We conduct long-term future research with a focus on investigating the dynamics of competition for the development of transformative AI. On the one hand, we use game theory to analyze games similar to the game presented in the publication Racing to the Precipice; on the other hand, we use computational modeling to analyze models which are intended to be more representative of real-world AI development dynamics. To achieve the latter, we formalized and implemented AI competition models that combine best practices from game theory, economics, technological races, and AI safety literatures. While we love sharing our insights, we always make sure that they aren’t considered information hazards. Therefore, some of our research isn’t publicly available.
In the paper Racing to the Precipice Racing to the Precipice, Armstrong, Bostrom, and Shulman presented a static AI competition game. We built a probabilistic version of their game and identified the Nash Equilibrium strategies for the extended model using a computational approach. As part of this analysis, we discuss how uncertainty and differing marginal returns from safety investments jointly influence the risk of disaster.
In our technical report Safe Transformative AI via a Windfall Clause, we evaluated the Windfall Clause policy proposed by the Future of Humanity Institute when applied to the model presented in Racing to the Precipice. We examined how the policy changes the disaster risk of AI competition in different scenarios and found that joining the Windfall Clause is often in the firms’ best interests and encourages a safer competition for transformative AI.