Biomedical Engineering Reference
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METHODS AND TECHNIQUES OF COMPLEX
SYSTEMS SCIENCE: AN OVERVIEW
Cosma Rohilla Shalizi
Center for the Study of Complex Systems,
University of Michigan, Ann Arbor
In 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 sys-
tems, 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; cel-
lular automata; agent-based models; the evaluation of complex-systems models; informa-
tion 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.
1.
INTRODUCTION
A complex system, roughly speaking, is one with many parts, whose behav-
iors are both highly variable and strongly dependent on the behavior of the other
parts. Clearly, this includes a large fraction of the universe! Nonetheless, it is not
vacuously all-embracing: it excludes both systems whose parts just cannot do
very much, and those whose parts are really independent of each other. "Com-
plex systems science" is the field whose ambition is to understand complex sys-
tems. Of course, this is a broad endeavor, overlapping with many even larger,
Address correspondence to: Prof. Cosma Rohilla Shalizi, Statistics Department, Carnegie Mellon
University, Pittsburgh, PA 15213 (cahalizi@stat.cmu.edu).
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