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Exploration and Incentivizing Participation in Randomized Trials

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  • Yingkai Li
  • Aleksandrs Slivkins

Abstract

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 with estimated type frequencies.

Suggested Citation

  • Yingkai Li & Aleksandrs Slivkins, 2022. "Exploration and Incentivizing Participation in Randomized Trials," Papers 2202.06191, arXiv.org, revised Mar 2025.
    • Handle: RePEc:arx:papers:2202.06191
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