After my first attempt in mathematical modelling in IMMC, my interest in modelling only grew; I think it was hence quite a natural occurrence that I picked this book up at the Harvard Coop, which I had visited for HMMT. Other statistics books had a huge gap in the middle; they were either written for elementary students or undergraduate scholars. Thus, it was quite natural that the first few pages of the book captivated my attention.
No lengthy definitions. Methods of modelling. How to model human errors.
To an aspiring statistician tired of reading lists of platitudinous, basic knowledge and was hungry to see how that knowledge could be applied, the book was an ever more effective guide to learn about the basics of mathematical modelling. From classic models such as the macro-micro loop to the newest breakthroughs in big data, Scott E. Page’s book contains exactly the types of information that an aspiring “model thinker” would be seeking for.
Another remarkable aspect is the “density” of the book. As you, reader, might know, in many non-fiction books, the first thirty pages can be a rapid-fire of information, while the next three hundred pages are just a reiteration of the former. However, the Model Thinker continued to present me with new ideas until the very end of the book; the amount of information contained in the 300-page book was quite remarkable. In fact, the entire book is a compact written version of his ten-hour lecture, which is available on Coursera.
An area of the book that particularly engrossed me the most was modelling human actions, as it seemed like an area impossible to me. How could we possibly predict the turbulent human mind? The answer was simple--our minds are also dictated by a handful of rules, and by precisely defining such rules, we would be able to approximate human behaviour in different contexts. I could see a strong relationship with heuristics, as a heuristic that best models the human mind would produce a model closest to reality.
Overall, I would absolutely recommend the book to anyone who wishes to gain an introductory insight into mathematical modelling.