随机试验中的探索与参与激励
Exploration and Incentivizing Participation in Randomized Trials
AI总结：
本文研究了随机试验中探索与利用的权衡问题，提出了一种激励相容机制以提高参与度，并分析了不同代理类型的模型变体。
摘要：
参与激励是抑制医学随机对照试验（RCTs）的已知问题，也是在线平台中RCTs导致用户不满的潜在原因。我们将这一问题框架为一个非标准的探索-利用权衡：一个RCT希望尽可能均匀地探索，而每个'代理'（患者或用户）则偏好'利用'，即看起来最佳的治疗。我们通过试验与代理之间的信息不对称来激励参与。我们通过对抗性生成的结果下的最坏情况估计误差来衡量统计性能，这是RCTs的标准目标。我们获得了一个接近最优的解决方案，即在该目标下具有特定保证的激励相容机制，以及任何激励相容机制的近似不可能结果。我们考虑了三种模型变体：同质代理（相同'类型'包括信念和偏好）、异质代理，以及一个利用估计类型频率以减轻罕见但困难代理类型影响的扩展。
英文摘要：
Participation incentives is a well-known issue inhibiting randomized controlled trials (RCTs) in medicine, as well as a potential cause of user dissatisfaction for RCTs in online platforms. We frame this issue as a non-standard exploration-exploitation tradeoff: an RCT would like to explore as uniformly as possible, whereas each "agent" (a patient or a user) prefers "exploitation", i.e., treatments that seem best. We incentivize participation by leveraging information asymmetry between the trial and the agents. We measure statistical performance via worst-case estimation error under adversarially generated outcomes, a standard objective for RCTs. We obtain a near-optimal solution in terms of this objective: an incentive-compatible mechanism with a particular guarantee, and a nearly matching impossibility result for any incentive-compatible mechanism. We consider three model variants: homogeneous agents (of the same "type" comprising beliefs and preferences), heterogeneous agents, and an extension that leverages estimated type frequencies to mitigate the influence of rare-but-difficult agent types.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
