### Adaptive Design and Refusal Conversions

For me, the idea of adaptive design was influenced by work from the field of clinical trials on multi-stage treatments. Susan Murphy introduced me to adaptive treatment regimes as an approach to the problem. She points to methods developed in the field of reinforcement learning as useful approaches to problems of sequential decisionmaking.

Reinforcement learning describes some policies (i.e. a set of decision rules for a set of sequential decisions) as myopic. A policy is myopic if it only looks at the rewards available at the next step. I'm reading Decision Theory by John Bather right now. He uses an example similar to the following to demonstrate this issue. The following is a simple game. The goal is to get from the yellow square to the green square with the lowest cost. The number in each square is the cost of moving there.Diagonal moves are not allowed.

The myopic policy looks only at the next option and goes down a path that ends up with only expensive options to reach the target. The myopic policy is shown in the following picture:

The total cost is 7. The optimal policy looks for the sequence with the lowest cost (since the reward function in this game is to find the lowest cost path). The optimal policy starts out with a a more expensive move, but ends up overall less costly:

I find myself in a similar situation with the telephone experiment that I've been running. It is more efficient in the first step (before refusal conversions). But it is less efficient for refusal conversions. So much so that the overall efficiency is the same for the experimental and control groups.

On the other hand, maybe I can locate a policy for refusal conversions that will be better than either the current experimental or control methods. Even if I'm not able to find such a solution, I still think this is an interesting problem.

### The Cost of a Call Attempt

We recently did an experiment with incentives on a face-to-face survey. As one aspect of the evaluation of the experiment, we looked at the costs associated with each treatment (i.e. different incentive amounts).

The costs are a bit complicated to parse out. The incentive amount is easy, but the interviewer time is hard. Interviewers record their time for at the day level, not at the housing unit level. So it's difficult to determine how much a call attempt costs.

Even if we had accurate data on the time spent making the call attempt, there would still be all the travel time from the interviewer's home to the area segment. If I could accurately calculate that, how would I spread it across the cost of call attempts? This might not matter if all I'm interested in is calculating the marginal cost of adding an attempt to a visit to an area segment. But if I want to evaluate a treatment -- like the incentive experiment -- I need to account for all the interviewer costs, as best…

### 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…