In the early days of my career in meteorology, which included determining the impact of air pollution emissions using mathematical models, one of my bosses, concerned about the outcome of a computer model I was using to assess a contentious industrial operation, asked me: “What will the model show?” I replied facetiously: “Well, what do you want it to show?”

In no way was I going to manipulate a model to get the results I or anyone else wanted. But the point is that models can produce results intentionally or unintentionally skewed by the modelers. Intentional tampering is akin to the adage “Figures don’t lie, but liars figure,” while unintentional bias can manifest itself in almost innumerable ways.

In “Escape From Model Land: How Mathematical Models Can Lead Us Astray and What We Can Do About It,” Erica Thompson provides a thorough, thoughtful treatment of the whys and wherefores of modeling and how to improve the practice, avoiding unintentional bias. Ms. 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 doctorate from Imperial College and many years of modeling experience. As such, she is well qualified to expound and comment on the world of modeling.

“Escape From Model Land” contains 10 well-written, accessible, relatable chapters that seamlessly incorporate engaging pertinent vignettes. The book progresses from defining the idealized locale of Model Land in Chapter 1 to showing with honed insight and practical guidance how to escape from Model Land in Chapter 10. The five principles for responsible modeling spelled out in Chapter 10 are especially helpful for improving modeling and the modeler.

The journey between Chapters 1 and 10 includes many stops that frequently prompt reflection on the personal bias of modelers, the role of expert opinion in model construction, expanding perspective to improve model applicability and reality, and the like. One chapter addresses a topic that the author is especially well versed in and is appropriately titled “The Atmosphere Is Complicated.” Other chapters adeptly focus on financial and pandemic modeling.

“Escape From Model Land” rightly reminds the reader of the ubiquitous paramount importance of knowing the assumptions and limitations of any model. For instance, the book notes that “there is a responsibility for misunderstandings or failures to communicate the limitations of models and the pitfalls inherent in using them to inform public policy-making. If modeling is to be taken seriously as an input to decision-making, we need to be clearer on this front, and part of that is acknowledging the social element of modeling rather than taking it to be a simple prediction tool that can be either right or wrong.”

Along this same vein, but in the wider context of science practice and public perception, “Escape From Model Land” observes: “If we are serious about addressing lack of confidence in science, it is necessary for those who currently make their living from and have built their reputation on their models to stop trying to push their version of reality on others.”

I frequently train professionals on the fundamentals of air pollution dispersion modeling. And regarding the reliability of model output, I remind my students that besides remembering the adage that “computers help you make mistakes at the speed of light,” once you get a result, ask yourself, “Does the answer make sense?”

Furthermore, relative to the essentials and uses of modeling, in my experience, a model is a tentative representation of an observation based on the interpretation of available information, a tool used to simulate real-world conditions. Yet among other things, “Escape From Model Land” shows that models are also “metaphors that facilitate communication, frame narratives and include value judgments with scientific information.”

Physicist Richard Feynman once observed: “It doesn’t matter how beautiful your theory is, it doesn’t matter how smart you are. If it doesn’t agree with experiment, it’s wrong.” By extension, if your model does not sufficiently represent reality, it’s wrong, or at least a large dose of humble rethinking of your model inputs is necessary. “Escape From Model Land” is a book that can help with that rethink and get you from Model Land to Realityville.

This piece originally appeared at WashingtonTimes.com and has been republished here with permission.