As a professional Business Intelligence Analyst (BIA) this is the perfect non-fiction book for my desk. I do data aggregation, reporting, and analysis at my day job. And we are constantly trying to determine what the best correlation, representation or model is for analysis the data available to us.
Scott E. Page starts us off talking about WHY. This is an often overlooked piece of any business work. The why. Why do we do something? Why do we care? Why use X over Y? And so on...
In this case Page is able to eloquently argue that in today's world of "big data" we need to be more aware of what options are available to us for analysis. It's no longer appropriate to use one model to analysis a problem. Instead we need to leverage the multi-model approach and look at complex issues, problems or phenomena (as he calls them) from many angles. As the world has gotten more complex, our data has gotten larger and more granular; which means we need to look at it from many different perspectives.

Seriously Detailed
I'm on a team of BIA's, many more senior than I, and so I chatted with them about the core concepts in Page's book and we all agreed on one thing. It's comprehensive! If there is a major model not represented in The Model Thinker I'd be shocked. Page does a great job of touching on 30+ models and giving three very specific things for each:
1) The definition and general usage
2) The actual mathematical breakdown
3) A real-life, relevant example of using the model.
The best part of reading any portion of Page's epic selection of models is easily the examples. From health care to criminal form to food quality to the stock market to population growth; we are given applicable scenarios to understand the nuances of each model and why it's the best choice. Plus there are tons of little tidbits and facts that are fun to share at cocktail parties (or if you're me, in random elevator conversations) in here!

Creativity
There is one thing that really surprised me in The Model Thinker and that is Page's emphasis on creativity. As someone who has a BA in Communications, Marketing & Design, and used to work in the magazine industry, I was surprised to find that my current Analytical career had such a basis in creativity. Reflecting upon Page's statements and my job I realized that he is right. When we program/code, develop visualizations or infer outcomes from data; we are looking at something and creatively manipulating it. Perhaps this explains how I went from an Art Director to a Analyst in one lifetime.

Overall
This is a book that I will be purchasing for my office shelf. I have already gone back to my eARC copy multiple times to look things up and to continue learning the models. It's not a book you will likely read cover to cover at any given time. But the first 50 pages of introduction and concepts are superb and well worth reading in order. After that you can jump around to the models you are most interested in, or if you're looking for the right solution for data crunching, read the intros to each chapter to determine if there might be applicable use to your situation. I know that The Model Thinker has already been picked up by some mainstream Universities and Colleges as a required textbook and certainly I can see why. In one book you gain the knowledge of hundreds of years worth of calculations and analyzation. Whether you currently work as a Data or Business Analyst, have a desire to learn to use big data, are a programmer or just a geek that loves graphs; The Model Thinker is likely to fill a void, you didn't even know existed, by giving you more models, examples and calculations than you will ever need.

Please note: I received an eARC of this book from the publisher via NetGalley. This is an honest and unbiased review.

Scott Page is a political scientist, but he's perhaps become best known as an advocate of social science models. This book is a great guide to models, why they're useful, and how that can help in your work. The models range from relatively simple game theoretic concepts and collective action problems to Markov chains and networks. Page manages to make all of this accessible to readers without dumbing the concepts down. This is one of those books that you might read through once, but will probably better serve you as a reference book you'll consult every once in a while if and when you encounter new problems. Recommended for anyone dealing with data professionally.

In this well-organized and well-edited book, Page clearly demonstrates why his course in Coursera is a hugely popular one. Anyone who has to deal with or would like a more formal approach for thinking about decisions, processes, or systems will significantly benefit from this book. One could argue that the models presented are not new - and thats true; what is beneficial to the reader is the well-orchestrated progression of types of models and their assumptions, structure, limitations and examples.

Page first makes a well-reasoned plea in favor of approaching any problem with a diverse set of models - each have various assumptions and may therefore implicitly capture (or discard) certain aspects of the system. After motivating the reader with basics of distributions, Page covers an extensive range of models ( a glimpse of the table of contents is sufficient for a potential reader to understand that various modeling techniques and learning mechanisms are covered). Page uses a variety of examples drawn from various domains. The common theme is mostly "think strategically, in a tractable, explainable way'. The earlier chapters discussing aspects of a model in itself is a useful discussion.

While the editing of the book is excellent (no extraneous information, fillers), the book is essentially a text book or a reference source. A student in engineering, policy, business/management and any practitioner in these fields, particularly consultants, will benefit from this book. One wishes there were more examples or challenges to the reader as a "homework'. Nevertheless, this is a must-have book for any one interested in modeling strategic and operational decisions and informatics/machine learning students.

I really like the subject of this book. Model thinking is one of the best subjects I have taken in Coursera. The concepts are really useful and practical. It helps you to frame your thinking about numerous things in our world.

I would not give this book a 5 stars because I think this subject is best convey through other format like videos, lectures, or course. The topic is a bit complex especially for those who ate not comfortable with numerical reasoning. By putting it in book format it limited its audience.

Since it is already written as a book, my suggestion would be to have more illustrations instead of text. This would aid in easy understanding.

Aside from illustrations, more practical examples would help.

The idea of this book is good, but i found the book itself too.. i guess dense is a good word for it.
I kept reading and my thoughts start to wander of to other things, for example what else i could be doing instead of sitting here reading the book since i was just not enjoying how it was written.
I am sure this book has its audience, i personally need my nonfiction book to be a bit more engaging and readable to enjoy them!

I had heard about the author's popular MOOC but had not taken it myself and was excited to check out its "book companion." Unfortunately I would rather pass on any more instruction in this vein.

Looking at the table of contents, I saw the majority of chapters were given to discussing various models (network models, random walks, etc.). However, the wind-up to get to these chapters is painfully slow as the author spends nearly 50 pages just getting us ready to dig into the models themselves.

This is really heady stuff and while well-written I just don't know who the audience for this book would be. The reader is bombarded with everything from the Condorcet jury theorem to bootstrapping to rational choice theory before they are really even off the ground in this 400-page tome.

Get into the real forest of the content and it's even tougher to navigate. You'll be presented with equations, theorems and models. Getting through this book requires tenacity.

Neither a textbook nor a "business book" and certainly not a "beach read," I just don't who this material is aimed at. The subtitle is "What You Need to Know to Make Data Work for You." This to me is not quite accurate as it's really more a guide for you as a layman to interpret social scientists' research.

It will certainly give you a conceptual heads-up as I wonder how many professional social scientists themselves are familiar with all these models. However for the layperson this is just not the right material to "make data work for you."