A Bayesian decision model based on expected utility and uncertainty risk

Risk is caused by the uncertainty of state of nature and a decision maker’s selection, and the result may appear to be an unfavorable outcome. Therefore, a decision maker wants to maximize an expected return with minimal risk exposures. In this paper, we propose an expected utility and uncertainty risk (EU–UR) model based on the reference prior, which extends the classical decision model under uncertainty. The EU–UR model is made by making a compromise between measures of expected utility and uncertainty. The model is empirically validated by applying to the Levy’s case and the Allais paradox.