考虑决策影响操纵成本，提升算法抗博弈能力
Robust Strategic Classification under Decision-Dependent Cost Uncertainty
- 构建两阶段鲁棒优化框架，成本随历史决策动态变化
- 实验证明该方法能有效降低长期策略性行为
- 适合关注公平性和系统稳定性的算法设计者
面对算法决策系统，人类会通过改变输入数据（付出代价）来操纵结果以获取有利判别（增加算法成本）。现有策略性分类研究多假设操纵成本固定且与分类器决策无关。然而实践中，操纵成本随历史决策动态演化：当前决策影响未来成本。本文提出一种两阶段鲁棒优化框架，引入依赖决策的不确定性集来刻画这种依赖关系。研究发现，意识到政策依赖成本不仅能减少不确定性，还能更有效地在长期内抑制策略性行为。
原文摘要 · Abstract (English)
Humans facing algorithmic decision systems have been found to ``game'' them by altering their input data (at a cost to them) in order to favorably change the algorithmic outcomes they receive (at a cost to the algorithm). The growing literature on strategic classification seeks to develop robust machine learning algorithms that account for, and reduce, unwanted strategic behavior. A limitation of these existing works is that they assume the cost of strategic behavior to be fixed and independent of the classifier's decision. In practice, however, manipulation costs evolve and depend on past algorithmic decisions: today's decisions influence tomorrow's costs. This paper proposes and analyzes a two-stage robust optimization framework with a decision-dependent uncertainty set to capture such dependencies. We highlight that awareness of policy-dependent costs not only reduces uncertainty, but also better curtails gaming of the algorithmic system over time.
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