2022/04/30 by Mengjing Chen, Pingzhong Tang, Chen, Mengjing +7
Computer Science · #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences #cs.GT
paper · pdf · doi:10.48550/arxiv.2205.00192
20 pages, 2 figures
arxiv created 2022/04/30 · arxiv updated 2022/05/03
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.