Expert Prediction: hard and soft

Written by: Stephen Hsu

Primary Source: Information Processing

Jason Zweig writes about Philip Tetlock’s Good Judgement Project below. See also Expert Predictions, Perils of Prediction, and this podcast talk by Tetlock.

A quick summary: good amateurs (i.e., smart people who think probabilistically and are well read) typically perform as well as or better than area experts (e.g., PhDs in Social Science, History, Government; MBAs) when it comes to predicting real world outcomes. The marginal returns (in predictive power) to special “expertise” in soft subjects are small. (Most of the returns are in the form of credentialing or signaling ;-)

WSJ: … I think Philip Tetlock’s “Superforecasting: The Art and Science of Prediction,” co-written with the journalist Dan Gardner, is the most important book on decision making since Daniel Kahneman’s “Thinking, Fast and Slow.” (I helped write and edit the Kahneman book but receive no royalties from it.) Prof. Kahneman agrees. “It’s a manual to systematic thinking in the real world,” he told me. “This book shows that under the right conditions regular people are capable of improving their judgment enough to beat the professionals at their own game.”

The book is so powerful because Prof. Tetlock, a psychologist and professor of management at the University of Pennsylvania’s Wharton School, has a remarkable trove of data. He has just concluded the first stage of what he calls the Good Judgment Project, which pitted some 20,000 amateur forecasters against some of the most knowledgeable experts in the world.

The amateurs won — hands down. Their forecasts were more accurate more often, and the confidence they had in their forecasts — as measured by the odds they set on being right — was more accurately tuned.

The top 2%, whom Prof. Tetlock dubs “superforecasters,” have above-average — but rarely genius-level — intelligence. Many are mathematicians, scientists or software engineers; but among the others are a pharmacist, a Pilates instructor, a caseworker for the Pennsylvania state welfare department and a Canadian underwater-hockey coach.

The forecasters competed online against four other teams and against government intelligence experts to answer nearly 500 questions over the course of four years: Will the president of Tunisia go into exile in the next month? Will the gold price exceed $1,850 on Sept. 30, 2011? Will OPEC agree to cut its oil output at or before its November 2014 meeting?

It turned out that, after rigorous statistical controls, the elite amateurs were on average about 30% more accurate than the experts with access to classified information. What’s more, the full pool of amateurs also outperformed the experts. …

In technical subjects, such as chemistry or physics or mathematics, experts vastly outperform lay people even on questions related to everyday natural phenomena (let alone specialized topics). See, e.g., examples in Thinking Physics or Physics for Future Presidents. Because these fields have access to deep and challenging questions with demonstrably correct answers, the ability to answer these questions (a combination of cognitive ability and knowledge) is an obviously real and useful construct. See earlier post The Differences are Enormous:

Luis Alvarez laid it out bluntly:

The world of mathematics and theoretical physics is hierarchical. That was my first exposure to it. There’s a limit beyond which one cannot progress. The differences between the limiting abilities of those on successively higher steps of the pyramid are enormous.

… People who work in “soft” fields (even in science) don’t seem to understand this stark reality. I believe it is because their fields do not have ready access to right and wrong answers to deep questions. When those are available, huge differences in cognitive power are undeniable, as is the utility of this power.

Thought experiment for physicists: imagine a professor throwing copies of Jackson’s Classical Electrodynamics at a group of students with the order, “Work out the last problem in each chapter and hand in your solutions to me on Monday!” I suspect that this exercise produces a highly useful rank ordering within the group, with huge differences in number of correct solutions.

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Stephen Hsu
Stephen Hsu is vice president for Research and Graduate Studies at Michigan State University. He also serves as scientific adviser to BGI (formerly Beijing Genomics Institute) and as a member of its Cognitive Genomics Lab. Hsu’s primary work has been in applications of quantum field theory, particularly to problems in quantum chromodynamics, dark energy, black holes, entropy bounds, and particle physics beyond the standard model. He has also made contributions to genomics and bioinformatics, the theory of modern finance, and in encryption and information security. Founder of two Silicon Valley companies—SafeWeb, a pioneer in SSL VPN (Secure Sockets Layer Virtual Private Networks) appliances, which was acquired by Symantec in 2003, and Robot Genius Inc., which developed anti-malware technologies—Hsu has given invited research seminars and colloquia at leading research universities and laboratories around the world.
Stephen Hsu

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