# Introduction to the Modeling and Analysis of Complex Systems

Hiroki Sayama, State University of New York at Binghamton

Copyright Year: 2015

ISBN 13: 9781942341093

Publisher: Open SUNY

Language: English

## Conditions of Use

Attribution-NonCommercial-ShareAlike

CC BY-NC-SA

## Reviews

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... read more

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.

I find no issues with accuracy.

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.

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

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.

The book if fine in terms of modularity.

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.

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

I find no problems with grammar.

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

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.

## Table of Contents

- Introduction
- Fundamentals of Modeling
- Basics of Dynamical Systems
- Discrete-Time Models I: Modeling
- Discrete-Time Models II: Analysis
- Continuous-Time Models I: Modeling
- Continuous-Time Models II: Analysis
- Bifurcations
- Chaos
- Interactive Simulation of Complex Systems
- Cellular Automata I: Modeling
- Cellular Automata II: Analysis
- Continuous Field Models I: Modeling
- Continuous Field Models II: Analysis
- Basics of Networks
- Dynamical Networks I: Modeling
- Dynamical Networks II: Analysis of Network Topologies
- Dynamical Networks III: Analysis of Network Dynamics
- Agent-Based Models

## Ancillary Material

## About the Book

*Introduction to the Modeling and Analysis of Complex Systems* introduces students to mathematical/computational modeling and analysis developed in the emerging interdisciplinary field of Complex Systems Science. Complex systems are systems made of a large number of microscopic components interacting with each other in nontrivial ways. Many real-world systems can be understood as complex systems, where critically important information resides in the relationships between the parts and not necessarily within the parts themselves.

This textbook offers an accessible yet technically-oriented introduction to the modeling and analysis of complex systems. The topics covered include: 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. Most of these topics 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 and instructors to flexibly choose relevant materials based on their objectives and needs. Python sample codes are provided for each modeling example.

## About the Contributors

### Author

**Hiroki Sayama**, D.Sc., is an Associate Professor in the Department of Systems Science and Industrial Engineering, and the Director of the Center for Collective Dynamics of Complex Systems (CoCo), at Binghamton University, State University of New York. He received his BSc, MSc and DSc in Information Science, all from the University of Tokyo, Japan. He did his postdoctoral work at the New England Complex Systems Institute in Cambridge, Massachusetts, from 1999 to 2002. His research interests include complex dynamical networks, human and social dynamics, collective behaviors, artificial life/chemistry, and interactive systems, among others. He is an expert of mathematical/computational modeling and analysis of various complex systems. He has published more than 100 peer-reviewed journal articles and conference proceedings papers and has edited eight books and conference proceedings about complex systems related topics. His publications have acquired more than 2000 citations as of July 2015. He currently serves as an elected Board Member of the International Society for Artificial Life (ISAL) and as an editorial board member for Complex Adaptive Systems Modeling (SpringerOpen), International Journal of Parallel, Emergent and Distributed Systems (Taylor & Francis), and Applied Network Science (SpringerOpen).