Description

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The analysis of complex systems--from financial markets and voting patterns to ecosystems and food webs--can be daunting for newcomers to the subject, in part because existing methods often require expertise across multiple disciplines. This book shows how a single technique--the partition decoupling method--can serve as a useful first step for modeling and analyzing complex systems data. Accessible to a broad range of backgrounds and widely applicable to complex systems represented as high-dimensional or network data, this powerful methodology draws on core concepts in network modeling and analysis, cluster analysis, and a range of techniques for dimension reduction. The book explains these and other essential concepts and provides several real-world examples to illustrate how a data-driven approach can illuminate complex systems.

• Provides a comprehensive introduction to modeling and analysis of complex systems with minimal mathematical prerequisites
  
• Focuses on a single technique, thereby providing an easy entry point to the subject
  
• Explains analytic techniques using actual data from the social sciences
  
• Uses only linear algebra to model and analyze large data sets
  
• Includes problems and real-world examples
  
• An ideal textbook for students and invaluable resource for researchers with a wide range of backgrounds and preparation
  
• Proven in the classroom
  

About the Author

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Greg Leibon is chief technology officer and cofounder of Coherent Path, a company specializing in predictive analytics. Scott D. Pauls is professor of mathematics at Dartmouth College. Dan Rockmore is the William H. Neukom 1964 Distinguished Professor of Computational Science at Dartmouth.