Strategic Data Sharing between Competitors
This addresses a strategic dilemma for firms in competitive markets considering collaborative learning, though it is incremental as it builds on existing economic models.
The paper tackles the problem of data sharing between competing firms by introducing a framework to analyze the trade-off between improving machine learning models and reducing profits due to benefiting competitors, finding that reduced competition and harder learning tasks foster collaboration.
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.