Exploration and Incentivizing Participation in Randomized Trials
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2022
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909986053947392 |
|---|---|
| author | Li, Yingkai Slivkins, Aleksandrs |
| author_facet | Li, Yingkai Slivkins, Aleksandrs |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2202_06191 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Exploration and Incentivizing Participation in Randomized Trials Li, Yingkai Slivkins, Aleksandrs Computer Science and Game Theory Theoretical Economics 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. |
| title | Exploration and Incentivizing Participation in Randomized Trials |
| topic | Computer Science and Game Theory Theoretical Economics |
| url | https://arxiv.org/abs/2202.06191 |