Decision Making under Model Misspecification: DRO with Robust Bayesian Ambiguity Sets

TL;DR

This paper introduces DRO-RoBAS, a novel approach combining distributionally robust optimization with Bayesian methods to handle model misspecification, improving decision-making robustness under uncertain data distributions.

Contribution

It proposes a new ambiguity set centered at a robust posterior predictive distribution, with a dual formulation in RKHS and probabilistic guarantees, addressing model misspecification in DRO.

Findings

Outperforms existing Bayesian and empirical DRO methods in out-of-sample tests

Provides probabilistic guarantees on ambiguity set tolerance levels

Effective in Newsvendor and Portfolio problems with misspecified models

Abstract

Distributionally Robust Optimisation (DRO) protects risk-averse decision-makers by considering the worst-case risk within an ambiguity set of distributions based on the empirical distribution or a model. To further guard against finite, noisy data, model-based approaches admit Bayesian formulations that propagate uncertainty from the posterior to the decision-making problem. However, when the model is misspecified, the decision maker must stretch the ambiguity set to contain the data-generating process (DGP), leading to overly conservative decisions. We address this challenge by introducing DRO with Robust, to model misspecification, Bayesian Ambiguity Sets (DRO-RoBAS). These are Maximum Mean Discrepancy ambiguity sets centred at a robust posterior predictive distribution that incorporates beliefs about the DGP. We show that the resulting optimisation problem obtains a dual formulation in the Reproducing Kernel Hilbert Space and we give probabilistic guarantees on the tolerance level of the ambiguity set. Our method outperforms other Bayesian and empirical DRO approaches in out-of-sample performance on the Newsvendor and Portfolio problems with various cases of model misspecification.