Risk-Adaptive Local Decision Rules

Abstract

The decision rules developed in this paper help managers recognize relationships between data and decisions and prescribe courses of actions that are guaranteed near-optimal as well as transparent and interpretable. Transparency and interpretability are prerequisites for any decision support tool that needs to be conveyed, understood, and justified broadly. An understanding of the relationship between data and decisions is important for managers as they identify the critical factors, prioritize their focus, guide the resource allocation, and develop mitigation strategies for handling data uncertainty. The relationship between data and decision can be especially convoluted for combinatorial optimization problems. We consider simple decisions rules that ensure transparency and interpretability, but also allow nearly any continuous decision rule within a unified framework of analysis. Our preferred adaptive minimum decision rule prescribes decisions with 1% relative suboptimality gap, on average, even when facing distributional shifts in out-of-sample testing.