Defection-Free Collaboration between Competitors in a Learning System

• URL: http://arxiv.org/abs/2406.15898v1
• Date: Sat, 22 Jun 2024 17:29:45 GMT
• Title: Defection-Free Collaboration between Competitors in a Learning System
• Authors: Mariel Werner, Sai Praneeth Karimireddy, Michael I. Jordan,
• Abstract summary: We study collaborative learning systems in which the participants are competitors who will defect from the system if they lose revenue by collaborating. We propose a more equitable, *defection-free* scheme in which both firms share with each other while losing no revenue.
• Score: 61.22540496065961
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study collaborative learning systems in which the participants are competitors who will defect from the system if they lose revenue by collaborating. As such, we frame the system as a duopoly of competitive firms who are each engaged in training machine-learning models and selling their predictions to a market of consumers. We first examine a fully collaborative scheme in which both firms share their models with each other and show that this leads to a market collapse with the revenues of both firms going to zero. We next show that one-sided collaboration in which only the firm with the lower-quality model shares improves the revenue of both firms. Finally, we propose a more equitable, *defection-free* scheme in which both firms share with each other while losing no revenue, and we show that our algorithm converges to the Nash bargaining solution.

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