by Angela Guess
In a recent article, Jeff Jonas explains how data beats math, noting, “Over the years, folks have often asked me what kind of math am I using to create large scale, real-time, context accumulating systems (e.g., NORA). Some fond of Bayesian speculate I am using Bayesian techniques. Some ask if I am using neural networks or heuristics. A math professor said I was doing advanced work in the field of Set Theory. My answer is always, ‘I don’t know any math. I didn’t finish high school. But I can explain how it works, step-by-step, and it is really quite simple.’”
Jonas goes on to tell a story about how some IBM Fellows tried to express his methods in a mathematical paper and failed every time to get it quite right: “One of the things demonstrated by this mathy paper might have been the notion that ‘data beats math’ – at least when it comes to Assertion Algorithms. Based on the available observation space, can an assertion be made? Yes or no. In short, there comes a point where sufficient evidence exists such that an assertion can be made as a ‘no-brainer’ without feeling compelled to split hairs with probability math.”
Jonas continues, “I’d rather look for corroborating and/or dissenting evidence than look to math for estimated probabilities. And if a really important outcome might come from such an assertion, I would continue to seek observations until it was so obvious you could show the board of directors and they would say ‘duh.’ If you run out of available observations and you are still not sure … then you have a few choices: 1) locate and collect the kinds of observations you need, 2) wait until you luck into a future observation related to the assertion in question (letting the existing ambiguity fester), or 3) pound on it with math. But I say only pound on it with math if it is going to be worth the additional effort/compute (e.g., you are playing high-stakes poker in Vegas).”
Read the full article here.