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.

研究如何设计评估机制以最大化群体中的总体质量得分，并在模型的特定设置下研究了找到最大化社会福利评估机制的算法方面，其中评分是线性的且机制使用可观察特征的线性评分规则时，最优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，我们设计了一个多项式时间算法，并在概率分布足够光滑且具有寻找密集区域的 Oracle 的情况下实现了（1/4）- 近似保证

激励感知评估机制下的福利最大化

Maximizing Welfare with Incentive-Aware Evaluation Mechanisms

This chapter considers the computational and statistical aspects of learning
linear thresholds in presence of noise. When there is no noise, several
algorithms exist that efficiently learn near-optimal linear thresholds using a
small amount of data. However, even a small amount of adversarial noise makes
this problem notoriously hard in the worst-case. We discuss approaches for
dealing with these negative results by exploiting natural assumptions on the
data-generating process.

本文探讨计算和统计方面的学习线性阈值的问题，特别是噪音的存在对该问题带来的影响及其应对方法。