We introduce DOSE-Dynamically Optimized Sequential Experimentation-to elicit preference parameters.DOSE starts with a model of preferences and a prior over the parameters of that model, then dynamically chooses a customized question sequence for each participant according to an experimenter-selected information criterion.After each question, the prior is updated, and the posterior is used to select the next, informationally-optimal, question.Simulations show that DOSE produces parameter estimates that are approximately twice as accurate as those from established elicitation methods.DOSE estimates of individual-level risk and time preferences are also more accurate, more stable over time, and faster to administer in a large representative, incentivized survey of the U.S. population (N = 2,000).By reducing measurement error, DOSE identifies a stronger relationship between risk aversion and cognitive ability than other elicitation techniques.DOSE thus provides a flexible procedure that facilitates the collection of incentivized preference measures in the field.
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