Caspar Oesterheld

Caspar Oesterheld, a PhD student and assistant director at CMU's Foundations of Cooperative AI Lab, studies multi-agent AI safety and program equilibria. He explains program games where programs read each other's code. They cover simulation-based versus proof-based cooperation, epsilon-grounded simulation tricks, shared randomness fixes, and why correlated versus private randomness changes what cooperation is possible.

Caspar Oesterheld discusses cooperative AI, its applications, and interactions between AI systems. They explore AI arms races, game theory limitations, and the challenges of aligning AI with human values. The podcast also covers regret minimization in decision-making, multi-armed bandit problem, logical induction, safe Pareto improvements, and similarity-based cooperation. They highlight the importance of communication, enforcement mechanisms, and the complexities of achieving effective cooperation and alignment in AI systems.