In “Calibration of Robust Empirical Optimization Models,” Gotoh, Kim, and Lim study the statistical properties ?-divergence distributionally robust optimization with concave rewards. They show that worst-case sensitivity expected reward to deviations from nominal is equal in-sample variance significant out-of-sample (sensitivity) reduction possible little impact on mean if robustness parameter ...