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Escape from Model Land: How Mathematical Models Can Lead Us Astray and What We Can Do About It

by Erica Thompson (Author)
4.2 4.2 out of 5 stars 104 ratings
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Why mathematical models are so often wrong, and how we can make better decisions by accepting their limits   

Whether we are worried about the spread of COVID-19 or making a corporate budget, we depend on mathematical models to help us understand the world around us every day. But models aren’t a mirror of reality. In fact, they are fantasies, where everything works out perfectly, every time. And relying on them too heavily can hurt us.  

In Escape from Model Land, statistician Erica Thompson illuminates the hidden dangers of models. She demonstrates how models reflect the biases, perspectives, and expectations of their creators. Thompson shows us why understanding the limits of models is vital to using them well. A deeper meditation on the role of mathematics, this is an essential book for helping us avoid either confusing the map with the territory or throwing away the map completely, instead pointing to more nuanced ways to Escape from Model Land. 

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  1. ISBN-10
    1541600983
  2. ISBN-13
    978-1541600980
  3. Publisher
    Basic Books
  4. Publication date
    December 6, 2022
  5. Language
    English
  6. Dimensions
    6.3 x 1.05 x 9.55 inches
  7. Print length
    256 pages
  8. See all details
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Editorial Reviews

Review

“Escape from Model Land offers a contemplative, densely encapsulated summary of her reflection and research... Ms. Thompson emphatically counsels not abstinence but discipline and humility… But it’s up to us to learn from models without being drawn in by their seductive elegance, and to ensure that the lessons from Model Land find substantive expression where it actually matters: in our messy, material, magnificent world”
 ―Wall Street Journal

"[A] highly engaging work of popular science, in which accessibility to the ideas is given priority over academic muscle-flexing."―Engineering & Technology

“Math-based models have become the secret machinery of our society, and this book draws back the curtain for a close look…. A complex subject rendered in accessible terms, with good advice for using models without drowning in data.”―Kirkus (starred)

"An eye-opening account of the limits and uses of mathematical models... a thoughtful, convincing look at how data works."―Publishers Weekly (starred)

“Data, computing power, AI, and the models that use them will continue to proliferate. The wisdom, life experience, and humility to make the best use of those powerful tools will remain scarce. This delightfully wide-ranging book offers heaps of the latter to help us generate genuine insights from the former.”―Charles J. Wheelan, New York Times-bestselling author of Naked Statistics

“A brilliant account of how models are so often abused and of how they should be used.”―John Kay, author of Other People’s Money

“Escape from Model Land demystifies the process of making the mathematical models that are increasingly used to make decisions about our lives, from the financial markets to the pandemic to climate change. A thought-provoking and helpful guide for data scientists and decision makers alike.”―Stephanie Hare, author of Technology Is Not Neutral

“A wise, lucid and compelling guide to how mathematical modelling shapes our world. Thompson teaches us how to go from being unthinking consumers of models to sophisticated users, combining a rich variety of vivid examples and case studies with deep conceptual expertise, presented in a lively and accessible way.”―Stian Westlake, CEO, Royal Statistical Society

About the Author

Erica Thompson is a senior policy fellow at the London School of Economics’ Data Science Institute and a fellow of the London Mathematical Laboratory. With a PhD from Imperial College, she has recently worked on the limitations of models of COVID-19 spread, humanitarian crises, and climate change. She lives in West Wales.  

Product details

  • Publisher ‏ : ‎ Basic Books (December 6, 2022)
  • Language ‏ : ‎ English
  • Hardcover ‏ : ‎ 256 pages
  • ISBN-10 ‏ : ‎ 1541600983
  • ISBN-13 ‏ : ‎ 978-1541600980
  • Item Weight ‏ : ‎ 15.5 ounces
  • Dimensions ‏ : ‎ 6.3 x 1.05 x 9.55 inches
  • Customer Reviews:
    4.2 4.2 out of 5 stars 104 ratings

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Erica Thompson

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Customer reviews

4.2 out of 5 stars
4.2 out of 5
104 global ratings

Top reviews from the United States

Gary Moreau, Author
5.0 out of 5 stars In a world of models, none deliver the ultimate truth.
Reviewed in the United States on January 3, 2023
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Erica Thompson is a senior fellow at the London School of Economics’ Data Science Institute and a fellow of the London Mathematical Laboratory. She has worked on models relating to COVID-19, public policy, and climate change. She might be the last person you would expect to say, therefore, that “follow the science” can be a fool’s folly.

Despite the world’s current worship of data as holding the answer to all questions, data are dumb. They know nothing. They rely on models to add meaningful relationships between them. Said slightly differently, all data exist in context. And no context relating to the reality of the world we live in can ever be completely knowable. It is simply too complex to draw it all together in a definable mathematical form.

Which is why she quotes British statistician, George Box, as famously noting that, “All models are wrong.” She notes, “The forecast is a part of a narrative…An engine, not a camera; a co-creator of truth, not a predictor of truth.” “Even if the model is ‘made’ by an artificial intelligence rather than by a human expert, it inherits in a complex way the priorities of those original creators.”

That is not to say, however, that we should simply jettison all of the modelers and revert to simple intuition or folklore for guidance in dealing with complex issues like weather, climate change, and pandemics. We just need to use their models responsibly. We need to understand their limitations and potential biases that may be unknowingly built into them.

And she concludes the book with five recommendations that should be adopted to help do just that. And they all make sense to me.

I live in the world of business, not public policy. And in the 45 years I have toiled in the corporate world I have been alarmed at the degree to which business people today rely on data and the financial models they create with them to make important decisions that greatly impact the lives of real people. Intuition and experience are largely dismissed, which is one of the reasons youth is in and those who have been there before are largely relegated to the back of the room.

Virtually no project gets approved today if it is not supported by an elaborate financial model. And if you can say that it was built with Big Data it is almost a shoe-in. Yet are our corporate decisions today any more accurate, or just less considerate of the company’s constituencies, particularly its employees.

Common sense is the oldest form of intuition there is and this book is full of it. Wherever you stand on the largest issues of the day, like climate change, this book will help you become more informed and better able to represent your position.

And it’s all written in language that any of us can follow. No mathematical notations or dense scientific jargon. The writing is very conversational and easy to follow.

Well done.
20 people found this helpful
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Harold Lund
4.0 out of 5 stars A worthwhile read
Reviewed in the United States on December 20, 2023
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Good but not great. Many valid observations about models and modeling are made. However, the book is longer than it needs to be as it seems to grind the observations into the ground.
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Tom Redman
5.0 out of 5 stars All Who Use Models Should Read this Book
Reviewed in the United States on January 19, 2023
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This is a terrific book and I hope everyone who uses any kind of model will give it a careful read. It covers an incredible range of important topics, from the fitness for use of a model, to exchangeability, to explainability, to the dangers of a single focus. I particularly enjoyed two discussions: the first that “you can’t fight complexity with complexity” and the idea that if you constantly reframe the problem, you delay action.

One nit: While the book hints at the problem of poor data quality, it does not give it the attention it deserves. Especially with high-powered tools, garbage in is still garbage out. Those trapped in model land fall prey to this issue.
4 people found this helpful
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laup
4.0 out of 5 stars Thought Provoking and Lively Reading
Reviewed in the United States on January 24, 2023
This book is well written, reads quickly, and has many fine examples of problems. It blows many whistles appropriately. It could be a good antidote for those too enamored of their own or others' models. It is a good provocative book for those modelers and thinkiers in the mood for reflection. It also has suggestions, such as the need for alternative conceptual models reflecting different perspectives. That, in requires being aware of different perspectives. She also notes the special role of "performative models" that may be useful but have big problems.

One of her provocative claims is that modeling provides a framework to use, which can be useful even if the framework isn't right. That's an astute observation, although--obviously--a "bad" framework can sometimes have very nasty consequences and support dangerous nonsense (e.g., astrology.

The book's biggest weakness, perhaps, is ascribing too much virtue to human discourse and reasoning that is not explicitly model-based. It sometimes seems as though she is saying the solution is to have better experts. or more common sense. If only it were so easy. Another problem is a tendency to generalize --focusing on examples of bad practice. She gives little space to practices that can do better. For example, muti-resolution, multi-perspective modeling and broad-reaching exploratory analysis under uncertainty can help. Making sharp distinctions between using models for description, explanation, postdiction, exploration and prediction is important. Agent-based modeling can represent possible behaviors and heuristics. Including uncertain qualitative factors can help. More generally, vigorous discussion, review, and debate can help. She doesn't adequately distinguish between statistical "modeling" with the benefit of empirical data and more speculative causal modeling to help in dealing with futures for which appropriate data is often lacking.

I would have been happier if her conclusion had been more a call for balance and a description of what kinds of balance researchers might seek. I would also have welcomed candid recognition that the quality of practitioners matters. Some practitioners are simply far better and wiser than others.
7 people found this helpful
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Thomas W.
5.0 out of 5 stars Well written.
Reviewed in the United States on October 9, 2023
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Reading it made me think of questions to ask. This is a good characteristic.
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Lane W Sharman
5.0 out of 5 stars Kudos!
Reviewed in the United States on April 4, 2023
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Ms Thompson has elucidated the primary dichotomy. Models are not the source of values but can and should lead to a considered discussion of value based decisions as a result of looking at models and their objective approximations.
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A M
3.0 out of 5 stars The book ignores the relevant discussions in the philosophy of science
Reviewed in the United States on January 2, 2023
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The models that this book discusses are scientific theories (sometimes small theories). The philosophy of science literature discusses questions such as What are the criteria for being a scientific theory? How should we choose between competing theories? But this book almost totally ignores these discussions. I failed to learn anything that might be relevant.
11 people found this helpful
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Top reviews from other countries

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emanuela marra
5.0 out of 5 stars Bel libro
Reviewed in Italy on January 6, 2024
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Consiglio la lettura
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Christian Mullon
5.0 out of 5 stars Essential.
Reviewed in France on July 25, 2023
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If you want to understand what is at stake in the common use of models in economics or climate, don't pass by this book, well and simply written.
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MR S T
5.0 out of 5 stars Refreshing analysis of "model land"
Reviewed in the United Kingdom on January 7, 2023
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A well written book exposing the hidden biases of the use of models. It argues well for the idea that diversity of models requires also diversity of modellers, an important idea often missing from popular texts.

I felt it was surprisingly kind to economists, who it seems to me are more frequently lost in model-land than many other disciplines - and so would do well to read this book. I'd always thought that the joke about the economist assuming a can-opener to open their tin of beans related to the tendency of economists to hang on to assumptions that have been proved wrong (unlike other disciplines where this signals the need to find alternatives). In the book, though this is used more to look at the imaginary can-openers we all carry around.

I look forward to a future book which dwells a little more on the solutions to complement this book which articulates well the problems of modelling without getting lost in the minutiae of how individual models work.
2 people found this helpful
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ROBIN
4.0 out of 5 stars Qualitative over quantitative
Reviewed in the United Kingdom on February 26, 2024
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Some good stuff here. Not a Nate Silver or Taleb easy read for a modelling audience as too qualitative. More numbers please. Finance discussion excellent but too short. Climate too long and dominates?
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mcdowella
4.0 out of 5 stars Process, not Product
Reviewed in the United Kingdom on November 5, 2023
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This is not a book on judging models on their merits. There are hints of this in the beginning, and at least one useful reference, but it soon zooms out to consider requirements, stakeholders, and social science. There is a justifying analogy for this in "Waterfall" software development, which declares that it is not practical to certify a computer program as highly reliable by testing alone; it is also necessary to inspect the process that developed it. This analogy is not perfect, because this book recommends assembling a diverse team with the explicit goal that the personal experiences of the team members will inform the modeling process, whereas traditional software development requires a replicable process, so one not influenced by the background and perhaps the current political opinions of a changing team.

This book mostly covers single interactions, as when a single report is presented to decision-makers. It considers its effect as a single action even when, as with the IPCC, that report may be years in production or revision. Another example of a single interaction would be the modelling advice leading to the first covid lockdowns. It does not consider a process of dialogue, or of a team of modellers gaining or losing a reputation with decision makers over time. The software development precedent for this would be Agile development, where, drawing on what is now a large collection of widely used software infrastructure, a software developer can provide early versions of their program to user representatives and repeatedly modify it to take account of their reaction to it, and still deliver on time. Perhaps, if decision makers engaged in repeated dialogues with modellers who followed a consistent process, decision makers would find Model Land a less treacherous place.
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