Explaining First-Stage Decisions of Two-Stage Stochastic Programs: Modeling, Reformulations and Solution Methods
Explainability is a growing concern for optimization models used as decision-making tools. It aims to help a stakeholder understand why a model recommends a given outcome, typically by exhibiting the elements of the input that account for this outcome. We study this question for linear two-stage stochastic programs, and explain the returned decision by a … Read more