最大化NFT激励：引用使你富有
Maximizing NFT Incentives: References Make You Rich
AI总结：
本文针对现有NFT激励机制的孤立性，提出基于DAG的引用激励模型，通过最大化NFT间连接来累积奖励，并理论及实践验证其最优效用。
摘要：
本文研究了如何优化现有的非同质化代币（NFT）激励措施。在探索大量与NFT相关的标准和现实世界项目后，我们得出一个意外发现：当前的NFT激励机制通常以孤立和一次性使用的形式组织，往往忽视了其在可扩展组织结构方面的潜力。我们提出、分析并实现了一种新颖的引用激励模型，该模型本质上构建为基于有向无环图（DAG）的NFT网络。此模型旨在最大化NFT之间的连接（或引用），使每个孤立的NFT能够扩展其网络并积累来自后续或订阅NFT的奖励。我们对模型进行了理论和实践分析，证明了其最优效用。
英文摘要：
In this paper, we study how to optimize existing Non-Fungible Token (NFT) incentives. Upon exploring a large number of NFT-related standards and real-world projects, we come across an unexpected finding. That is, the current NFT incentive mechanisms, often organized in an isolated and one-time-use fashion, tend to overlook their potential for scalable organizational structures. We propose, analyze, and implement a novel reference incentive model, which is inherently structured as a Directed Acyclic Graph (DAG)-based NFT network. This model aims to maximize connections (or references) between NFTs, enabling each isolated NFT to expand its network and accumulate rewards derived from subsequent or subscribed ones. We conduct both theoretical and practical analyses of the model, demonstrating its optimal utility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
