# Online Mechanism Design for Information Acquisition

arXiv: https://arxiv.org/abs/2302.02873 · HTML: https://mlsanity.com/paper/2302.02873/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2023.

## Built on by (most cited first, 2 of 2)

- [Markov Persuasion Processes: Learning to Persuade from Scratch](https://mlsanity.com/paper/2402.03077/) (2024)
- [Stochastic Principal-Agent Problems: Efficient Computation and Learning](https://mlsanity.com/paper/2306.03832/) (2023)

## Abstract (arXiv)

We study the problem of designing mechanisms for information acquisition scenarios. This setting models strategic interactions between an uniformed receiver and a set of informed senders. In our model the senders receive information about the underlying state of nature and communicate their observation (either truthfully or not) to the receiver, which, based on this information, selects an action. Our goal is to design mechanisms maximizing the receiver's utility while incentivizing the senders to report truthfully their information. First, we provide an algorithm that efficiently computes an optimal incentive compatible (IC) mechanism. Then, we focus on the online problem in which the receiver sequentially interacts in an unknown game, with the objective of minimizing the cumulative regret w.r.t. the optimal IC mechanism, and the cumulative violation of the incentive compatibility constraints. We investigate two different online scenarios, i.e., the full and bandit feedback settings. For the full feedback problem, we propose an algorithm that guarantees tildemathcal O(√ T) regret and violation, while for the bandit feedback setting we present an algorithm that attains tildemathcal O(Tα) regret and tildemathcal O(T1-α/2) violation for any α∈[1/2, 1]. Finally, we complement our results providing a tight lower bound.