### Myopic Calling Strategies

I'm interested in sequential decision-making problems.In these problems, there is a tension between exploration and exploitation. Exploitation is when you take actions with more certainty about the rewards. The goal of exploitation is to get maximum reward to the next action given what is currently known. Exploration is when you take actions with less certainty. The goal is to discover what the rewards are for actions about which little is known.

A strategy that always exploits is called myopic since it always tries to maximize the reward of the current action without any view to long-term gains.

Calling algorithms certainly face this tension. For example, evenings might be the best time on average to contact households. If I know nothing else, then that would be my guess about when to place the next call. But it would be foolish to stay with that option if it continues to fail. If I have failures in that call window, I might explore another call window to try and see if the reward is greater in that window for this particular household.

The following is a simple example, taken from Kulka et al. (1988). The goal is to establish contact. The contact strategy $$a_j$$ can take on any of the following five values: WDM=weekday morning, WDA=weekday afternoon, WDE=weekday evening, SAT=Saturday, SUN=Sunday. We want to know which 3-call ($$j=1,2,3$$) sequence produces the highest contact rate. Using our notation, if $$Y_i=1$$ denotes contact for the $$i^{th}$$ case on any of the 3 calls, then the goal is to find the 3-call sequence that leads to the highest $$Pr(Y_i=1)$$. A myopic strategy would choose $$a_1$$ by comparing the probability of contact for each of the five possible treatments. The choice of $$a_2$$ and $$a_3$$ would be made in the same way. A non-myopic strategy would look at all 125 ($$5*5*5$$) possible sequences and determine which one had the highest overall probability of contact. That's basically what Kulka and colleagues did (looking at all possible combinations).

We could extend this approach across several stages of the survey process by looking at how the contact strategy impacts the ability to gain cooperation at later stages. For instance, a three-call sequence that placed three calls in the middle of the night might have a high contact rate, but would likely have a low rate of completing interviews.

### "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…

### Future of Responsive and Adaptive Design

A special issue of the Journal of Official Statistics on responsive and adaptive design recently appeared. I was an associate editor for the issue and helped draft an editorial that raised issues for future research in this area. The last chapter of our book on Adaptive Survey Design also defines a set of questions that may be of issue.

I think one of the more important areas of research is to identify targeted design strategies. This differs from current procedures that often sequence the same protocol across all cases. For example, everyone gets web, then those who haven't responded to  web get mail. The targeted approach, on the other hand, would find a subgroup amenable to web and another amenable to mail.

This is a difficult task as most design features have been explored with respect to the entire population, but we know less about subgroups. Further, we often have very little information with which to define these groups. We may not even have basic household or person chara…