Book Description
The leading authority on system dynamics explains this approach to organizational problem solving, emphasizing simulation models to understand issues such as fluctuating sales, market growth and stagnation, the reliability of forecasts and the rationality of business decision-making. The CD includes modeling software from Vensim, ithink, and PowerSim.
Customer Reviews:
One of the best SD books with connection to practical work.......2007-10-06
"Business Dynamics" is a great book leading the newcomer -as myself- into the field of SD and the experienced system dynamicist can use it as a knowledge pool.
Even though the book is rather expensinve -and heavy alike- it covers great wisdom of John Sterman (he is by the way a scholar of the founder of the field, Jay W. Forrester) and is more than worthwhile buying if you are strongly interested in the field.
I was lucky to meet John and Jay this summer during a specific SD workshop at MIT and the yearly System Dynamics Society Conference and could chat with both of them (they are both very practicably using SD with a strong academic background). Learning and getting more experienced in System Dynamics and the use for daily problem solving is a dynamic and evolving process of wisdom with lots of feedback ("Business Dynamics" can help a lot in getting deeper insights.
Best regards
Ralf
Excellent.......2007-08-29
Excellent guide to systems thinking, clear examples, clear thinking and very interesting conclusions reached. highly recommended
buen libro.......2007-02-22
como parte de la materia lo llevo, me salio mas barato que en mexico y me es util para mi carrera
Amazing.......2007-01-12
The definitive book on Business dynamics !
It may look dificult to follow, but it isn`t really easy to read and follow !
The cd brings good examples.
Edward Garrity, Professor of Information Systems.......2007-01-04
Sterman's book is far and away the best and most comprehensive book on system dynamics for business. Although the title is "Business Dynamics" it really is a text on applying system dynamics to both business and larger social issues/systems.
If you are a strong student of business and systems then this is the book for you. It is highly recommended if you are interested in learning the most you possibly can regarding system dynamics and how to model systems. However, if you are searching for an introduction to this area - watch out. The book is actually quite advanced - it is used at MIT and other graduate business programs at top universities.
What is amazing about this book is that it blends absolute precision, correctness and detail with practical modeling advice and insight and it is written in a relaxed style with numerous examples. It is one of the best examples of clear writing about a complex topic. Dr. Sterman's years of system work has allowed him a level of clarity that is unsurpassed in the business and systems area.
As mentioned earlier, the book can be overwhelming and I would recommend a newcomer to this area to pick up several books first, to provide a more gentle introduction. This is not a contradiction of what I wrote earlier, it is simply that the book packs so much information in its pages, and it is still very long.
Highly recommended.
Book Description
This book provides the first clear, comprehensive, and accessible account of complex adaptive social systems, by two of the field's leading authorities. Such systems--whether political parties, stock markets, or ant colonies--present some of the most intriguing theoretical and practical challenges confronting the social sciences. Engagingly written, and balancing technical detail with intuitive explanations, Complex Adaptive Systems focuses on the key tools and ideas that have emerged in the field since the mid-1990s, as well as the techniques needed to investigate such systems. It provides a detailed introduction to concepts such as emergence, self-organized criticality, automata, networks, diversity, adaptation, and feedback. It also demonstrates how complex adaptive systems can be explored using methods ranging from mathematics to computational models of adaptive agents.
John Miller and Scott Page show how to combine ideas from economics, political science, biology, physics, and computer science to illuminate topics in organization, adaptation, decentralization, and robustness. They also demonstrate how the usual extremes used in modeling can be fruitfully transcended.
Customer Reviews:
Annie Wu -- Book #1.......2007-08-10
I am a purchasing agent who buys books for my faculty, and as far as I know, this faculty member is very impressed with this particular book.
The Emergence of Convergence .......2007-08-04
At the time of writing this review, this book isn't searchable through Amazon, that's too bad because if you're reading the reviews wondering if it's worth buying, just browsing through any page from the intro or appendix B would clearly resolve any remnant hesitation. This book is a must have for anyone even remotely interested in complex adaptive systems. Scott Page and John Miller dress the landscape and state of the art of computational social science, the issues are motivated from the ground up and the existing approaches to resolve them explicitly detailed, yet using clear and jargon free language. For example, descriptions of the many concepts repeatedly used in the scientific method (of CAS et al) such as ergodicity or optimization theory are refreshing and insightful, simply stuff you don't get from textbooks, but rather that one would learn over years of experience doing.
In summary, the authors are handing us an expert summary of literature and developments of a complex field in a concise, fun and delightful read, it would be a shame to miss it.
Book Description
If you specialize in complex sales, the business-to-business transactions that involve multiple decisions made by multiple people from multiple perspectives, this is the book for you! It presents The Prime Process—a diagnostic, customer-centered approach that clearly sets you apart from your competition and positions you with respect and credibility as a valued and trusted advisor. If the stakes are high and you’re expected to win, this book will give you the edge you’ve been looking for.
Buy your copy today!
Customer Reviews:
Avoid unpaid consulting and become a leader not only a manager of the sales process.......2007-10-07
Jeff Thull is one of the 'fathers' of consultative selling and you really can't go wrong by reading and studying any of his books. This book is not only a primer on the subject but a resource that you'll find yourself going back to over and over again.
Business to Business Selling (B2B), that is complex, high dollar, capital intensive selling is different than the transactional sales of a Sandler, Tracey, or Hopkins. The long sales cycles and intense competitive pressures of complex sales require skills that move beyond techniques, closing, and relationship building. A salesperson must manage the sale through a long and involved process with an entire continuum of issues, stakeholders, and obstacles. As Thull says "we need to get beyond selling to managing decisions," "beyond problem solving to managing change," beyond meeting needs to managing expectations," beyond transactions to managing relationships," and "beyond reacting to managing clear communications." This book is the roadmap for doing those things.
In some ways this book moves past the idea of consultative selling because everyone claims to be doing that. Often customers don't have a rational or clear buying process themselves. Salespeople have to navigate these turbulent waters while preventing "unpaid consulting" and loosing to a competitor for unseen reasons. Thull offers constructive and timely advice on how to lead not simply manage, to drive change instead of being a victim of other people's processes.
"New Solution Selling" offers a better 'system' complete with tactical advice and forms to implement a complex selling process. However for important concepts and a philosophical sales foundation this book is a important read.
I have 37 years in sales...........2007-08-23
I am familar with many of the concepts and ideas in this book. I enjoyed the overview of the three eras of sales; particularly since I have lived thru them all! And, the fighter jet complex sale made me appreciate the less complex sales I work with day to day. I believe this is a good book for those new to the world of complex sales and provides a good overview upon which to build and increase your knowledge of the sales process.
Refreshing approach.......2007-08-15
Tired of the same old "wedge foot in door, give presentation then close" mentality? This manual offers a refreshing view of a better method for client management. If you feel your time and resources are too valuable to waste them putting the hard sell tactics on uninterested parties, purchase this book.
Good overview for beginners.......2007-04-10
This is the first book I have read that provides a good foundation for salesman who are beginners.
Develop an unfair advantage & truly differentiate your offering.......2006-09-14
Tremendous foundation of how to change the fundamentals, rules of engagement, the game and give yourself a wonderfully unfair advantage in business development that involves complex sales. Excellent read prior to reading Jeff Thull's other two (excellent) books, The Prime Process and Exceptional Selling, which get into more detailed tactics and how-to's. Consultative selling is not a way to differentiate yourself and your offering any longer. Most salespeople now use that approach. As Thull puts it, to truly differentiate yourself, you've got to be a diagnostic business developer. This book book will teach you if you how if you choose to implement the fundamentals of the Prime Process described. He makes the point that spectacular results always begin with unspectacular (unglamorous) preparation.
Book Description
This textbook provides a thorough introduction to the field of learning from experimental data and soft computing. Support vector machines (SVM) and neural networks (NN) are the mathematical structures, or models, that underlie learning, while fuzzy logic systems (FLS) enable us to embed structured human knowledge into workable algorithms. The book assumes that it is not only useful, but necessary, to treat SVM, NN, and FLS as parts of a connected whole. Throughout, the theory and algorithms are illustrated by practical examples, as well as by problem sets and simulated experiments. This approach enables the reader to develop SVM, NN, and FLS in addition to understanding them. The book also presents three case studies: on NN-based control, financial time series analysis, and computer graphics. A solutions manual and all of the MATLAB programs needed for the simulated experiments are available.
Customer Reviews:
An extremely good book.......2006-11-16
This is a very good book. Another reviewer has commented on Vojislav Kecman being an excellent teacher. I whole-heartedly second that opinion. Often times, while reading this book, you will pause with a doubt or question. What you will find surprising is that almost certainly the author has answered that question in the next paragraph. Many times, the author's answers will tally your own answers.
The first chapter of the book (entitled: Learning and Soft Computing: Rationale, Motivations, Needs, Basics) is 119 pages long. It is an essential reading. By the time you finish reading this chapter the things will start falling into place and you will be more motivated and ready to read the remaining chapters. Until you are highly aware of this topic, do not skip this chapter.
A book is made up of a lot of things other than the text that it covers. Does it contain many/any stupid jokes? Is it printed on the highest quality paper? Is the font size good? Is it printed too dense? Is the cover page inviting enough? Are the dimensions/weight of the book correct? On all these counts the book scores high.
Consistent with the subject matter that it covers, this is not an easy book. You will perhaps like to read it with paper and pencil. But if you are willing to spend time with this book, this book will do a lot of good to you. This is a very good book.
An excellent book on Machine Learning.......2003-02-27
What strikes me each time I open this book is Mr Kecman's sense of pedagogy: it is a lesson in the matter. Not only his book delivers the - sometimes complex - techniques in a highly readable manner, but the concepts behind each of the main tools (SVM, NN & FL) he chose to highlight are always brilliantly put in context. One comes out of the reading with more than a set of equations but rather with a clearer picture of the field.
Mr Kecman is - without a doubt - a great teacher.
This effort to deliver a clear message is furthermore underlined through the numerous original figures: if you are like me and feel that a (good) picture speaks more than a thousand words, you will sure appreciate the way the illustrations complement the text and truly help the understanding.
I have read several other books on the subject but if I had to chose one for teaching purposes, this would be the one. I you want to build a better understanding of the field, get this book: it will pay on the long term.
Excellent, useful book!.......2001-07-23
This book is a nice and, I would say, a successful attempt to provide a unified survey of important theoretical and practical machine learning tools: neural networks (NN), support vector machines (SVM) and fuzzy systems (FS).
Book consists of nine chapters, covering SVMs, one- and multi-layer perceptrons and radial-basis function networks, as variants of neural networks, and basics of fuzzy theory. This is followed by interesting case-studies (in financial, control and computer graphic applications) and concluded by basics of optimization theory and an overview of necessary mathematical tools. All the MATLAB programs needed for the simulated experiments are available on the book web site.
Authored by Vojislav Kecman, a prominent researcher in the field of soft computing and previous MIT visiting professor, this book is an excellent material for advanced undergraduate and introductory graduate courses in machine learning applications and soft computing....
Book Description
Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics--particularly in machine learning, scientific modeling, and artificial life--and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics.
Customer Reviews:
Good Theoretical GA Textbook.......2005-05-06
This book primarily deals with the theoretical side of genetic algorithms. If you are looking for practical knowledge of how to implement a GA you should look elsewhere. For all intents and purposes this is a textbook. It's heavy on theory and proofs, but doesn't always explain everything in depth (that's what class time is for). There are problems at the end of each chapter that can be assigned to students.
There are case studies of many academic projects that seem to drone on forever and aren't really that useful in helping you learn how to write your own GA. Chapter 1 gives an overview and provides all of the appropriate terminology. Chapter 5 gives an high-level overview of how to implement a GA. Those are the 2 must-read chapters, all of the others can be used as torture for CS students.
To recap, if you're teaching a class in artificial intelligence this book is good. If you're trying to figure out how to implement a GA to solve a practical problem not so good. That evens out to 3 stars for my rating. I recommend searching the web, there are a few good sites on GA programming.
Not for beginners.......2004-02-04
I have an engineering degree, and I found this to be a little tough to follow for two reasons:
1. Not enough step by step prodecure especially at the beginning. Mitchell is too quick to start with the math formulas. It turns out that Genetic Algorithms are fairly straight forward and easy to follow, but you have to read this book twice before you "get it" because Mitchell clouds the discussion with proofs and mathematical representations of systems. It is tough to follow.
2. Mitchell does a poor job of selecting meaningful examples to illustrate the points. A nice simple set of examples where the average person easily picture the system would have been delightful. Instead this author chooses to illustrate the Genetic Algorithms through uncommon neural networks amoung other exotic applications. I found myself struggling to understand both the example (I didn't know a thing about neural networks!) and the genetic algorithm.
When buying an Introduction type book, I expected it to be more 'down to earth'. this book is for advanced minds!
An introduction and much more.......2004-01-26
First it must be said that the book is not an introduction that the non-scientist will easily understand. Some knowledge of computer programming is assumed. It acknowledges this in the last paragraph of the preface. Many of the notations in the book are unfamiliar to business or financial readers. There is no mathematics beyond algebra so the aforementioned prerequisites are the main hills to climb.
Mitchell's book is an overview of genetic algorithm analysis techniques as of 1996. The author gives a history of pre-computer evolutionary strategies and a summary of John Holland's pioneering work. A description of the basic terminology is presented and examples of problems solved using a GA (such as the prisoner's dilemma). The second chapter discusses evolving programs in Lisp and cellular automata. Also included in this chapter is a discussion of predicting dynamical systems. This was the section that has the most interest for me. Also interesting was the summary in this chapter about putting GAs into a neural network so that the ANNs could evolve.
The fifth chapter discusses when to employ a GA for maximum success. I appreciate the clearly thought out discussion of when to choose a GA for a problem. Sometimes authors of these types of books mimic the man with a hammer that thinks everything looks like a nail.
A Great Introduction to Genetic Algorithms.......2002-12-07
This is a great place to start to learn about genetic algorithms. The writing is clear and not bogged down by jargon. The book is not overly technical; it is written for the layman and has a casual conversational style that is a pleasure to read.
About half of the book is devoted to presenting examples of studies that have used genetic algorithms. These examples are interesting in themselves and also serve to illustrate the variety of genetic approaches that are available. The book also presents conflicting points of view of experts about which algorithms work best and why. This is helpful in combatting the impression that a beginner sometimes gets that everything is simple and all the answers are known.
Good introduction for such a short book.......2002-04-07
Although short, this book gives a good introduction to genetic algorithms for those who are first entering the field and are looking for insight into the underlying mechanisms behind them. It was first published in 1995, and considerable work has been done in genetic algorithms since then, but it could still serve as an adequate introduction. Emphasizing the scientific and machine learning applications of genetic algorithms instead of applications to optimization and engineering, the book could serve well in an actual course on adaptive algorithms. The author includes excellent problem sets at the end of each chapter, these being divided up into "thought exercises" and "computer exercises", and in the latter she includes some challenge problems for the ambitious reader.
Chapter 1 is an overview of the main properties of genetic algorithms, along with a brief discussion of their history. The role of fitness landscapes and fitness functions is clearly outlined, and the author defines genetic algorithms as methods for searching fitness landscapes for highly fit strings. An elementary example of a genetic algorithm is given, and the author compares genetic algorithms with more traditional search methods. The author emphasizes the unique features of genetic algorithms that distinguish them from other search algorithms, namely the roles of parallel population-based search with stochastic selection of individuals, and crossover and mutation. A list of applications is given, and two explicit examples of applications are given that deal with the Prisoner's Dilemna and sorting networks. The author also gives a brief discussion as to how genetic algorithms work from a more mathematical standpoint, emphasizing the role of Holland schemas. The reader more prepared in mathematics can consult the references for more in-depth discussion.
The next chapter stresses the role of genetic algorithms in problem solving, beginning with a discussion of genetic programming. Automatic programming has long been a goal of computer scientists, and the author discusses the role of genetic programming in this area, particularly the work of John Koza on evolving LISP programs. In addition, she discusses the current work on evolving cellular automata and its role in automatic programming. The latter discussion is more detailed, this resulting from the author's personal involvement in artificial life research. Those interested in time series prediction tools will appreciate the discussion on the use of genetic algorithms to predict the behavior of dynamical systems, with an example given on predicting the behavior of the (chaotic) Mackey-Glass dynamical system. The author also gives applications of genetic algorithms in predicting protein structure, an area of application that has exploded in recent years, due to the importance of the proteome projects. The area of neural networks has also been influenced by genetic algorithms, and the author discusses how they have replaced the familiar back-propagation algorithm as a method to find the optimal weights.
Chapter 3 is more in line with what the author intended in the book, namely a discussion of the relevance of genetic algorithms to study the mechanisms behind natural selection. She discusses the "Baldwin effect", which gives a connection between what an organism has learned (a small time-scale process) to the evolutionary history of the Earth (a long time-scale process). A simple model of the Baldwin effect is given using a genetic algorithm, along with a discussion of the Ackley-Littman evolutionary reinforcement learning model, which involves the use of neural networks, and which is another computational demonstration of the Baldwin effect. In addition, the author discusses models for sexual selection and ecosystems based on genetic algorithms. These are the "artificial life" models that the author has been involved in, and she gives a very understandable overview of their properties.
Chapter 4 should suit the curiosity of the mathematician or computer scientist who wants to understand the theoretical justification behind the use of genetic algorithms. Again employing the Holland notion of schemas and adaptation as a "tension between exploration and exploitation", the author formulates a mathematical model, called the Two-Armed Bandit Problem, of how genetic algorithms are used to study the tradeoffs in this tension. The level of mathematics used here is very elementary with the emphasis placed on the intuition behind this model, with only a sketch of the model's solution given. To address the role of crossover in genetic algorithms, the author discusses in detail a class of fitness landscapes, called "Royal Road functions" that she and others have developed. The performance of the genetic algorithm employed is then compared against the three different hill-climbing methods. Formal mathematical models of genetic algorithms are also discussed, one of which involves dynamical systems, another using Markov chains, and one using the tools of statistical mechanics. The latter is very interesting from a physics standpoint but is only briefly sketched. The interested physicist reader can consult the references given by the author for further details.
Practical use of genetic algorithms demands an understanding of how to implement them, and the author does so in the last chapter of the book. She outlines some ideas on just when genetic algorithms should be used, and this is useful since a newcomer to the field may be tempted to view a genetic algorithm as merely a fancy Monte Carlo simulation. The most difficult part of using a genetic algorithm is how to encode the population, and the author discusses various ways to do this. She also details various "exotic" approaches to improving the performance of genetic algorithms, such as the "messy" genetic algorithms. One must also choose a selection method when employing genetic algorithms, and the author shows how to do this using various techniques, such as roulette wheel and stochastic universal sampling. In addition, genetic operators must also be chosen in implementing genetic algorithms, and the author emphasizes crossover and mutation for this purpose. Lastly, the values of the parameters of the genetic algorithm, such as population size, crossover rate, and mutation rate must be chosen. The author discusses various approaches to this. Although brief, she does give a large set of references for further reading.
Average customer rating:
- Is a good start but needs more...
- The arcane made accessible
- Great Starting Point in Agent based Modelling
- interesting, but describes an old version of the software
- Invention - on all levels
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Turtles, Termites, and Traffic Jams: Explorations in Massively Parallel Microworlds (Complex Adaptive Systems)
Mitchel Resnick
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ASIN: 0262680939 |
Book Description
How does a bird flock keep its movements so graceful and synchronized? Most people assume that the bird in front leads and the others follow. In fact, bird flocks don't have leaders: they are organized without an organizer, coordinated without a coordinator. And a surprising number of other systems, from termite colonies to traffic jams to economic systems, work the same decentralized way. Turtles, Termites, and Traffic Jams describes innovative new computational tools that can qhelp people (even young children) explore the workings of such systems--and help them move beyond the centralized mindset.
Customer Reviews:
Is a good start but needs more..........2007-06-13
Book needs some more content, it gives a good first level intro but does not develop it into more interesting/advanced things.
50% of the book is a 'sales' pitch for StarLogo.
If you have experience with writing code for simulations this book is a bit light technically.
Still has some interesting ideas in it.
The arcane made accessible.......2006-03-28
On the surface, the book seems focused on computers and even video games. This makes the activities appealing for middle and high school students. However, the ideas in this book are actually relevant to two entirely different fields: 1) genetics, and 2) artificial intelligence. Both these fields have at their centers the concept of complexity arising out of simplicity. Students who work their way though the activites in this book will find that Dr. Resnick offers a window into a deep mathematical world that has applications far beyond computers and games.
Great Starting Point in Agent based Modelling.......2005-10-17
Mitchel Resnick shares a lovely sense of wonder, discovery and fascination in this slim, easily read volume about agent based modelling. Using micro-worlds (termites, ants and other modelling metaphors) Resnick shows us how collective behaviours are more than a simple sum of the parts - and through his experiments using StarLogo programming he shows the nature of emergent behaviours that come through decentralised thinking.
His reading list is a great starting point for anyone fascinated by Complexity and Agent Based Modelling, and so too is his elegant list of guiding heuristics that he has learned through toying with various ants, termites, forest fires and traffic jams.
- Positive feedback (in models) often plays a vital role.
- Randomness can help create order. Random isn't always chaotic.
- A Flock is not a big bird. The behaviour of groups should not be confused with the behaviour of individuals.
- A traffic jam is not just a collection of cars. Emergent objects have an ever-changing composition.
- The hills are alive. Don't just focus on the individual objects - look also at their environment.
This volume helped our research team design approach agent-based modelling, and put us in touch with other avenues of decentralised thinking: for example Network Theory. Best of all, Resnick helped us lighten-up. This book (and its subject matter) has profound ideas, but never loses a delicious sense of awe. Recommended.
interesting, but describes an old version of the software.......2003-09-28
This is a book describing the research of a team at MIT using a version of the educational language "Logo". Running in a simple graphical environment which supports multiple parallel operation of code in the same shared space. Write a few lines of code for an "ant", then let 1000 of them loose. The current version of this "StarLogo" system is written in Java, and available as a free download for anyone to play with.
The use of Logo is both a strength and a weakness of the approach. The strength is that the code is concise and easy to understand. The weakness is that there is only one source of the software, and anyone wishing to try it is limited to the available download. This would not be such a limitation if the book described the same version, but unfortunately things have moved on a lot since the book was written, and few (if any) of the examples will work without alteration.
As well as the development of the StarLogo system, the book covers experiments in emergent behaviour. Typical sections include how parameter and environment changes can affect the growth and development of simulated ant colonies, and a theoretical basis for those "phantom traffic jams" we have all experienced.
This book is certainly interesting if you are interested in developing parallel software simulations, or if you are interested in marginal computer languages, but don't expect the code to work without effort.
Invention - on all levels.......2002-10-14
This book provided the motivating force to write my first, and last, review for Amazon.com.
Over the past 5 years since my first reading Mitchel Resnick's Turtles Turmites and Traffic Jams, the book has come up on numerous occasions related to several topics, two of which most basically:
1) Writing style - Resnick's clear, well-researched, simple yet profound style. His background as journalist and inventor enables TT&T to walk a new line between source material and criticism.
2) Content - Resnick's theoretical application of emergent behavior to education is robust; his practical educational tools (starlogo and later, mindstorms) are a fundamentally clear and wondrous collapsing of idea into artifact.
I will include this book with few others in my life bibliography.
Book Description
What is life? Has molecular biology given us a satisfactory answer to this question? And if not, why, and how to carry on from there? This book examines life not from the reductionist point of view, but rather asks the question: what are the universal properties of living systems and how can one construct from there a phenomenological theory of life that leads naturally to complex processes such as reproductive cellular systems, evolution and differentiation? The presentation has been deliberately kept fairly non-technical so as to address a broad spectrum of students and researchers from the natural sciences and informatics.
Customer Reviews:
poor.......2007-07-07
Hidden Order is a seminar re-arranged into a book. The outcome is not a good read. It is tedious and lacks interesting cases.
If complex adaptive systems are of interest, take a class and skip this book.
A milestone in understandin complexity.......2005-09-16
It gave me the basic concepts in very clear and rogorous way
Not the Best Intro Book for Everyone.......2004-03-19
I think this is an excellent book for someone interested in constructing complex adaptive systems. It clearly lays out the technical guidelines that you would need. And of course, it was written by the man who originated genetic algorithms!
However, if you are new to the phenomena of complex adaptive systems (CAS) or agent-based models (ABM), this might not be the best intro book for you. This is particularly true if you are wondering what a genetic algorithm is right now. I think you will get the most out of the book if you are already somewhat familiar with CAS and ABM as Holland does not dwell on illustrative examples. (Yes there are examples, but they are very short compared to other authors on this topic.) Because of this, I think this book will be rather dry and technical and non-intuitive for a real newbie. If you have no idea where to begin, try _Growing Artificial Societies_ by Joshua Epstein and Robert Axtell.
One final comment: for excellent in-depth look at the reiterated Prisoner's Dilemna model with genetic algorithms that Holland briefly discusses, read _The Complexity of Cooperation_ by Robert Axelrod. (Axelrod and Holland mention each other in their books.)
Interesting ideas, bad writing.......2004-02-10
The ideas presented in this book by John Holland are no doubt interestring. The thought of spontaneous self-organization though hardly new has intrigued humans for centuries. Intuitively it makes sense and appeals to most peoples physical and methaphysical sensibilities. We know and hope that there is more to life than our common sense knowledge of it and the often dull and mechanistic accounts of natural science. Emergence theory therefore has an immediate appeal. Holland manages to keep this "flame" alive for one chapter (the first one) then the wholle enterprise is drowned in techno babble and most "non-hacker" readers are bored and dissappointed. This book is one more instance of a genuinly interesting idea being mercilessly slaughtered by bad writing. It's a true waste. It makes you wish writing courses were made compulsory for natural scientists and techo folks. Unfortunatelly it is hard to reckommend a better book on this subject... Most of the existing books are either written by litterary incompetent but hard core techno devotees or by soft science writers ruminating the self evident and riding the tidal wave of hype. All for the buck and a snapshot in the spotlight. A non-trivial sign of an over-hyped field of inquiry, dangerously bordering the realms of pseudoscience. At least chaos theory had Edward Lorenz as a respectable and astute front figure, managing to keep the delicate balance between scientific integrity and popular appeal. As for emergence theory that post is still vacant. Holland may be an important contributor to the field of emergence theory but he fails the requirements for that post.
John Holland is the master.......2002-07-05
In the worlds of Complexity and Artificial Intelligence, the name of John Holland is revered, and for good reason. One of the most important contributions to both fields was Holland's invention of genetic algorithms, a class of optimization techniques that applies a survival-of-the-fittest heuristic to a broad range of otherwise intractable problems. He is certifiably a genius, and his words on the subject of complexity should be considered close to the gospel.
To the benefit of all mankind, this god of complexity has seen fit to lay down his word on the subject in a manner suitable to the masses. He posits seven basic properties of complex adaptive systems (worth reading and memorizing in their own right), then uses the rest of the book to demonstrate that adaptive systems possess these properties and shows us how a computer can capture such adaptive mechanisms. Pure gold and totally accessible.
This book excels as an exposition of complex adaptive systems for the masses, and as a tutorial for the technically inclined. If you are so technically inclined, follow this book with Holland's "Emergence" and "Adaptation in Natural and Artificial Systems." Then head on over to Goldberg's book on genetic algorithms and maybe some Koza (a quick Amazon search can find these for you).
Book Description
Combines the theoretical foundations of intelligent problem-solving with he data structures and algorithms needed for its implementation. The book presents logic, rule, object and agent-based architectures, along with example programs written in LISP and PROLOG. The practical applications of AI have been kept within the context of its broader goal: understanding the patterns of intelligence as it operates in this world of uncertainty, complexity and change.
The introductory and concluding chapters take a new look at the potentials and challenges facing artificial intelligence and cognitive science. An extended treatment of knowledge-based problem-solving is given including model-based and case-based reasoning. Includes new material on: Fundamentals of search, inference and knowledge representation AI algorithms and data structures in LISP and PROLOG Production systems, blackboards, and meta-interpreters including planers, rule-based reasoners, and inheritance systems. Machine-learning including ID3 with bagging and boosting, explanation based
learning, PAC learning, and other forms of induction Neural networks, including perceptrons, back propogation, Kohonen networks, Hopfield networks, Grossberg learning, and counterpropagation. Emergent and social methods of learning and adaptation, including genetic algorithms, genetic programming and artificial life. Object and agent-based problem solving and other forms of advanced knowledge representation
Customer Reviews:
Superficial and unclear.......2005-05-27
Trying to gather the greatest audience possible, this book is superficial, completly unclear and boring. Why? Topics are quickly introduced, concepts are rarely analized deeply, it's more discorsive than formal. With so many subjects of AI in the same book not enough space can be given to all of them, so most of the chapters are lists of important algorithms or concepts, barely explained. Do you want to verify it? See the table of contents and the number of pages, and try to see how much space can be given to every point... not enough.
Fantastic Introduction to AI.......2005-01-06
This book really stands out among the AI texts (I've read 4 others). First, the language is clear and simple enough for undergrads to grasp. Second, there are consistent examples that pervade the text to help the reader apply each method to an established problem. Third, the explanations of algorithms/structures are crafted and phrased to TEACH, not merely to summarize a bunch of material for reference purposes. Finally, the programming chapters allow the student to realize the material, and really think about the problems by implementing them and hashing out the details.
I cannot complain about any lack of depth - the length already exceeds 900 pages. To those that desire more, look into academic journals - this is an intro. Moreover, robotics, vision, neural nets, and other topics already have their own "forked" research fields, with textbooks of comparable length focusing on those topics alone!
Enjoy! This text is sure to get you started!
this book not cover much.......2003-07-14
I bought this book for my introduction course in AI. I feel that this book has lack of somethings which are very important, neural networks, and Ai and robotics to name a few. I found that the text is very hard to understand. Again he didn't use enough example to explain some of the topics. I am lost reading this book. The book is not well structured and turned me bored after 30 minutes reading it. The reason are, AI term definations are not included as other book do, few visual diagrams, objective is not well defined. Once again, he didn't include introduction/review of what we acpect to learn of each of every chapters. Reading it is like reading a "white bible". Only plain text and unprofessional layout. This book discorage me reading it. I think i should buy other book that have a wider coverage topics in AI and yet easy to understand, consistent with my AI course syllibus and yet easy for my eyes.
Good For Beginners in AI.......2002-12-05
This is a very good book for anyone wanting to get an insight. Good for the first college course in AI too. It introduces the different areas of AI quite well, and develops logic before doing that. Prolog and LISP are also introduced.
The only reason I wouldn't give this book 5 stars is because
1) The Prolog and LISP features aren't all that great. They could have done better than just explaining what they did.
2) There was very little or almost no depth in the material covered. I wanted to go on reading more about the advanced features, but that never happened. So, I had to go to the library and look for something there.
But a great book for a college course. I wouldn't recommend this for a Grad course in CS...A grad student should be knowing beyond what this book covers.
Don't miss it!.......2002-05-24
This is the best general AI book I've seen this far. It introduces all the popular branches of AI clearly. If you are serious about AI, you should own this book...
Average customer rating:
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Geometrical Dynamics of Complex Systems: A Unified Modelling Approach to Physics, Control, Biomechanics, Neurodynamics and Psycho-Socio-Economical Dynamics ... and Intelligent Systems Engineering)
Vladimir G. Ivancevic , and
Tijana T. Ivancevic
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover
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Book Description
This volume presents a comprehensive introduction into rigorous geometrical dynamics of complex systems of various natures. By "complex systems", in this book are meant high-dimensional nonlinear systems, which can be (but not necessarily are) adaptive. This monograph proposes a unified geometrical approach to dynamics of complex systems of various kinds: engineering, physical, biophysical, psychophysical, sociophysical, econophysical, etc. As their names suggest, all these multi-input multi-output (MIMO) systems have something in common: the underlying physics. Using sophisticated machinery composed of differential geometry, topology and path integrals, this book proposes a unified approach to complex dynamics – of predictive power much greater than the currently popular "soft-science" approach to complex systems. The main objective of this book is to show that high-dimensional nonlinear systems in "real life" can be modeled and analyzed using rigorous mathematics, which enables their complete predictability and controllability, as if they were linear systems. The book has two chapters and an appendix. The first chapter develops the geometrical machinery in both an intuitive and rigorous manner. The second chapter applies this geometrical machinery to a number of examples of complex systems, including mechanical, physical, control, biomechanical, robotic, neurodynamical and psycho-social-economical systems. The appendix gives all the necessary background for comprehensive reading of this book.
Books:
- Business Dynamics: Systems Thinking and Modeling for a Complex World with CD-ROM
- Classical Dynamics of Particles and Systems
- Commutative Algebra: with a View Toward Algebraic Geometry (Graduate Texts in Mathematics)
- Comparative Biomechanics: Life's Physical World
- Concurrency: State Models & Java Programs
- Construction Drawings and Details for Interiors: Basic Skills
- Convex Analysis (Princeton Landmarks in Mathematics and Physics)
- Cracking the AP Calculus AB and BC Exams, 2006-2007 Edition (College Test Prep)
- Data Structures and Algorithms in C++
- Deformations of Algebraic Schemes (Grundlehren der mathematischen Wissenschaften)
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