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Bayes, Keynes, King and Fritz: fake news and academics with fixed ideas

By Paul Alper

“When the facts change, I change my mind. What do you do, sir?” – John Maynard Keynes

On the Fritz – Unknown. Attested from 1902, originally meaning “in a bad way” or “in bad condition”, malfunctioning of an appliance. Perhaps from German name Fritz, or by onomatopoeia (here, imitating the sound of electric sparks jumping).

In statistics, Bayesianism plays an important role.  According to Wikipedia, Thomas Bayes was an English statistician, philosopher and Presbyterian minister who is known for formulating a specific case of the theorem that bears his name: Bayes’ theorem. Bayes never published what would become his most famous accomplishment; his notes were edited and published after his death.

Bayes’ theorem is the way of combining what one currently believes with the new data in order to come up with an updated belief. In fact, this is how things like machine learning and weather forecasting are done successfully. Eventually, enough new data can override/supplement initial beliefs. Keynes was thus a Bayesian, albeit a couple of hundred years after Bayes.

Giving up a previous position is never easy – people are not machines – and note that the Keynesian quotation is, in fact, apocryphal. To illustrate further how resistant to updating humans are, consider Karen King, a distinguished Harvard professor, as discussed in Ariel Sabar’s book, Veritas: A Harvard Professor, A Con Man and the Gospel of Jesus’s Wife

Karen King would seem to me to be a classic case of someone, who despite (because of?) being very accomplished, just cannot accept that her idée fixe – Mary Magdalen was both wife and lead disciple of Jesus – could be wrong. In passing, I should note that my long-standing personal idée fixe is that academics in general suffer the same affliction. She is also typical in that when evidence arises that counters her convictions, she actively attempts to dismiss its importance. For details regarding her (mis)weighing of the data, see Mark Oppenheim’s review of Sabar’s book in the New York Times.

The other main character in the book is the con man, Walter Fritz, who may indeed for all I know, be an expert on Bayesianism. He seems to be knowledgeable in Egyptology, papyrology, sex video productions and, for good measure, he was the head of the Stasi museum in Berlin. More importantly, he knew how to exploit the weakness of his mark. He delivered to King precisely what she wanted to be true. Sabar expends a great deal of shoe-leather journalism to find the gaps in Fritz’s storyline and King’s willingness to be a believer.  Almost right to the end, she never was onto Fritz or heard the electric sparks jumping.

But apparently this failure to hear the sparks jumping also afflicts the half of the US who believe deeply, truly and incorrectly that the 2020 election was rigged by The Deep State, an entity so shadowy that finding no evidence of its existence is further proof of its existence. Currently, because of the mob raid on the United States Capitol, Republican legislators are in a Bayesian quandary as to if, when and how to leave the Trump ship. The new data really aren’t all that different from the old data but after a while, as they say, enough is enough, especially when fascist-type behavior is captured on video immediately after deep-south Georgia elected two Democrats to the United States Senate. I am tempted to repeat my favorite quotation from a TV program of my youth:

“You can fool some of the people all of the time, and all of the people some of the time—and them’s pretty good odds.”

Paul Alper is an emeritus professor at the University of St. Thomas, having retired in 1998. For several decades, he regularly contributed Notes from North America to Higher Education Review. He is almost the exact age of Woody Allen and the Dalai Lama and thus, was fortunate to be too young for some wars and too old for other ones. In the 1990s, he was awarded a Nike sneaker endorsement which resulted in his paper, Imposing Views, Imposing Shoes: A Statistician as a Sole Model; it can be found at The American Statistician, August 1995, Vol 49, No. 3, pages 317 to 319.