Optimal Anonymous Independent Reward Scheme Design

TL;DR

This paper investigates the design of reward schemes that incentivize high-quality content creation, proving NP-hardness in general, and providing efficient solutions for convex cases and analysis of linear and proportional schemes.

Contribution

It introduces the concept of anonymous independent reward schemes (AIRS), proves their optimality under convex costs, and analyzes their approximation ratio compared to simpler schemes.

Findings

Optimal AIRS can be formulated as a convex optimization problem.

Linear reward schemes have a 1/2-approximation ratio, which is tight.

Proportional schemes can perform arbitrarily poorly compared to AIRS.

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.