Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Model
Abstract
We study the incentivized information acquisition problem, where a principal hires an agent to gather information on her behalf. Such a problem is modeled as a Stackelberg game between the principal and the agent, where the principal announces a scoring rule that specifies the payment, and then the agent then chooses an effort level that maximizes her own profit and reports the information. We study the online setting of such a problem from the principal's perspective, i.e., designing the optimal scoring rule by repeatedly interacting with the strategic agent. We design a provably sample efficient algorithm that tailors the UCB algorithm (Auer et al., 2002) to our model, which achieves a sublinear -regret after iterations. Our algorithm features a delicate estimation procedure for the optimal profit of the principal, and a conservative correction scheme that ensures the desired agent's actions are incentivized. Furthermore, a key feature of our regret bound is that it is independent of the number of states of the environment.
Cite
@article{arxiv.2303.08613,
title = {Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Model},
author = {Siyu Chen and Jibang Wu and Yifan Wu and Zhuoran Yang},
journal= {arXiv preprint arXiv:2303.08613},
year = {2023}
}
Comments
35 pages, adding an impossible result (Lemma 3.2) with its proof in Section D.1