AGENTS, NETWORKS, EVOLUTION

Scientific progress during the last three decades has greatly profited from our advances in understanding complex systems. Fundamental modeling approaches were considerably improved, particularly agent-based modeling, network science, nonlinear dynamics, and system science. At the same time, these approaches have been applied to and adopted by various scientific disciplines, ranging from physics and chemistry to engineering, molecular biology, economics, and the social sciences. This book reflects the success of complexity science both from a historical and a modeling perspective. It uses 25 articles from different disciplines, published over 25 years, to demonstrate the power and problems of modeling complex systems. The book's four parts, Agent-based Models, Network Models, Models of System Dynamics, and Models of Evolution, each provide an informative synopsis of the respective modeling approach. An introductory overview summarizes each approach's essential concepts, addresses the main research directions, and reviews applications in various disciplines. The selection of reprinted publications is motivated by their scientific relevance and methodological contributions to understanding complex phenomena. A chronological list of publications details the development of each modeling approach over the past 25 years.