James Owen Weatherall
Scientist, Mathematician, and Author of The Physics of Wall Street
—Lee Smolin, author of The Trouble with Physics
Weatherall is a physicist, philosopher, and mathematician. His first book—and a New York Times bestseller—The Physics of Wall Street: A Brief History of Predicting the Unpredictable, is a compelling study of scientific discovery, economic turbulence, financial innovation, risk, reward, failure, and success. Michael Brown, a past chairman of NASDAQ, says Physics is “an important book for anyone who embraces the scientific method for improving the lot of mankind.”
James Owen Weatherall holds graduate degrees from Harvard, the Stevens Institute of Technology, and the University of California, Irvine, where he is presently an assistant professor of logic and philosophy of science and a member of the Institute for Mathematical Behavioral Sciences. He has written for Slate and Scientific American, and was the managing editor of Philosophy of Science, the official journal of The Philosophy of Science Association.
The Physics of Wall Street
What runs modern Wall Street? The answer is Quants, with their high-tech mathematical models. In this sweeping talk, James Weatherall tells the story of how in the aftermath of World War II, some innovative physicists and mathematicians saw surprising connections between physics, gambling, and finance—and put them to use to become the first quants. Captivating and accessible, Weatherall reveals the forces that have shaped financial markets, while presenting a timely perspective on how to strengthen economic models and stabilize Wall Street in the process.
An Economic Manhattan Project
Mathematical models have transformed finance, often by introducing new strategies and enabling new financial products. In some cases, however, these models have done more damage than good, making markets less stable and introducing new systemic risk. This makes some people think we should abandon models in finance. But that's the wrong solution. Instead, we should make models better—and become more sophisticated in how we use them. In this talk, James Weatherall describes what he see as an opportunity for making market regulation and policy-making more effective by embracing the very mathematical methods that give many people pause. He outlines his plan for an Economic Manhattan Project, the goal of which would be to develop the next generation of economic and financial theory, designed to make markets stronger and more stable.
What’s Wrong with Common Sense?
Financial markets, some say, are so complex that there's no way to tame them except with the kind of deep understanding gained from years of hands-on experience. But this argument is bogus, for two reasons. It undersells the value of models, but just as importantly, it overstates the value of our instincts. Psychological research over the last fifteen years has revealed that we are terrible at reasoning about probability and risk. Even when real money is at stake, we make systematically irrational decisions. Meanwhile, studies consistently show that expert opinions and predictions based on them do worse than even the simplest, least realistic mathematical models. In this practical talk, James Weatherall argues that it's time to abandon the cult of the expert and recognize that our only hope of understanding complex decision-making scenarios is to embrace the most sophisticated mathematical models we can come up with.
Phisics, Phynance, & Filosophy: Why Investors Need More Philosophers
Many recognize that to understand today's biggest problems—from global warming to the global financial crisis—voters and policy-makers need a modicum of scientific knowledge. But in fact, we need more than that. We need to understand how to put scientific knowledge into context, and to use it effectively in decision-making. This requires not just scientific literacy, but also philosophical literacy. The issue isn't whether we can build models of financial markets. We can. The important question, as James Weatherall explains in this talk, is what to make of those models and when to trust them. And this is ultimately a philosophical question—but one that lurks very close to the surface, with ramifications for policy-makers and even investors.
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