The Premature Kingdom: Hoisted from the Archives
Karl Marx’s Intuitions: Marx’s Enthusiasm for the Market

One of the most intriguing anomalies in all of behavioral economics is the so-called Monty Hall problem—many people refuse to believe that revealing apparently irrelevant information—that there is a goat behind door #2—about where the automobile is can and should change your assessment of likelihoods and thus your optimal decision. Perhaps this is because we are wired at a fairly deep level to believe in no correlation without causation—that we have to see a causal link between two phenomena in order to be willing to believe that they are correlated. Whether that is the explanation or not, it is clear that those of us who are bears of little brain need a lot of systematic help in parsing out issues of causation in complicated systems. And here Dana Mackenzie and Judea Pearl's _The Book of Why is going to be of enormous help in providing a gentle introduction to the issues and framework for thought. Here it is reviewed by the extremely sharp Lisa Goldberg:

Lisa Goldberg: Review of "The Book of Why: https://www.ams.org/journals/notices/201907/rnoti-p1093.pdf: "Pearl’s co-author Dana Mackenzie spoke on causal inference.... It concluded with an image of the first self-driving car to kill a pedestrian.... With a lead time of a second and a half, the car identified the object as a pedestrian. When the car attempted to engage its emergency braking system, nothing happened. The NTSB report states that engineers had disabled the system in response to a preponderance of false positives in test runs. The engineers were right, of course, that frequent, abrupt stops render a self-driving car useless. Mackenzie gently and optimistically suggested that endowing the car with a causal model that can make nuanced judgments about pedestrian intent might help.... Professor Judea Pearl has given us an elegant, powerful, controversial theory of causality. How can he give his theory the best shot at changing the way we interpret data? There is no recipe for doing this, but teaming up with science writer and teacher Dana Mackenzie, a scholar in his own right, was a pretty good idea...


#noted #2019-11-26

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