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Maximizing NFT Incentives: References Make You Rich

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Listed:
  • Guangsheng Yu
  • Qin Wang
  • Caijun Sun
  • Lam Duc Nguyen
  • H. M. N. Dilum Bandara
  • Shiping Chen

Abstract

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.

Suggested Citation

  • Guangsheng Yu & Qin Wang & Caijun Sun & Lam Duc Nguyen & H. M. N. Dilum Bandara & Shiping Chen, 2024. "Maximizing NFT Incentives: References Make You Rich," Papers 2402.06459, arXiv.org.
    • Handle: RePEc:arx:papers:2402.06459
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    References listed on IDEAS

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    1. Bao, Te & Ma, Mengzhong & Wen, Yonggang, 2023. "Herding in the non-fungible token (NFT) market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
    2. Arthur Charpentier & Romuald Élie & Carl Remlinger, 2023. "Reinforcement Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 425-462, June.
    3. Philip J. Reny, 1999. "On the Existence of Pure and Mixed Strategy Nash Equilibria in Discontinuous Games," Econometrica, Econometric Society, vol. 67(5), pages 1029-1056, September.
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