#TechModTuesday - Door Number 1

This week, we get a bit philosophical in that #TMT introduces a new way of thinking; one that you'll probably grasp intuitively and will immediately begin applying in other areas of your life. Let's goooooo!

The Monty Hall Problem

The Monty Hall problem goes like this: You're a game show contest (LOVE THIS ALREADY!). You've made it to the final round (HERE FOR IT!). There are 3 doors in front of you, one of which has a new Corvette behind it (INTO THIS!). One of the doors has nothing behind it (NO THANK YOU), and the third one has a goat (LESS INTO THIS). You get to take home whatever is behind the door that you open as your prize for playing.

You pick door number 2. Monty Hall, the host of the game show, opens door number 3 and reveals that the goat was behind that door (GOODBYE CHEVRE). Monty then says, "Hey you, would you like to switch doors?" What do you do? Did you pick correctly with door number 2? Or is that Corvette behind door number 1?

You're probably thinking, "Why switch? I've got a 50/50 shot at a Corvette!" And you're sort of right? But in reality, you had a 1 in 3 shot of getting it right and Monty had a 2 in 3 chance of keeping that Corvette. He's offering you the chance to switch him odds.

Just to really drive the point home, imagine there are 100 doors and Monty's opened 98 of them. You had a 1 in 100 chance to begin with, Monty had a 99 in 100 chance. Would you switch places with Monty? Absolutely.

Reverend Thomas Bayes

In the above, you figured out that when you uncover new information, it's smart to respond accordingly! A reverend,?Thomas Bayes, was the first to observe this and turn this into a mathematical formula to be used to solve some probability problems. While I'd love to dive into the equation itself, we'll leave that for another time.

Fun story about Bayes is that he wrote the formula and never actually published it! The theorem eventually got published by another guy, and then built on even more by a French mathematician, Pierre-Simon Laplace, who generalized it to be used in other forms. Fun fact about Laplace — he first suggested that black holes could exist!

Whew, what a diversion! Back to our regularly scheduled programming...

So What?

And as always, you should be asking "What does this have to do with insurance?" And the answer is, surprisingly "It applies in a bunch of places!" Take, for instance, some of our machine learning models that are in use today. The model makes a prediction, observes what happens, and then updates the model itself to make a better prediction next time. It took a statistical model, an actual observation, and then updated to accommodate it.

Perhaps you could use this to test some Mark Twain-ish things —?those things "people know that ain't so."?Your prior assumption ("The world works this way"), meets a real assumption ("except in this case"), and then might be worth updating your assumption ("The world doesn't actually work this way").

In?The Black Swan, Taleb highlights this with the example of literally a black swan changing how Europeans considered the species altogether. If you'd only ever seen white swans and then came across a black one, you'd have to re-evaluate your model of the swan population (Note: there's plenty more to Taleb's example, but we'll stop here).

Finally, in the insurance world, we might consider those things we know for a fact (Insurance is sold, not bought; Customers don't want to hear from their insurance company; etc) and begin to poke at them, making sure we update our mental model along the way. If we find a lot of "except in this case", we should probably rethink the validity of the model altogether.

As we implement more and more data science/machine learning models at Mutual of Omaha AND as we continue to run experiments as a company, we'll have plenty of opportunities to take our previous ideas about how the world works and update them with new information.

(ir)relevant Music

Prince - Little Red Corvette

Mark Ronson - Find U Again (Feat. Camila Cabello)