Fraud-Proof Revenue Division on Subscription Platforms
Fuente:
arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866917065630154752 |
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| author | Ghosh, Abheek Neoh, Tzeh Yuan Teh, Nicholas Tyrovolas, Giannis |
| author_facet | Ghosh, Abheek Neoh, Tzeh Yuan Teh, Nicholas Tyrovolas, Giannis |
| contents | We study a model of subscription-based platforms where users pay a fixed fee for unlimited access to content, and creators receive a share of the revenue. Existing approaches to detecting fraud predominantly rely on machine learning methods, engaging in an ongoing arms race with bad actors. We explore revenue division mechanisms that inherently disincentivize manipulation. We formalize three types of manipulation-resistance axioms and examine which existing rules satisfy these. We show that a mechanism widely used by streaming platforms, not only fails to prevent fraud, but also makes detecting manipulation computationally intractable. We also introduce a novel rule, ScaledUserProp, that satisfies all three manipulation-resistance axioms. Finally, experiments with both real-world and synthetic streaming data support ScaledUserProp as a fairer alternative compared to existing rules. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_04465 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fraud-Proof Revenue Division on Subscription Platforms Ghosh, Abheek Neoh, Tzeh Yuan Teh, Nicholas Tyrovolas, Giannis Computer Science and Game Theory Artificial Intelligence Machine Learning Theoretical Economics We study a model of subscription-based platforms where users pay a fixed fee for unlimited access to content, and creators receive a share of the revenue. Existing approaches to detecting fraud predominantly rely on machine learning methods, engaging in an ongoing arms race with bad actors. We explore revenue division mechanisms that inherently disincentivize manipulation. We formalize three types of manipulation-resistance axioms and examine which existing rules satisfy these. We show that a mechanism widely used by streaming platforms, not only fails to prevent fraud, but also makes detecting manipulation computationally intractable. We also introduce a novel rule, ScaledUserProp, that satisfies all three manipulation-resistance axioms. Finally, experiments with both real-world and synthetic streaming data support ScaledUserProp as a fairer alternative compared to existing rules. |
| title | Fraud-Proof Revenue Division on Subscription Platforms |
| topic | Computer Science and Game Theory Artificial Intelligence Machine Learning Theoretical Economics |
| url | https://arxiv.org/abs/2511.04465 |