Epistemic utility estimation

We apply an epistemological model of rational decision making developed by Isaac Levi to the estimation problem. The estimators thus developed are optimal with respect to an epistemic utility function that accounts for the cost of error, the decision agent's assessment of informational value, and the decision agent's specified willingness to risk errors in exchange for information acquisition; the estimators are inherently set-valued. We apply Levi's stable acceptance procedure to the estimation process, facilitating the use of unnormalized probability densities. As an example of the theory, we develop algorithms to apply an epistemic utility estimator to the output of a set-valued Kalman filter.