I'm still thinking about this problem that gets posed in machine learning -- exploitation vs. exploration. If you want to read more on the topic, I'd recommend Sutton and Barto's Reinforcement Learning. The idea is deceptively simple. Should you take the highest reward given what you currently know, or explore actions for which you don't know the reward?
In machine learning, they try to balance the two objectives. For example, in situations of greater uncertainty, you might spend more resources on exploration.
In surveys, I've tended to look at experiments as discrete events. Run the experiment. Get the results. Implement the preferred method. But we know that the efficacy of methods changes over time. The simplest example is incentive amounts. What's the right amount? One way to answer this question would be to run an experiment every so often. And change your incentive amounts based on each experiment. Another approach might be to keep a low level of exploration going at all times. The level of experimentation might change as the uncertainty changes. This kind of design is very intriguing to me. And it seems like a natural fit for ongoing surveys.
In machine learning, they try to balance the two objectives. For example, in situations of greater uncertainty, you might spend more resources on exploration.
In surveys, I've tended to look at experiments as discrete events. Run the experiment. Get the results. Implement the preferred method. But we know that the efficacy of methods changes over time. The simplest example is incentive amounts. What's the right amount? One way to answer this question would be to run an experiment every so often. And change your incentive amounts based on each experiment. Another approach might be to keep a low level of exploration going at all times. The level of experimentation might change as the uncertainty changes. This kind of design is very intriguing to me. And it seems like a natural fit for ongoing surveys.
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