Defection-Free Collaboration between Competitors in a Learning System (2406.15898v1)

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.