3 prompts to being Bayesian

How much should new information matter? Or, is this time different? Because sometimes it’s not.

“Merlin (Heidemanns) said that essentially the polls gain more weight. It’s not that we construct a model and weight the polls. We don’t take a weighted average of the polls, we estimate latent parameters and the polls are data. That said, you can roughly approximate the estimate as a weighted average.”

Andrew Gelman

According to Gelman, priors like “the economy, stupid” never exit the model. According to Nate Silver, the final poll removes all prior data.

In college sports priors matter more than Gelman allows for politics. Nearly two-thirds of college basketball teams who start a season ranked in the top-25, finish in the top-25. College football is the same.

How much new information should matter is a tricky question, but it’s helpful and why the Wharton Moneyball co-hosts encourage each other to become more Bayesian.

At the start of a football season we can guess (or hope) on a team’s chances. With more information, each play, game, and season, we update our idea. Eventually our guess at the start of the year gives way to the information of the year.

It’s hard to do though because we don’t know is this time different. Most of the time it’s not different enough, and base rates work best. But there are three general frameworks which might help us become more Bayesian.

First is to ask a Marc Andreessen like question: is software eating the world? How has the system changed and what does that mean? From FAANG to Testa joining the S&P, it seems like a systemic shift toward technology. Ditto for passing in football.

Second is to ask the Michael Mauboussin question: how much of this was luck? There’s a lot more luck in a single football play than an entire football game. It’s always a mix of skill and luck, but in what ratio?

Third is to consider our identity: am I attached to a position for unacknowledged reasons? This category includes biases like sunk cost and personal influences like ego or status.

The 2021 vaccine rollout is a good instance of practicing Bayesianism. Start with the base rate for vaccines. Watch for evidence. Adjust accordingly.

Baseline data

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One of the coronavirus problems, one of any system’s problems, is lack of good data. When data is precise and simple it’s just a math problem. This is why we have to gamble with coronavirus.

In mid-March I started to feel kinda ill. Did I have it? Everything pointed to yes.

I’d traveled through airports. I felt congested and achy. The news talked more about coronavirus than allergies. Wait. What? The noise of the news made me overlook the color of my car, which was a nicely tinged yellow thanks to an above average pollen count in central Florida. 

My problem was that the ‘fifth vital sign’ had overtaken all the others. Or put differently, the only data I was using was highly subjective. Instead of continuing my confoundedness I started counting. 

IMG_5490.jpeg

Regularly tracking my temperature showed nothing to worry about.

The other potential problem at the the time was toilet paper. 

Well before we were storming stores and short sheets I had stocked up. But watching the paper pandemonium I had no idea how long our stockpiles would last. So, I counted. Our  conservative count is two rolls per person per month. Prior to counting, I’d never have known.

Now do emergency funds

Good data is an objective tool to use alongside the subjective. If we kinda feel ill, we can take temperatures. If we see toilet paper rolling out of stores, we can use a rule of thumb. If we’re worried about finances, we can compare spending to savings. Good data is the base rate, our adjustments are the subjective. 

In any quantitative field three things matter: counts, computations, and communications.

Without accurate counts, we know nothing. 

Without accurate counts and computations, we infer nothing. 

Without accurate counts, computations, and communications, we do nothing. 

Sometimes we jump the gun. We build a model and share it to the world. #dataisbeautiful. Sometimes though we just need to start at the beginning and count. 

Thanks for reading.