Bayesian Incremental Utility Elicitation with Apoplication to Hearing Aids Personalization

Medical devices such as hearing aids often contain many tunable parameters. The optimal setting of these parameters depends on the patient’s preference (utility) function, which is often unknown. This raises two questions: (1) how should we optimize the parameters given partial information about the patient’s utility? And (2), what questions do we ask to efficiently elicit this utility information? In this paper, we present a coherent probabilistic decision-theoretic framework to answer these questions. In particular, following [2] we will derive incremental utility elicitation as a special case of Bayesian experimental desig