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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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!
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.
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.
There is the problem you can only click TryIt once. If you want to repeat it you have to refersh the page.
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.
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...
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.
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)
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.
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.