Two Johns Hopkins economists recently wrote a Wall Street Journal opinion piece titled, “Jerome Powell Is Wrong. Printing Money Causes Inflation.” Their argument is that Federal Reserve chair Powell is mistaken in his assertions that there is not a close relationship between money and inflation. As evidence, they offer the chart below, showing that the rate of inflation can be predicted almost perfectly from the rate of increase of M2, a broad measure of money. The authors explain: The theory rests on a simple identity, the equation of exchange, which demonstrates the link between the money supply and inflation: MV=Py, where M is the money supply, V is the velocity of money (the speed at which it circulates relative to total spending), P Read More ›
David Auerbach has picked The Phantom Pattern Problem (2020) by Gary Smith and Jay Cordes as one of the top books of 2020 in the science and tech category. Auerbach, who describes himself as “a writer and software engineer, trying to bridge the two realms,” is the author of BITWISE: A Life in Code (2018). He has an interesting way of choosing books to recommend: Those that resist the “increasingly desperate and defensive oversimplification” of popular culture: I hesitate to mention too many other books for fear of neglecting the others, but I will say that of the science and technology books, several deal with subjects that are currently inundated with popularizations. In my eye, those below are notably superior Read More ›
Economist Gary Smith and statistician Jay Cordes have a new book out, The Phantom Pattern Problem: The mirage of big data, on why we should not trust Big Data over common sense. In their view, it’s a dangerous mix: Humans naturally assume that all patterns are significant. But AI cannot grasp the meaning of any pattern, significant or not. Thus, from massive number crunches, we may “learn” (if that’s the right word) that Stock prices can be predicted from Google searches for the word debt. Stock prices can be predicted from the number of Twitter tweets that use “calm” words. An unborn baby’s sex can be predicted by the amount of breakfast cereal the mother eats. Bitcoin prices can be Read More ›
Pomona College economics professor Gary Smith, author with Jay Cordes of The Phantom Pattern Problem (Oxford, October 1, 2020), tackles an age-old glitch in human thinking: We tend to assume that if we find a pattern, it is meaningful. Add that to the weaknesses of current artificial intelligence and “Houston, we have a problem,” he warns: The scientific method tests theories with data. Data-mining computer algorithms dispense with theory and search through data for patterns, often aided and abetted by slicing, dicing, and otherwise mangling data to create patterns. Gary Smith, “Phantom patterns: The big data delusion” at IAI News (August 24, 2020) Many of the patterns so detected are obviously spurious, for example: A computer algorithm for evaluating job Read More ›
Dr. Smith thinks that the most dangerous error is putting data before theory. Many data-mining algorithms that are now being used to screen job applicants, price car insurance, approve loan applications, and determine prison sentences have significant errors and biases that are not due to programmer mistakes and biases, but to a misplaced belief in data-mining.