发表机构
California Institute of Technology; University of Chicago（加州理工学院; 芝加哥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出战略决策聚焦学习，研究预测影响博弈均衡的问题，发现预测准确性与均衡收益可能非单调，并设计算法在保护基准上验证，强调预测器需考虑战略互动。
AI 中文摘要
机器学习（ML）预测越来越多地被用于指导决策，从而引发了决策聚焦学习（DFL）问题，在该问题中，预测器不仅针对准确性进行优化，还针对下游决策质量进行优化。然而，大多数现有工作假设单一决策者在孤立环境中进行优化。本文正式提出了战略决策聚焦学习，其中机器学习系统预测一个外生状态，某些智能体在参与博弈之前会观察到该状态。例如，公园管理员可以预测野生动物位置，以针对战略偷猎者分配反偷猎巡逻。虽然外生状态不受智能体行为的影响，但预测会影响智能体的策略及由此产生的均衡。我们发现，战略考量从根本上改变了学习问题。特别是，我们证明了预测准确性与均衡收益之间的景观可能是非单调的，即更好的预测可能会降低性能。我们提出了解决这些挑战的算法方法，并在野生动物保护和基础设施保护的基准上进行了验证。我们的理论和实验强调了在设计预测器时考虑战略互动的重要性。
英文摘要
Machine learning (ML) predictions are increasingly being used to guide decision-making, giving rise to the problem of decision-focused learning (DFL) where predictors are optimized for downstream decision quality rather than accuracy alone. However, most existing work assumes a single decision-maker optimizing in isolation. This paper formalizes strategic decision-focused learning, where an ML system predicts an exogenous state that some agents observe before playing a game. For example, a park ranger may predict wildlife locations to allocate anti-poaching patrols against strategic poachers. While the exogenous state is unaffected by agent actions, predictions influence agents' strategies and the resulting equilibrium. We find that strategic considerations fundamentally change the learning problem. In particular, we show the prediction accuracy-equilibrium payoff landscape can be non-monotonic, i.e., better predictions can degrade performance. We propose algorithmic approaches to address these challenges and validate them across benchmarks in wildlife conservation and infrastructure protection. Our theory and experiments highlight the importance of accounting for strategic interactions when designing predictors.
