Simply Complexity: A Clear Guide to Complexity Theory

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.

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.If you found this review helpful please click "Yes".

Very interesting but somewhat verbose text with a lack of quantitative analysis. Very much geared to the general public but the prose is not engaging enough for the stated audience and not quantitative enough for those who want a deeper understanding.

Awesome book!! Excellent way to start to grasp the wide ranging applicability of complexity theory

I have read better books on the subject.

After reading the book you are left where you started. This book is too popular in the sense that it does not help you to gain more than anecdotal knowledge of the field.

good