“Making Statistics Work: Information Theory and Bayesian Inference”

I took a look at the above-titled book by economists Duncan Foley and Ellis Scharfenaker. It’s an interesting read, in many ways a throwback to the 1950s when a group of mathematicians brewed a heady mix of operations research, game theory, probability theory, and economics in an attempt to create a unified theory of social science, or to map the limitations of this effort. Important figures in this effort include John Maynard Keynes, John Von Neumann, Jimmie Savage, Milton Friedman, Duncan Luce, Howard Raiffa, Kenneth Arrow, Herbert Simon, Ed Jaynes, . . . a whole bunch of people who are still remembered today. Back in the day, Bayes was seen alternately as Jesus or the Devil, and there were hopes of a grand synthesis of subjective probability and local information in markets, a connection between formal statistical inference and individual decision making. In retrospect, the cognitive science of the 1950s wasn’t all there, and hierarchical modeling hadn’t been integrated into…