Skip to main content

Mired in Myopia?

Reinforcement Learning (RL) deals with multi-step decision processes. One strategy for making decisions in a multi-step environment is always choose the option that maximizes your immediate payoff. In RL, they call this strategy "myopic" since it never looks beyond the payoff for the current action.

The problem is that this strategy might produce a smaller total payoff at the end of the process. If we look at the process as a whole, we may identify a sequence of actions that produces a higher overall reward while not maximizing the reward for each individual action.

This all relates to an experiment that I'm running on contact strategies. The experiment controls all calls other than appointments and refusal conversion attempts. The overall contact rate was 11.6% for the experimental protocol, and 9.0% for the control group. The difference is statistically significant.

But establishing contact is only an intermediate outcome. The final outcome of this multi-step process is completing an interview. It appears that the control and experimental groups produce interviews at about the same rate (147 control vs 153 experiment). Timestamps on the call indicate that the experimental contact strategy reduces time spent achieving the first contact by about 10%, but overall savings are only about 2% (i.e. not really different).

This looks like a case where a myopic policy (i.e. improving only the contact stage) does not lead to overall gains. The current experimental protocol will need to be expanded to all stages somehow. That's a big step...
Labels:

Comments

Post a Comment

Popular posts from this blog

Tailoring vs. Targeting

One of the chapters in a recent book on surveying hard-to-reach populations looks at "targeting and tailoring" survey designs. The chapter references this paper on the use of the terms among those who design health communication. I thought the article was an interesting one. They start by saying that "one way to classify message strategies like tailoring is by the level of specificity with which characteristics of the target audience are reflected in the the communication." That made sense. There is likely a continuum of specificity ranging from complete non-differentiation across units to nearly individualized. But then the authors break that continuum and try to define a "fundamental" difference between tailoring and targeting. They say targeting is for some subgroup while tailoring is to the characteristics of the individual. That sounds good, but at least for surveys, I'm not sure the distinction holds. In survey design, what would constitute
36 comments
Read more

What is Data Quality, and How to Enhance it in Research

  We often talk about “data quality” or “data integrity” when we are discussing the collection or analysis of one type of data or another. Yet, the definition of these terms might be unclear, or they may vary across different contexts. In any event, the terms are somewhat abstract -- which can make it difficult, in practice, to improve. That is, we need to know what we are describing with those terms, before we can improve them. Over the last two years, we have been developing a course on   Total Data Quality , soon to be available on Coursera. We start from an error classification scheme adopted by survey methodology many years ago. Known as the “Total Survey Error” perspective, it focuses on the classification of errors into measurement and representation dimensions. One goal of our course is to expand this classification scheme from survey data to other types of data. The figure shows the classification scheme as we have modified it to include both survey data and organic forms of d
Post a Comment
Read more

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 assu
4 comments
Read more