Multi-Level Strategic Classification: Incentivizing Improvement through Promotion and Relegation Dynamics
This addresses the issue of dishonest manipulation by self-interested agents in classification systems, offering a novel incentive mechanism.
The paper tackles the problem of incentivizing honest effort in strategic classification by designing a multi-level promotion-relegation framework with thresholds, proving that agents can achieve arbitrarily high levels through genuine improvement under mild conditions.
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.