Abstract
The standard assumptions that underlie many conceptual and quantitative frameworks do not hold for many complex physical, biological, and social systems. Complex systems science clarifies when and why such assumptions fail and provides alternative frameworks for understanding the properties of complex systems. This review introduces some of the basic principles of complex systems science, including complexity profiles, the tradeoff between efficiency and adaptability, the necessity of matching the complexity of systems to that of their environments, multiscale analysis, and evolutionary processes. Our focus is on the general properties of systems as opposed to the modeling of specific dynamics; rather than provide a comprehensive review, we pedagogically describe a conceptual and analytic approach for understanding and interacting with the complex systems of our world. This paper assumes only a high school mathematical and scientific background so that it may be accessible to academics in all fields, decision-makers in industry, government, and philanthropy, and anyone who is interested in systems and society.
Highlights
How can we scientifically approach the study of complex systems—physical, biological, and social? Empirical studies, while useful, are by themselves insufficient, since all experiments require a theoretical framework in which they can be interpreted
The sum of a system’s complexity at each scale will equal the sum of each individual component’s complexity [9]. us, for any system, there is a fundamental tradeoff between the number of behaviors a system can have and the scale of those behaviors
Competition between players statistical physics is that in spite of the impossibility of describing the details of trillions of molecules, the macroscopic properties of the molecules can be well understood by analyzing their space of possible behaviors, rather than their specific configurations and motions
Summary
While useful, are by themselves insufficient, since all experiments require a theoretical framework in which they can be interpreted While many such frameworks exist for understanding particular components or aspects of systems, the standard assumptions that underlie most quantitative studies often do not hold for systems as a whole, resulting in a mischaracterization of the causes and consequences of large-scale behavior. Is paper provides an introduction to complex systems science, demonstrating a few of its applications and its capacity to help us make more effective decisions in the complex systems of our world. It focuses on some general properties of complex systems, rather than on the modeling of specific dynamics as in the subfields of dynamical systems, agent-based modeling and cellular automata, network science, and chaos theory.
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