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Introduction to computational social science : principles and applications (2nd ed.)
Cioffi-Revilla C., Springer International Publishing, New York, NY, 2017. 607 pp. Type: Book (978-3-319501-30-7)
Date Reviewed: Mar 29 2019

For computer scientists, the title of this volume may seem an oxymoron. The social sciences (classically including psychology, sociology, anthropology, political science, and economics) use many qualitative methods that have little use for computers beyond word processing and spreadsheets for preparing research budgets. However, the very term “statistics” (coined, in 1749, to describe data about the political state) reflects a long-standing concern of these disciplines for quantitative analysis, and the increased accessibility of computational power has led to the rapid growth of computational social science, represented by a network of regional societies (such as the Computational Social Science Society of the Americas, CSSSA) and journals such as the long-standing Journal of Artificial Societies and Social Simulation (JASSS) and the newly inaugurated Journal of Computational Social Science (JCSS). This volume, by a recognized leader in the field, is intended as a university textbook introducing the field, but will also be a convenient desk reference for researchers who interact with the field.

For many, computational social science consists mainly of computer simulations of social systems. This volume does discuss social simulation (in the last two chapters), but the first seven chapters discuss three other areas where computers touch social science: automated information extraction, social networks, and the study of social complexity.

The first chapter gives an overview of the field and its history, highlighting the concept of society as a complex adaptive system. Chapter 2 reviews computation, describing the basic function of a computer, different computer languages, and the abstract modeling of objects and data structures. Chapter 3 reviews the use of computation for information extraction, focusing on two techniques: Osgood’s semantic differential (which used factor analysis to discover the ubiquity of three semantic axes in human vocabulary) and data mining. Chapter 4 is devoted to methods of studying social networks, both those made up of relations among people and those that relate concepts and beliefs into larger systems.

The author devotes three full chapters to the discussion of social complexity. Chapter 5 motivates the subject by exploring different kinds of social organization, ranging from kinship groups at one extreme to empires at the other, and offers a set of formal measures based on the network structures defined in chapter 4. Chapter 6 offers two approaches to formalizing social complexity: structural (whether components combine in series or in parallel) and distributional. This latter category leads to an extensive discussion of power-law distributions in social data. Chapter 7 discusses theories of social complexity, with formal definitions of the emergence of social complexity and the event functions that govern it, along with a series of theorems over these constructs (offered without proofs).

The discussion of social complexity is formal and mathematical in tone, but does not in itself define computational processes. Rather, it provides the theoretical grounding for the discussion of social simulation in the last two chapters, a tool that can be used to test and explore the consequences of theories of social complexity. These chapters review different types of social simulation, including equation-based modeling focused on system variables (notably, the system dynamics approach), cellular automata, and agent-based modeling. One would like some discussion of the benefits and drawbacks of each of these approaches. There is no mention of the very important technique of model docking, introduced by the author’s colleague Robert Axtell at George Mason University. This approach recommends modeling a given system using different approaches (such as an equation-based model and an agent-based model) to see which results are invariant and which ones are method dependent (alerting the researcher to possible artifacts of the simulation method).

The book is an impressive compendium of formal methods in social science that can benefit from computational support, with special focus on social systems as complex adaptive systems and a formalism for studying their evolution through time. Each chapter after the first two begins with a timeline of researchers and milestones in the topic of the chapter, and each chapter ends with problems designed to test the reader’s comprehension of the material, as well as more open-ended exercises; an appendix contains answers to the problems. These features, as well as the breadth of coverage, make the volume very attractive for its intended classroom application.

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Reviewer:  H. Van Dyke Parunak Review #: CR146500 (1906-0226)
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