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What enables individually simple insects like ants to act with such precision and purpose as a group? How do trillions of neurons produce something as extraordinarily complex as consciousness? In this remarkably clear and companionable book, leading complex systems scientist Melanie Mitchell provides an intimate tour of the sciences of complexity, a broad set of efforts that seek to explain how large-scale complex, organized, and adaptive behavior can emerge from simple interactions among myriad individuals. Based on her work at the Santa Fe Institute and drawing on its interdisciplinary strategies, Mitchell brings clarity to the workings of complexity across a broad range of biological, technological, and social phenomena, seeking out the general principles or laws that apply to all of them. Richly illustrated, Complexity: A Guided Tour--winner of the 2010 Phi Beta Kappa Book Award in Science--offers a wide-ranging overview of the ideas underlying complex systems science, the current research at the forefront of this field, and the prospects for its contribution to solving some of the most important scientific questions of our time.
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She captures the excitement of research. (Ian D. Couzin, Science )
She writes in an unpretentious style with frequent entertaining and useful anecdotes. (Iain D. Couzin. Science ) --このテキストは、 ハードカバー 版に関連付けられています。
She writes in an unpretentious style with frequent entertaining and useful anecdotes. (Iain D. Couzin. Science ) --このテキストは、 ハードカバー 版に関連付けられています。
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143 人中、142人の方が、｢このレビューが参考になった｣と投票しています。
Best introduction to complexity I know of
2009/5/14
形式:ハードカバー|Amazonが確認した購入
This book is easily the best introductory "guided tour" of complexity I know of. It has several key strengths:
1. Mitchell covers many of the major topics which can reasonably be grouped under the umbrella of complexity, so the breadth of the book is excellent. For my benefit and yours, here are the main topics covered, roughly in the order they appear in the book: chaos, information, thermodynamics, Godel's theorem, Turing machines, evolution, genetics, measures of complexity, fractals, self-reproducing automata, genetic algorithms, cellular automata, artificial life, information processing in living systems, analogy-finding algorithms, game theory, networks, power laws, metabolic scaling, random boolean networks, and historical foundations of complex systems research (cybernetics, general systems theory, synergetics, etc.). This long list leaves out some significant complexity topics, but Mitchell's scope is still plentiful for an introductory guided tour.
2. The topics are covered in sufficient depth to clearly convey the key concepts, which reflects the fact that Mitchell is a scientist who really knows the subject. Though the treatment is certainly introductory, rest assured that this isn't a superficial journalistic popularization which drops lots of names and terminology without getting into any real content.
3. Mitchell's writing style is concise and precise, but still friendly and not at all terse. The book is quite easy to read if you have a decent background in general science.
4. General readers will appreciate that there isn't much formal math in the book, yet Mitchell explains things in a way that nicely intimates the outlines of the math for readers who are math-savvy.
5. Mitchell's presentation is sober and honest. She naturally highlights the potentials and promise of complex systems science, but she also openly acknowledges its past dead ends and likely future limitations.
6. There are biographical notes interspersed throughout the book, which adds a nice human touch.
For completeness, I'll note that I did notice a few technical errors in Mitchell's initial discussion of immunology. But these errors don't invalidate the general message, and can be overlooked, considering the overall excellence of the book.
The bottom line is that I highly recommend this book to anyone interested in complexity (how could you not be?). It's a perfect introduction for beginners, and people well-versed in the subject will also appreciate the convenience of having a high-quality broad overview within the covers of just one book.
1. Mitchell covers many of the major topics which can reasonably be grouped under the umbrella of complexity, so the breadth of the book is excellent. For my benefit and yours, here are the main topics covered, roughly in the order they appear in the book: chaos, information, thermodynamics, Godel's theorem, Turing machines, evolution, genetics, measures of complexity, fractals, self-reproducing automata, genetic algorithms, cellular automata, artificial life, information processing in living systems, analogy-finding algorithms, game theory, networks, power laws, metabolic scaling, random boolean networks, and historical foundations of complex systems research (cybernetics, general systems theory, synergetics, etc.). This long list leaves out some significant complexity topics, but Mitchell's scope is still plentiful for an introductory guided tour.
2. The topics are covered in sufficient depth to clearly convey the key concepts, which reflects the fact that Mitchell is a scientist who really knows the subject. Though the treatment is certainly introductory, rest assured that this isn't a superficial journalistic popularization which drops lots of names and terminology without getting into any real content.
3. Mitchell's writing style is concise and precise, but still friendly and not at all terse. The book is quite easy to read if you have a decent background in general science.
4. General readers will appreciate that there isn't much formal math in the book, yet Mitchell explains things in a way that nicely intimates the outlines of the math for readers who are math-savvy.
5. Mitchell's presentation is sober and honest. She naturally highlights the potentials and promise of complex systems science, but she also openly acknowledges its past dead ends and likely future limitations.
6. There are biographical notes interspersed throughout the book, which adds a nice human touch.
For completeness, I'll note that I did notice a few technical errors in Mitchell's initial discussion of immunology. But these errors don't invalidate the general message, and can be overlooked, considering the overall excellence of the book.
The bottom line is that I highly recommend this book to anyone interested in complexity (how could you not be?). It's a perfect introduction for beginners, and people well-versed in the subject will also appreciate the convenience of having a high-quality broad overview within the covers of just one book.
113 人中、108人の方が、｢このレビューが参考になった｣と投票しています。
A gentle but thought-provoking introduction
2009/3/15
形式:ハードカバー
From reviews of the book that appear on the back cover:
"...scholarly yet entertaining..."
"...best general book on this topic."
"...entertains and informs all the way..."
I agree with all of the above. Unlike many books on complexity, this book is easy to read and highly accessible to general readers. More importantly to me as a graduate student, this book is more fascinating and in many ways more thought-provoking than math-heavy textbooks for specialists/academics.
I bought the book because of my interest in artificial intelligence, and I highly recommend this book to anyone interested in artificial intelligence, computer science, or biology. What I like most about the book is that it provides me with a fresh perspective/synthesis that pulls together what has been going on in different fields and subfields. For example, in computer science, we are taught all the time about how important it is for programs to be able to scale, but we are not given a biological perspective of how genes scale so well. This book does that in it's chapter on scaling.
Each chapter includes historical perspectives and/or real-world examples. For example, the chapter on genetic algorithms includes a quick survey of the companies and organizations that have recently benefited from using them.
The book also includes a chapter on why computers are still pretty dumb (lack general intelligence). The chapter reiterates that analogy understanding may be the holy grail to developing artificial general intelligence. (Like most people, I agree with the author that artificial general intelligence, AGI, is not going to happen anytime soon.) Some relevant info about the author from Wikipedia: "She received her PhD in 1990 from the University of Michigan under Douglas Hofstadter and John Holland, for which she developed the Copycat cognitive architecture. She is the author of "Analogy-Making as Perception.""
"...scholarly yet entertaining..."
"...best general book on this topic."
"...entertains and informs all the way..."
I agree with all of the above. Unlike many books on complexity, this book is easy to read and highly accessible to general readers. More importantly to me as a graduate student, this book is more fascinating and in many ways more thought-provoking than math-heavy textbooks for specialists/academics.
I bought the book because of my interest in artificial intelligence, and I highly recommend this book to anyone interested in artificial intelligence, computer science, or biology. What I like most about the book is that it provides me with a fresh perspective/synthesis that pulls together what has been going on in different fields and subfields. For example, in computer science, we are taught all the time about how important it is for programs to be able to scale, but we are not given a biological perspective of how genes scale so well. This book does that in it's chapter on scaling.
Each chapter includes historical perspectives and/or real-world examples. For example, the chapter on genetic algorithms includes a quick survey of the companies and organizations that have recently benefited from using them.
The book also includes a chapter on why computers are still pretty dumb (lack general intelligence). The chapter reiterates that analogy understanding may be the holy grail to developing artificial general intelligence. (Like most people, I agree with the author that artificial general intelligence, AGI, is not going to happen anytime soon.) Some relevant info about the author from Wikipedia: "She received her PhD in 1990 from the University of Michigan under Douglas Hofstadter and John Holland, for which she developed the Copycat cognitive architecture. She is the author of "Analogy-Making as Perception.""
45 人中、45人の方が、｢このレビューが参考になった｣と投票しています。
Superb overview of Complexity
2009/5/7
形式:ハードカバー|Amazonが確認した購入
This book really lives up to its title, "Complexity: A Guided Tour." Dr. Mitchell has turned her Santa Fe Institute lectures on the foundations of Complexity into a very interesting, readable book suitable for academics, professionals, students, and interested laypeople. She explains how complexity fits into the history of scientific knowledge. She relates it to the rapidly expanding field of information science, as influenced by biological rather than mechanical models. She even explains how computer models relate to living systems as information processors.
Having read many scholarly papers on these topics, I can vouch for the clarity and accuracy of her work. She certainly doesn't need any endorsement, though; as a successful doctoral student under the renowned Doug Hofstadter and now a professor at Santa Fe, she is in the inner circle of complexity scientists today. If only her book had come out a year or two ago! It puts in one place many ideas we used to have to search out and integrate on our own!
One note: the mathematics of complexity science can be daunting. Dr. Mitchell has done a terrific job expressing & explaining those concepts. Unlike many of the complexity books in print, hers is both intelligent and accessible. Highly recommend it!
Having read many scholarly papers on these topics, I can vouch for the clarity and accuracy of her work. She certainly doesn't need any endorsement, though; as a successful doctoral student under the renowned Doug Hofstadter and now a professor at Santa Fe, she is in the inner circle of complexity scientists today. If only her book had come out a year or two ago! It puts in one place many ideas we used to have to search out and integrate on our own!
One note: the mathematics of complexity science can be daunting. Dr. Mitchell has done a terrific job expressing & explaining those concepts. Unlike many of the complexity books in print, hers is both intelligent and accessible. Highly recommend it!
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