The Model Thinker: What You Need to Know to Make Data Work for You

The Model Thinker: What You Need to Know to Make Data Work for You

The Model Thinker: What You Need to Know to Make Data Work for You

The Model Thinker: What You Need to Know to Make Data Work for You

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Overview

Work with data like a pro using this guide that breaks down how to organize, apply, and most importantly, understand what you are analyzing in order to become a true data ninja.

From the stock market to genomics laboratories, census figures to marketing email blasts, we are awash with data. But as anyone who has ever opened up a spreadsheet packed with seemingly infinite lines of data knows, numbers aren't enough: we need to know how to make those numbers talk. In The Model Thinker, social scientist Scott E. Page shows us the mathematical, statistical, and computational models—from linear regression to random walks and far beyond—that can turn anyone into a genius. At the core of the book is Page's "many-model paradigm," which shows the reader how to apply multiple models to organize the data, leading to wiser choices, more accurate predictions, and more robust designs. The Model Thinker provides a toolkit for business people, students, scientists, pollsters, and bloggers to make them better, clearer thinkers, able to leverage data and information to their advantage.

Product Details

ISBN-13: 9781549110399
Publisher: Basic Books
Publication date: 03/16/2021
Product dimensions: 5.70(w) x 5.60(h) x 1.10(d)

About the Author

Scott E. Page is the Leonid Hurwicz Collegiate Professor of Complex Systems, Political Science, and Economics at the University of Michigan and an external faculty member of the Santa Fe Institute.

Jamie Renell is a voice talent and audiobook narrator.

Table of Contents

Preface xi

Prologue xiii

1 The Many-Model Thinker 1

2 Why Model? 13

3 The Science of Many Models 27

4 Modeling Human Actors 43

5 Normal Distributions: The Bell Curve 59

6 Power-Law Distributions: Long Tails 69

7 Linear Models 83

8 Concavity and Convexity 95

9 Models of Value and Power 107

10 Network Models 117

11 Broadcast, Diffusion, and Contagion 131

12 Entropy: Modeling Uncertainty 143

13 Random Walks 153

14 Path Dependence 163

15 Local Interaction Models 171

16 Lyapunov Functions and Equilibria 181

17 Markov Models 189

18 Systems Dynamics Models 201

19 Threshold Models with Feedbacks 213

20 Spatial and Hedonic Choice 227

21 Game Theory Models Times Three 243

22 Models of Cooperation 253

23 Collective Action Problems 269

24 Mechanism Design 283

25 Signaling Models 297

26 Models of Learning 305

27 Multi-Armed Bandit Problems 319

28 Rugged-Landscape Models 327

29 Opioids, COVID-19, and Inequality 339

Notes 375

Bibliography 403

Index 431

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