders on fraud (in my view). It is a deliberate attempt to deceive its customers into believing that they are purchasing something more current and relevant than is actually the case.

No comments about An Introduction to Systems Analysis Techniques (2nd Edition).

No comments about Mobilities.

5 comments about Facts and Mysteries in Elementary Particle Physics.

1. If you "understand" at least to some extent quantum theory you will enjoy this book. It is not described by math equations but Good writing and Analogies.You Must understand elementary particles to get quantum physics and mechanics to understand how they work since it's so different from our daily reality, you must visualize. I have read enough books and looked up info on the net to grasp the sense of quantum theory and it's counterparts, Read "Parallel Worlds" By Michio Kaku, He describes things so easily. 4 stars because no book is a five yet for me except the book previously Mentioned because of it's simplicity and wide variety of topics covered. Good luck opening your mind.



2. 
   This is a unique book.
   
   First of all, the paper, font, diagrams, and cover are wonderful. It's really a nice looking book cover to cover.
   
   Next, the author includes biographies of people involved in the field. The writing is candid and humorous. The biographies don't read like a textbook at all. They include his own opinions, as well as interesting anecdotes about the people.
   
   Finally, the author includes some of his own personal story in the book, regarding his work in particle physics. It's nice to see a first-hand account. I enjoy his commentary.
   
   All these things make this a special book, and worth reading.
   
   The author can be somewhat grumpy, but you have to take that with a sense of humor. Consider that physicists (I am one) tend to be literal and often TOO honest, at the risk of being blunt or awkward. So try not to be put off.
   
   Some parts of the book are a bit tedious. If you really want to understand the topic, read some other books along with this one. If there's only one book to get, try Oerter's "Theory of Almost Everything". But if you want a few books, then definitely include this one.



3. Muy bien explicado si tus conocimientos sobre física de partículas no son excelentes. Matemáticamente sencillo de comprender



4. This is a well structured book which describes developments in modern physics in an in-depth and comprehensive way.
   .
   After a preliminary discussion of basic physical issues, the author launches into a detailed, yet non mathematical, outline of the standard model of particle physics which he rightly says is a beautiful model indeed. His discussion of this is a highlight of the book and the book is worth buying for this chapter alone. He then goes on to discuss quantum mechanics as well as discussing aspects of relativity pertinent to particle physics.
   
   Understanding the basic elements of the universe did not happen overnight but rather was the fulfilment of a combined effort of a large number of people. At all stages throughout the book, the author illustrates the contribution of the various personalities involved, and does it so that the reader appreciates the erstwhile contribution each person made. The author himself made a significant contribution.
   
   Of course, not just the `who' is relevant. How they achieved the various breakthroughs is also important and the book's discussion of the history and development of modern accelerators and particle colliders is of particular interest.
   
   Finally the discussion of the theory of particles and of interactions within particles concludes what is an enjoyable and interesting book on topics that are justifiably regarded as complicated, yet are dealt with in the book in an easy and very readable way .
   
   This book is recommended for all who wish to appreciate current ideas about the basic elementary particles of nature and would like to have an understanding of these incredible `building blocks' of our wonderful universe..



5. Martinus Veltman has a rare gift - to have indepth knowledge of a complex subject, and be able to give the layman a plausible explanation of it. I have almost completed my second reading of the book. Such was the wealth of information, and my eagerness to read, that I could not take it all in on the first reading. The reader must persist with some of Dr Veltman's language quirks , but this is a minor criticism - the effort is handsomely repaid. His character profiles (occasionally caricatures), and personal stories, add a human dimension, and serve to point out that it takes many brilliant and hard working people, not just theorists and not just Nobel Prize winners, to create an edifice as grand (and yet fragile) as the Standard Model. Highly commended - a beautiful legacy for future generations.

5 comments about An Introduction to General Systems Thinking (Wiley Series on Systems Engineering & Analysis).

1. In computing, a timeless classic is anything that is worth reading for any reason other than to obtain a historical context after five years. If that still holds true after twenty five years, then it is truly an extraordinary piece of work. That label applies to this book. It is not about computing per se, but about how humans think about things and how "facts" are relative to time, our personal experience and environmental context.
   Human thinking is a complex operation and that is the point of this book. The problems and examples presented are not those in computing, but problems in how we think about the world and how that world can be different from person to person. In many ways, Weinberg anticipates the development of the science of chaos, where small changes lead to disproportionate large changes. His example of the "small" change of a single character is a classic. A man was considering the purchase of a piece of real estate, but when told the cost was fourteen million dollars, sent the response by telegram, "No, price too high." However, somehow a character was dropped, so the agent received the message, "No price too high", purchased the property and so a classic error was invented.
   Weinberg uses science and mathematics as the genesis point for most of his examples. The laws of thermodynamics, chance and simulations in state spaces are used to demonstrate the points. As someone with a wide background in science, I found his examples of how scientific thought gives us an anchor but yet alters over time excellent learning material. Thought problems are included at the end of each chapter and they cover many different areas. Some involve mathematics, others science and many could be the point of a vigorous philosophical debate. Together they form the best collection of thought experiments and points of contention that I have ever seen gathered together in one location.
   This is a book that is a true classic, not in computing but in the broad area of scholarship. It is partly about the philosophy and mechanisms of science; partly about designing things so they work but mostly it is about how humans view the world and create things that match that view. This book will still be worth reading for a long time to come and it is on my list of top ten computing books of the year.



2. Weinberg distills the essence from von Bertalanffy's classic and manages to present it in a very accessible fashion. The book has been out of print for quite a while so it is great to see a new edition. The message and information contained in here, although originally published in 1975, is now more relevant than ever.

   Weinberg covers many aspects of systems theory beginning with the main stumbling block with the present scientific paradigm: the idea that the universe is mechanistic. His treatment is much more general than Robert Rosen's in "Life Itself" but still conveys why the mechanistic notion is flawed.

   He then outlines the general systems theory approach before leading into the idea that a system is simply a way of looking at the world. He then outlines the principle of indifference. This leads straight into two sections outlining various aspects of making observations. Finally he discusses behaviour and then some general systems questions.

   Throughout the book he uses many examples from disparate fields in conjunction with questions for further research. It is great to see someone who doesn't preach systems but actually uses the ideas.

   Definitely a must-read as we decided how to solve the myriad of issues before us.




3. I was searching for an alternative to the out-of-print book 'Quality Software Management, vol.1: Systems Thinking', written by the same Author.
   Having read few Sofware Management series books from the same Author, which I rate at the very top of my list, I was biased on very high expectations. Surprisingly, I have found it being quite verbose and in the end, I couldn't get too much inspiration out of it.



4. This book is excellent. I first read this book in graduate school in 1976, and I continue to find Weinberg's ideas useful. It was outstanding then, and it has held up with time.



5. Weinberg's book will not teach you how to be a systems thinker. It will, however, provide a stimulating discussion and thoughtful examination of an alternative approach to problem analysis and solution. The book is not so much about how the systems approach works or how it can be applied to complex problems as it is an invitation to his readers to explore their perceptions of what they think they know versus what they really do know. Throughout the book, Weinberg follows the strategy of leading the reader through a series of logical discussions designed to bring them face to face with their biases and misconceptions about systems vs. reductionist thinking. In doing so, Weinberg exposes the shortcomings of the reductionist approach to problem solving by demonstrating to his readers that the real solutions to some familiar and apparently simple problems are very complex.
   
   Through his examples, Weinberg shows that by viewing a system holistically within its environment, we may be able to discern patterns of behavior/actions and recognize interactions, interrelationships, and interdependencies among the components that will be missed in a reductionist approach. From that view, we can better understand the system and, perhaps, better predict how it will evolve over time. The success of his approach is demonstrated by the fact that people are still reading and quoting his book 25+ years after it was written.
   
   One facet of this book which I found beneficial may be a drawback for some readers. Weinberg wrote from the viewpoint of a computer programmer and a scientist. A person not versed in either field might have difficulty understanding his examples.

5 comments about Turbulent Mirror: An Illustrated Guide to Chaos Theory and the Science of Wholeness.

1. While this book does make some interesting points about chaos, I found that the book's blatant disregard for accepted science very hard to stomach. I currently attend Harvey Mudd College, a small, but highly regarded science and engineering school, so I like to think that I know something about the subject.

   For example, at one point the authors are describing solitons (a term I had never heard before), states a theory that by generating an extra bit of energy we could put the universe out of the unstable equilibrium it currently exists in and cause it to "begin to boil." While this is all well and good, it makes vast assumptions that the authors neglect to mention. Most importantly it assumes that the universe is in an unstable equilibrium, a fact which although highly unlikely is not impossible. Secondly it assumes that the universe is completely clean of these bits of extra energy currently. They draw this parallel to an example of superheating water because without external particles to build upon no bubbles can form to release the steam. This is also true, but it is still impossible because it is impossible to have a perfect system like this. There are always going to be minute cracks in the pot, or imperfections in the water (fractal theory, covered earlier in the book, even states this!), and so while this might be theoretically possible it will not happen in any real world environment. The book has many other places like this where the authors conveniently leave out details that might weaken their arguments. I find this to make the book as a whole very frustrating to read, even if some of their points are valid.

   Another reason that I find the book to be very frustrating is that everything is very sensationalized. At the beginning of the description of fractals the authors say that the first person to think of a fractal curve created "a panic among mathematicians that took some fifty years to resolve." I find it truly hard to believe that the entire mathematical community was pulling their collective hair for fifty years trying to explain this curve, but by phrasing it this way the authors make it seem like science as a whole does not want to accept new ideas because it would make them look bad. In reality though I think the scientific community is ready to accept anything that can be strongly proven theoretically, or experimentally (just look at relativity, or quantum).

   Because of all of these failings I would not recommend this book. I am sure that there are many other better books about chaos theory that do an excellent job of describing it without disregarding the rest of science, or trying to place it in places where it does not necessarily belong.




2. I've finished this book's Chinese version today. In the last year, I'm trying my best effort to absorb knowledge of Chaos Theory, Complexity, and Catastrophe Theory. It's quite hard to get a in-depth guild of the above knowledge to common people in Hong Kong.

   My purpose to get the above knowledge is just in order to find the hidden order of financial market, and, of course, to make profit from the market. That's why I find this book is good to serve my purpose. It explained clearly on fractals, the relationship between chaos and order, and non-linearness.

   I knew E. Peters has using fratals / Elloit Wave Theory to analyze financial market. Of course, it needs more intra-day data to try to find such fratals in a small scale period, e.g. in a 5-minute charts. But I guess that, such fractal are existing in the market, if you watching index movement everyday.

   On another aspest, the technique of plotting data in a phase space is a tool to get the picture of financial market to me. This tools can be compared with weighted moving average, MACD, or other technical indicators. Though, phase space analysis is quite uneasy to a man without advanced mathematics. I'm quite sure such mathematical technique may apply to financial trading.

   Besides, the idea of "quasi-periodic" is likely describing financial market. Though I got less knowledge from the book on this topic. It sounds like some ideas from William Gann, and other cyclist writings.

   Hince, I'm benefitted from the book to enlighten new view point to see the world, and the market. I recommend any financial market practitioner to read this Chaos Theory guild and then reread some technical analysis classics, and reviewing their trading strategies. I believe that shall be worthy in one's trading life.

   N.B. The picture 2.7 is missing (P.76), and there is some printing errors in its Chinese version which printed in 20.6.1997




3. A wonderful synthesis of science at the edge. A grasp of how scientific methodology is changing to accommodate the revelations of chaos theory. The used edition I read was from 1990 and is prescient even now (alas). The informed and illuminating evidence that revolutionizes the current Neo-Darwinistic paradigm of molecular evolutionary theory towards the end of the book was particularly refreshing. John Briggs and F. Peat's thinking is so strikingly lucid, informed, and visionary that this book will fail to make almost any lecture list where it is most needed for years to come.



4. Here is an easy to read exposition of the theory of order out of chaos and how the natural world arises from basic natural processes repeated over and over again. The relevance of fractals to this study is given as well as a description of psychic processes. A must read for anyone interested in the new science. All the more complex theories of interest to the magical endeavor are based on the ideas presented in this book. It's an excellent companion to James Gleick's "Chaos."



5. This book is hard to find, contains a bit too much math (you can kind-of skip it if it overwelms you), it is out of print, hard to find and exagerates a bit some times but, if you read through it your perspective on life, causality and human behaviour will change and you will have a better understanding of un-undestandable things.