通过升降级机制设计，激励个体长期诚实努力提升。
Multi-Level Strategic Classification: Incentivizing Improvement through Promotion and Relegation Dynamics
- 构建多层级升降级框架，用阈值动态调节激励。
- 证明在温和条件下，个体可仅靠真实努力达任意高阶。
- 适合研究公平决策系统与长期行为激励的学者。
战略分类研究自利个体为获得有利分类结果而操纵自身表现的问题，当欺骗成本低于真实努力时，个体常选择不诚实行为。现有研究多聚焦于动态分类器权重优化，本文则转向分类器阈值与难度递进的设计，提出多层级晋升-降级框架。该模型捕捉了个体长远考虑、技能保留及资格积累的自我强化效应。我们刻画了个体最优长期策略，并证明：在适度条件下，设计合理的阈值序列可有效激励诚实努力。关键结论是，只要条件满足，个体仅凭真实进步即可达到任意高阶水平。
原文摘要 · Abstract (English)
Strategic classification studies the problem where self-interested individuals or agents manipulate their response to obtain favorable decision outcomes made by classifiers, typically turning to dishonest actions when they are less costly than genuine efforts. While existing studies on sequential strategic classification primarily focus on optimizing dynamic classifier weights, we depart from these weight-centric approaches by analyzing the design of classifier thresholds and difficulty progression within a multi-level promotion-relegation framework. Our model captures the critical inter-temporal incentives driven by an agent's farsightedness, skill retention, and a leg-up effect where qualification and attainment can be self-reinforcing. We characterize the agent's optimal long-term strategy and demonstrate that a principal can design a sequence of thresholds to effectively incentivize honest effort. Crucially, we prove that under mild conditions, this mechanism enables agents to reach arbitrarily high levels solely through genuine improvement efforts.
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