Complex Adaptive Systems: An Introduction to Computational Models of Social Life

As social and natural science inquiry moves toward a new paradigm that encourages a greater use of emergence and pattern recognition, the social work field remains underrepresented. Those familiar with the concepts of complex adaptive systems (CAS) and, more specifically, agentbased modeling (ABM) may ask how these concepts apply to social workers. Miller and Page’s Complex Adaptive Systems: An Introduction to Computational Models of Social Life provide possible answers. Although the computational nature of complex adaptive systems may strike fear in many, it provides a valuable bottoms-up framework that accounts for individual client (agent) behavior resulting in larger patterns rather than a top-down approach to the testing of models. It potentially provides a more sensitive method for investigating individuals and their responses to social interventions. In this volume, the authors develop a concise introduction to what many refer to as the science of the 21st century. The authors’ strength comes from multiple perspectives developed through the application and study of complexity science. Because of its adaptive nature, complexity science—understanding what attracts agents (clients), how these agents then self-organize in order to lead to an emergent behavior—provides a promising investigative framework adaptable within both qualitative and quantitative methods. The authors take a 5-part approach to explain their method. Part I introduces the reader to the complexity present in our social world. It gives readers unfamiliar with complex adaptive systems a solid orientation to several key concepts. While several of these concepts may initially be unfamiliar to social work researchers (e.g., cellular automata, self-organized criticality, and emergence theory), these concepts nonetheless are essential to grasping and applying complexity components. Part II briefly discusses preliminary information on the basics of modeling and emergence theory. Part III begins a lengthy discussion on computational modeling and its role in social inquiry. By discussing 10 aspects of applying complexity, the authors cover a broad range of advantages in using the approach. Given that the authors’ backgrounds lie primarily in economics, political science, and computational social sciences, the material remains accessible and applicable to social service research. Foremost are flexibility versus precision whereby a wider range of behaviors can be included and being process oriented while not being oversimplified in a reductionist sense. Other valuable concepts that separate the complex adaptive systems framework from traditional inquiry include the use of adaptive agents who are inherently dynamic and heterogeneous and the relative costs of conducting research. Part IV is the most technical section because it discusses several models of complex adaptive social systems. The chapters include a basic classifying framework based on the Buddhist Eightfold Path of right view, right intention, right