Methods and Techniques of Complex Systems Science: An Overview

dc.creatorShalizi, Cosma Rohilla
dc.date2003-07-09
dc.date2006-03-24
dc.date.accessioned2026-07-07T06:36:14Z
dc.date.available2026-07-07T06:36:14Z
dc.descriptionIn this chapter, I review the main methods and techniques of complex systems science. As a first step, I distinguish among the broad patterns which recur across complex systems, the topics complex systems science commonly studies, the tools employed, and the foundational science of complex systems. The focus of this chapter is overwhelmingly on the third heading, that of tools. These in turn divide, roughly, into tools for analyzing data, tools for constructing and evaluating models, and tools for measuring complexity. I discuss the principles of statistical learning and model selection; time series analysis; cellular automata; agent-based models; the evaluation of complex-systems models; information theory; and ways of measuring complexity. Throughout, I give only rough outlines of techniques, so that readers, confronted with new problems, will have a sense of which ones might be suitable, and which ones definitely are not.
dc.description96 pages, 8 figures. Versions 2 and 3: corrects minor typographical errors. Version 4: Expanded examples, updated references (through late 2004), matches published version up to changes in formatting
dc.identifierhttps://arxiv.org/abs/nlin/0307015
dc.identifierhttp://arxiv.org/abs/nlin/0307015
dc.identifierChapter 1 (pp. 33--114) in Thomas S. Deisboeck and J. Yasha Kresh (eds.),_Complex Systems Science in Biomedicine_ (New York: Springer, 2006)
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/100017
dc.subjectAdaptation and Self-Organizing Systems
dc.subjectStatistical Mechanics
dc.subjectChaotic Dynamics
dc.subjectCellular Automata and Lattice Gases
dc.subjectData Analysis, Statistics and Probability
dc.subjectQuantitative Methods
dc.titleMethods and Techniques of Complex Systems Science: An Overview
dc.typetext

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