When AI systems are deployed in the real world, many cooperating AI agents will share the same source code or neural network weights. This motivates the study of symmetric team theory. In this talk, Scott shares the results of a new CHAI research paper: For Learning in Symmetric Teams, Local Optima are Global Nash Equilibria. There’s a mix of good and bad news, showing conditions when symmetric cooperation is both stable and unstable.