  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.
