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Exploration vs exploitation

Once more on this theme that I discussed on this blog several times last year. This is a central problem for the field of research known as reinforcement learning. I'd recommend taking a look at Sutton and Barto's book if you are interested. It's not too technical and can be understood by someone without a background in machine learning.

As I mentioned in my last post, I think learning in the survey environment is a tough problem. The paper that proposed the upper confidence bound rule said it works well for short run problems -- but the short run they envisioned was something like 100 trials.

In the survey setting, there aren't repeated rewards. We're usually looking for one interview. You might think of gaining contact as another reward, but still. We're usually limited to a relatively small number of attempts (trials). We also often have poor estimates of response and contact probabilities to start with. Given that reward structure, poor prior information, and the limitation on the usual small number of trials, it may be that surveys should lean toward the exploration side of the problem.

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