Mr. Bayes and Mr. Occam, Still Popular after All These Years
April 14, 2020
In the early 2000s, I met a self appointed expert who turned red when he talked about the good Reverend Bayes. The spark for his crimson outrage was the idea that one would make assumptions about the future and plug those assumptions into the good Reverend centuries old formula. If you have forgotten it, here it is:
Why the ire? I think the person was annoyed with Autonomy’s use of the theorem in its enterprise search system and neuro-linguistic marketing. On top of that, if not trained in an appropriate manner and then retrained, Autonomy’s integrated data operating layer would drift; that is, return results less precise than before. Most licensees were not able to get their manicured nails into this concept of retraining. As a result, the benighted would rail at the flaws of the UK’s first big software company that seemed to make money.
And Occam? Well, this self appointed expert (akin to a person who gets a PhD online and then wants everyone to call him/her “doctor”) did not know about William and his novacula Occami. This church person lived several centuries before the good Reverend Bayes. William’s big idea was KISS or keep it simple stupid. One of my now deceased instructors loved to call this lex parimoniae, but in this blog KISS is close enough for horse shoes. (A variant of this game may have been played a century before Willie was born in the 1280s.)
So what?
I read “How Should We Model Complex Systems?” The write up in my opinion makes the case for the good Reverend’s method with a dash of Willie’s as guiding principles. No doubt the self-appointed expert will be apoplectic if he reads this blog post. But the highlight of the write up is a comment by Yandon Zhang. The failings of modeling reality can be addressed in part by adding more data.
That is a triple play: Bayes’, Willie, and more data.
The result? Models are good enough despite the fancy math that many firms layer on these three legs of the predicting footstool.
What’s that mean in reality? Something is better than nothing. What is often overlooked is that guessing or operating in the manner of Monte Carlo might generate results closer to reality. Want an example? Maybe Covid models?
Stephen E Arnold, April 13, 2020
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