What do you think?
Rate this book
Complex Adaptive Systems: An Introduction to Computational Models of Social Life
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.
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.
284 pages, Paperback
First published January 1, 2007
103 people are currently reading
2,243 people want to read
About the author
John H. Miller
4books9followersJohn Howard Miller, 1959-
Experiments with economic principles, c1997: t.p. (John H. Miller) p. iii (assoc. prof., economics, Carnegie Mellon U., Pittsburgh, 1989- ;Ph.D., U. of Michigan)
Experiments with economic principles, c1997: t.p. (John H. Miller) p. iii (assoc. prof., economics, Carnegie Mellon U., Pittsburgh, 1989- ;Ph.D., U. of Michigan)
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
Displaying 1 - 30 of 31 reviews
January 30, 2018
"English below"
نیمه اول کتاب به عنوان یک کتاب علمی برای عموم، نیمه جذابی است اما رفته رفته در نیمه دوم ظاهری آکادمیک و با جذابیت کمتر پیدا می کنند. چرا که سرشار از مثالها و مدلهایی می شود که برای کتاب عمومی جزئیات بیش از حد دارند و برای کتاب آکادمیک چندان کاربردی نیستند، چرا که به تصویرسازی و البته شبیه سازی کامپیوتری برای فهم بیشتر نیاز دارند
برای مطالعه مقدماتی بهتر برای درک موضوع کتاب پیچیدگی از ملانی میچل کتابی مفیدتر و با قلمی منسجم تر است گرچه از زاویه علوم کامپیوتر به مسئله پرداخته است و انگیزه من برای مطالعه این کتاب همین امر بود تا از زاویه علوم سیاسی و اجتماعی به موضوع بپردازم، نیازی که در انتها به شیوه ای رضایت بخش برآورده نشد
The first half is an enjoyable reading as a general science book, but it gets more and more academic, and less and less interesting in the second half; especially since lots of the examples and models described are too detailed to be interesting for general audience and not practical to be in an academic book, since they need more visual representation or simulation exercises to comprehend! For a better introduction to the subject "Complexity: a guided tour" is a better and well-written book. However, it is a book from a computer science perspective and I chose this one to have a better understanding of complex adaptive systems in social and political science, which at the end was a bit disappointing.
نیمه اول کتاب به عنوان یک کتاب علمی برای عموم، نیمه جذابی است اما رفته رفته در نیمه دوم ظاهری آکادمیک و با جذابیت کمتر پیدا می کنند. چرا که سرشار از مثالها و مدلهایی می شود که برای کتاب عمومی جزئیات بیش از حد دارند و برای کتاب آکادمیک چندان کاربردی نیستند، چرا که به تصویرسازی و البته شبیه سازی کامپیوتری برای فهم بیشتر نیاز دارند
برای مطالعه مقدماتی بهتر برای درک موضوع کتاب پیچیدگی از ملانی میچل کتابی مفیدتر و با قلمی منسجم تر است گرچه از زاویه علوم کامپیوتر به مسئله پرداخته است و انگیزه من برای مطالعه این کتاب همین امر بود تا از زاویه علوم سیاسی و اجتماعی به موضوع بپردازم، نیازی که در انتها به شیوه ای رضایت بخش برآورده نشد
The first half is an enjoyable reading as a general science book, but it gets more and more academic, and less and less interesting in the second half; especially since lots of the examples and models described are too detailed to be interesting for general audience and not practical to be in an academic book, since they need more visual representation or simulation exercises to comprehend! For a better introduction to the subject "Complexity: a guided tour" is a better and well-written book. However, it is a book from a computer science perspective and I chose this one to have a better understanding of complex adaptive systems in social and political science, which at the end was a bit disappointing.
January 11, 2011
A well-thought and clearly-written introduction to agent-based modeling, with main focus on its most popular application to social systems.
Such an introduction fills a vacancy in literature. Though the concept of ABM is intuitive, there are many aspects that need to be grasped before their full potentialities and limitations are entirely explicit. The first half of the book is dedicated to reflections on the concept of model itself, on its utility as a simplified map of the phenomena of interest, and on the many trade-offs on which a good model is built (few versus many agents, homogeneity versus heterogeneity, updating rules, information and comunication). This may result boring to people interested in real models - to which only the central part of the book is dedicated, and only few games and settings are delved in details, in simple topologies - but it is nonetheless the part of most interest of the work.
The authors repeatedly states that the "interest in the between" is the key to understand where agent-based models play a new role. It means that ABM allows to explore the part of the solution spaces and boundaries that lies between the extremes, which are on the contraty the usual regions that are only accessible to standard modeling like analytics and numericals. This region is actually the largest part of the solution space, and probably the most interesting, if only because it is closer to the conditions found in real life.
And in this vast middle land all kind of behaviours can appear, which may be very different from the extreme cases and standard scenarios. What it interesting is anyway the structure possibly behind it, the emergence of patterns. Information, adaptivity, communication, updating, topology (networks) - all play an important role in the models, which in turn allow a deeper appreciation of such concepts.
The book ends with a proposed agenda for further investigations, and few rules for correct modeling.
All in all, a referential point to start and pretty well didactic.
Such an introduction fills a vacancy in literature. Though the concept of ABM is intuitive, there are many aspects that need to be grasped before their full potentialities and limitations are entirely explicit. The first half of the book is dedicated to reflections on the concept of model itself, on its utility as a simplified map of the phenomena of interest, and on the many trade-offs on which a good model is built (few versus many agents, homogeneity versus heterogeneity, updating rules, information and comunication). This may result boring to people interested in real models - to which only the central part of the book is dedicated, and only few games and settings are delved in details, in simple topologies - but it is nonetheless the part of most interest of the work.
The authors repeatedly states that the "interest in the between" is the key to understand where agent-based models play a new role. It means that ABM allows to explore the part of the solution spaces and boundaries that lies between the extremes, which are on the contraty the usual regions that are only accessible to standard modeling like analytics and numericals. This region is actually the largest part of the solution space, and probably the most interesting, if only because it is closer to the conditions found in real life.
And in this vast middle land all kind of behaviours can appear, which may be very different from the extreme cases and standard scenarios. What it interesting is anyway the structure possibly behind it, the emergence of patterns. Information, adaptivity, communication, updating, topology (networks) - all play an important role in the models, which in turn allow a deeper appreciation of such concepts.
The book ends with a proposed agenda for further investigations, and few rules for correct modeling.
All in all, a referential point to start and pretty well didactic.
September 28, 2018
This book is not that much about application of agent based computational models in a social life modeling as about computationaml models as a tool for research. So don't expect here a fascinating journey to different social phenomena. Though some of them are scratched a bit in order to demonstrate some particular features of computational models. You can think this book as an introduction into modeling adaptive systems. And Miller covers here many quite basic metascientific questions. Like, for example, he has to address the scepticism and suspiciousness of those who doesn't trust computational modeling to be the same reliable and pure as mathematical analysis where you have a set of equations describig a particular phenomenon.
Miller has been underlining through many chapters what makes computational modeling so attractive. First of all, it's so called 'the interest in between', when a phenomenon consists of a few agents. Traditional models usually are capable to cope with systems of 1, 2 or infinite number of objects. But if we talk for example about political parties then their number is far from being infinite but also higher than 2. Another intresting aspect is spatial relations. Traditional models usually have to assume that either all objects are randomly spreaded in a model space or exist on a pinpoint. Which is not always close to reality. Computational modeling allows to introduce various spatial relations among agents like families and friends or colleagues.
Basically the main goal of computational modeling is investigation of emergence: when from interaction of basic objects - on a micro level - a new entity or system on a macro level, with its behavior and laws, is born. As an example Miller mentions a sequence of sciences: chemistry laws are based on ground laws of physics, biology is based on chemistry, psychology is based on biology, and sociology is built on the top of psychology. And it's quite fascinating to discover how macrobehavior is born from microbehavior. Usually it is subconciously assumed that opposite directions of a microbehavior would lead to different macrobehavior. But there are examples when they lead to the same outcome.
Also Miller considers computational modeling in close cooperation with genetic algorithms which make a system of interacting agents to adapt, to evolve, to live. And results of application of such apparatus conform to observations of real phenomena in social life and economics. I think it's a crucial moment in computational modeling.
Apparently this book was not aimed to a broad audience but most likely to students. Probably this explains the fact that Miller is not shy to use math equations and formulaes and even, oh Lord!, theorems and their full proves! At the same time ocassionally the book looks uneven. Some quite obvious things are covered very thoroughly with very detailed and redundant explanations. But some more tricky questions are explained briefly as if they were selfevident. For example I was not convinced if it's correct to model an organization as a binary automaton representing some binary function and then making conclusions regarding abilities of such organization to solve different problems. Sometimes I found that Miller uses two or three words for likely the same notion, but it's not clear completely, and a reader is left to guess if it's so.
Miller has been underlining through many chapters what makes computational modeling so attractive. First of all, it's so called 'the interest in between', when a phenomenon consists of a few agents. Traditional models usually are capable to cope with systems of 1, 2 or infinite number of objects. But if we talk for example about political parties then their number is far from being infinite but also higher than 2. Another intresting aspect is spatial relations. Traditional models usually have to assume that either all objects are randomly spreaded in a model space or exist on a pinpoint. Which is not always close to reality. Computational modeling allows to introduce various spatial relations among agents like families and friends or colleagues.
Basically the main goal of computational modeling is investigation of emergence: when from interaction of basic objects - on a micro level - a new entity or system on a macro level, with its behavior and laws, is born. As an example Miller mentions a sequence of sciences: chemistry laws are based on ground laws of physics, biology is based on chemistry, psychology is based on biology, and sociology is built on the top of psychology. And it's quite fascinating to discover how macrobehavior is born from microbehavior. Usually it is subconciously assumed that opposite directions of a microbehavior would lead to different macrobehavior. But there are examples when they lead to the same outcome.
Also Miller considers computational modeling in close cooperation with genetic algorithms which make a system of interacting agents to adapt, to evolve, to live. And results of application of such apparatus conform to observations of real phenomena in social life and economics. I think it's a crucial moment in computational modeling.
Apparently this book was not aimed to a broad audience but most likely to students. Probably this explains the fact that Miller is not shy to use math equations and formulaes and even, oh Lord!, theorems and their full proves! At the same time ocassionally the book looks uneven. Some quite obvious things are covered very thoroughly with very detailed and redundant explanations. But some more tricky questions are explained briefly as if they were selfevident. For example I was not convinced if it's correct to model an organization as a binary automaton representing some binary function and then making conclusions regarding abilities of such organization to solve different problems. Sometimes I found that Miller uses two or three words for likely the same notion, but it's not clear completely, and a reader is left to guess if it's so.
June 23, 2020
I picked up Miller & Page to learn about complex adaptive systems, which was a reasonable expectation given the title. I was not particularly successful.
First, this is not a textbook or reference. It reads more like a pop-sci treatment of the subject, albeit one on the more difficult side. It is *very* wordy. I like words as much as the next person, but here words are often used where mathematics or an illustration would be greatly more effective in conveying the idea. The discussion of Wolfram's automata classification scheme would have been greatly clarified if this scheme was given quantitatively, as would the presentation of power-laws and heavy-tailed distributions. This makes it seem like the authors deliberately avoided a more technical treatment of the subject, which was a mistake if true.
Beyond this, my misgivings are that one doesn't really get a comprehensive feel for this subject from this text. The overarching theme appears to be "interacting automata with certain rules for how to evolve and certain interconnections might do something interesting; if you let them adapt these rules as they evolve, they might do something still more interesting." It reads, as advertised, like a collection of examples meant to illustrate this broad idea. Sometimes these examples are apt vehicles for the concepts (like the 2D forest fire model that exhibits self-organization in interesting ways), but sometimes they fall flat (like the section on organizational decision making, which used a weak and almost trivial example that didn't illuminate the potential power of this area).
Overall, the book is a bag of computational examples with very little conceptual foundation tying it all together. Now, this might just be the state of the subject, which apparently lacks basic principals and unifying formalism, and so perhaps there's little the authors could have done to improve the presentation. Regardless of who's to blame -- the authors or an entire field of research -- the book just didn't do it for me.
First, this is not a textbook or reference. It reads more like a pop-sci treatment of the subject, albeit one on the more difficult side. It is *very* wordy. I like words as much as the next person, but here words are often used where mathematics or an illustration would be greatly more effective in conveying the idea. The discussion of Wolfram's automata classification scheme would have been greatly clarified if this scheme was given quantitatively, as would the presentation of power-laws and heavy-tailed distributions. This makes it seem like the authors deliberately avoided a more technical treatment of the subject, which was a mistake if true.
Beyond this, my misgivings are that one doesn't really get a comprehensive feel for this subject from this text. The overarching theme appears to be "interacting automata with certain rules for how to evolve and certain interconnections might do something interesting; if you let them adapt these rules as they evolve, they might do something still more interesting." It reads, as advertised, like a collection of examples meant to illustrate this broad idea. Sometimes these examples are apt vehicles for the concepts (like the 2D forest fire model that exhibits self-organization in interesting ways), but sometimes they fall flat (like the section on organizational decision making, which used a weak and almost trivial example that didn't illuminate the potential power of this area).
Overall, the book is a bag of computational examples with very little conceptual foundation tying it all together. Now, this might just be the state of the subject, which apparently lacks basic principals and unifying formalism, and so perhaps there's little the authors could have done to improve the presentation. Regardless of who's to blame -- the authors or an entire field of research -- the book just didn't do it for me.
June 27, 2012
A tremendously useful introduction to a set of new lines of research in - every field imaginable. Moderate in its claims, starting from very elementary and easy to follow and developing to some moderately complex (for me) stats and algebra, at about the undergrad level, and eminently skippable for anyone not interested in mathematical modeling.
Really worth at least skimming for anyone looking to stay current in social science methods and their implications.
Really worth at least skimming for anyone looking to stay current in social science methods and their implications.
September 17, 2012
My friend Jon loaned this to me as an academic read I might find interesting, and it lived up to the promise. Though it's written for an introductory course in complex systems, it reads like rigorous pop science for the first two-thirds with interesting examples of modeling systems with lots of agents (e.g. voting, biology, etc.). The last third gets harder to read at it shifts to proofs based on very simple systems, but by then you're so close to the end that you just have to finish.
November 17, 2018
One thing is sure, it would be a little bit harder to follow through this book, if you didn't or aren't simultaneously following the Model Thinking course by Scott E. Page himself. (it's a notorious MOOC, and is very enjoyable indeed...)
Model thinking, if I had to describe it, is a more sophisticated version of Mind Games, that tries to understand the world around us.
I first was interested in VUCA studies after discovering the opportunities it offers.
This book sums up the essential things one needs to know before diving in such as : Emergent phenomena, Equilibria, Game Theory, The Prisoner's Dilemma,... (All the fun), although it's not enough, but the authors put tge right effort to quench one's curiosity and captivate me at least. It's the most pluridisciplinary field I have ever came across.
This is one big adventure that just started by the end of this reading.
From Boolean functions to computational modelling, I was positively surprised by the genius of all the scientists to understand and solve problems/phenomena using some unexpected shortcuts...
"A life spent making mistakes is not only more honorable, but
more useful than a life spent doing nothing."
--George Bernard Shaw
Model thinking, if I had to describe it, is a more sophisticated version of Mind Games, that tries to understand the world around us.
I first was interested in VUCA studies after discovering the opportunities it offers.
This book sums up the essential things one needs to know before diving in such as : Emergent phenomena, Equilibria, Game Theory, The Prisoner's Dilemma,... (All the fun), although it's not enough, but the authors put tge right effort to quench one's curiosity and captivate me at least. It's the most pluridisciplinary field I have ever came across.
This is one big adventure that just started by the end of this reading.
From Boolean functions to computational modelling, I was positively surprised by the genius of all the scientists to understand and solve problems/phenomena using some unexpected shortcuts...
"A life spent making mistakes is not only more honorable, but
more useful than a life spent doing nothing."
--George Bernard Shaw
February 1, 2011
The most simple language text i could find on the topic. that being said, still very dense and hard to follow. more of a fault of the abstract and complex nature of CAS then the author but still.
May 9, 2011
Really fantastic overview of the current state of CAS theories and models. So good, in fact, that its become an essential part of my dissertation!
April 24, 2020
In addition to the utilities suggested by the title that the book is an introduction to the computational models of social complex adaptive system, this is a good book to learn about the new development in formalism in adaptive system, or more broadly nonlinear system. The formalism developed before the complexity science in mathematics is the formalization of process that could be characterized precisely with linearity and limit. The operations are categorized as algebra, analysis, geometry, topology. The formalism for nonlinear phenomenon is different. For the nonlinear phenomenon, the process is unrolled nonlinearly, and thus the linear and limiting argument is not enough to characterize the process. We could analyze the condition that a solution could be reached or not, or the parameters that influence the development of the process, or design a process that fits our purpose ultimately. However, among these predictive process, there are many of which the behaviors are just chaotic. Thus, to understand and analyze such phenomenon, either for the purpose of understanding them, or utilizing the understanding for further goals, the algorithmic nature of the problem is inherent, and could only be analyzed with algorithmic tools.
In this book, the algorithmic side of formalism is explored. Though the authors do not analyze the formalism from the philosophy of mathematics side, the development in this book is a good source to understand the overview of such algorithmic formalism in the recent decades.
Only a sketchy reading of the book is made.
December 26, 2017
A thought-provoking introductory exploration to modeling social systems, covering ideas for rule-based agents within a variety of rule-based systems, moving onto evolutionary-like automata and organization of agents to solve problems. Underlying some of the ideas, one could see references to deeper concepts, e.g., nonlinearity, attractors, emergence, and complexity, none of which was explained explicitly. At times, I did find the writing tedious, as some ideas were too obvious to spend time detailing, but overall, a well-written easy to digest text.
January 17, 2019
A very good introduction to complex adaptive systems and modelling them. Lots of food for thought, some sort of new way of thinking, many different insights on how micro dynamics emerge in macro behaviour, how simple things may produce complex ones or complex ones simple. World will never be the same for a person reading such science work thoroughly. Modelling the world is definitely the future!
February 8, 2019
At first, I was a little disappointed by the lack of discussion of (code/pseudocode) implementation of models, frameworks, etc. (I think I went into this looking for a something more like an O'Reilly book.) But overall this was a very solid qualitative discussion of and around modeling, adaptive systems, etc.
February 8, 2022
Interesting book. The idea of what complex adaptive systems mean and how they can be modeled is discussed. Applications are e.g. in economy, biology, sosiology and antropology.
Concrete modeling examples are discussed quite shortly but there are links to the original stories. I think I will use quite a lot of time with studing those examples and maybe creating web based illustrations.
PS. I have earlier done a prototype using Javascript on the distribution dynamics of co-operation. See and click TryIt. The game is a repeated prisoner's dilemma in a two-dimensional space were agents interact with their neighbours only (blue meaning Co-operate, Blue meaning Defect).
There is the problem you can only click TryIt once. If you want to repeat it you have to refersh the page.
Concrete modeling examples are discussed quite shortly but there are links to the original stories. I think I will use quite a lot of time with studing those examples and maybe creating web based illustrations.
PS. I have earlier done a prototype using Javascript on the distribution dynamics of co-operation. See and click TryIt. The game is a repeated prisoner's dilemma in a two-dimensional space were agents interact with their neighbours only (blue meaning Co-operate, Blue meaning Defect).
There is the problem you can only click TryIt once. If you want to repeat it you have to refersh the page.
December 22, 2022
By looking at the collective behavior of a group as a whole, we can see patterns and trends emerge that are not apparent when considering the behavior of just one individual. By using computational models, we can gain a more rigorous, mathematical understanding of the factors that influence the behavior of complex systems and how they adapt and change over time.
May 18, 2024
Very knowledgeable author but not sure who is the target audience. The book seems rather unorganised without a coherent message, jumping from hopeless verbal descriptions of computational modelling to verbose descriptions of some modelling cases.
February 14, 2018
Scott page is great. This book is an excellent companion to his Coursera course.
July 22, 2018
Not engaging for people new to the space. Lacking real world context, most examples take the form of ABC equations.
August 11, 2021
See last update
February 25, 2023
Interesting book, though I was expecting it to be more about what was being modeled rather than how to do the modeling
January 24, 2009
Hard work! Lots of references to scientific/academic papers particularly annoying when the references are to papers produced by the authors themselves, and not enough detail on the examples -. That said an interesting book � I am not sure I would call it an ‘introduction� though a lot of prior knowledge is required to understand the topics or a desire to stop every few pages and investigate exactly what they mean by...
February 2, 2014
I am slogging my way through this as I don't have a statistics or economics education which would be helpful as a basis for understanding the math in this book.
Regardless, it is great information especially if it is supplemented with other courses that are produced by Scott Page through Coursera and The Great Courses series of DVD's .
Regardless, it is great information especially if it is supplemented with other courses that are produced by Scott Page through Coursera and The Great Courses series of DVD's .
August 7, 2014
Brilliant introduction to the model of complex adaptive systems as applied to social organizations. A must read for social scientists who are interested in the application of the complex adaptive systems model to their own interests.
February 27, 2016
An excellent book on complexity and computer science. It resonated with my drivers on understanding complexity. Important for those wanting to understand internet related complexity (from connectiveness to big data)
September 30, 2016
Absolutely amazing book - does not read like a textbook. Authors delve into the philosophical possibilities and theoretical underpinnings of complexity while laying out a beginner's guide to the methodology, even offering best practices.
December 3, 2016
A heavy read that provides a deep insight into how models can be used to provide new ways of viewing the complex world around us. Great to read in parallel with Scott E. Page "Model Thinking" in Coursera.
December 11, 2009
The nerd guide to the Galaxy ...
I think if this can be combined with game theory and chaotic modeling scientists might be able to socialize like any normal human ..
I think if this can be combined with game theory and chaotic modeling scientists might be able to socialize like any normal human ..
Want to read
July 22, 2010applications to social dynamics
May 25, 2013
It's not a bad book, but it's a very slow introduction to the topic, primarily aimed as social scientists. I wouldn't recommend it to anyone who can grasp computer-related topics quickly.
June 12, 2016
Not terrific. I wouldn't read another book by this author. Long on verbiage, short on substance.
Displaying 1 - 30 of 31 reviews
Can't find what you're looking for?
Get help and learn more about the design.