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Human judgment enables a model to reflect human information, but it also introduces the potential for bias from using our perception to build models and interpret their results. This inclination can sometimes be a feature, as in weather prediction’s improvements over time by searching for and testing for patterns, or a bug, as in cases like finance and baseball, where bias can lead to less accurate prediction.
Some of Silver’s chapters cohere with the central Bayesian theme better than others do, and Silver does not consistently maintain the distinction between risk and uncertainty. Still, his skillful writing and storytelling make The Signal and the Noise an enjoyable read, even if you are not a prediction junkie. Overall it is a thoughtful, well-cited work with informative attention to detail.
Similarly, James Owen Weatherall’s The Physics of Wall Street is an engaging, well-written history of the work of physicists, mathematicians, and statisticians on modeling financial markets since the late 19th century. Weatherall, a physicist, mathematician, and philosopher, unearths research from some unjustifiably underappreciated mathematicians, and he narrates a lively story about their work while making challenging ideas easier to understand.
Some of Weatherall’s subjects, such as Benôit Mandelbrot, discovered entirely new fields of inquiry (in Mandelbrot’s case, fractal geometry and chaos theory) as they developed theories to solve concrete problems. Others, such as physicist Fischer Black, pioneered the application of physics models to complicated finance problems like options pricing. In all cases Weatherall shows that intellectual nonconformity and interdisciplinary collaboration were key to his subjects’ successes.
Weatherall’s main theme is that the methodology of physics involves developing appropriately simple models, being honest about their assumptions, testing those models, and then revising them based on their performance and/or when the assumptions are invalid. Based on this foundation, he argues that the physicists and other quants are not entirely to blame for failures to predict financial market downturns such as the recent 2008 crisis, nor even for having developed models and financial innovations that made financial markets more brittle and less resilient.
“Putting all of the blame for the 2007–2008 crisis on Li’s model, or even securitized consumer loans, is a mistake,” Weatherall writes. “The crisis was partly a failure of mathematical modeling. But even more, it was a failure of some very sophisticated financial institutions to think like physicists. The model worked well under some conditions, but like any mathematical model, it failed when its assumptions ceased to hold.”
Weatherall’s research and argument are broadly persuasive but incomplete. His account does not address the fact that physicists develop these models within a framework of human institutions, the sets of formal and informal rules that govern how individuals act and interact in financial markets and the broader economy. These institutions shape the incentives of all kinds of people—including quants and the people who employ them—in the complex network of markets.
So Weatherall’s conclusion is accurate, but the financial crisis was largely a failure of institutions and incentives that made financial markets more brittle, not solely a failure of mathematical modeling per se. While the Warren Zevon fan in me appreciates his epilogue’s invocation to “send physics, math and money!” to enable better outcomes in financial markets, it’s a prescription that overlooks the distorted incentives that existed, and persist, in financial markets.
These two books have several shared attributes that make them worth reading—lively writing that humanizes a difficult topic, attempts to understand modeling and prediction in the face of uncertainty, and application to well-examined case studies in finance, weather, earthquakes, and poker. A common theme is the danger of assuming that the risks of bad outcomes are independent of each other instead of related, especially in financial markets. Having faith that your model will work regardless of conditions leads to poor predictions and unexpected outcomes. Good modeling requires constant testing and humility, even (or especially) after a spectacularly successful election prediction.