An Intrinsic Integrity-Driven Rating Model for a Sustainable Reputation System
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866911355760541696 |
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| author | Wen, H. Huang, T. Xiao, D. |
| author_facet | Wen, H. Huang, T. Xiao, D. |
| contents | In the era of digital markets, the challenge for consumers is discerning quality amidst information asymmetry. While traditional markets use brand mechanisms to address this issue, transferring such systems to internet-based P2P markets, where misleading practices like fake ratings are rampant, remains challenging. Current internet platforms strive to counter this through verification algorithms, but these efforts find themselves in a continuous tug-of-war with counterfeit actions.
Exploiting the transparency, immutability, and traceability of blockchain technology, this paper introduces a robust reputation voting system grounded in it. Unlike existing blockchain-based reputation systems, our model harnesses an intrinsically economically incentivized approach to bolster agent integrity. We optimize this model to mirror real-world user behavior, preserving the reputation system's foundational sustainability. Through Monte-Carlo simulations, using both uniform and power-law distributions enabled by an innovative inverse transform method, we traverse a broad parameter landscape, replicating real-world complexity. The findings underscore the promise of a sustainable, transparent, and formidable reputation mechanism. Given its structure, our framework can potentially function as a universal, sustainable oracle for offchain-onchain bridging, aiding entities in perpetually cultivating their reputation. Future integration with technologies like Ring Signature and Zero Knowledge Proof could amplify the system's privacy facets, rendering it particularly influential in the ever-evolving digital domain. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_09143 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | An Intrinsic Integrity-Driven Rating Model for a Sustainable Reputation System Wen, H. Huang, T. Xiao, D. Computational Engineering, Finance, and Science Social and Information Networks In the era of digital markets, the challenge for consumers is discerning quality amidst information asymmetry. While traditional markets use brand mechanisms to address this issue, transferring such systems to internet-based P2P markets, where misleading practices like fake ratings are rampant, remains challenging. Current internet platforms strive to counter this through verification algorithms, but these efforts find themselves in a continuous tug-of-war with counterfeit actions. Exploiting the transparency, immutability, and traceability of blockchain technology, this paper introduces a robust reputation voting system grounded in it. Unlike existing blockchain-based reputation systems, our model harnesses an intrinsically economically incentivized approach to bolster agent integrity. We optimize this model to mirror real-world user behavior, preserving the reputation system's foundational sustainability. Through Monte-Carlo simulations, using both uniform and power-law distributions enabled by an innovative inverse transform method, we traverse a broad parameter landscape, replicating real-world complexity. The findings underscore the promise of a sustainable, transparent, and formidable reputation mechanism. Given its structure, our framework can potentially function as a universal, sustainable oracle for offchain-onchain bridging, aiding entities in perpetually cultivating their reputation. Future integration with technologies like Ring Signature and Zero Knowledge Proof could amplify the system's privacy facets, rendering it particularly influential in the ever-evolving digital domain. |
| title | An Intrinsic Integrity-Driven Rating Model for a Sustainable Reputation System |
| topic | Computational Engineering, Finance, and Science Social and Information Networks |
| url | https://arxiv.org/abs/2310.09143 |