In this area, we would like to see proposals for work that can improve our understanding of some specific types of multi-agent dynamics involving advanced AI systems. This includes emergent phenomena (behaviours, goals and capabilities) that are not present in the individual agents or systems, but that arise specifically in the multi-agent system. We believe that this kind of work will be important to identify, monitor and mitigate new risks that arise as deployment of advanced and interacting AI systems becomes more widespread throughout society.

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• Work on destabilising dynamics, which could aim to answer questions about the conditions under which multi-agent systems involving AI have undesirable dynamics and how such phenomena can be monitored and stabilised. Such work may cover aspects such as how the number of agents, their objectives, and the features of their environment might precipitate undesirable dynamics.
• Work on prevention of correlated failures, that could arise due to similarities and shared vulnerabilities among agents in the multi-agent system. This could include work on the impact of AI agents learning from data generated by each other on shared vulnerabilities, correlated failure modes, and their ability to cooperate/collude.
• Work on which network structures and interaction patterns lead to more robust or fragile networks of AI agents, and the development of tools for overseeing and controlling the dynamics and co-adaptation of networks of advanced AI agents. This might include ‘infrastructure for AI agents’ such as interaction protocols. 
• Theoretical and empirical work on establishing the conditions under which unexpected and undesirable goals and capabilities might emerge from multiple AI agents, how robust such phenomena are, and how quickly they can occur. Comparisons across specific scenarios could help to establish conditions under which these emergent phenomena are more likely, such as the degree of competition, complementarity of agents, access to particular resources, or task features.

We expect it to be challenging to do work in this area that will be informative about advanced AI systems. Proposals should explicitly aim for results that can be expected to generalise to large and complex systems consisting of advanced AI agents, and it is important that the phenomena studied can reasonably be expected to be significant for the societal impact of AI systems in the long term.

• From Chaos to Order: Symmetry and Conservation Laws in Game Dynamics
• Constants of Motion: The Antidote to Chaos in Optimization and Game Dynamics
• The Prevalence of Chaotic Dynamics in Games with Many Players‍
• Exploration-Exploitation in Multi-Agent Learning: Catastrophe Theory meets Game Theory‍
• Catastrophic Cascade of Failures in Interdependent Networks‍
• Evolutionary Dynamics of Multi-Agent Learning: A Survey‍
• Visibility into AI Agents‍
• Ecosystem Graphs: The Social Footprint of Foundation Models‍
• Collective Intelligence for Deep Learning: A Survey of Recent Developments