论文标题
探索并激励参与随机试验
Exploration and Incentivizing Participation in Randomized Trials
论文作者
论文摘要
参与激励措施是一个众所周知的问题，抑制了医学中的随机对照试验（RCT），也是在线平台中RCT不满意的潜在原因。我们将这个问题框架为非标准探索 - 探索 - 折衷方案：RCT希望尽可能统一地探索，而每个“代理人”（患者或用户）都喜欢“剥削”，即看起来最好的治疗方法。我们通过利用试验与代理之间的信息不对称来激励参与。我们通过在对抗产生的结果下通过最坏情况估计误差来衡量统计性能，这是RCT的标准目标。我们从这个目标方面获得了近乎最佳的解决方案：具有特定保证的激励兼容机制，以及任何与激励兼容机制的几乎匹配的不可能结果。我们考虑三个模型变体：均质剂（包括信念和偏好的相同类型），异质剂，以及利用估计类型频率的扩展，以减轻稀有但缺陷剂类型的影响。
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.
