This was a very challenging read and not quite what I expected. I was anticipating the book to summarize more technical concepts and lean more heavily into the ethics of data analysis. My experience of the book was the exact opposite. There is a lot of discussion of models in their theoretical states, which is not a concept I'm as comfortable with. Someone with more experience in the field or who has read a better introductory book may gain a lot more from this than I did. Definitely a moderate to advanced read.

This was a brilliant book. I don't think I've highlighted as many passages in my life. Not only is Erica Thompson's writing good, the whole premise of this book is one that I think many of us working in the sciences need to grapple with. Models are mathematically convenient tools for describing reality, but they are only as good as their assumptions. Thompson's point is that we need to be more careful about how we make the leap from a model to reality.

If you're a scientist (particularly working with the social sciences), I would recommend checking out this book.

ESCAPE FROM MODEL LAND by Erica Thompson is subtitled "How Mathematical Models Can Lead Us Astray and What We Can Do About It." Thompson, a statistician and senior policy fellow at the London School of Economics, provides a somewhat dense but thought-provoking reflection in the limitations of the many mathematical models on which we rely. One key point she addresses is the inherent bias of model designers, noting for example how educated middle-class modelers during the pandemic under- and over-valued certain costs for lockdowns and other policy decisions. Making this easy to remember, she notes the WEIRD (Western, Educated, Industrial, Rich, Developed) assumptions which may underlie a model without the developer's conscious knowledge. Stressing that models are "ubiquitous now in everyday life," Thompson encourages readers to use them for exploration, to gauge their sensitivity, and to ask probing questions: "How are models constructed? To whom do they deliver power? How should we regulate them? How can we use them responsibly?" ESCAPE FROM MODEL LAND received starred reviews from Kirkus and Publishers Weekly and was reviewed recently in both The Economist and The Wall Street Journal.

review links:
https://www.economist.com/culture/2022/11/30/two-new-books-explore-the-upside-of-big-data-and-ai
https://www.wsj.com/articles/escape-from-model-land-review-seduced-by-numbers-11672182577

Escape from Model Land’s title and description grabbed my attention. I have loved reading books about the ethics of technology/models. However, I did find this book a challenge to read. This book reads more like a research paper than a book and gets very technical. I enjoyed the topics Thompson covered, but didn’t feel like I fully grasped them. This book is probably best suited for those with advanced knowledge of statistics and may be difficult for the layman.

Astrology == Mathematics. For Sufficiently Large Values Of 2 While Imagining Spherical Cows. Thompson does a truly excellent job here of showing how and where mathematical models of real-world systems can be useful, and where they can lead us astray - perhaps a bit *too* good, as at times she has to jump through a few mental hoops to excuse the inadequacies of preferred models such as those related to climate change and the spread of COVID. On climate models in particular, she actually raises one of the several points Steven Koonin did in 2021's Unsettled - namely, just how wide each cell of the model is by necessity and how much variation there is within these cells in reality yet models must - again by necessity - use simply an average value throughout the cell. But she discusses a wide variety of models in addition to climate, and again, she truly does an excellent job of showing their benefits and how they can harm us. One star is lost due to the extremely short "future reading" section in place of a more standard bibliography (20% or so is fairly standard in similar nonfiction titles). The other star is lost because this book does have a robust discussion of the numerous COVID models and *I DO NOT WANT TO READ ABOUT COVID*. I am waging a one-man war on any book that references this for any reason at all, and the single star deduction is truly the only tool I have in that war. Still, again, this book really is quite good - as a narrative alone, indeed better than the three star ranking would seem to indicate. Very much recommended.