Exploration and Incentivizing Participation in Randomized Trials

TL;DR

This paper addresses the challenge of incentivizing participation in randomized controlled trials by framing it as an exploration-exploitation tradeoff and proposing mechanisms that improve statistical performance while encouraging agent involvement.

Contribution

It introduces a novel incentive-compatible mechanism for RCT participation, providing near-optimal solutions and analyzing different agent heterogeneity scenarios.

Findings

Achieves near-optimal worst-case estimation error guarantees.

Provides a nearly matching impossibility result for incentive-compatible mechanisms.

Extends the model to heterogeneous agents and uses estimated type frequencies.

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 that leverages estimated type frequencies to mitigate the influence of rare-but-difficult agent types.