Born of the fruitful collaboration between the domains of human-societal disciplines and computer and information technology and grown with support from, among others, mathematical linguistics, network analysis, dynamic system theory, and system simulation techniques, the emerging field of computational social science (CSS) now benefits from large socio-databases generated by many web-network platforms or created by interdisciplinary public or private projects.

In fact, one of the book’s main parts focuses on the subject of automated information extraction from this kind of database, known as a data mining process. Several methods of big data exploitation from lexical, semantic, and even emotional analysis to categorization, clustering, and anomaly detection procedures are listed.

Another application-dedicated issue deals with structural and quantitative analysis of social networks, seen as the interaction totalities established between individuals, groups, or organizations and between their opinions, collective beliefs, or private interests. Starting from a matrix-based structure description and statistical characteristics of networks, some real-world examples of social network analysis (SNA) applications are presented, including human cognition and belief systems, supply chain networks, and the social structure of so-called small worlds.

The complexity of human societies, understood as dynamic, emergent, adaptive, and far from equilibrium systems, represents the theoretical core of the book and also the most formalized of its subjects. We are guided to discover social complexity in a gradual way through its quantitative indicators, distribution laws, adaption via artifacts, or canonical-integrative theories. These complexity theories are described from two complementary points of view: the forward logic perspective, probabilistically modeling the outcomes of social evolution, and the backward logic perspective, modeling its causes.

The CSS research area of variable-oriented and agent-based mathematical models along with the most significant software products used for system modeling and simulation are also extensively covered. System dynamics, queueing, cellular automata, and agent-based models involved in social system simulation are unitary, as described by the motivation design implementation verification validation analysis (MDIVVA) methodology.

As with all valuable textbooks, this book is organized in a rigorous manner: each chapter includes an introductory abstract, a short chronology of the main achievements related to the chapter’s topic, well-balanced formalized-intuitive knowledge content, a significant number of questions and problems followed by some proposed exercises, and finally a list of future readings. For didactic reasons, the author added an overview on computer basics that contains an introduction to unified modeling language (UML) notation, used for diagram representation of social complexity, and also an appendix with answers for the end-of-chapter problems.

Therefore, I think Claudio Cioffi-Revilla’s work hits its assumed target: to be an affordable textbook for students and, at the same time, a useful support manual for instructors interested in learning or teaching computational social science.