### "Call Scheduling Algorithms" = Call Scheduling Algoritms + Staffing

We think about call scheduling algorithms as a set of rules about when cases should be called. However, staffing is the other half of the problem. For the rules to be implemented, the staff making the calls need to be there. And, there can also be issues if the staff is too large. The rules need to account for both of these situations.

Probably the more difficult problem is a staff that is too large. For example, imagine that all active cases have been called. There is an appointment in 45 minutes. The interviewer can wait, or call cases that have already been called on this shift. Calling cases again would be inefficient and a violation of a rule of the algorithm. Still, it seems bad to not make he calls.

I wrote a paper on a call scheduling algorithm. I assigned a preferred calling window to each case. These windows changed over time as calls were placed and the results of previous calls were used to inform the assignment of preferred window. I spent a lot of time analyzing data to see how often calls were placed in the preferred window.

This issue comes up in the paper by Greenberg and Stokes. Their algorithm recommended placing 30% of the calls on the first night of the study. This is likely infeasible as a scheduling problem.

In any event, call scheduling is a complicated problem. The complications, I think, stem in part from the difficulty of implementing staffing that will match the rules of the calling. And extending results across organizations with different approaches to staffing is difficult. In evaluating these algorithms, we need to know the rules, the violations of the rules, and the results.

### "Responsive Design" and "Adaptive Design"

My dissertation was entitled "Adaptive Survey Design to Reduce Nonresponse Bias." I had been working for several years on "responsive designs" before that. As I was preparing my dissertation, I really saw "adaptive" design as a subset of responsive design.

Since then, I've seen both terms used in different places. As both terms are relatively new, there is likely to be confusion about the meanings. I thought I might offer my understanding of the terms, for what it's worth.

The term "responsive design" was developed by Groves and Heeringa (2006). They coined the term, so I think their definition is the one that should be used. They defined "responsive design" in the following way:

1. Preidentify a set of design features that affect cost and error tradeoffs.
2. Identify indicators for these costs and errors. Monitor these during data collection.
3. Alter the design features based on pre-identified decision rules based on the indi…

### An Experimental Adaptive Contact Strategy

I'm running an experiment on contact methods in a telephone survey. I'm going to present the results of the experiment at the FCSM conference in November. Here's the basic idea.

Multi-level models are fit daily with the household being a grouping factor. The models provide household-specific estimates of the probability of contact for each of four call windows. The predictor variables in this model are the geographic context variables available for an RDD sample.

Let $\mathbf{X_{ij}}$ denote a $k_j \times 1$ vector of demographic variables for the $i^{th}$ person and $j^{th}$ call. The data records are calls. There may be zero, one, or multiple calls to household in each window. The outcome variable is an indicator for whether contact was achieved on the call. This contact indicator is denoted $R_{ijl}$ for the $i^{th}$ person on the $j^{th}$ call to the $l^{th}$ window. Then for each of the four call windows denoted $l$, a separate model is fit where each household is assum…

### 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…