Book Review

Abstract

Complex systems science is a rather new discipline that has recently gained particular attention in several domains such as economy, biology, computer science, physics, and chemistry. To quote Stephen Hawking, “Complexity is the science of the 21st century” [5]. The idea of complex systems is captured precisely by Aristotleʼs Metaphysics, in which he remarkably describes “the whole [as] more than the sum of its parts” [11]. In fact, the collective behavior of a complex system cannot be inferred by merely looking at the behavior of the simple parts. What is interesting is looking at the interactions and relationships between the parts that give rise to the collective behavior. The concept of complexity is connected to the notion of edge of chaos [6], a region in the state space of a system between complete order (i.e., periodic or no change) and total randomness or chaos (i.e., aperiodic change) where the complexity is maximal. Besides the more philosophical aspects, complex systems can be analyzed and simulated with the help of unconventional models (e.g., cellular automata, networks, agent-based models). Such models are, in general, fairly simple, but their mathematical/computational analysis and implementation may represent a challenge for students and for researchers outside mathematical and computational studies. Instructors may also be challenged by teaching complex systems modeling and analysis to a broad audience of students from different backgrounds, not necessarily familiar with the terminology and simulation techniques. With that in mind, Sayama has been actively involved in a multitude of summer and winter schools, seminars, conference tutorials, and academic courses within complex systems. One of the main challenges that Sayama and colleagues (myself included) face when teaching complex systems science to students from diverse disciplines is the lack of simple, easily accessible basic teaching material that could be used for this purpose. On the other hand, several somewhat advanced research-oriented books exist [2, 14, 3] that tackle complexity from a more rigorous theoretical and mathematical perspective. The novelty of the book reviewed here, Introduction to the Modeling and Analysis of Complex Systems, lies in its ability to introduce the broad discipline of complex systems to beginners in the field, both students and instructors. This is done in a simple and clear way without ignoring technical aspects and with easily reproducible examples even for those not necessarily familiar with computer programming and scripting/coding abilities. Sayamaʼs book is suitable for advanced high school, undergraduate, and graduate students in the natural sciences, the social sciences, technology and engineering, management, and economics, among others. The book is available under the OpenSUNY initiative [10] and is downloadable for free in PDF format. The book provides Python source code for all the given examples, together with solutions to the presented exercises. It uses a freely available Python-based simulation framework for complex systems modeling, called PyCX [12]. Sayama was also involved in the development of the PyCX example library. The book is divided into three main parts: an introduction that covers brief historical notes, the key concepts of emergence and self-organization (recurring themes underlying all the subsequent chapters), and the basics of creating models; a second part that introduces key concepts such as