Introduction to the Modeling and Analysis of Complex Systems

Reviewed by Lida Hagh, Assistant Professor, Old Dominion University on 5/28/24

Comprehensiveness rating: 5 see less

This unique approach provides a comprehensive view of related concepts and techniques and allows readers, students, and instructors to flexibly choose relevant materials based on their objectives and needs.

Content Accuracy rating: 5

This book's content is error-free.

Relevance/Longevity rating: 5

This book has 19 chapters which is one of the best sources for complex systems.

Clarity rating: 5

The book content is written in accessible prose and well-organized sections.

Consistency rating: 5

The consistency of this book is visible, and parts and sections are consistent to each other.

Modularity rating: 4

The organization and structure of the contents look good enough.

Organization/Structure/Flow rating: 5

The organization flow of the contents, code, samples and developed models appearance are fine.

Interface rating: 5

Images and codes and other features are shown clearly.

Grammatical Errors rating: 5

The book content doesn't show any grammatical errors.

Cultural Relevance rating: 5

This book uses codes, samples, and examples in each case and a model to show the application in each case.

Comments

In this book, Professor Sayama has produced a very inclusive and complete introduction and overview of complexity system modeling. This book has 19 chapters which is one of the best sources for dynamic systems and complex systems. The good aspect of this book is that it uses the open-source software Python and numerous hands-on exercises on complex system modeling. This can also help the student to develop their code and see the implementation result visually. The main topics of this book are discussed in two chapters, one focusing on computational modeling and the other on mathematical analysis. This unique approach provides a comprehensive view of related concepts and techniques and allows readers, students, and instructors to flexibly choose relevant materials based on their objectives and needs. Several examples of Python codes are provided in each modeling example. This book chapters covers the fundamentals of modeling, basics of dynamical systems, discrete-time models, continuous-time models, bifurcations, chaos, cellular automata, continuous field models, static networks, dynamic networks, and agent-based models. Dr Sayama divided this book into three main parts; Part I describes a basic introduction and fundamentals of model building and complex system modeling. Part II discusses systems that have a small number of variables, and it covers the modeling and analysis of both discrete- and continuous-time systems systematically. Part III discusses the simulation of systems with a large number of variables.

Reviewed by Sudhir Trivedi, Professor, Southern University and A&M College, Baton Rouge, Louisiana on 6/20/17

Comprehensiveness rating: 4 see less

This is more like an encyclopedia of modeling complex systems. Please take it as a compliment.

This is an excellent book on the topic which can be used by engineers, computer scientists, physicists, bio-scientists, and almost everyone who needs in-depth knowledge of modeling. The book is adequately comprehensive except I would have preferred inclusion of fuzzy set models and details on artificial neural networks. Artificial neural networks and fuzzy sets are versatile modeling tools which are involved in our day-to-day lives.

Use of Python for simulation is a great idea. I believe, a brief and formal discussion of computational complexity of various systems would have added to this book.

Content Accuracy rating: 5

I find no issues with accuracy.

Relevance/Longevity rating: 4

Modeling is a growing concept in theory and in practice, particularly owed to rapid progress in computing. This book can always be used to provide fundamental information in modeling complex systems. However, in the long run, updates will be necessary as the new concepts are discovered and implemented.

Clarity rating: 5

Very clear. Of course, one has to be aware of basic terminology in mathematics, statistics, and computing in order to follow the book.

Consistency rating: 5

I am fine with consistency. I very much appreciate the fact that the book has been organized in three parts: (i) fundamentals, (ii) systems with small number of variables, and (iii) systems with large number of variables. This provides consistency in introducing concepts.

Modularity rating: 5

The book if fine in terms of modularity.

Organization/Structure/Flow rating: 4

The book has been organized in three parts: (i) fundamentals, (ii) systems with small number of variables, and (iii) systems with large number of variables. Each part is divided in chapters. This provides clarity in organization.

This should be noted that the chapters are not arranged in order of the degree of difficulty. Easier chapters on "Modeling" precede less easier chapters on "Analysis" on a particular topic. This is not a negative or positive. This is how the book is structured.

Interface rating: 4

The book has no interface problems except some issues with formatting such as continuation of a small table on the following page (page 153).

Grammatical Errors rating: 5

I find no problems with grammar.

Cultural Relevance rating: 5

I believe science is culture-neutral. Additionally, I see no examples which would give an impression of cultural bias.

Comments

A very good and interesting book for modelers. Use of Python for simulation is a great idea.

The book is adequately comprehensive except I would have preferred inclusion of fuzzy set models and details on artificial neural networks. Artificial neural networks and fuzzy sets are versatile modeling tools which are involved in our day-to-day systems.

I believe, a brief and formal discussion of computational complexity of various systems would have nicely added to this book.