Strategic Data Sharing between Competitors

TL;DR

This paper presents a framework analyzing the trade-offs and incentives for data sharing between competing firms, highlighting how market conditions influence collaboration decisions in machine learning contexts.

Contribution

It introduces a novel framework combining economic and machine learning perspectives to analyze data sharing incentives among competitors.

Findings

Reduced competition encourages data sharing.

Harder learning tasks promote collaboration.

Market conditions significantly influence sharing incentives.

Abstract

Collaborative learning techniques have significantly advanced in recent years, enabling private model training across multiple organizations. Despite this opportunity, firms face a dilemma when considering data sharing with competitors -- while collaboration can improve a company's machine learning model, it may also benefit competitors and hence reduce profits. In this work, we introduce a general framework for analyzing this data-sharing trade-off. The framework consists of three components, representing the firms' production decisions, the effect of additional data on model quality, and the data-sharing negotiation process, respectively. We then study an instantiation of the framework, based on a conventional market model from economic theory, to identify key factors that affect collaboration incentives. Our findings indicate a profound impact of market conditions on the data-sharing incentives. In particular, we find that reduced competition, in terms of the similarities between the firms' products, and harder learning tasks foster collaboration.