Maximizing NFT Incentives: References Make You Rich

In this paper, we study <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">how to optimize existing non-fungible token (NFT) incentives</i>. Upon exploring a large number of NFT-related standards and real-world projects, we uncover an unexpected finding: current NFT incentive mechanisms, often organized in an isolated and one-time-use fashion, tend to overlook their potential for scalable organizational structures. To address this, we propose, analyze, and implement a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reference incentive</i> model, inherently structured as a directed acyclic graph (DAG)-based NFT network. Leveraging the Stackelberg game framework and deep reinforcement learning (DRL), this model aims to maximize connections (or references) between NFTs, enabling isolated NFTs to expand their networks and accumulate rewards from subsequent or subscribed ones. Through both theoretical and practical analyses, we demonstrate the optimal utility of the proposed model.