Maximizing NFT Incentives: References Make You Rich
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910324321419264 |
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| author | Yu, Guangsheng Wang, Qin Sun, Caijun Nguyen, Lam Duc Bandara, H. M. N. Dilum Chen, Shiping |
| author_facet | Yu, Guangsheng Wang, Qin Sun, Caijun Nguyen, Lam Duc Bandara, H. M. N. Dilum Chen, Shiping |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06459 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Maximizing NFT Incentives: References Make You Rich Yu, Guangsheng Wang, Qin Sun, Caijun Nguyen, Lam Duc Bandara, H. M. N. Dilum Chen, Shiping Computer Science and Game Theory Computational Engineering, Finance, and Science Cryptography and Security Computers and Society General Economics Economics 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. |
| title | Maximizing NFT Incentives: References Make You Rich |
| topic | Computer Science and Game Theory Computational Engineering, Finance, and Science Cryptography and Security Computers and Society General Economics Economics |
| url | https://arxiv.org/abs/2402.06459 |