The model thinker What you need to know to make data work for you

Scott E. Page

Book - 2018

"We confront no end of complex problems: why is inequality on the rise? Why are more and more Americans clinically obese? Does a racially diverse team make better decisions? How can we predict the outcomes of elections? At the same time, we find ourselves awash in data, be it on the opioid crisis, college admissions, genetic correlates of disease, financial transactions, or athletic performance. To confront such complexity and put that data to use, we need models: we can use linear regression to predict sales growth, or a power-law distribution to explain city sizes and book sales. Although each model offers insight, any single model will be wrong--just ask the physicist who, trying to understand barnyard animals, imagined a spherical ...cow. We must be able to do better. The question is simply how. In [this book], Scott E. Page gives us the answer: many-model thinking. By applying multiple diverse frameworks, we can achieve greater insights--indeed, using many models enables us to scale a hierarchy encompassing data, information, knowledge, and ultimately wisdom. Underpinning this, Page presents twenty-five broad classes of models--including models of growth, random walks, entropy, Markov chains, and many more--in a user-friendly and highly readable format, while teaching us how and when to apply them. Whether you work in science, business, government, or even literary studies, you confront complex problems, and you have more data than ever before. The Model Thinker will show how models can make that data work for you."--Jacket.

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Subjects
Published
New York : Basic Books [2018]
Language
English
Main Author
Scott E. Page (author)
Edition
First edition
Physical Description
xiii, 427 pages : illustrations ; 25 cm
Bibliography
Includes bibliographical references (pages 357-409) and index.
ISBN
9780465094622
  • Prologue
  • 1. The Many-Model Thinker
  • 2. Why Model?
  • 3. The Science of Many Models
  • 4. Modeling Human Actors
  • 5. Normal Distributions: The Bell Curve
  • 6. Power-Law Distributions: Long Tails
  • 7. Linear Models
  • 8. Concavity and Convexity
  • 9. Models of Value and Power
  • 10. Network Models
  • 11. Broadcast, Diffusion, and Contagion
  • 12. Entropy: Modeling Uncertainty
  • 13. Random Walks
  • 14. Path Dependence
  • 15. Local Interaction Models
  • 16. Lyapunov Functions and Equilibria
  • 17. Markov Models
  • 18. Systems Dynamics Models
  • 19. Threshold Models with Feedbacks
  • 20. Spatial and Hedonic Choice
  • 21. Game Theory Models Times Three
  • 22. Models of Cooperation
  • 23. Collective Action Problems
  • 24. Mechanism Design
  • 25. Signaling Models
  • 26. Models of Learning
  • 27. Multi-Armed Bandit Problems
  • 28. Rugged-Landscape Models
  • 29. Opioids, Inequality, and Humility
  • Notes
  • Bibliography
  • Index
Review by Choice Review

Page, a prominent social science researcher and complex systems scholar (Univ. of Michigan), has written a tremendously significant book embracing a creative, innovative approach for thinking about the complex mechanisms of social and natural phenomena. Page emphasizes a many-model thinking approach rather than a stand-alone model to understand and solve various real-world problems. His many-model thinking approach has certain parallels to multiscale mathematical modeling and ensemble modeling in data science. Page introduces models from various disciplines to facilitate many-model thinking. For each individual model, he thoroughly describes all levels of components, from related assumptions, to mathematics equations, to applications and implications. At the end of the book, he ultimately demonstrates how opioid pandemics and income inequality can be explained by the many-model approach. The Model Thinker could serve as a complementary textbook to mathematically and computationally intensive courses in econometrics, statistics, and data science. The text is accompanied by detailed notes and references for further study. This book is suitable for both college and graduate students as well as general readers who are interested in learning about various models and their ensemble. Summing Up: Highly recommended. Advanced undergraduates through faculty and professionals; general readers. --Seong-Tae Kim, North Carolina A&T State University

Copyright American Library Association, used with permission.