Algorithmic decision-making in high-stakes domains often involves assigning
decisions to agents with incentives to strategically modify their input to the
algorithm. In addition to dealing with incentives, in many domains of interest
(e.g. lending and hiring) the decision-maker only observes feedback regarding
their policy for rounds in which they assign a positive decision to the agent;
this type of feedback is often referred to as apple tasting (or one-sided)
feedback. We formalize this setting as an online learning problem with
apple-tasting feedback where a principal makes decisions about a sequence of
$T$ agents, each of which is represented by a context that may be strategically
modified. Our goal is to achieve sublinear strategic regret, which compares the
performance of the principal to that of the best fixed policy in hindsight, if
the agents were truthful when revealing their contexts. Our main result is a
learning algorithm which incurs $\tilde{\mathcal{O}}(\sqrt{T})$ strategic
regret when the sequence of agents is chosen stochastically. We also give an
algorithm capable of handling adversarially-chosen agents, albeit at the cost
of $\tilde{\mathcal{O}}(T^{(d+1)/(d+2)})$ strategic regret (where $d$ is the
dimension of the context). Our algorithms can be easily adapted to the setting
where the principal receives bandit feedback -- this setting generalizes both
the linear contextual bandit problem (by considering agents with incentives)
and the strategic classification problem (by allowing for partial feedback).

本文探讨了在高风险领域中的算法决策，其中涉及到对代理的决策，在对其进行策略性修改的激励下，研究了在线学习问题，使其达到亚线性策略后悔，并提出了实用的解决方案。