Externally indexed torrent

If you are the original uploader, contact staff to have it moved to your account

Textbook in PDF format

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.
Complex systems are commonly defined as systems comprising many strongly interacting elements. These system elements are denoted as agents, a term with roots in Computer Science and economics. Today, agent-based modeling is used in various disciplines, notably in the social sciences. In biology and ecology, it is also known as individual-based modeling. Early concepts in distributed Artificial Intelligence (AI) used the term actor-oriented modeling.
The ultimate aim of complex systems research is to explain systemic properties on the “macro” level based on the properties of the agents and their interactions on the “micro” level. The systemic properties are often denoted as emergent properties because they result from the collective interactions of many agents. Consequently, agent-based models are multi-agent models.
Preface
Introduction
Agent-Based Models
Network Models
System Dynamics Models
Models of Evolution