Why Open Source? A Game-Theoretic Analysis of the AI Race
For AI developers and policymakers, this work provides a formal framework to understand and potentially influence open-source dynamics in competitive AI development.
The authors propose a game-theoretic model to analyze open-sourcing vs. closed-sourcing decisions in the AI race, showing that finding discrete Nash equilibria is NP-hard but tractable via MIP, while continuous equilibria exist and are tractable. They derive policy-relevant insights from the analysis.
In recent years, with the advancement of frontier AI, we have observed certain dynamics in open-sourcing and closed-sourcing decisions. We propose a game-theoretic model to analyze these dynamics in the current landscape of the AI race. Our model builds on an R&D race framework under a winner-takes-all setting, and it accounts for the cases where the players' actions can be either discrete or continuous (i.e., partial open-sourcing, such as open weights). We show that determining the existence of a discrete pure non-trivial Nash equilibrium is NP-hard in general but that we can transform the discrete Nash existence computation into a MIP (Mixed-Integer Programming) problem, making it tractable for small instances using a standard MIP solver. Next, we show the existence and tractability of pure Nash equilibria in the continuous version of our problem, leveraging standard convex analysis results, and constructing an equivalent MIP formulation. Throughout this work, we leverage both our main technical results as well as surrounding technical analysis, to derive socially relevant insights that we believe can serve both to understand already existing decisions and dynamics and to potentially inform new policies.