Robust control theory is a tool for assessing decision rules when a decision maker distrusts either the specification … control theory to the so-called multiplier and constraint preferences that have been used to express ambiguity aversion …. Detection error probabilities can be used to discipline empirically plausible amounts of robustness. We describe applications to …

We experimentally explore decision-making under uncertainty using a framework that decomposes uncertainty into three … distinct layers: (1) physical uncertainty, entailing inherent randomness within a given probability model, (2) model … uncertainty, entailing subjective uncertainty about the probability model to be used and (3) model misspecification, entailing …

framework is model-free and allows for stresses on the output such as (a) the mean and variance, (b) any distortion risk measure … including the Value-at-Risk and Expected-Shortfall, and (c) expected utility type constraints, thus making the reverse … sensitivity analysis framework suitable for risk models. …