Customer Reviews: Simply Complexity: A Clear Guide to Complexity Theory
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on February 6, 2010
If you are unfamiliar with Complexity Theory ("The Science of Sciences") then this is a great book to start with. Neil Johnson has done an impeccable job of keeping the intricacies of Complexity within a very manageable framework that any layman can understand. Take this quote for example: "Complexity can be summed up by the phrase "Two's company, three is a crowd." In other words, Complexity Science can be seen as the study of the phenomena which emerge from a collection of interacting objects - and a crowd is a perfect example of such an emergent phenomenon, since it is a phenomenon which emerges from a collection of interacting people." The real strength of this book lies in Johnson's unsophisticated and plain approach towards Complexity Science which he couples with many real world examples. But neither does Johnson leave anything out; Self-Similarity, Fractals, Power-Laws, Networks, etc. - it's all here.

My only complaint about this book comes on page 100. Here, Johnson explains how the "six degrees of separation" network was conceived by Stanly Milgram in 1967. I am sure that Johnson knows that this was debunked by later research, but Johnson fails to mention this in the book (one only has to look to Wikipedia, Complexity: A Guided Tour by Melanie Mitchell or The Numbers Game: The Commonsense Guide to Understanding Numbers in the News, in Politics, and inLife for confirmation. I do not fault Johnson here because given the 'basic' level at which this book was written, he probably didn't feel like complicating the issue - the point he was trying to make was satisfied - and he therefore surely didn't feeling like going into the whole mess by upending the urban legend. So, with that aside, I do recommend this book as a great introduction to Complexity and recommend Complexity: A Guided Tour by Melanie Mitchell for the interested reader as a great book to continue learning about Complexity Science.
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on July 7, 2010
Complexity science is a broad field with vague boundaries, so no single book can cover the whole field in depth. In this book, Neil Johnson focuses on a definition of complexity associated with a particular class of computational models, and he describes these models and their resulting behaviors at a level suitable for the general reader (somewhat detailed descriptions, but essentially no formal math). He has a PhD in physics and has himself done considerable research on these types of models (see the references at the end of the book), so his knowledge in this area is fairly authoritative.

For Johnson, a complex system has the following characteristics:

(1) A population of multiple (at least three) interacting objects or "agents" which typically form a network. These objects may be very simple, but they don't have to be.

(2) Competition among the objects for limited resources. As part of this overall competition, there can also be local cooperation within the system.

(3) Feedback processes, which give the system memory and history.

(4) Ability of the objects to adapt their strategies in response to their history.

(5) Ability of the system to interact with its environment.

(6) Self-organization of system behavior, without the need for a central controller.

(7) Emergence of non-trivial patterns of behavior, including a complicated mixture of ordered and disordered behavior. This can include chaotic behavior, as well as extreme ordered behavior (eg, traffic jams, market crashes, human diseases and epidemics, wars, etc.).

Johnson gives many examples of complex systems, and a jazz band is among the most interesting of these examples (the jazz performance is the behavior of the system).

Here are some of the key results from the models he describes:

(1) Even if the objects comprising the population of the system are complicated and heterogeneous (eg, people), this variability tends to "average out" in a way that allows the objects to be modeled as being fairly simple and homogeneous (at least as a first approximation).

(2) Due to competition, the population of objects will often become polarized into two opposing groups (eg, bears and bulls in financial markets, opposing political parties, etc.). This competition tends to reduce fluctuations in the behavior of the system.

(3) It's sometimes possible to steer the behavior of a system by manipulating a subset of the system's objects.

(4) Network structure tends to make complex systems more robust.

(5) The overall behavior of a system, and the ability of individual objects in the system obtain resources, depends on both the amount of available resources and the level of connectivity (network structure) between objects. When resources are only moderate, adding a small amount of connectivity widens the disparity between successful and unsuccessful objects, whereas adding a high level of connectivity reduces this disparity. By contrast, when resources are plentiful, adding a small amount of connectivity is sufficient to increase the average success rate and enable most objects to be successful. These patterns are consistent with what I've observed in the competition among engineering firms over the years (including during the current recession, a time of reduced resources).

(6) The behavioral outcomes of complex systems often follow a power law distribution, with smaller events being most common, but with extreme events also occurring more often than one might expect.

One of my main motivations to read this book was to get insight into how malignant tumors might be modeled as complex systems, with the hope that such models might provide clues regarding more effective ways to treat cancer. I was pleased to see that Johnson does discuss cancer at several points in the book, but I was disappointed to find that his discussion of cancer modeling is relatively superficial. Nevertheless, I'm firmly convinced that cancer is best modeled as a complex system, so I believe that much more research along these lines is (urgently) needed.

Overall, I do recommend this book. Johnson is qualified to write it, and it works well as an easily understood introduction at a level of detail suitable for general readers. However, again, keep in mind that the scope of the book is fairly narrow, so many important topics aren't mentioned at all. As a result, the book provides a good understanding of some of the trees in the forest of complexity science, but not much sense of the overall forest. For a broader introduction to complexity science, I recommend Complexity: A Guided Tour by Melanie Mitchell.
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on May 9, 2011
The book is composed of two parts: the first titled "what exactly is complexity theory?", and the second "what can complexity science do for me?". While I pretty much liked the first part, I got some mixed feeling with respect to the second which I'll try to explain below.

Part one describes the ideas behind the complexity field of research, its properties and provides some toy examples (such as mob behavior). The text is very clear, easy to follow and explained in a way that *anyone* can follow. On a personal note, while most was already known to me, I really enjoyed the Jazz music analogy in chapter 3. Generally, this part was very interesting; I was missing some discussions about the differences between the complexity theory and other related (or equivalent) ideas that can be found under different umbrellas such as "agent based models", "multi agent systems".

The problem starts with Part two of the book. In this part the goal of each chapter (six of them) is to show the application of the complexity ideas to various domains: from financial markets, through warfare and terrorism, to quantum physics. My criticism is that while the author spends lots of space to describe each model, he makes very little effort to discuss the results/theorems/conclusions that can be derived from the model and their impact on reality. That is, we learn to appreciate the nice model for couple of pages but than, as the model is an extremely simplified description of reality, I kept baffling at what valuable information can be actually derived from it. The author, with only few vague sentences about the actual impact of the model, does not make a good point with that regard.

For example, chapter 10 ends with a model on sheep-wolf-dog game where one needs to decide whether to send the dogs to attack the wolf or keep them to defend the sheep. One of the conclusions is that for small numbers, attack is the best defense. That is a nice slogan but obviously not something that we can really conclude from the model. Moreover, the author claims that this result is analogous to a navy boats problem from WW2, who were hunted by German U-boat submarines. The navy ships put on a device to change course randomly to avoid contact. I think that a more accurate description for the success of the random strategy might actually come from a game theory analysis which includes mixed strategies (as oppose to the suggested game). The whole part of critical evaluation with discussion on the limitation of the models and the presentation of alternative ideas is severely lacking in this book.

That problem was pretty much consistent with all the chapters, and left me questioning whether the complexity ideas are as strong as was advocated in part one of the book. Another issue that I had while reading is the poor writing style: there are numerous repetitions of the phrases "in other words" and "in particular", often several times in the same paragraph. Going back to my mixed feeling here, I grade the book with three stars.
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on August 21, 2012
The book description might not be a lie, but i feel deceived - the preface says "There is, however, one problem. We don't yet have a fully-fledged "theory" of Complexity. Complexity. Instead, I will use this book to assemble all the likely ingredients of such a theory..."

I'm also confused by author's choice of technical terms - he never says "entropy", always "disorder" when talking about thermodynamic laws and arrow of time, but he finds it ok to say "In physics-jargon, this effect is called frustration." while i have no idea what "this" nor "frustration" means in this context (i get the meaning only from reading the following pages).

So the order in which he presents his ideas is really strange - e.g. when talking about the most important example of biasing in Physics:
1. first he uses an undefined concept (bias due to temperature): "the biasing effect of temperature is analogous to that of a tired secretary who is getting ready to leave the office at night",
2. then he provides a definition: "the temperature controls the amount of energy available for arranging objects, and this in turn biases the arrangements",
3. and a page later i finally get the idea: "The way in which water passes from ice at low temperatures to steam at high temperatures is a great example of this effect."

Also i don't like when someone defines a situation as an unsolvable problem instead of explicitly stating the reasons why obvious solutions should be ignored (e.g. "Friday night bar problem" = 100 people want to go to a bar with 60 seats, each person will "win" if he goes to bar and it won't be overcrowded, or if he stays at home if the bar is overcrowded - how should they decide without communication? => why obvious solutions like reservation system or moving to a bigger place are ignored???).

Finally, i find it annoying to use mathematical symbols without any attempt to define any rules using these symbols - like let's have a memory size m = 2 to store 2 past events and probability p = 0 if an agent makes always opposite decision than implied from past events, ... what decision should an agent make if the 2 past events were 0 and 1???

That all being said, i find the actual ideas presented in the first 3 chapters very inspiring as one of the possibilities of how to look at systems and i hope to find more practical ideas in the rest of the book.
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on March 9, 2015
I came to this book as a physicist with a hobby-level interest in complexity theory. And to me this was a disappointment. Mathematical depth is non-existent. In its course, the book goes over several problems, approaching them from the point of view of complexity theory. This perspective is interesting and instructive, sure enough. However, these explanations tend to be too long and convoluted. A point that could be made effectively in a paragraph often takes several pages. Even for a non-scientist looking for an overview of a subject, I can't imagine this is a helpful treatment.

Basically, imagine someone's dad went to a lawn mower convention, and now insists on telling everyone all the cool lawn mower gadgets he saw. It's exactly like that, except it's complexity theory and not lawn mowers. And it goes on for over 200 pages.

Melanie Mitchell's "Complexity: A guided tour" is a far better book. Just get that one.
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on August 6, 2015
One of the best books I've read this year. I HIGHLY recommend it to anyone who wonders how complicate problems (hint: they all involve human behavior) could be modeled and possibly solved. So insightful yet very easy and enjoyable to read. Get this book! Read it! You will be amazed.
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on September 28, 2013
Complexity theory can be a difficult topic to learn and there is a wide body of literature with varying descriptions of what complexity means. Johnson's book is a great starting point for many readers because it is conversational in tone, free of complicated equations, covers a wide range of topics and does not assume a prior knowledge of complexity theory.

Johnson begins with a patient and detailed introduction to complexity and then introduces the role of disorder to build the groundwork for defining chaos, taking care to explain that chaotic does not equate to randomness when defined in scientific terms. His description of the eight key components of complexity (p. 15) are particularly valuable insights for those new to the topic.

An important feature of this book is Johnson's ability to make certain core concepts of complexity science clear to his readers. Examples include topics such as "pockets of order" (p. 21), "strange attractors" (p. 46), and "anti-crowds" (p. 72). Yet, Johnson's extended example of disordered files and filing cabinets quickly grows tedious. Nevertheless, for those who can endure the details, the example provides an effective way to explain some rather obscure concepts in complexity theory (e.g. strange attractors, chaos).

Johnson also articulates a very clear explanation for the formation and function of fractals as emergent outcomes in certain complex systems. This unique approach to explaining fractals is especially valuable for non-mathematicians who are curious about their relationship to system outcomes. Yet, because many people are confused by the role of fractals in complex systems it would have been helpful for the author to contrast the types of systems where fractals are, and are not expected to form. There appears to be considerable confusion about this in the business community and the popular press, especially related to organizations as complex systems.

Another topic which often seems confusing to those learning complexity is the role of feedback, especially in the organizational context where information is the medium of exchange that alters the system. Johnson depicts a framework (p. 26) of how feedback can influence complexity and provides order to a system, but fails to emphasize how feedback operates differently in a complex system, compared with a cybernetic system. He does indicate that feedback incorporates learning and memory into human dynamics but seems to suggest that feedback is the key ingredient that moves systems from order to disorder and back (p. 110). Johnson could be much clearer that complex systems don't experience feedback as a regulatory mechanism to maintain equilibrium as seen in more traditional dynamical systems. And that disorder or emergent outcomes in complex systems are also related to other mechanisms. This is conceptually important, because complex systems are generally "far from equilibrium", a characteristic that has deep implications for expected system behavior.

Overall, these are minor issues that don't mitigate the value of this excellent book, which represents an accessible and thorough treatment of complexity science at the introductory level. Lastly, don't overlook the appendix for an extensive annotated list of references and resources about complexity topics.

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on February 4, 2014
One of the best books on this subject I've read. I've referenced this multiple times in my research and recommend this book to others interested in this subject, particularly those lacking a solid foundation in the hard sciences.
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on July 12, 2013
This was one of the most boring and incomprehensible books I've read in a while. Instead of making complexity theory more approachable the author completely turned me off the subject. The analogies and tools he uses to explain the theory instead add more "complexity" to even the simplest points. Here's a game you can play: count the times the author says, "in other words...". Hint, it's a whole lot! In other words, the author goes over twice what should have originally been explained (once) in the simplest way possible.

The book is lacking as an introductory overview or as a text book. Better illustrations would have helped (the ones in the book are pathetic) as would better case studies. Complexity theory is really about applying tools such as probability and pattern recognition to complex systems. Neil Johnson gets some of these points across but drags you through Dante's nine levels of boredom to get there.
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on June 21, 2015
This text provides a relatively straight forward approach to examining common situations in life that can be categorized as 'complex'. These examples then enable the reader to view these, and other, complex systems from a new perspective. It makes for an interesting read, but don't expect it to provide you "the meaning of life".
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