Economics models the behavior of people, firms, and other decision makers, as a means to understand how these decisions shape the pattern of activities that produce value and ultimately satisfy (or fail to satisfy) human needs and desires. The field adopts rational models of behavior, either of individuals or of behavior in the aggregate.

Artificial Intelligence (AI) research is also drawn to rationality concepts, which provide an ideal for the computational agents that it seeks to create. Although perfect rationality is not achievable, the capabilities of AI are rapidly advancing, and AI can already surpass human-level capabilities in narrow domains.

We envision a future with a massive number of AIs, these AIs owned, operated, designed, and deployed by a diverse array of entitites. This multiplicity of interacting AIs, apart or together with people, will constitute a social system, and as such economics can provide a useful framework for understanding and influencing the aggregate. In turn, systems populated by AIs can benefit from explicit design of the frameworks within which AIs exist. The proposed research looks to apply the economic theory of mechanism design to the coordination of behavior in systems of multiple AIs, looking to promote beneficial outcomes.

When a massive number of AIs are owned, operated, designed, and deployed by a diverse array of firms, individuals, and governments, this multi-agent AI constitutes a social system, and economics provides a useful framework for understanding and influencing the aggregate. In particular, we need to understand how to design multi-agent systems that promote beneficial outcomes when AIs interact with each other. A successful theory must consider both incentives and privacy considerations.

Mechanism design theory from economics provides a framework for the coordination of behavior, such that desirable outcomes are promoted and less desirable outcomes made less likely because they are not in the self-interest of individual actors. We propose a program of fundamental research to understand the role of mechanism design, multi-agent dynamical models, and privacy-preserving algorithms, especially in the context of multi-agent systems in which the AIs are built through reinforcement learning (RL). The proposed research considers two concrete AI problems: the first is experiment design, typically formalized as a multi-armed bandit process, which we study in a multi-agent, privacy-preserving setting. The second is the more general problem of learning to act in Markovian dynamical systems, including both planning and RL agents.