个性化激励政策中操纵的局限性
原标题：The Limits of Manipulation within Personalized Incentive Policies
AI 摘要
该论文研究了个人化激励政策中代理人的策略性操纵问题。作者将监管者与代理人之间的交互建模为Stackelberg博弈，其中监管者基于观察到的历史行为学习代理人画像，代理人则可能模拟虚假画像以获取更高激励。分析表明，由于代理人需持续按模拟画像行为并放弃真实偏好，会产生被以往研究忽视的操纵成本；分类不确定性虽降低个性化精度，却可能通过威慑操纵使监管者受益。基于伦敦出行方式选择数据集的模拟验证了该威慑效应，该框架可用于政策部署前的鲁棒性压力测试。
正文节选
The Limits of Manipulation within Personalized Incentive Policies Abstract Demand management policies aim to shift individual behavior toward sustainable choices (e.g., goods, transport modes). Personalized incentive policies tailor subsidies to agents’ needs and constraints, but personalization also exposes the regulator to agents’ strategic manipulative behavior. The extent to which agents can profitably manipulate such policies remains poorly understood, which limits their applicability. This