### New Calling Experiment

Since the results of the experiment on call scheduling were good (with the experimental method having a slight edge over the current protocol), I've been allowed to test the experimental method against other contenders. The experimental method is described in a prior post.

This month, I'm testing the experimental method which uses the predicted value for contact probabilities (MLE) across the four windows against another method which uses the Upper Confidence Bound (UCB) of the predicted probability. This quite often implies assigning a different window for calling than the experimental method.

The UCB method is designed to attack your uncertainty about a case. Lai ("Adaptive Allocation and the Multi-Armed Bandit Problem," 1987) proposed the method. Other than the fact that our context (calling households to complete surveys) is a relatively short process (i.e. few pulls on the Mult-Armed Bandit), the multi-armed bandit analogy fits quite well.

In my dissertation, I did some simulations that suggested that the UCB approach might beat out the MLE approach over the long run. But that it would fall behind early. In other words, it learns early and then exploits that learning later. Those data were simulated. We'll see how it works on the real thing...

### Goodhart's Law

I enjoy listening to the data skeptic podcast. It's a data science view of statistics, machine learning, etc. They recently discussed Goodhart's Law on the podcast. Goodhart's was an economist. The law that bears his name says that "when a measure becomes a target, then it ceases to be a good measure." People try and find a way to "game" the situation. They maximize the indicator but produce poor quality on other dimensions as a consequence. The classic example is a rat reduction program implemented by a government. They want to motivate the population to destroy rats, so they offer a fee for each rat that is killed. Rather than turn in the rat's body, they just ask for the tail. As a result, some persons decide to breed rats and cut off their tails. The end result... more rats.

I have some mixed feelings about this issue. There are many optimization procedures that require some single measure which can be either maximized or minimized. I think thes…