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arXiv · 2205.00192

Optimal Anonymous Independent Reward Scheme Design

Mengjing Chen·Pingzhong Tang·Zihe Wang·Shenke Xiao·Xiwang Yang
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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.

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Mengjing Chen, Pingzhong Tang, Zihe Wang, Shenke Xiao, Xiwang Yang. 2022-04-30. Optimal Anonymous Independent Reward Scheme Design. https://arxiv.org/abs/2205.00192

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