The Model Thinker: What You Need to Know to Make Data Work for You | Scott E. Page

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 listener 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.  

Editorial Reviews

Review

Choice award for outstanding academic title

Scott Page's The Model Thinker is a deeper dive into the theory of mental models and the math behind them. Page is a professor at the University of Michigan, and his book explores mental models in a wonderful way.―Tomasz Tunguz

"A tremendously significant book embracing a creative, innovative approach for thinking about the complex mechanisms of social and natural phenomena"―S-T. Kim, North Carolina A&T State University, Choice

A hands-on reference for the working data scientist, "The Model Thinker" challenges us to consider that the historical methods we have used for data analysis are no longer adequate given the complexity of today's world....What has given this book a place in my permanent library is its deep dives into dozens of models. Equations and the diagrams are here, but so are applications."―Carol Wells, Inside Big Data: Your Source for Machine Learning

"This book offers a remarkably comprehensive and insightful introduction to mathematical models in the social sciences, written by one who is a master of the field and a brilliant teacher."―Roger Myerson, Winner of the Nobel Memorial Prize in Economic Sciences (2007) and Glen A. Lloyd Distinguished Service Professor of Economics at the University of Chicago

"An original and thought-provoking book, and a challenging one for a one-model thinker like myself. Brace yourself for an entirely new perspective."―Daron Acemoglu, professor of economics at MIT and co-author of Why Nations Fail

"The clarity of Scott's thinking has been awing me since our days together as doctoral students at Kellogg. Beautifully written, this book teaches us how to stay logical, coherent and effective at work and at life more broadly--amidst a world awash in ever more data, distraction and complexity."―Sally Blount, former dean of the Kellogg School of Management at Northwestern University

"With the exception of physics, science--and particularly the social sciences--currently resides in a liminal period characterized by hints of universal principles and the tantalizing possibility of robust prediction. In this accessible and pragmatic book, Page persuasively shows us how in these transition periods we can use the wisdom of crowds to improve our decision-making. The twist is that the 'crowd' is not made of individuals but of well-chosen models each of which offers a different window on the world."―Jessica Flack, professor at the Santa Fe Institute and director of the Collective Computation Group

"Page explains the value of applying several models to a single problem, and then provides a conceptual toolkit for doing so. His book is a labor of love."―Art Friedman, co-author of Quantum Mechanics

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.