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.
Thompson, a senior policy fellow at the London School of Economics, debuts with an eye-opening account of the limits and uses of mathematical models. Thompson explains that models are metaphors for the real world, and that it's crucial to avoid taking them too literally. "Force equals mass times acceleration is the ‘correct model' to use to solve the question" of when a truck would reach 60 mph, for example, but real-world conditions contain variables that the model can't account for. Thompson offers a host of lessons, among them that every model depends upon value judgments to determine what's included in them, that models should be understood as "not an objective mathematical reality, but a social idea," and that models contain the biases of those who make them, so increased diversity among modelers is essential for "greater insight, improved decision-making capacities and better outcomes." Thompson wraps up with a list of principles for "responsible modelling," including deciding "to what purpose(s)" models should be applied, and if "decisions informed by this model will influence other people or communities" who weren't considered or consulted in the making of the model. The result is a thoughtful, convincing look at how data works.