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Very Short Introductions #392
Complexity: A Very Short Introduction
The importance of complexity is well-captured by Hawking's "Complexity is the science of the 21st century". From the movement of flocks of birds to the Internet, environmental sustainability, and market regulation, the study and understanding of complex non-linear systems has become highly influential over the last 30 years.
In this Very Short Introduction , one of the leading figures in the field, John Holland, introduces the key elements and conceptual framework of complexity. From complex physical systems such as fluid flow and the difficulties of predicting weather, to complex adaptive systems such as the highly diverse and interdependent ecosystems of rainforests, he combines simple, well-known examples -- Adam Smith's pin factory, Darwin's comet orchid, and Simon's 'watchmaker' -- with an account of the approaches, involving agents and urn models, taken by complexity theory.
ABOUT THE The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable.
In this Very Short Introduction , one of the leading figures in the field, John Holland, introduces the key elements and conceptual framework of complexity. From complex physical systems such as fluid flow and the difficulties of predicting weather, to complex adaptive systems such as the highly diverse and interdependent ecosystems of rainforests, he combines simple, well-known examples -- Adam Smith's pin factory, Darwin's comet orchid, and Simon's 'watchmaker' -- with an account of the approaches, involving agents and urn models, taken by complexity theory.
ABOUT THE The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable.
112 pages, Paperback
First published July 1, 2014
170 people are currently reading
1036 people want to read
About the author
John H. Holland
15 books49 followersJohn Henry Holland was an American scientist and professor of psychology and electrical engineering and computer science at the University of Michigan, Ann Arbor. He was a pioneer in what became known as genetic algorithms.
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Displaying 1 - 30 of 53 reviews
January 11, 2018
This short booklet mainly provides some tools to look at complex systems. Perhaps it is a good state of affairs (I can not judge that), but it remains very theoretical. What is also striking is that most of the tools can only be used in the form of mathematical or schematic models, and consequently are only applicable to a limited number of complex systems. Holland suggests that in the future a global theory of complex systems is possible, but at the same time he indicates that many questions remain open. For my purpose, - the use of complexity thinking in the study of history -, this book did not offer many relevant things.
February 9, 2023
Karmaşık uyarlanabilir sistemler/ complex adaptive systems bilgi akışının ve bilgi yönetiminin yükseldiği çağımızda yeni başlayan dönemin öncül ve önemli fikirlerinden. Çok güzel, mutlaka tavsiye olunur. John Holland in diğer kitaplarına bir başlangıç, uzun bir yolculuğa çıkarabilir meraklıları...
This entire review has been hidden because of spoilers.
April 7, 2020
I'm a big fan of this whole "Very Short Introduction" series. I have several, mostly in Kindle but this one snuck it's way into my pile at a bookstore. Most are 4* and really do give a quick survey of a whole field, so I usually read if I'm starting to study or my knowledge is old. This one is quite similar but it misses several aspects of Complex Adaptive Systems (CAS) that I'm already aware of, while going into depth on others.
I re-read it and dropped my rating considerably. A more critical reading found a considerable number of important error. I had skimmed over the very non-standard definition of an ecological niche (he thinks the human body is a single niche for organisms that inhabit it), unsupported generalisations (e.g. all complex adaptive systems become more complex, while in fact the opposite happens in many cases, e.g. parasites becoming simpler than the free-living organisms they evolved from), category mistakes (it's not individual systems that necessarily become simpler or more complex but evolutionary lines (whether that is an animal vs a species or a town market versus a computerized commodity market).
Holland explains the big division in Complexity studies between CAS complexity and complex physical systems such as the atmosphere but then spends very little time on the latter. This was fine by me as today I'm interested in CAS. But then, maybe it would have been 5* if he'd at least listed some of the other approaches to CAS at the same level of detail as physical systems. For example he does not even mention Bayesian Networks . Now I think of it, his whole approach is constructive (building CAS from bottom up) with little mention of analytic (understanding CAS top down via learning models).
There is a small amount of mathematics in the book, but you don't need to know how to work it. For example, it is useful to know what a partial differential equation is, but you don't need to know how to construct one, let alone solve it.
I re-read it and dropped my rating considerably. A more critical reading found a considerable number of important error. I had skimmed over the very non-standard definition of an ecological niche (he thinks the human body is a single niche for organisms that inhabit it), unsupported generalisations (e.g. all complex adaptive systems become more complex, while in fact the opposite happens in many cases, e.g. parasites becoming simpler than the free-living organisms they evolved from), category mistakes (it's not individual systems that necessarily become simpler or more complex but evolutionary lines (whether that is an animal vs a species or a town market versus a computerized commodity market).
Holland explains the big division in Complexity studies between CAS complexity and complex physical systems such as the atmosphere but then spends very little time on the latter. This was fine by me as today I'm interested in CAS. But then, maybe it would have been 5* if he'd at least listed some of the other approaches to CAS at the same level of detail as physical systems. For example he does not even mention Bayesian Networks . Now I think of it, his whole approach is constructive (building CAS from bottom up) with little mention of analytic (understanding CAS top down via learning models).
There is a small amount of mathematics in the book, but you don't need to know how to work it. For example, it is useful to know what a partial differential equation is, but you don't need to know how to construct one, let alone solve it.
August 25, 2018
به شدت لذت بردم.
در مورد سیستمهای پیچیدهس و با این که خیلی خلاصه نوشته شده، ولی ایدههایی که میده به درد استفاده تو سایر حوزهها و فهم سازوکارهای تقریبا همه چیز میخوره. مگه هر چیزی به نوعی یه سیستم پیچیده نیست؟
اگه انگلیسی سختتونه، کتاب پیچیدگی شعبانعلی هم خیلی خوبه. گرچه هنوز کلی راه داره کامل باشه و هنوز تو اوایل کار.
کتاب شعبانعلی خیلی خوب توضیح داده، اما هنوز چون اوایلشه دیدی که این کتاب به شما میده رو نمیده. یه دید خیلی کلی ساده برای شناخت سطحی سیستمهای پیچیده و کارکردشون.
در مورد سیستمهای پیچیدهس و با این که خیلی خلاصه نوشته شده، ولی ایدههایی که میده به درد استفاده تو سایر حوزهها و فهم سازوکارهای تقریبا همه چیز میخوره. مگه هر چیزی به نوعی یه سیستم پیچیده نیست؟
اگه انگلیسی سختتونه، کتاب پیچیدگی شعبانعلی هم خیلی خوبه. گرچه هنوز کلی راه داره کامل باشه و هنوز تو اوایل کار.
کتاب شعبانعلی خیلی خوب توضیح داده، اما هنوز چون اوایلشه دیدی که این کتاب به شما میده رو نمیده. یه دید خیلی کلی ساده برای شناخت سطحی سیستمهای پیچیده و کارکردشون.
September 24, 2018
I was a bit disappointed with this book.
I was disappointed probably because I expect so much of books in this series. I have quite a few of the OUP's "A Very Short Introduction" series, on a wide variety of topics. They are variable, but mostly I have found them excellent introductions to topics, which have often let me start and work on deeper and longer studies to get a better understanding. You have to have a realistic view of how much such a short book can provide - but they are usually very clear and a great starting point.
This book was an exception. I accept complexity is going to be at least complicated. But I would have expected an introduction to be introductory - I found this to be hard going. At first I was going to give this 2 stars. But then I realised it's not really that poor - just not typical for the series. I have learnt something about Complexity.
Little of the book is actually about complexity or the interesting property of emergence - mainly about how we model it, and through modelling I assume hope to understand it better, but this modelling is all in the early stages. I would have appreciated a few more examples of complexity and emergence, and a little less of the modelling.
I was disappointed probably because I expect so much of books in this series. I have quite a few of the OUP's "A Very Short Introduction" series, on a wide variety of topics. They are variable, but mostly I have found them excellent introductions to topics, which have often let me start and work on deeper and longer studies to get a better understanding. You have to have a realistic view of how much such a short book can provide - but they are usually very clear and a great starting point.
This book was an exception. I accept complexity is going to be at least complicated. But I would have expected an introduction to be introductory - I found this to be hard going. At first I was going to give this 2 stars. But then I realised it's not really that poor - just not typical for the series. I have learnt something about Complexity.
Little of the book is actually about complexity or the interesting property of emergence - mainly about how we model it, and through modelling I assume hope to understand it better, but this modelling is all in the early stages. I would have appreciated a few more examples of complexity and emergence, and a little less of the modelling.
April 6, 2020
2018.06.22–2019.01.12
Contents
Holland JH (2014) Complexity - A Very Short Introduction
Preface
List of illustrations
• 01. A cellular automaton
• 02. A snowflake curve
• 03. Small-world networks
• 04. Ising model
• 05. Turing’s model of symmetry-breaking in morphogenesis
• 06. An agent and the corresponding network
• 07. Successive changes of strength under a bucket brigade
• 08. Producing new rules by cross-over
• 09. Urn model of coupled reactions
• 10. Generating faces by recombination
• 11. Substructures and structures using children’s building blocks
• 12. Lynx/Hare oscillation
• 13. Diffusion between urns
• 14. Ball arrangements in urns
• 15. Criterion for a valid model
1. Complex systems
• What is complexity?
• Complicated vs. complex
• The behaviours of complex systems
• Two kinds of complexity
• Analysing complexity
2. Complex physical systems (CPS)
• Characteristics of complex physical systems
• Snowflake curves and fractals
• Scaling
• Networks
• Dynamics
3. Complex adaptive systems (CAS)
• Characteristics of complex adaptive systems
• Agent structure
• CAS theory
4. Agents, networks, degree, and recirculation
• Agents and networks
• Loops
• Tree-like approximations and evolutionary games
5. Specialization and diversity
• Specialists
• Diversity
• Origins of production lines
6. Emergence
• Building blocks revisited
• Building blocks mediate emergence
• Boundaries and emergence
7. Co-evolution and the formation of niches
• What is a niche?
• Niche theory
• Niche formation through co-evolution
• Mechanisms producing niche evolution
8. Putting it all together
• The role of an overarching framework
• A closer look at generated systems
• A generated framework for CAS
• Important questions
• Interpretation of the framework
• A short summary
Further reading
Index
Contents
Holland JH (2014) Complexity - A Very Short Introduction
Preface
List of illustrations
• 01. A cellular automaton
• 02. A snowflake curve
• 03. Small-world networks
• 04. Ising model
• 05. Turing’s model of symmetry-breaking in morphogenesis
• 06. An agent and the corresponding network
• 07. Successive changes of strength under a bucket brigade
• 08. Producing new rules by cross-over
• 09. Urn model of coupled reactions
• 10. Generating faces by recombination
• 11. Substructures and structures using children’s building blocks
• 12. Lynx/Hare oscillation
• 13. Diffusion between urns
• 14. Ball arrangements in urns
• 15. Criterion for a valid model
1. Complex systems
• What is complexity?
• Complicated vs. complex
• The behaviours of complex systems
• Two kinds of complexity
• Analysing complexity
2. Complex physical systems (CPS)
• Characteristics of complex physical systems
• Snowflake curves and fractals
• Scaling
• Networks
• Dynamics
3. Complex adaptive systems (CAS)
• Characteristics of complex adaptive systems
• Agent structure
• CAS theory
4. Agents, networks, degree, and recirculation
• Agents and networks
• Loops
• Tree-like approximations and evolutionary games
5. Specialization and diversity
• Specialists
• Diversity
• Origins of production lines
6. Emergence
• Building blocks revisited
• Building blocks mediate emergence
• Boundaries and emergence
7. Co-evolution and the formation of niches
• What is a niche?
• Niche theory
• Niche formation through co-evolution
• Mechanisms producing niche evolution
8. Putting it all together
• The role of an overarching framework
• A closer look at generated systems
• A generated framework for CAS
• Important questions
• Interpretation of the framework
• A short summary
Further reading
Index
September 2, 2020
Update: My current suggestion is Networks: A Very Short Introduction as a better first book on complexity. After that one, this one should make a good deal more sense, though it falls short of ideal. I've listed a number of other suggestions below this review.
I can't really recommend this as a first introduction to complexity science. For that, I would highly recommend Steven Johnson's book Emergence. It is not technical. And it's exciting and a breeze to read, aside from all the fun ideas that'll latch into your brain and keep you thinking for long after. You'll definitely walk away with an intuitive grasp of what complexity is all about, though you will not be a practitioner (yet).
As for this book, which I did actually really enjoy and get a number of insights from, I'll have to expand my review later, but here's a start. John Holland was a genius who was ahead of his time even compared to most of the founders of the field described in Waldrop's Complexity. You've heard about his work if you've heard of genetic algorithms, as he invented/discovered those. He also helped design IBM's CPU instruction set and was one of the earliest researchers to experiment with neural networks and artificial ecosystems. This little volume, effectively 90 pages, is a sort of gamble. It's written at too high a level for a beginner, and its focus on Holland's own genre of complex system modeling, with the terminology he personally liked to use, feels more arbitrary than it actually is. Reading this too fast is an exercise in futility; it'll start to sound repetitive. He tries to avoid jargon and math, but the result is that often what he's saying doesn't feel grounded. 90 pages just isn't a lot of space, especially for a field that, as of the writing of this volume, did not have a unified theory. So it isn't like he could just parade that out.
But this does not mean the book is a bad idea. What he's presenting is what's sometimes called an "idea model"—actually several that are connected. Idea models are not complete pictures or theories. They are toy examples that can illustrate how something tends to work. His "bucket brigade" auction of if-then rules (which can quite literally evolve, at least in theory, any kind of process from scratch on a computer) and his related Markov (statistical) process that diffuses those rules among "gated urns" (which can simulate and help you understand the formation of functional units on multiple levels seen interacting everywhere around you in nature, eg, organelles, organs, organisms, and superorganisms—or similarly atoms, molecules, objects, planets, solar systems, galaxies) seem too specific, and then the attached discussion seems too vague and hand-wavy. Working through it can feel a bit like playing an alternate-reality game—it's almost as if you're falling down a fictional world's "rabbit hole" and deciphering clues. But he leaves a trail for you, and I do not believe he is trying to be abstruse at all. It just isn't a total success. For the effort, though, you'll be taught by someone who revealed and clarified the basic ideas of complexity to many scientists at the first meetings of the Santa Fe Institute. Re-reading bits can pull those words out of the haze and attach sense to them.
It was pretty much perfect for where I am. Later I'll list a few more books that would be good starting points (edit: done!), and more details to suggest who would benefit most from this one (edit: attempted, but still thinking about that).
Note: the VSI series has books on Fractals, Chaos, Networks, The Computer, Information, Probability, Evolution, Systems Biology, and other areas that figure into complexity, which happens to be extremely multidisciplinary. This book does seem to avoid overlapping with those too much. When combined with "90 pages" and "no unified theory yet," "multidisciplinary" and "avoiding overlap" may begin to account for the feeling of many readers that its focus is narrower than expected.
—For those looking to get into complexity—
Here are some more suitable books to consider as starting points, the best ones I know:
Emergence (Johnson) - The first book I'd recommend for most readers. May remind you of Wired magazine, but personally I liked that. Written well for a popular audience.
Growing Artificial Societies (Axtell & Epstein) - Hands-on simulation design. Starting at ground level, it'll patiently walk you through how to consider a social phenomenon of interest and create your own rules to simulate it. Though assigned in the second rather than the first class I took, it's a terrific intro to agent-based modeling, the flagship simulation technique we use.
Complexity (Waldrop) - Awesome journalism about the field's founders and the sleuthy thrill of their discoveries. After reading this, you can search for complexity books online and feel like you personally know many of the authors and what makes them tick.
Micromotives and Macrobehavior (Schelling) - Surprisingly smooth, pleasant read by a Nobel Laureate in economics who won partly on account of this book. Introduced the idea of "tipping points" to readers in 1978. Discusses how personal interactions affect neighborhoods and lead to urban decay, gentrification, and segregation.
Complex Adaptive Systems (Miller & Page) - Highly respected introductory guide, mildly academic tone but nicely written. Packs a punch with central concepts, lots to think about, also practical advice on designing simulations. This was the most useful text in the first course I took... can't go wrong with it unless "academic" scares you.
Complexity: A Guided Tour (Mitchell) - The full panorama! Probably the best and gentlest book for a beginner wanting more depth/breadth/detail than you'll find in Johnson's Emergence. Assumes no prior knowledge. Happens to be a favorite with big names in the field (for example Holland cites it in the book I'm reviewing). It's relatively new and short and also popular in intro courses. Very likely it's the book you want. (But I'm only reading it now FWIW.)
The Sciences of the Artificial (Simon) - Not recommended as a first book for most people, but I'm including it because it's a complete classic, downright poetic, quotable on every page, with some of my own favorite quotations anywhere (not to mention John Holland quotes it in Complexity: A Very Short Introduction and then brings it up several more times). Imagine an especially deep Carl Sagan book full of giant, timeless thoughts, written by a founder of AI and several other fields. He based it on a key lecture series he gave at MIT in 1968 (then updated several times and expanded with new topics in 1996), but you wouldn't know it started as anything but inspired writing. It's another text from my first course, and I loved it then but found it a bit difficult and didn't finish it for a few years. Basically the genesis of the field—so yes, it has been an introduction for many. Certainly it's dated here and there, but the nice thing about original and accurate thinking is that it never goes out of style. More than a great book on the subject, it's a great book full stop.
Dynamics: The Geometry of Behavior (Abraham, Shaw) - This is kind of a 4-part math comic book, where the "characters" if you will are aspects of chaos theory. It's highly visual, advancing mostly via colorful graphs and their captions. Somewhat difficult to get hold of, but I found it in a local university library, and the first volume was recently reprinted, and the ebooks are available from the author's website. Many scientists got their start in chaos (non-linear dynamics) by reading this series from the 80s. It's a real mind-bender and technically requires almost no math knowledge, though if you haven't taken a calculus course, you'd have to really like math to read the whole thing. Anyway, if this sounds like your cup of tea, it's amazing.
Introduction to the Theory of Complex Systems (Thurner, Hanel, & Klimek) - Not for the faint of heart, but if you have a science/math background, I gather it's the most thorough and complete textbook going. A few people will want to jump straight to this one. (It's also one I'm only just reading now.)
Incidentally, there's almost nothing you can do that's more instructive than simply downloading NetLogo and playing with the models that come with it. In the same way that some people pooh-pooh popular science books as if somehow those just shouldn't exist, some experts pooh-pooh NetLogo as if it should do exactly what they want even if that makes it abstruse and/or highly specific to their endeavor. In reality, NetLogo is incredibly useful, both as a learning tool and as a way to prototype most any complex system you care to name. It is also something of a standard. Put aside any pomposity you hear against it and check it out.
Also, there are good free courses online at ComplexityExplorer. (Currently I'm taking the one on fractals and can recommend it.)
Best of luck!
I can't really recommend this as a first introduction to complexity science. For that, I would highly recommend Steven Johnson's book Emergence. It is not technical. And it's exciting and a breeze to read, aside from all the fun ideas that'll latch into your brain and keep you thinking for long after. You'll definitely walk away with an intuitive grasp of what complexity is all about, though you will not be a practitioner (yet).
As for this book, which I did actually really enjoy and get a number of insights from, I'll have to expand my review later, but here's a start. John Holland was a genius who was ahead of his time even compared to most of the founders of the field described in Waldrop's Complexity. You've heard about his work if you've heard of genetic algorithms, as he invented/discovered those. He also helped design IBM's CPU instruction set and was one of the earliest researchers to experiment with neural networks and artificial ecosystems. This little volume, effectively 90 pages, is a sort of gamble. It's written at too high a level for a beginner, and its focus on Holland's own genre of complex system modeling, with the terminology he personally liked to use, feels more arbitrary than it actually is. Reading this too fast is an exercise in futility; it'll start to sound repetitive. He tries to avoid jargon and math, but the result is that often what he's saying doesn't feel grounded. 90 pages just isn't a lot of space, especially for a field that, as of the writing of this volume, did not have a unified theory. So it isn't like he could just parade that out.
But this does not mean the book is a bad idea. What he's presenting is what's sometimes called an "idea model"—actually several that are connected. Idea models are not complete pictures or theories. They are toy examples that can illustrate how something tends to work. His "bucket brigade" auction of if-then rules (which can quite literally evolve, at least in theory, any kind of process from scratch on a computer) and his related Markov (statistical) process that diffuses those rules among "gated urns" (which can simulate and help you understand the formation of functional units on multiple levels seen interacting everywhere around you in nature, eg, organelles, organs, organisms, and superorganisms—or similarly atoms, molecules, objects, planets, solar systems, galaxies) seem too specific, and then the attached discussion seems too vague and hand-wavy. Working through it can feel a bit like playing an alternate-reality game—it's almost as if you're falling down a fictional world's "rabbit hole" and deciphering clues. But he leaves a trail for you, and I do not believe he is trying to be abstruse at all. It just isn't a total success. For the effort, though, you'll be taught by someone who revealed and clarified the basic ideas of complexity to many scientists at the first meetings of the Santa Fe Institute. Re-reading bits can pull those words out of the haze and attach sense to them.
It was pretty much perfect for where I am. Later I'll list a few more books that would be good starting points (edit: done!), and more details to suggest who would benefit most from this one (edit: attempted, but still thinking about that).
Note: the VSI series has books on Fractals, Chaos, Networks, The Computer, Information, Probability, Evolution, Systems Biology, and other areas that figure into complexity, which happens to be extremely multidisciplinary. This book does seem to avoid overlapping with those too much. When combined with "90 pages" and "no unified theory yet," "multidisciplinary" and "avoiding overlap" may begin to account for the feeling of many readers that its focus is narrower than expected.
—For those looking to get into complexity—
Here are some more suitable books to consider as starting points, the best ones I know:
Emergence (Johnson) - The first book I'd recommend for most readers. May remind you of Wired magazine, but personally I liked that. Written well for a popular audience.
Growing Artificial Societies (Axtell & Epstein) - Hands-on simulation design. Starting at ground level, it'll patiently walk you through how to consider a social phenomenon of interest and create your own rules to simulate it. Though assigned in the second rather than the first class I took, it's a terrific intro to agent-based modeling, the flagship simulation technique we use.
Complexity (Waldrop) - Awesome journalism about the field's founders and the sleuthy thrill of their discoveries. After reading this, you can search for complexity books online and feel like you personally know many of the authors and what makes them tick.
Micromotives and Macrobehavior (Schelling) - Surprisingly smooth, pleasant read by a Nobel Laureate in economics who won partly on account of this book. Introduced the idea of "tipping points" to readers in 1978. Discusses how personal interactions affect neighborhoods and lead to urban decay, gentrification, and segregation.
Complex Adaptive Systems (Miller & Page) - Highly respected introductory guide, mildly academic tone but nicely written. Packs a punch with central concepts, lots to think about, also practical advice on designing simulations. This was the most useful text in the first course I took... can't go wrong with it unless "academic" scares you.
Complexity: A Guided Tour (Mitchell) - The full panorama! Probably the best and gentlest book for a beginner wanting more depth/breadth/detail than you'll find in Johnson's Emergence. Assumes no prior knowledge. Happens to be a favorite with big names in the field (for example Holland cites it in the book I'm reviewing). It's relatively new and short and also popular in intro courses. Very likely it's the book you want. (But I'm only reading it now FWIW.)
The Sciences of the Artificial (Simon) - Not recommended as a first book for most people, but I'm including it because it's a complete classic, downright poetic, quotable on every page, with some of my own favorite quotations anywhere (not to mention John Holland quotes it in Complexity: A Very Short Introduction and then brings it up several more times). Imagine an especially deep Carl Sagan book full of giant, timeless thoughts, written by a founder of AI and several other fields. He based it on a key lecture series he gave at MIT in 1968 (then updated several times and expanded with new topics in 1996), but you wouldn't know it started as anything but inspired writing. It's another text from my first course, and I loved it then but found it a bit difficult and didn't finish it for a few years. Basically the genesis of the field—so yes, it has been an introduction for many. Certainly it's dated here and there, but the nice thing about original and accurate thinking is that it never goes out of style. More than a great book on the subject, it's a great book full stop.
Dynamics: The Geometry of Behavior (Abraham, Shaw) - This is kind of a 4-part math comic book, where the "characters" if you will are aspects of chaos theory. It's highly visual, advancing mostly via colorful graphs and their captions. Somewhat difficult to get hold of, but I found it in a local university library, and the first volume was recently reprinted, and the ebooks are available from the author's website. Many scientists got their start in chaos (non-linear dynamics) by reading this series from the 80s. It's a real mind-bender and technically requires almost no math knowledge, though if you haven't taken a calculus course, you'd have to really like math to read the whole thing. Anyway, if this sounds like your cup of tea, it's amazing.
Introduction to the Theory of Complex Systems (Thurner, Hanel, & Klimek) - Not for the faint of heart, but if you have a science/math background, I gather it's the most thorough and complete textbook going. A few people will want to jump straight to this one. (It's also one I'm only just reading now.)
Incidentally, there's almost nothing you can do that's more instructive than simply downloading NetLogo and playing with the models that come with it. In the same way that some people pooh-pooh popular science books as if somehow those just shouldn't exist, some experts pooh-pooh NetLogo as if it should do exactly what they want even if that makes it abstruse and/or highly specific to their endeavor. In reality, NetLogo is incredibly useful, both as a learning tool and as a way to prototype most any complex system you care to name. It is also something of a standard. Put aside any pomposity you hear against it and check it out.
Also, there are good free courses online at ComplexityExplorer. (Currently I'm taking the one on fractals and can recommend it.)
Best of luck!
January 9, 2019
This was my first 'A Very Short Introduction' book, so there is no comparison there. Complexity is not always the most inviting subject and certainly difficult to put in a short introduction.
Since this was not my first book on complexity, as I read Stuart Kauffman's 'At Home in the Universe' before, I was aware of most of the concepts. Sadly, the book provided not much more than Kauffman's book, though I wish now that I had read Holland before, just to speed up my capability of following Kauffman. Time savings business case for a first dive in complexity theory, Holland definitely wins.
To have earned 4 or even 5 stars, Holland could have reached a little bit more out to what complexity could mean for humanity, and especially our social networks, companies or institutions. Much further than a rainforest analogy, or some checkers game examples or the workings of our human cells, Holland did not get and he dared not touch too much upon the many unclarities around complex adaptive systems, which organisations, social groups or governments are.
Therefore, my next complexity read will focus on complex adaptive leadership.
Since this was not my first book on complexity, as I read Stuart Kauffman's 'At Home in the Universe' before, I was aware of most of the concepts. Sadly, the book provided not much more than Kauffman's book, though I wish now that I had read Holland before, just to speed up my capability of following Kauffman. Time savings business case for a first dive in complexity theory, Holland definitely wins.
To have earned 4 or even 5 stars, Holland could have reached a little bit more out to what complexity could mean for humanity, and especially our social networks, companies or institutions. Much further than a rainforest analogy, or some checkers game examples or the workings of our human cells, Holland did not get and he dared not touch too much upon the many unclarities around complex adaptive systems, which organisations, social groups or governments are.
Therefore, my next complexity read will focus on complex adaptive leadership.
May 13, 2018
Chapter 1: Complex systems
Chapter 2: Complex physical systems (CPS)
Chapter 3: Complex adaptive systems (CAS)
Chapter 4: Agents, networks, degree, and recirculation
Chapter 5: Specialization and diversity
Chapter 6: Emergence
Chapter 7: Co-evolution and the formation of niches
Chapter 8: Putting it all together
Chapter 2: Complex physical systems (CPS)
Chapter 3: Complex adaptive systems (CAS)
Chapter 4: Agents, networks, degree, and recirculation
Chapter 5: Specialization and diversity
Chapter 6: Emergence
Chapter 7: Co-evolution and the formation of niches
Chapter 8: Putting it all together
August 21, 2015
Hm. I have a bit of mixed feeling about this book, though Holland is definite the scientist who impact me the most in understanding complex systems. I think I just have mixed feelings about this Very Short Introduction (VSI) series - it's too short. In Shannon's phrases, I may say, the carrying capacity of the book format is smaller than the entropy of the information source. Also, this Introduction is not so much of a introduction. In my opinion, it's more like a concluding chapter of Holland's life-long ingenious contribution to the science, on his final vision of the fundamental framework of complexity science that is yet to be substantiated. He was so close, but now he's not with us anymore. :( Majority of the text is a hurry review of "Signals and Boundaries", which is not too long in itself but the formulation of the gated/tagged urn metaphor is more in depth. I imagine a introduction for people who are not familiar with his work or complex system sciences in general should contain more historical evolution of the subject. But I can't really imagine, I'm too biased. Overall, this book is very, say, Holland :) .. and he's quite special.
January 29, 2020
I grabbed the book in order to gain some structured insight of complexity and, especially, CAS. As a Public Health professional, complexity and CAS, right next to probabilities, play a crucial role in my field. The verdict: the book can be classified as a guide that will take you from the abstract concept of complexity, through the essentials of complex adaptive systems (CAS), all the way to the mathematical depths of probability, Markov models and Turing's model of symmetry-breaking morphogenesis. In introducing complexity, the book makes use of all(?) disciplines utilizing the concept and, while certain section of it were rather difficult to grasp from the first read by a non-mathematician/physicit, it has to be highlighted that the 90 pages can be read and utilized by professionals of all disciplines even slightly related to the logical mindset of probabilities and complexity.
December 29, 2019
Read less like an accessible introduction for a layperson than a condensed summary for a specialist. It was possible to grasp to gist in places, but this was not written with the general reader in mind.
September 25, 2019
A giudicare dal mio entusiasmo provato per i primi tre capitoli di questo libro - intitolati rispettivamente What is complexity, Complex Physical Systems e Complex Adaptive Systems - è un po' sorprendente che io alla fine mi sia ritrovata a dare tre stelle a questo Complexity, di John Holland; ma il motivo è presto detto: la seconda parte di questo libro è confusa, scombinata, senza target, senza un focus.
Infatti, se nei primi tre capitoli sembrava non fosse necessario nessun background scientifico, in quelli successivi si descrive esplicitamente - e in due pagine - il modello di Lokta-Volterra per il rapporto preda-predatore, compreso di equazioni, per poi lanciarsi in una breve dissertazione sulle catene di Markov con tanto di matrice di transizione; se nei primi tre capitoli sembrava che l'obiettivo di questo libro fosse mettere ordine, chiarire, spiegare con calma alcuni concetti fighissimi (self similarity, scaling, networks, emergence), in seguito ci si concentra su astrusi modelli di signal processing, spiegati frettolosamente, che per qualche ragione (poco chiara, a dire il vero) dovrebbero riuscire a schematizzare matematicamente i sistemi complessi adattivi; per poi dedicarsi a pagine e pagine di spiegazioni teoriche senza un vero filo conduttore, intervallate da qualche esempio banale e inutile e da qualche altro esempio fin troppo specifico per poterne comprendere il collegamento con il ragionamento iniziale.
Intendiamoci: non voglio leggere libri universitari nel mio tempo libero, ma non mi dispiacciono i libri anche un po' più tecnici, se ho le competenze per affrontarli; ma non mi piace che si passi da un target all'altro cambiando capitolo.
Se quindi volete un mio consiglio, trovate il modo di leggere i primi tre capitoli, che valgono davvero la pena, e poi buttatevi su qualcos'altro che approfondisca la parte che vi interessa di questa grossa e confusionaria materia, anche facendovi guidare dalla (scarna) bibliografia alla fine del libro.
Infatti, se nei primi tre capitoli sembrava non fosse necessario nessun background scientifico, in quelli successivi si descrive esplicitamente - e in due pagine - il modello di Lokta-Volterra per il rapporto preda-predatore, compreso di equazioni, per poi lanciarsi in una breve dissertazione sulle catene di Markov con tanto di matrice di transizione; se nei primi tre capitoli sembrava che l'obiettivo di questo libro fosse mettere ordine, chiarire, spiegare con calma alcuni concetti fighissimi (self similarity, scaling, networks, emergence), in seguito ci si concentra su astrusi modelli di signal processing, spiegati frettolosamente, che per qualche ragione (poco chiara, a dire il vero) dovrebbero riuscire a schematizzare matematicamente i sistemi complessi adattivi; per poi dedicarsi a pagine e pagine di spiegazioni teoriche senza un vero filo conduttore, intervallate da qualche esempio banale e inutile e da qualche altro esempio fin troppo specifico per poterne comprendere il collegamento con il ragionamento iniziale.
Intendiamoci: non voglio leggere libri universitari nel mio tempo libero, ma non mi dispiacciono i libri anche un po' più tecnici, se ho le competenze per affrontarli; ma non mi piace che si passi da un target all'altro cambiando capitolo.
Se quindi volete un mio consiglio, trovate il modo di leggere i primi tre capitoli, che valgono davvero la pena, e poi buttatevi su qualcos'altro che approfondisca la parte che vi interessa di questa grossa e confusionaria materia, anche facendovi guidare dalla (scarna) bibliografia alla fine del libro.
February 27, 2022
This was one of the more difficult entries in the Very Short Introduction series for me. Even though the book is generally well-written, with few awkward sentences, and even though many of the topics were at least recognizable to me from prior reading (and some abstract algebra from way back in graduate school), I still found this book hard going. I'm not really sure why, but I had difficulty maintaining my reading focus, with wandering thoughts, having to re-read passages that I blanked on, and finding myself suddenly engaged in other activities. So if you're anything like me and if you've ever struggled with distractions, this book might be a form of exercise.
Having said that, I look back and struggle now to form a clear impression on what I just read. This might relate to the late author's concession that complexity lacks a comprehensive theory yet, even though it abounds with example cases from a diversity of physical, biological, mathematical, and social domains. I just didn't get much I could sink my teeth into, something I might use, or some fresh insight into the endless array of social, political, medical, environmental, philosophical, and various other *-al problems that assail humankind. However, complexity is certainly at the root of most if not nearly all of them, which means understanding this book's topic seems like a necessary step on the way to solving the hard problems. (Do we have any simple problems? We might have some that sound simple enough to summarize, such as "People are basically dumb", but problems that are until now intractable are generally so because they are complex, and therefore defy simple and thus tractable solutions.)
So I give this book four stars for content, the subject mastery of the author (who gets automatic points for having invented genetic algorithms), and the generally good writing style. Even if I might have to read more books in this area to feel like I'm getting it.
Having said that, I look back and struggle now to form a clear impression on what I just read. This might relate to the late author's concession that complexity lacks a comprehensive theory yet, even though it abounds with example cases from a diversity of physical, biological, mathematical, and social domains. I just didn't get much I could sink my teeth into, something I might use, or some fresh insight into the endless array of social, political, medical, environmental, philosophical, and various other *-al problems that assail humankind. However, complexity is certainly at the root of most if not nearly all of them, which means understanding this book's topic seems like a necessary step on the way to solving the hard problems. (Do we have any simple problems? We might have some that sound simple enough to summarize, such as "People are basically dumb", but problems that are until now intractable are generally so because they are complex, and therefore defy simple and thus tractable solutions.)
So I give this book four stars for content, the subject mastery of the author (who gets automatic points for having invented genetic algorithms), and the generally good writing style. Even if I might have to read more books in this area to feel like I'm getting it.
June 14, 2022
An enjoyable and insightful quick tour of some of the big ideas of Complexity. Holland is one of the foremost early thinkers on Complexity Science, and having bought several of his works on the topic, I figured this book, his last and shortest, would be the best entry point to his thought.
There's two essences to this book. First, introducing the ideas of complexity as a field, what it is, how complex systems work and what these ideas offer. Second, establishing that even if we don't have a overarching theory of complexity, one is plausible. That second question leads to lots of interesting, if slightly challenging attempts at modeling and designing such systems.
Mitchell Waldrop's book 'Complexity' is probably the best starting point. I certainly benefited from knowing a bit about the subject before reading this. The second theme in particular is more of an introduction for those wanting to contribute to the field, than the general public wanting to know what the field is about. That's no bad thing, as Holland is a very good guide, both in his knowledge but also clear and welcoming passion for the ideas involved. I'm now looking forward to diving into his longer works when i get the opportunity.
There's two essences to this book. First, introducing the ideas of complexity as a field, what it is, how complex systems work and what these ideas offer. Second, establishing that even if we don't have a overarching theory of complexity, one is plausible. That second question leads to lots of interesting, if slightly challenging attempts at modeling and designing such systems.
Mitchell Waldrop's book 'Complexity' is probably the best starting point. I certainly benefited from knowing a bit about the subject before reading this. The second theme in particular is more of an introduction for those wanting to contribute to the field, than the general public wanting to know what the field is about. That's no bad thing, as Holland is a very good guide, both in his knowledge but also clear and welcoming passion for the ideas involved. I'm now looking forward to diving into his longer works when i get the opportunity.
July 14, 2023
A difficult subject for a short introduction, I'd say, but Holland seems to make a valiant effort to stay true to the computational and mathematical underpinnings of complexity approaches while nevertheless striving for accessibility.
I came away with the impression that Complexity is a sort of hybrid scientific field, but much younger and developmental than more pure, traditional forms of scientific inquiry.
In places, it is quite mind-blowing to think of these complex, adaptive systems of networks and evolving processes underpinning everything from natural evolution to economic markets and cells and the Internet.
A minor criticism is that I think there could have been more frank discussion of the limitations of the field as it stands, as well as a greater variety of applied real-world problems and examples. But I came away with a renewed sense of awe and humility in relation to things like specialisation, diversity, and emergence. Perhaps AI could have been touched on a little more, as Holland briefly mentions how far away we are from creating/modelling complex artificial systems that resemble anywhere near the complexity of a human central nervous system. AI enthusiasts and scaremongers alike would do well to reflect on this.
I came away with the impression that Complexity is a sort of hybrid scientific field, but much younger and developmental than more pure, traditional forms of scientific inquiry.
In places, it is quite mind-blowing to think of these complex, adaptive systems of networks and evolving processes underpinning everything from natural evolution to economic markets and cells and the Internet.
A minor criticism is that I think there could have been more frank discussion of the limitations of the field as it stands, as well as a greater variety of applied real-world problems and examples. But I came away with a renewed sense of awe and humility in relation to things like specialisation, diversity, and emergence. Perhaps AI could have been touched on a little more, as Holland briefly mentions how far away we are from creating/modelling complex artificial systems that resemble anywhere near the complexity of a human central nervous system. AI enthusiasts and scaremongers alike would do well to reflect on this.
April 9, 2021
Kitabın adının "CAS & CPS a different perspective" olması gerektiğini belirtmeliyim. Kitap kesinlikle bir giriş kitabı değil. Karmaşık sistemler üzerine bilginiz varsa aynı zamanda fizik ve matematik tahsiliniz de varsa size ilgi duyduğunuz bu konu hakkında farklı bir perspektif sunabilecek bir özet. Ve muhtemelen bu özet de yazarın bir diğer kitabı olan Emergence üzerinden yazılmış. Kesinlikle bir popüler kitap olmamasının yanında daha önce görmüş olduğum klasik CPS (Complex Physical Systems) yanında CAS (Complex Adaptive Systems) ile tanıştırdı ve de ilk defa bir karmaşık sistemin cebir üzerinden anlatıldığını görmüş oldum. John Holland'ın Santa Fe Enstitüsü'nde çalıştığını öğrenmemle beraber Emergence kitabını hemen sipariş ettiğimi ve diğer kitaplarını da merakla takip listeme eklediğimi not düşmeliyim.
April 1, 2023
https://fromtheheartofeurope.eu/complexity-a-very-short-introduction-by-john-h-holland/
On the basis of reading two books from the series, I’m rather impressed with the Very Short Introductions from Oxford University Press (the other one I have read is Modern China, by my old friend Rana Mitter). I complained after reading one of the earlier accounts of complexity that I was still looking for a good introduction to the topic, and I think I have found it. Mathematics is not really my thing these days, but I found this a very helpful overview of the theoretical side of complex adaptive systems, pulling together a lot of topics that I vaguely knew about. I still need to find something on the more organisational management side of it, but this is a good start.
On the basis of reading two books from the series, I’m rather impressed with the Very Short Introductions from Oxford University Press (the other one I have read is Modern China, by my old friend Rana Mitter). I complained after reading one of the earlier accounts of complexity that I was still looking for a good introduction to the topic, and I think I have found it. Mathematics is not really my thing these days, but I found this a very helpful overview of the theoretical side of complex adaptive systems, pulling together a lot of topics that I vaguely knew about. I still need to find something on the more organisational management side of it, but this is a good start.
January 12, 2024
Holland does a very good job striking a balance between "very briefly introducing" and refraining from condescension. Sometimes books from the VSI series can assume way too much knowledge in the reader, making them impenetrable to laymen while simultaneously being too scant for the initiated. Other times they can be way too broad and "spare" you the more meaty bits. This book however was very well balanced. The analogies and examples were well selected. I generally had a grasp on the subject matter from moment to moment, but was still periodically baffled, which is pretty ideal in my opinion. I want to be challenged, but at the same time I am reading for pleasure. I doubt that I'll dig any deeper into complex systems any time soon, but it was nice putting a new little tool in my toolbox for understanding the world around me.
May 26, 2020
I wouldn't recommend this for someone who wants an approachable way to learn more about complexity science. I mean just it's name might be a hindrance your curiosity let alone this text.
It is definitely informative, but you can only read on if you have an assignment or as in my case an honest review to write.
Yes there were many helpful classifications that has helped memorize some definitions and cluster, but this is not the best book I read that aims at explaining these theories : I would recommend Scott E. Page (he has a nice blog), and his co-author Miller...
I have one author that I haven't read yet and that's Pr. Melanie Mitchell!
In short, this book is a lovely review if you are already acquainted with the topic, and honestly that's far from an introduction... Sorry
It is definitely informative, but you can only read on if you have an assignment or as in my case an honest review to write.
Yes there were many helpful classifications that has helped memorize some definitions and cluster, but this is not the best book I read that aims at explaining these theories : I would recommend Scott E. Page (he has a nice blog), and his co-author Miller...
I have one author that I haven't read yet and that's Pr. Melanie Mitchell!
In short, this book is a lovely review if you are already acquainted with the topic, and honestly that's far from an introduction... Sorry
May 23, 2017
A good start to learn about complex adaptive system
This book is a good start to learn about complex adaptive system. The subject is quite complicated and a concise presentation is a quite daunting task. For this reason the book becomes increasingly difficult to follow, especially if the reader is lacking the basic theoretical elements, hence requiring to read some sections multiple times. The summary at end ties all together helping to consolidate what has been absorbed up to that point. Good references help to further explore the subject.
This book is a good start to learn about complex adaptive system. The subject is quite complicated and a concise presentation is a quite daunting task. For this reason the book becomes increasingly difficult to follow, especially if the reader is lacking the basic theoretical elements, hence requiring to read some sections multiple times. The summary at end ties all together helping to consolidate what has been absorbed up to that point. Good references help to further explore the subject.
June 11, 2022
Complexity is a unique book. It describes very interesting properties that distinguish complex from complicated systems: emergence, self-organisation, butterfly effect, fat-tailed behavior, and adaptive interaction. Complex physical vs adaptive systems were discussed in much depth with great visuals, and analogies. The examples described were effective and quite representative: cellular automation for CPS algorithms, markets & rainforests for CAS feedback loops. Complexity analysis was helpful too.
January 17, 2023
Generally a big fan of this series but this topic didn't translate as well. Still contained a lot of interesting stuff, but it was unclear sometimes what was discussion of models, and what was discussion of the phenomena being modeled. For example a lot of the book is dedicated to Complex Adaptive Systems, which are modeled as signals, and processes which recieve signals and output signals, which makes sense in the modelling world but to me felt a bit strange when talking about the actual processes being discussed. Still a decent read.
March 30, 2024
More than anything a reflection of the benefits of the format of the series. Really great introduction, and for a topic that seems (unfortunately or fortunately depending on your perspective) taken on by mathematicians, doesn't focus too much on said maths. Side point on how ironic it is that complexity explains a non-precise universe but the field is mostly populated by precision seeking mathematicians. The varied aspects explored in the book, the subtopics, the areas, set about a great foundation in complexity and one of, if not the best, starting points to the topic
February 1, 2017
Not having any experience with the subject matter, I cannot testify to the quality of information. I did, however find the book easy to read and learned quite a bit about a fascinating topic. As a primer, I found it very useful. While accessible to the layperson, it does get into respectable depth.
June 18, 2022
Türkçe çevirisini hiç beğenmedim. Bazı terimler yerine oturmuyor, çevirmen konuya hakim değil. İngilizce üzerinden devam etmek zorunda kaldım. Kitap konuya kısa giriş için okunabilir, ama bazı konular tekrarlı ve iyi kurgulanmamış bence. Garip benzetmeler var. Konunun kendisi mi böyle bilemiyorum, incelenebilir.
October 28, 2017
New Concepts in Complexity
I found this book to provide a good overview of boundaries, hierarchies and rules, concepts which I have not seen explained in detail in previous books on the subject.
I found this book to provide a good overview of boundaries, hierarchies and rules, concepts which I have not seen explained in detail in previous books on the subject.
January 8, 2018
I own a few other books from these series, and this is as good as the others. However, the constraints of these books is they have to limit themselves to a great degree of abstraction and lack of detail. In all, I should be reading this as a companion to a more substantive work on the same subject.
February 14, 2021
Karmaşıklık konusunda daha önce de okumalarım olmasına rağmen bazı yerlerde anlamakta zorlandım diyebilirim. Başlığının "Kısa bir giriş" olmasına rağmen giriş seviyesinde değil. Özellikle konu hakkında hiç bilgisi olmayanlar için okuması kolay olmayabilir.
October 23, 2021
A very short introduction indeed. I like how it tries to formalize evolution of complex systems but it's too short to make a real impact. Nice thing is that I have a better understanding of what a markov chain is though. ;)
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