A Pareto Dominance Principle for Data-Driven Optimization
提出一种在不确定环境下基于数据做决策的方法，寻找在未见数据上表现好且失望风险低的决策，并证明其存在条件，适用于简单或复杂问题及非均匀数据。
Our paper proposes an effective way to make decisions based on data for uncertain situations. In simple terms, a data-driven decision is just a choice we make by looking at the available data. We express this choice as the best one according to a model we create from the data. The quality of this decision is judged by how well it performs in situations not seen during training. We also consider how often it disappoints in those situations. The challenge is that we do not know the exact probability of generating the data. An ideal data-driven decision should work well for any possible probability. However, such ideal decisions are usually not possible. Therefore, we look for decisions that work well on unseen data, considering the chances of disappointment. We prove that such effective decisions exist under certain conditions, allowing for practical applications. This approach holds regardless of whether the original problem is simple or complex, and it works even when the data are not uniformly collected. Our study also uncovers how the characteristics of the data-generating process influence the optimal decision-making model.