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.

翻译：参与激励是医学随机对照试验中一个众所周知的抑制因素，同时也是在线平台随机对照试验中可能导致用户不满的潜在原因。我们将此问题构建为一个非标准的探索-利用权衡：随机对照试验希望尽可能均匀地探索，而每个“智能体”（患者或用户）则偏好“利用”，即选择看似最佳的处理方案。我们通过利用试验与智能体之间的信息不对称性来激励参与。我们通过对抗性生成结果下的最坏情况估计误差来衡量统计性能，这是随机对照试验的标准目标。我们针对该目标获得了近乎最优的解决方案：一种具有特定保证的激励相容机制，以及针对任何激励相容机制的近乎匹配的不可能性结果。我们考虑了三种模型变体：同质智能体（具有相同“类型”，包含信念和偏好）、异质智能体，以及一种利用估计类型频率来减轻罕见但困难智能体类型影响的扩展模型。