{
  "annotation_id": "e01fbcae-60f0-4799-bbee-38eda185fc37",
  "date_created": "2026-02-17T05:53:19.965000Z",
  "date_modified": "2026-02-17T05:53:19.965000Z",
  "file_hash": "c7d1d9dd61fd1659f331c9a3c90a5b52897f71411cfad732e6dc4876b887ce23",
  "private": false,
  "record": {
    "abstract": "Click-fraud is commonly seen as a key vulnerability of pro-rata revenue sharing on music streaming platforms, whereas user-centric is largely immune. This paper develops a tractable non-cooperative model in which artists can purchase fraud activity that generates undetectable fake streams up to a technological limit. We show that pro-rata can be fraud-robust: when fraud technology is weak, honesty is a strict dominant strategy, and an efficient fraud-free equilibrium obtains. When fraud technology is strong, a unique fraud equilibrium arises, yet aggregate fake streams remain bounded. Although fraud is inefficient, the resulting redistribution may improve fairness in some cases. To mitigate fraud without abandoning pro-rata, we introduce a parametric weighted rule that interpolates between pro-rata and user-centric, and characterize parameter ranges that restore a fraud-free equilibrium under technology constraint. We also discuss implications of Spotify\u0027s modernized royalty system for fraud incentives.",
    "arxiv_id": "2601.09573",
    "authors": [
      "Hao Yu"
    ],
    "categories": [
      "econ.TH"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "On click-fraud under pro-rata revenue sharing rule",
    "url": "https://arxiv.org/abs/2601.09573",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "981a7784-bc69-491a-9ef8-a807840f1534",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}