Shocks, Crises, and False Alarms

Between 1970 and 2019, the Global Terrorism Database recorded over 200,000 terrorist attacks. Yet, during that same period, the average number of annual global deaths from terrorism was approximately 3,300—a figure comparable to the number of annual drowning deaths in the United States alone. In the two decades following the September 11th attacks, Americans were eight times more likely to be killed by a police officer than by a terrorist. This statistical imbalance highlights a profound human tendency: we are terrible at calibrating our fear to the actual data. The most visible, dramatic threats capture our attention and drive policy, while the quieter, more probable dangers are often ignored.

This gap between perception and reality is what Philipp Carlsson-Szlezak and Paul Swartz have spent their careers analyzing. As the former and current Chief Economists at the Boston Consulting Group's Henderson Institute, they’ve had a front-row seat to the cycles of panic and paralysis that grip boardrooms and governments. They witnessed firsthand how leaders, armed with immense data, still struggled to distinguish a genuine five-alarm fire from a faulty smoke detector. This book emerged from their work developing a systematic framework to help decision-makers pierce through the noise, assess the true scale of a threat, and avoid the costly overreactions that often cause more damage than the initial shock itself.

Module 1: Rejecting the Master-Model Mentality

We've all been trained to seek out experts and models for definitive answers. In fields like physics, this works. But in economics, it's a trap. The authors argue that economies are not static systems governed by fixed laws. They are complex, adaptive, and shaped by human behavior. Trying to predict them with a single, all-encompassing framework is what they call the "Master-Model Mentality."

This approach is seductive. It promises certainty through precise, quantitative forecasts. But it consistently fails when we need it most. The authors insist that you must abandon the search for a single, predictive "master model." Before the 2008 financial crisis, for example, many standard economic models simply assumed away the workings of the financial system. It was a fatal oversimplification. The models were weakest right when the crisis hit. Similarly, during the early days of the COVID-19 pandemic, data was extreme. Unemployment soared. GDP plunged. These numbers were so far outside historical norms that any model trying to forecast a recovery was just guessing.

So what's the alternative? Instead of seeking prediction, the authors urge us to cultivate judgment. This leads to their next core idea: Practice "economic eclecticism" by drawing from multiple disciplines to build a narrative. This means you don't rely on just one theory. You pull from economics, finance, history, and even political science. You build a story about what's happening. During the post-COVID recovery, an eclectic approach would have looked beyond just mobility data. It would have incorporated the massive policy response, the rapid shift to online spending, and the speed of vaccine development. This narrative-based judgment explained the strong recovery far better than any single model could.

And here's the thing. This is about recognizing that the constant stream of negative headlines creates a powerful bias. Doom sells. Crises get clicks. A crucial part of your new approach is to actively discount doom-mongering. In 2020, major publications predicted a "Greater Depression." In 2022, they warned of 1970s-style "forever inflation." Both were false alarms. The cost of believing these narratives can be huge. Automakers who cut semiconductor orders in 2020, believing the depression story, found themselves at the back of the line when the economy rebounded, creating massive supply chain bottlenecks for themselves.