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Complex Adaptive Systems: An Introduction to Computational Models of Social Life (Princeton Studies in Complexity)

3.94  ·  Rating details ·  383 ratings  ·  25 reviews

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

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Kindle Edition, 271 pages
Published November 28th 2009 by Princeton University Press (first published 2007)
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Amin
Dec 31, 2016 rated it liked it  ·  review of another edition
Shelves: science
"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
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Mangoo
Jan 11, 2011 rated it really liked it  ·  review of another edition
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
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John Carter McKnight
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.
Rob T
Sep 17, 2012 rated it really liked it  ·  review of another edition
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.
Alex Whalen
May 09, 2011 rated it it was amazing  ·  review of another edition
Shelves: complex-systems
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!
Дмитрий Филоненко
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 ...more
Dona Baker
Feb 02, 2014 rated it really liked it  ·  review of another edition
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 https://www.coursera.org/ and The Great Courses series of DVD's http://www.thegreatcourses.com/greatc....
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Philip Williams
Dec 02, 2016 rated it it was amazing  ·  review of another edition
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.
ShawnLeeZX
Apr 23, 2020 rated it really liked it  ·  review of another edition
Shelves: complex_system

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, to
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Safiya
Nov 17, 2018 rated it liked it  ·  review of another edition
Shelves: rmtd, vuca
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 n
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James Igoe
Dec 13, 2016 rated it really liked it
Shelves: systems
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 deta ...more
Denis Romanovsky
Jan 17, 2019 rated it it was amazing  ·  review of another edition
Shelves: favorites
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!
Daniel Cunningham
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.
Daniel Arges
Jul 31, 2018 rated it liked it  ·  review of another edition
Shelves: social
Good book, although too much theoretical, with so few practical examples.
The reading is quite boring.
Will
Jul 22, 2018 rated it it was ok  ·  review of another edition
Not engaging for people new to the space. Lacking real world context, most examples take the form of ABC equations.
Daniel Barker
Feb 14, 2018 rated it really liked it  ·  review of another edition
Scott page is great. This book is an excellent companion to his Coursera course.
S.P.
Jan 21, 2009 rated it liked it  ·  review of another edition
Shelves: non-fiction, science
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...
John
Aug 03, 2014 rated it it was amazing  ·  review of another edition
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.
Paulo Fernando Vieira de Carvalho
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)
Srav Chag
Sep 30, 2016 rated it it was amazing  ·  review of another edition
Shelves: thesis
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.
DJ
Jul 22, 2010 marked it as to-read  ·  review of another edition
applications to social dynamics
Vikram
Feb 01, 2011 rated it it was ok  ·  review of another edition
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.
Ahmad
Aug 09, 2009 rated it really liked it  ·  review of another edition
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 ..
Robin Berjon
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.
Eric Waggoner
Not terrific. I wouldn't read another book by this author. Long on verbiage, short on substance.
Kevin Krosley
rated it really liked it
Nov 21, 2018
Edwin
rated it it was amazing
May 17, 2018
David Borenstein
rated it liked it
Jun 26, 2016
Monte
rated it really liked it
Jun 13, 2014
Prasanth
rated it really liked it
Sep 07, 2012
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John 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)

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