论文标题
通过激励性评估机制最大化福利
Maximizing Welfare with Incentive-Aware Evaluation Mechanisms
论文作者
论文摘要
在诸如大学入学和保险费率诸如确定诸如申请之类的启发下，我们提出了一个评估问题，其中投入由战略个人控制，他们可以以一定的代价修改其功能。学习者只能部分观察这些功能，并旨在将个人在质量分数方面进行分类。目的是设计一种评估机制，以最大化整体质量得分，即福利，在人群中，考虑了任何战略性更新。我们进一步研究了在模型中两个特定环境下找到最大化评估机制的福利的算法方面。当分数是线性的，并且机制使用可观察到的特征的线性评分规则时，我们表明最佳评估机制是质量得分的适当投影。当机制必须使用线性阈值时，我们设计了一种多项式时间算法，具有（1/4） - approximation保证，当基础特征分布足够平滑并承认寻找密集区域的甲骨文时。我们将结果扩展到未知的设置，必须从样品中学习。
Motivated by applications such as college admission and insurance rate determination, we propose an evaluation problem where the inputs are controlled by strategic individuals who can modify their features at a cost. A learner can only partially observe the features, and aims to classify individuals with respect to a quality score. The goal is to design an evaluation mechanism that maximizes the overall quality score, i.e., welfare, in the population, taking any strategic updating into account. We further study the algorithmic aspect of finding the welfare maximizing evaluation mechanism under two specific settings in our model. When scores are linear and mechanisms use linear scoring rules on the observable features, we show that the optimal evaluation mechanism is an appropriate projection of the quality score. When mechanisms must use linear thresholds, we design a polynomial time algorithm with a (1/4)-approximation guarantee when the underlying feature distribution is sufficiently smooth and admits an oracle for finding dense regions. We extend our results to settings where the prior distribution is unknown and must be learned from samples.
