In this area, we would like to see proposals for work on how information and transparency affects cooperation. Information asymmetries (both strategic uncertainty about what agents will do and structural uncertainty about private information that others have) are a prime cause of cooperation failure. AI agents are adept at processing vast swathes of information and have features (such as being defined by software) that might make this a promising area in which AI agents could overcome the challenges faced by humans.

• Work on how the potential transparency and/or predictability of agents (e.g. through black-box or white-box access to their source code) can be used to understand and control the extent to which they cooperate. This predictability might emerge due to the similarity of agents and their ability to reason about each other.
• Work on scaling automated information design (e.g. “Bayesian persuasion”) to more complex agents and environments (including LLM agents).
• Implementing and scaling methods for secure information transmission/revelation between AI agents that enable cooperation. This might include work on the ability of agents to conditionally reveal and verify private information.
• The development of efficient algorithms for few-shot coordination in high-stakes scenarios. This could include theoretical work (for example, establishing the amount of information required to predict the behaviour of other agents) and empirical work (for example, on generalising or applying few-shot coordination algorithms to complex settings and advanced agents).

• We believe that there are relatively few crucial coordination problems that are inherently zero-shot (rather than few-shot), and we therefore do not expect to fund work on zero-shot coordination. However, if you believe you can make a good case for how such work would be important for improving the outcomes of real-world, high-stakes scenarios, you are welcome to submit a proposal on zero-shot coordination as well.
• For methods for secure information transmission/revelation, it is important that this enables cooperation that would not otherwise be possible (for example, due to strategic considerations that make agents reluctant to reveal private information). General work on secure information transmission is not something we expect to fund.

• Resolving social dilemmas using mutual transparency or similarity
  • Program Equilibrium
  • Game Theory with Translucent Players
  • Similarity-Based Cooperative Equilibrium
• Resolving problems with private information
  • Commitment Games with Conditional Information Disclosure
  • Information Design in Multi-Agent Reinforcement Learning
• Improving coordination
  • "Other-Play" for Zero-Shot Coordination
• Safe Pareto improvements
  • Safe Pareto Improvements for Delegated Game-Playing
  • Safe Pareto Improvements for Expected Utility Maximizers in Program Games