Defection-Free Collaboration between Competitors in a Learning System

TL;DR

This paper explores collaborative learning between competing firms, proposing a defection-free sharing scheme that ensures mutual benefit and converges to a fair Nash bargaining solution.

Contribution

It introduces a novel, equitable collaboration scheme for competing firms that prevents revenue loss and guarantees convergence to a fair outcome.

Findings

Fully collaborative sharing leads to market collapse.

One-sided sharing improves revenues for both firms.

The proposed scheme converges to the Nash bargaining solution.

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.