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Disclaimer: I'm not a philosopher, nor am I a physicist, so I can't really appreciate merits of harsh reviews from these perspectives on American Amazon. But I consider myself being a spet in enterprise software and a systems engineer, so what follows would be probably relevant only to people of my his book Paul Cilliers explores different aspects of complex systems, in particular self-organization and capacity to reflect the external world. What makes it interesting from my perspective is that the author postulates that a neural network is superior model for a complex system when compared with predicate-based models. This may sound odd, but the use of this rather surprising approach is well-justified and enables the author to create interesting conclusions from the wealth of theoretical and practical results available in the field of neural e property of self-organization is said to be enabled by the system being on the verge between active (self-inducing) and passive (balancing) stances, and the real self-organization is only possible when there's a proper balance between cooperation and tournament among elements, which is exactly the case for huge scale neural networks, such as, arguably, our neural ever, the most interesting part of the book is the discussion about representation of the external globe within a complex system. It is deemed to be a critical property of any complex system. An begin system without representation capacity is unable to anticipate and react to upcoming changes that would eventually tear it ul Cilliers argues that a predicate-based approach is inferior for representing complex reality. Instead, the author looks at the issue from the perspective of post-modernist (in particular, post-structuralist) philosophical school. In particular, he drew a lot from the understanding of a natural language that is pioneered by Jacques Derrida, who opposed the school of structural linguistics, originating from works of Ferdinand de Saussure. Derrida denied the existence of an independent and abstract semantics layer within a natural language, and according to him a meaning of a word could only be defined by (deferred to) the sum of relationship with other words in a language. It is demonstrated that Derrida's model of the method natural language represents the reality is ogous to the method a neural network solves a problem. The author draws an ogy between predicate-based modelling approach and structural linguistics and then concludes that post-structuralist approach based on the distributed representation is superior to the former for the purpose of representing complex and ambiguous is is quite interesting perspective with a lot of practical implications. For example, a prominent corollary for post-structuralist view is that it is impossible to construct a single consistent description of the reality (meta or grand narrative). That gives a fresh rather refreshing perspective on systems integration approaches based on a grand narrative of unified semantics such as ISO 15926, which may actually fail to deliver not because of some technical problems that eventually will be resolved, but because of violating the laws of the representation of rprisingly, despite addressing sophisticated matters the prose of the book is simple to follow, well though-out, and rather convincing, but to be honest, I've skipped most of the latest chapter that covers different aspects of the philosophy of science, as I'm lacking background that is important to create sense out of summary, being deeply anchored in IT, the book is definitely well worth reading for any IT spet, especially those who deal with applications integration and architecture.(This review was originally posted on Enterprise Systems Engineering blog -- see profile for URL)
Though it helps to have a better understanding of the modern philosophy of science, it is not important in order understand the idea the book presents to the reader. It is very well thought out, cogent and I would recommend it to anyone who is interested in cross-disciplined approaches to understanding concepts. I appreciate the process oriented nature of his thesis and how the classic Newtonian physics are inadequate to defining our rapidly changing universe. Be prepared to think, but prepare yourself for a amazing adventure.
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 let 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 latest 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 necessary 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 personal 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.
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 lliers 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 real 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 create one's skin crawl:On p. 6, as an example of a non-linear relationship: "money can keep compounded interest". In fact, this is a classic *linear* relationship (so common it's often used as an introductory issue 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 cash in an account, and n is the interest rate. The solution is Mo * e^(nt), where Mo is the initial amount of cash in the acc and 'e' represents 'exponential'. (Simply because compounded interest generates an exponential curve over time does not create the relationship non-linear; the underlying equation is linear.)On p. 4: "Any ysis 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' ysis is to *completely* represent a system without considering time. The elliptical relationship between velocity and momentum in a easy harmonic oscillator is a common example that a lot of 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 contains 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 a lot of of the traps Alan Sokal identified in Fashionable Nonsense.)I think that the primary concept behind the book could have been interesting, but due to Cilliers elementary-level grasp of half the topic matter with which he deals, the statement Cilliers himself makes on p. 133 (in reference to a latest 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."
As a professional engineer with a powerful interest in postmodern philosophy I identify closely with the author and I am very amazed at how he could relate the extremely abstract concepts of post-structuralism with the more concrete example of neural networks. His unmasking of the metaphysics of representation that underlies current research in artificial intelligence was a amazing insight for me. At only 142 pages, this book seemed very inviting and thus I bought it. But don't be misled, what this book lacks in length is more than created up by its density. For me, who prior to this had only read introductory books on postmodernism and had only vague notions about connectionism and neural networks, it turned out to be extremely challenging and demanding to read, and completing it gave me a sense of achievement related to being done with a hard project. I think some parts were unnecessarily abstract, which, knowing the author's talent for making ogies and examples, felt like a disappointment. Other parts, such as his comments on postmodern ethics, simply begged for further elaboration or at least to references on the works of others in this field. I think I will return to this book once I read more on Derrida and Lyotard for a better understanding. I really hope that by then the author will have come out with a sequel to this very interesting and groundbreaking line of work.
Cilliers has undertaken an necessary job - exploring the linkage between complexity thinking and postmodernism. He has created perfect use of some main writers on postmodernism and shown some necessary relation to studies of representation and self-organizing systems. He works hard to support us escape the locked-in positions of positivistic and foundationalist science, but his major conceptual base in connectionism displays an unabashed modernist view. While connectionism is an necessary tool in exploring the ideas about how the mind/brain works, it ignores other necessary ideas arising from the work of Maturana/Varela and Niklas Luhmann on auto-poiesis and John Holland on complex adaptive systems. More significantly, Cilliers is locked into the ideas of networks. It is a valuable tool for the technological advances, but for a full philosophical exploration he undertakes, we needs also to look at field thinking, particularly that arising in quantum fields discussion such as in Sunny Auyang' work.What I search most difficult in Cillier's retention of the modernist view of competition. Our cultures may be agonistic but is tournament fundamental to the development of human life?
The book combines elements of various 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.
I also realized that topic matter experts are useful to a degree in understanding systems but the outsider can see a issue very differently and that is valuable as well. The book does a amazing job of highlighting where systems thinking is also practical in improving broader things like family, squads and organizations along with businesses. It is definitely worth the read for anybody as it applies to daily interactions.
What an insightful book! Since reading this book I can honestly say I've been thinking in systems for all aspects of my work and life. I think this is such an necessary read not just for you education or professional development, but also private growth. Especially for designers who wish to "design think" and aspire to be the next Cooper and Tim Browns.
This book is a pager turner. It will hold your turning pages. If you are interested in how the business, political, economic globe is functioning, this is the book. I found myself up late at night reading, and reading. To me this book is amongst the amazing books that is 'needed' to be read by all who would like to examine how to develop a leading process with future success involved.
This book is a gives an accommodating prologue to the structure and standards of frameworks considering. I would exceedingly suggest is book is a pager turner. It will hold your turning pages. On the off possibility that you are keen on how the business, political, financial globe is working, this is the book:)
A must read for anyone who is interested in changing the globe or succeeding in anything over the long term. The first chapters, being primary introductions to systems thinking, seemed technical and unrelated to the challenges I face in my work and life. But in the second half, as she starts bringing these initial concepts alive with examples of how they relate to and impact larger problems that affect us all, the beauty and elegance of her thinking shines through. When I finished the book I understood why the introduction was necessary. I see the globe differently now.
I am not an engineer or politician and had never given systems theory much thought until I stumbled across this ere's a amazing paradox in this work. On the one hand we are systems within systems and while understanding that or viewing the globe that method doesn't give us the predictability that so a lot of people want they had. Is it any wonder as our systems become more and more complex we are (1) at a loss to control the globe and (2) are susceptible to those who act as if they know what the issues are and how to fix them. Recommended read!
This book lays the groundwork of systems thinking. As an engineer, I felt this book did a very amazing job in tying a lot of concepts together that is practical, usable in daily life. The book does a amazing job of highlighting where systems thinking is also practical in improving broader things like family, squads and organizations along with businesses. It is definitely worth the read for anybody as it applies to daily interactions.
One of the best books I've read in a very long time. Seems to me our society could really use a amazing dose of whole systems thinking. We've gotten so amazing at breaking things into parts that we've forgotten how connected reality is. This book helped me reconnect the pieces and start to experience the globe from a whole systems perspective, and that makes all the difference. Because changing the method we see things also changes the method we respond, whole systems thinking encourages us to figure out why things aren't working so we can improve our own society, instead of focusing on deciding who to blame.
This book is a unbelievable read. I purchased it randomly but I dont regret it. It really gets your mind thinking about how systems work in the globe around us, and how so a lot of things truly are linkedAfter finishing the book, I felt I had a beautiful amazing grasp on how to approach models and systems. The method to pose and ponder questions when looking at seemingly easy systems came easily after reading the book. I also realized that topic matter experts are useful to a degree in understanding systems but the outsider can see a issue very differently and that is valuable as well.
I search that these principles work in the business globe as well. I have my own business and found that these principles work quite well in it. I think the globe would be a better put if these ideas were implemented in a lot of various venues. I highly recommend reading and absorbing these ideas and systems regardless of your field of endeavor.
This book's author is full of himself and uses over complicated language. It is difficult reading. For example "The advent of individuality brings into existance Darwinian selection, as a specialized subset of Markovian stochastic processes." Really! NO THANKS.
I have several interests that seem tied together -- complexity, emergent properties (the whole being greater than the sum of the parts, as with the human brain and ant colonies) and the arrow of time, which I think is an emergent property of matter when sufficiently organized. This is not all simple reading, as it is edited lectures and such; but it is informative. No one yet has a complete handle on how nature creates greater complexity; but it's worth reading about current thinking on the matter.
Nice summary of current state-of-the-art of multi-disciplinary complexity science and entropy, but unfortunately I didn't search anything fresh to add further insight beyond what has already been written over the past 20 years on the subject. I found the numerous chapters delving into serious mathematics of entropy and complexity to be somewhat contrived and very abstract at best. The book reads like Stuart Kauffman plus physicists describe complexity with mathematics. Kauffman tends to be rather massive with mathematical concepts (for a biologist), and the physicists only up the ante. I found Melanie Mitchell's "Complexity: A Guided Tour" a more enjoyable contemporary treatment of the same topic matter with more fresh modern insight and less abstract math.
This is a series of essay of the subject of complexity. I am no expert but to me this is an interesting philosophical topic. I found the essays variously penetrating, or impenetrable. I strongly recommend Stuart Kauffman's thoughts on re-enchanting the world. The study of complexity is a vary incomplete science, maybe so incomplete it ought not be called science. Non-the-less it dances on the edge fresh and magical understanding of what our globe is.
I enjoyed some of the authors of what really is a compilation of articles about complexity and ultimately its relationship to life. I would have been more interested in reading about complexity and time. Unfortunately, that wasn't really the point of the manuscript. I didn't finish this book - and probably won't
FUCHS, Amin, Nonlinear Dynamics in Complex Systems: Theory and Applications for the Life-, Neuro- and Natural Sciences, ISBN 978-3-642-33552-5, 236 pages, Springer, 2013 Reviewed by D. Subbaram Naidu, Idaho State University (formerly Book Review Editor: IEEE Transactions on Automatic Control; Wiley International Journals of Robust and Nonlinear Control and Optimal Control: Applications and Methods and Elsevier International Journal Mechatronics: The Science of Smart Machines).This book presents a mathematical treatment of different dynamical systems arising in the so-called “soft” life, neuro and natural sciences. The book consists of three parts: Part I covering one-, two- and higher-dimensional systems including chaos and discrete maps and stochastic systems. Part II focusses mainly on Haken-Kelso-Bunz (HKB) Model for human movement behavior and Part III providing mathematical background important for the book including some numerical e style of writing is makes the reading interesting. At the same time the author maintains the important mathematical rigor. A couple of very appealing features of this book are numerical procedures and computer simulations using the standard software MATLAB and solutions provided at the end of the book to the issues at the end of each chapter. These features create this book an perfect choice for both self-reading and for teaching a course and is a welcome addition to the literature [1-3].References F.L. Chernoousko and L. Felix and I.M. Ananievski and S.A. Reshmin, Control of Nonlinear Dynamical Systems: Methods and Applications, Springer-Verlag, Berlin, Germany, 2008. eng and X. Hu and T. Shen, ysis and Design of Nonlinear Control Systems, Science Press and Springer-Verlag, Beijing, China and Berlin, Germany, 2010. K.M. Hangos and J. Boker and G. Szederikenyi, ysis and Control of Nonlinear Process Systems,Springer-Verlag, London, UK, 2004.
An perfect book covering non linear dynamics. It is well written and more importantly reads well, conveying the points in a brief yet illustrative fashion. It is also quite concise-moreso than Strogatz book, but appears just as comprehesive-apart from being slanted more to those with trajectories toward careers in biomedical research. There is a welcome chapter towards the end on the essential math methods required, which again would be very useful to those without a mathematically intensive background. There are a few minor first-edition bugs in the labeling of some diagram, but this is unlikely to be noticed by all but the most fastidious of ere is an entire chapter dedicated to the HKB model, which is not entirely surprising seeing that Dr K wrote the forward.If you are interested in NLD, and are torn between which of the two books to get, Fuchs or Strogatz, do what I did-get them both. The Strogatz has more applications and examples outside of the biomedical field and is quite inexpensive. Either I believe will serve you well.
The primary idea of Kauffman's book is that the complexity we see in nature (including life or technology) is contingent to math, i.e. can be explained and predicted by mathematical reasoning. The same is real of statistical thermodynamics and evolution. He states that Darwin's evolutionary theory explains only how complex life emerged from easy life, but it does not explain how easy life emerged from matter. There is probably a larger jump in complexity from matter to the first easy cell, than from that easy cell to a modern human being. Darwin does not explain that first jump. Kauffman doesn't either even though he is convincing in showing that life must have started through autocatalytic sets of molecules. He points out that these sets are self-organizing, stable and can vary as a reflex to external stimuli. What he mentions, but does not explain, is that autocatalytic sets can (or must) self-reproduce, a important step before evolution sets in. On page 66 of the paperback edition he states that "such breaking in two happens spontaneously as such [auto-catalytic] sets increase in volume", but, maddeningly, he does not explain how or why. One has to wonder: if life is such a important effect of matter (therefore the title "at home in the universe") why then has it proven so difficult to synthesize anything approaching life in the laboratory? He doesn't e book is full of incredibly interesting ideas. He explains ontogeny (the transformation of a fertilized egg to a highly complex and differentiated organism) using a easy model of on/off enzymes which allows him to build a Boolean network in which various cell types correspond to various "attractors", which are intrinsic in such a network. He shows that the same relationship that holds between number of attractors and size of a network, also holds between number of cell types and size of DNA of a wide range of organisms. Very impressive. He goes on to discuss things like fitness landscapes and genetic algorithms, the edge between boring order and supracritical instability where the really interesting items happens, the co-evolution of coupled systems, the structure of efficient companies or countries, and e only criticism I have is about his poetical language that does indeed resemble fluff; anyone who even partly understands his ideas would be excited enough without all that sauce. Also I missed a deeper development, the book does point into one interesting direction and then jumps into another matter, leaving one hungering for more. But maybe this is the author's is is an perfect book even though it resembles more a symphony of ideas than a theorem. Very highly recommended: a mind opener.
Whereas Darwinian Evolution theorizes that all life evolved from single-cell organisms via natural selection applied to variation, Kauffman focuses attention upon the source of variation, and his Theory of Emergence poses a plausible respond to evolution's stickiest question: how did cells arise. His "autocatalytic sets" spontaneously emerge "fully-grown" - and you'll understand why upon reading this book. He also suggests a principled reason for why life may depend upon - even flourish due to - chaos, without resorting to the wide-eyed speculation found in other chaos-theories on organic evolution & development.
In the still not quite emerged zone of emergence aka complexity/systems, some rare books look even better a lot of years after their original publication. At Home in the Universe almost ranks with Gregory Bateson's Steps to an Ecology of Mind: Collected Essays in Anthropology, Psychiatry, Evolution, and Epistemology and Mind and Nature: A Important Unity (Advances in Systems Theory, Complexity, and the Human Sciences) in that regard. This is in part because the zone has such a fragmented vocabulary, reflecting a range of approaches which Kauffman is largely across due to his leadership at Santa Fe Institute in its highly productive first decade when its find for interdisciplinary insight outranked the find for marketable applications. In a find I did for a 2007 paper, Kauffman was second only to Nobel laureate Ilya Prigogine for citations from the hard science end of the complex systems ere can be challenges in reading books like this and Bateson's in that significant parts have still not gained wider awareness. They can sound unfamiliar, lack substantive follow up and, I imagine, for those who have not long been on the emergence/complex/systems track, they may seem counter-intuitive. They predate catch phrases like "unknown unknowns" or "black swans" which might create their central relevance to today's challenges more obvious.Kauffman's unifying strategy is exploring how the least accessible parts of the panarchy of natural systems improve their position on fitness landscapes, a mathematical representation reliant on ready mental movement from higher dimensional state locations to the hills and valleys of familiar physical topography. To that end, he has applied simplified models to find for general principles. His results help a powerful case that huge gains and radical change come early, after which it becomes an ever more difficult struggle to gainfully inch further from an established viable position. This principle enables him to propose convincing scenarios for the origin of life, the Cambrian explosion and contemporary technological change; arguably the three most significant things we could seek to understand since the origin of the is book reflects the peak of Santa Fe's excitement over the edge of chaos--border of order, an idea for which I had come to feel the lone defender early this decade. It is a bemusing sidelight to finally reading this book in 2009, that my own as yet unpublished current research has finally created clear something that had been hinted at but never expressed outright since Wolfram's 1983 notion of Class 4 cellular automata: that we have set up a false dichotomy between chaos and order which we need to leave behind if we are ever to understand that essential complexity arises best in cirtances where there is creative synergy between even deterministic chaos and emergent only true quibble with Kauffman is that he came to this work with a want to justify his feeling that we should be "at home in the universe" rather than totally defined by historical contingencies, aka accidents. At least he is up front about wanting that finding and, by extension, an endorsement of capitalist/American triumphalism. It would have been preferable if he had come with an begin mind to whatever he might find. I suspect reality might prove a bit more contingent than he would be comfortable with, but that his broad principles of fitness landscape navigation might also prove ever more useful as they are better understood.
This necessary work leads the exodus from one-dimensional Darwinian selectionism in a fashion that does not sucb to transcendental explanation or abdication from naturalism, whatever the word means. The views of George Wald, Hoyle and Wickramasinghe and others have waited a long time for acknowledgement and some true attempts at scientific hypothesis formation. It is also real the street to true answers is a long one, and Kauffman's work does as much to reveal the difficulties as provide the final answers. Every step of the method is likely to keep scorn from those who would bypass the collosal difficulties with the attitude, 'I give up God did it'. This work is liberating and a snapshot of true scientific enquiry in action.
This book takes a hard look at how life on earth came to be. Rather than buy into the idea that somehow life evolved via the "blind watchmaker" scenario (i.e., related to the argument that an troops of monkeys sitting at typewriters would eventually compose a amazing novel), Stuart Kauffman builds a terrific case that the ingredients essential to life are bound to the rules that govern complex adaptive systems. And the very presence of these rules send a powerful signal that "we the living", are "we the intended."The author's conviction to both his argument and the science of complex systems is evident throughout the book. If you are coming to this book without much background in complex adaptive systems, you will not be short-changed here. In fact, Kauffman provides extremely rich examples with numerous easy diagrams to educate the reader as he builds his case. Considering the book was published some 7 years ago, I was surprised to see the concept of gene networks given so much attention in the text. Seeing how the recent trend in genomics research is looking at genes and proteins as a regulatory network and attempting to identify specific disease pathways, the science in this book is extremely relevant.
This outstanding book provides the basis for understanding the extremely complex systems on our some sense, I found it depressing because it provides scientific explanations for a lot of locations that I had attributed to another I found it extremely empowering because it helps to explain a lot of problems that we see in life. For example, there is one chapter that discusses the size of organizations. It provides an explanation for why we see companies divide into business troops only to recentralize two years later in an endless cycle.I echo all the other 5 star reviews on this page.
Stuart Kauffman is a brilliant renaissance man; a man who was a playwright, philosopher, physician, and ultimately seminal theorist of the principles of complexity and emergence at the Santa Fe Institute. His ideas are widely influential, groundbreaking, and bear upon the biggest questions: what is the origin of life? In the face of the 2nd law of thermodynamics, what explains the richness of order we observe all around us? What are the underlying mathematical principles of emergent phenomena? What are the underlying mathematical principles of evolution (both natural and technological)? Indeed, until Kauffman, I had problem defining evolution without a tautology at all (i.e. "evolution is survival of the fittest - and 'the fittest' are defined as the ones who survive"). Kauffman gives you a lexicon for understanding evolution, including why there is more radical divergence of form in early evolution and less later on. The principles of emergence bear directly on such diverse subjects as ontogeny, economics, the formation of galaxies, coevolution of similar species, etc... "At Home in the Universe" is Kauffman's synthesis - his attempt to tie up his decades of work in an explanatory method for the layman. The book is full of intricate diagrams illustrating the concepts discussed and, while sometimes challenging, is readable by any moderately educated person. These ideas have the power to change people's globe views - and certainly have transformed my own. Stuart Kauffman's ideas have resonated deeply for me and have inspired me in a multitude of , what's the problem? It's the writing. Kauffman can't seem to decide if he is writing a book of philosophy or a book of science. He spends an inordinate amount of zone discussing the philosophical implications of his ideas, often before he has even presented the ideas - allow alone the experimental or theoretical support. As a book of exposition of science, "At Home in the Universe" is almost inexcusably poor. He presents a complex idea accompanied by a complex diagram which he explains. Often, however, he fails to explain the nature of the experiment or research that generated the diagram. He doesn't describe experimental or theoretical help for these ideas. The paucity of descriptions of the science behind these strong ideas is doubly galling in the presence of repetitive presentation of inappropriate philosophical ysis. A lot of times in the course of this book I had to throw up my hands in frustration, wishing for exposition of the experiments hinted at in the diagrams - and being given long range cultural and religious context in its stead. For God's sake, allow me place the context together for myself! But please give me the conclusion, this book ultimately teases. If you have any interest in emergence or complexity theory you will need to read this - the ideas are that profound. However, having read it, you will have to look elsewhere for empirical or theoretical help for the strong ideas presented here.
Kaufman's explanation of the deep structure revealed in complex systems is far-reaching. The material emergin from the Santa Fe Institute consistently seems so expansive and innovative that I've come to expect revolutionary ideas. This book is no disappointment. I read this book 20 years ago and re-read it recently. For those who remain current in their reading on complexity theory and self organizing systems this book may seem dated. However, it was pioneering when first published and still remains broad in its scope and in its depth of understanding. If you read nothing else in this book, read the final chapter,"An Emerging Global Civilization". Latest year I read "The Better Angels of Our Nature" and when I see emergent phenomena like the Arab Spring I am hopeful that the self-organizing system of civilization will reveal deep order.
I will be brief with my review. Brief because this book forced me to think and introduces fresh paradigms in my routine. The key learning here is that as Engineers, we should always trade locations with our customers and that we should adopt user-centric approaches to systems e long narratives in the book could place the shortened attention reader to sleep and intimidate the weak at heart. But, there's a lot of wisdom hidden even in those sections. A second reading should reveal them.His discussion of "Holon" and Multi-minded systems as the foundation for socio-cultural systems was a visionary one, and exposed one reality: That platforms such as Fb and Twitter were possible even as far back as the 1960's if Engineers had changed their perspectives and embraced multi-mindedness as a design is book should be read alongside "The Ghost in the Machine (Arkana) by Arthur Koestler (Jun 5, 1990)".Jovita Nsoh, CISM, CISSP, CITA-PSenior Security ArchitectMicrosoftSeattle, WA
The least this book can do is to change your thinking paradigm. It's so full of wisdom and insight that it takes a while to absorb the essence of it. There are a few books on Systems Thinking out there, but this book is special it its 30,000 feet view of the discipline. It combines system dynamics and systems design so beautifully that you can't distinguish between them. In my opinion, it's a must read for senior leaders, consultants and change agents all across the world.
Very interesting. I start at the end: Is helpful to have the recreation of practical cases. The modular design thinking system Is complicated to understand. I pretend use this book as a tutorial for practical problems. We see how useful Is then
Amazing engineering book for the bookshelf. I have thirty years experience in Power Plant Engineering and found some subjects in this book that are difficult to search a amazing reference such as pipe freezing. The author has presented this in an simple to use format. I was also surprised with some amazing rules of thumb for plumbing. Glad I created the purchase.
I'm relatively fresh to these concepts, and this book is challenging and exciting to me. For someone who hasn't spent much time considering these concepts, it will seem beautiful revolutionary. I can't really compare it to any other literature on the topic, but I felt that it was a dense, high-level introduction to the topic, and I will likely read through it again and again (because I'm sure I'll explore more each time). This book is well written with easy-to-digest vocabulary, but the concepts are complex and should really create you think. Love it.
Everyone involved in some kind of management should have a look a it. Systems methodology is one of a kind, appropriate to the complexity which has always existed around us but, is now, evolving faster than ever.
I wanted to like this book. Unfortunately, it has a very huge amount of errors. I started jotting them down around chapter 5. After finishing the book I have 8 (4.5"x7") pages of these error notes. I did not go out of my method to search these; they were just the most blatant as I read through. I am sure you would search more if you did a rigorous review. It feels like the editor was asleep at the wheel. Here are some highlights (lowlights?):1.) Book jacket says it covers steam systems. The chapter on steam was removed after the 2nd edition. The editor/publisher doesn't even know or care what is inside this book.2.) Numerous typos and formatting problems.3.) Troops are often missing or wrong4.) A lot of in text references to tables and figures are wrong or the figure is missing.5.) Book calls 90 degree bends ¾ bends (p. 2.26). I really hope this was a not good editing job and not what the author really thinks.6.) Many, a lot of equations are wrong. Imagine a computer program that randomly assigns mathematical operators and you wouldn't be far off. My favorite was Pressure=Force + Zone (p. 15.9).7.) There is a lot of incorrect table data. For example, value of btu/gal of propane is off by a factor of 10 in Table 13.2.8.) Description of the pressure vacuum breaker (p. 9.51&2) gets 2/3 of the salient features wrong. The relevant figure shown is not even a PVB.I think people who gave this book a higher review just use it for occasional reference and haven't read it all the method through. I would be very cautious about using any info from this a side note, this book was originally $150. I bought it used for half that, and it is currently $30 new. That is telling.
Facility Piping Systems Handbook for Industrial,Commercial and Health Care Applications by FrankelMcGraw Hill Publishers 2010Reviewed by: Dr. Joseph S. MarescaThe work begins with an exhaustive list of professional institutesrelated to the subject of piping systems and processes. Examplesof the governing institutes are:o American Gas Instituteo American Petroleum Instituteo American Society of Testing / Materials and a host of othersThere are codes for piping systems, allowable materials,stresses, seismic loads, thermal heat expansion, fabrication,installations and e subject of corrosion is discussed extensively. Corrosionresistance is the ability of the pipe to resist internalcorrosive effects of fluid flow throughout , as well as externalcorrosive flows on the pipe itself .i.e. soils and the surrounding atmospheric conditionsCorrosion can be reduced or eliminated with suitable coatings,linings and cathodic protection .Cathodic protection has become a widely used way forcontrolling the corrosion deterioration of metallic structuresin contact with most forms of electrolytically conductingenvironments. Cathodic protection reduces the corrosionrate of a metallic structure by reducing its corrosion potential,bringing the metal closer to an immune neral corrosion describes pipe dissolution over the entireexposed surface where localized corrosion resides in a smallarea of the pipe. Corrosive cracking is thephysical deterioration and cracking of pipe wall due toincreased operating temperature, tensile stress on the pipeand chemicals. The author describes water impurities like turbidity(insolubles) , bacteria and viruses.Tensile stress occurs when a material undergoes a pulling orstretching force. Stress is defined as a force appliedover a cross-sectional zone in typical troops of pounds per square inch(psi) or Newtons per square e type of stress that a material is exposed to will depend on howthe force is being applied. The three primary types of stress aretensile, compressive, and e maximum tensile stress that a material can withstand beforefailure is known as tensile strength .The value of ultimate tensile strength varies widely fordifferent materials. Soft, malleable materials likemany plastics, rubber, and metals are considered elastic andwill undergo significant deformation beforea complete failure occurs. Hard and brittle materials,like concrete and glass, have small or no deformation beforecomplete failure. The ultimate tensile strength for manydifferent types of concrete, plastics, metal,wood, glass, rubber and ceramics is fairly described in manuals;such as this uble wall piping involves the installation of outer pipearound an inner pipe which prevents the release of hazardousliquids being transported in the inner pipe.Typical valve design considerations are temperature, pressure,shutoff valve operation, pressure drops, corrosive resistance,velocity, fire safety and hazardous nerally, no machine should be designed without the ability toshut it down by some means either electrical, mechanicalor otherwise. In addition, some applications areconstructed with redundant and tridundant capabilities toaddress contingency ere is an extensive section on insulation which includesfiberglass, cellular glass,expanded plastic foam,foamed plastic, calcium silicate, mineral fiber andinsulating cement.Overall, the volume enunciates a fairly complete renditionof applicable fluid principles and materials sciencestructure of matter intertwined with the relevantstandards setting authorities who promulgate standardsfor the engineering and allied professions . This acquisitionwould be excellent for the engineering professional in yourhousehold. Students of engineering fluid mechanicswill search this volume useful for exams and projects.
Perfect reference book for piping systems, pipe materials, general overview of system types, design specific criteria, etc. I cannot believe this book was only 38 dollars. A must have for a plumber, plumbing engineer, or process engineer.
The book does a very amazing job of covering all the use cases for ytics in healthcare at a high level; geared towards the healthcare professional; as a business intelligence practitioner - excited to take deeper dive with next book: Practical Predictive ytics and Decisioning Systems for Medicine: Informatics Accuracy and Cost-Effectiveness for Healthcare Administration and Delivery Including Medical Research (
A thorough review of a very broad and complex business zone - Health Care and Life Sciences represents the intersection point of rich social media type data and the human genetic code. The opportunities to use ytics to improve quality of life and reduce costs are huge.
Compilation of content from multiple ytics experts edited by Dwight McNeill, a healthcare ytics thought-leader.Have had the pleasure of working with Dwight on the Int'l. Inst. of ytics Healthcare & Life Sciences Advisory me very useful frameworks are provided including: Heatlhcare ytics Continuum - figure 6.1, Healthcare ytics "Needscape" (ytics taxonomy) - figure 6.2, an employer healthcare program ytics map (another ytics taxonomy) - figure apter 7 ytics Hack Sheet is a must read for any organization striving to move toward more advanced ytics; could be a little book in itself!
i love this book!!!! was unbelievable to read and so understood what he meant when he said the ambiguity of definitions can obtain one stuck in the mud and trying to understand this topic. Then he does show, definitions are, relevant to context of discussion. the definitions have to be tweaked evovle with the subject in general as applied to various opinion only, this book is a classic,, and just one of a lot of on this subject that is amazing to read. But I like the style of writing, insights, not to difficult to understand, but you will have to stop and think about some of his thoughts, but i like books that create you do that, feels like conversation. He does test to obtain away from pure mathematics but at same time he contains it, but you can still obtain much from reading this book even if the math is not your favorite topic, the method you process understanding ideas. I suggest, test the sample first, then buy the book if you like the sample. You feel a wisdom in the writing. that admits what is known, and not known. a lot of today write not willing to admit limitations in knowledge, and that gets annoying because amazing research always admits limitations. Again, my opinion only, and no i'm not fresh to this topic, so i did have prior knowledge on this subject before I started reading this book.
I was disinclined to like this book even though I am a product of one of Page's academic departments at o a lot of books based on formal modeling are based on assumptions that have small bearing on what actually happens in the globe I work in where, models or not, our job is to foster cooperation in conditions of diversity and ch too my surprise, I really like his use of formal modeling techniques and his ability to bring them down (near) the level of mere mortals who lack his economic and mathematical sophistication. I agreed with his conclusion at least in part because I wanted to.But seriously, the logic in his arguments includes germs of ideas we who work in the applied field should pay attention and even more than critics of cooperative issue solving ignore at their empirical and normative peril.
If you are looking for research to introduce you to the role of diversity in complex adaptive systems, then you must begin with Page's book. It explains the layered foundations of "how diversity happens, how it is maintained, and how it affects complex systems." This book examines the multi-level aspects of diversity (i.e., types, community compositions and interaction structures).
I like the book. In this work Page provides insight not necessarily finitions are easy, insight isn't. Definitions I can obtain anywhere or create them up myself. The invitation Page provides is simple: here's a couple of ways to think about the phenomenon under consideration. And he does so with a comfortable degree of depth and rigor. I have to admit, I didn't spend a lot of time thinking about the role of diversity in complex systems. So maybe now it isn't an is no Tom Clancy, but neither is the book boring. A bit of a warning though, if you do not know much about complexity, maybe it would be a amazing idea to read a primer first. Page gives an intro on complexity, but its a bit shallow. I think a novice ought to have a better appreciation for connectedness, inter-dependency, adaptability, etc. in general to provide a richer context.Let's face it, a lot of concepts in complexity science are axiomatic at best, and ill-defined/poorly understood to any degree of depth. I think it is very much worthwhile to take a characteristic of complex systems, i.e. diversity, and spend some time and effort exploring its nuances. Amazing job and a worthy addition to anyone's library on complexity.If I lost my copy, is the book worthwhile enough to buy it again? Yes.
Stein does a amazing job in explaining Spin Glasses in a simple, yet scientifically precise way. The book is written for a general audience but the references create it possible for one to dig into the technical info if needed.
I found the text unnecessarily complicated and difficult to follow. Examples were to few or not detailed enough. Could be amazing for someone who is a stronger math student, but this book is aimed at intro. level students and classes.
I don't know if it's just my smartphone or what but for some reason, this and this ebook alone loads EXTREMELY slow. Sometimes when flipping pages, the next page just won't load. This is a large issue especially when you need to refer to tables in the back of the book then flip back to the chapter that you were on. Other than that, it's a decent textbook that highlights necessary formulas and offers applicable example questions to solve.
This book really helped me learn a lot more about statistics. The only issue I have with this book is that some of the info on it was wrong and had me second guessing myself. I thought I was using the formula wrong because I wasn't coming up with the same respond in the book, I figured out that it was the book that had the wrong respond on the page. Now it wasn't a lot of problems, I can only count 3-4 of them in the whole book from various chapters. Other than that it was a amazing book I passed the class and I'm happy.
The author has the appearance of writing in a conversational style, but the book doesn't have much substance or originality behind it. The idea of diversity being valuable is nothing new. Complexity as a diverse population of connected agents doesn't tell you much. He's explaining complexity using more complex words, fluff, and metaphors. all of which is apparent in an online course he teaches, along with this and other books. One particular review says it best.What I got out of this course is given in the course description, which summarizes the totality of the content in a few easy points. What makes a system complex as defined by the science of complexity? The respond lies in the presence of four factors:• A population of diverse agents, all of which are • Connected, with behaviours and actions that are • Interdependent, and that exhibit • ere you have it. That’s the course. I agree that the course includes some insights, but I wouldn’t say it is “filled” with ics are brought to your attention such as discovering that (not “how”) your own consciousness is perhaps the ultimate example of a complex system, as billions of neurons coalesce and communicate to make the mystery of awareness. There is neither satisfying explanation nor insight n't test to impress me with your command of scientific or higher rhetoric. He also kept throwing out the names and publications of other authors, including subtitling their names and showing a copy of the books in full colour. I had the impression I was watching an infomercial each time and that Professor Page was getting paid commissions for each book he mentioned, which were far too a lot of and of no relevance given the amount of time that was given to them.
This is a very readable, not-too-difficult, but not at all easy, introduction to a difficult topic. The authors (DS/CN) aimed to pitch the book between Scientific American and the technical literature. At this they succeed, but that’s something like aiming between the top of a ping mall and low earth orbit. It would be more accurate to say that the presentation is somewhere between SciAm and an undergraduate stat mech textbook. There’s no way, for example, that you can read this book and tackle the popular review paper on the subject by Binder & Young (Rev. Mod. Phys. 1986) unless you already have a amazing number of math and physics courses under your belt. What the book can give you, though, is a road-map to a lot of of the subjects that paper will also give you an overview of some applications of the mathematical apparatus of spin glass research to other locations (Chap. 6). Here I really appreciated the authors’ taste: they avoid trendy subjects in economics or social physics, and stick to more reasonable fare: computational complexity, neural network computation and the protein folding problem. Cf. a paper on the arXiv about the spin glass physics of custody wars involving parents who either have more 2 or more kids with multiple exes, or have kids with one or more exes and are involved with a partner in a related situation — not only is the issue contrived but it’s very unlikely in true life to be entrusted to e survey of applications doesn’t end the book, though. It’s followed by a chapter on short-range spin glasses, a family of models distinguished from those that assume an infinite spin glass, such as the Sherrington-Kirkpatrick (SK) model, which are discussed earlier. This chapter is slightly more demanding mathematically, and the authors even suggest it’s skippable altogether — though I’d advise versus that, for a few reasons. First, if your math can handle the earlier chapters it can probably handle this one; second, this chapter is referred to several times in the conclusion. And third and most importantly, this material supports the conclusion’s point that it might not be justified to push ogies too far between various types of complex systems (or “quasi-complex” systems, as the authors believe spin glasses to be, since the latter aren’t adaptive). There might not be any “universality” in the behavior of complex systems — at some point, necessary behavior may be sui generis.Unfortunately, history shows that such insights will do small to discourage statistical physicists from branching into family counseling, or economists from spinning their very successful tales. For example, it’s been observed for years that the equilibrium models that underlie macroeconomics and some aspects of microeconomics as well are nonsense — a point created elegantly in this book, though without reference to economics, when the authors point out that a system that’s strongly characterized by its past is a system that’s out of equilibrium (@45). Of course even spin glass physicists use unrealistic models, like SK. But those physicists don’t create or influence any decisions that could affect your life, like whether to fire you, how much to pay you, or what should be the economic policy of the country as a whole. Even the financial crash of 2008 wasn’t enough to convince economists who hadn’t foreseen it that their models were wrong; the impact of this book on that problem is likely to be somewhat more modest.When I wrote to the authors to ask about a couple of points that had confused me, they both responded immediately, warmly, and effectively. Some of my questions grew out of my misreadings, but there are a couple of points a reader might wish to hold in mind. One is that what makes the hierarchical tree in Fig. 5.6 “ultrametric” isn’t just its structure, but also the particular metric used with it. The other is that although the different models aren’t written with an express dependence on temperature T, there is a factor of exp[-E/(k_B)T] buried in there (see sec. 1.5). The absence of explicit T-dependence confused me when Chap. 6 started talking about adjusting T as a parameter; maybe this will be more clearly expressed in a future e book contains a useful bibliography, though a small hard to scan because it’s organized in order of citation as in some science journals like Science or Nature, rather than alphabetically as in mathematics or some non-science fields. The index isn’t adequate: for example, ‘information’ is mentioned often in the text in different senses of that word, but none are indexed. The authors told me they’d done it themselves, because a professional job is expensive — since another Princeton U Press book I read recently had an even more amateurish index, I suspect that Princeton’s contract terms with its authors might be a small too greedy, shifting the indexing cost to them without regard to the impact on the book’s users. That’s a very minor point that’s not so pertinent to whether you should read this book. If you’re fresh to spin glasses, you should.
This book I bought for my daughter. She said the book was fine. The book does have a typo in it and it was a small beat up. This is why we gave it four stars. The book is much cheaper though to rent from Amazon then to buy at the school bookstore.
Used for an introductory behavioral stats class. Beautiful amazing explanation of concepts except I think the practice issues in the book are generally too simple for what has ever appeared on my exams. Amazing practice initially but I have to search other sources for difficult issues when preparing for an exam.