Problem
Research questions and friction points this paper is trying to address.
decision-focused learning
strategic interactions
machine learning predictions
game theory
equilibrium
Strategic Decision Focused Learning
🤖 AI Summary
本文探讨了在存在策略性交互的情况下，如何通过优化机器学习预测以提升决策质量的问题，并提出算法来应对由此产生的挑战。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
strategic decision-focused learning
non-monotonic relationship
algorithmic approaches
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