Optimal Anonymous Independent Reward Scheme Design
Abstract
We consider designing reward schemes that incentivize agents to create high-quality content (e.g., videos, images, text, ideas). The problem is at the center of a real-world application where the goal is to optimize the overall quality of generated content on user-generated content platforms. We focus on anonymous independent reward schemes (AIRS) that only take the quality of an agent's content as input. We prove the general problem is NP-hard. If the cost function is convex, we show the optimal AIRS can be formulated as a convex optimization problem and propose an efficient algorithm to solve it. Next, we explore the optimal linear reward scheme and prove it has a 1/2-approximation ratio, and the ratio is tight. Lastly, we show the proportional scheme can be arbitrarily bad compared to AIRS.
Cite
@article{arxiv.2205.00192,
title = {Optimal Anonymous Independent Reward Scheme Design},
author = {Mengjing Chen and Pingzhong Tang and Zihe Wang and Shenke Xiao and Xiwang Yang},
journal= {arXiv preprint arXiv:2205.00192},
year = {2022}
}
Comments
20 pages, 2 figures