Picture a fleet of drones, a network of sensors, or a set of traders in a market. Each one observes only a sliver of the world and must act on that sliver — yet their actions combine into a shared outcome. This is the world of decentralized decision-making, and it hides a question that is subtle even before you ask for a good strategy: does an optimal joint strategy exist at all? Infinite spaces and information split across agents can make the answer surprisingly delicate.
Existence first, then optimization
A recurring thread of my work establishes when team-optimal strategies exist. In On the existence of optimal policies for a class of static and sequential dynamic teams (2015) and Existence of Team-Optimal Solutions in Static Teams with Common Information (2020), we develop a topology-of-information approach: treat each agent’s strategy as shaping a probability measure and study continuity in the right topology. That machinery rests on a genuinely foundational result — The topology of information on the space of probability measures over Polish spaces (2014) — and extends to Teams with Countable Observation Spaces (2021). Existence sounds abstract, but without it, “find the optimal decentralized policy” is not even a well-posed request.
Common information: taming asymmetry
When agents know different things, equilibria are hard to compute. The common information approach cuts through this: reformulate the problem around what everyone knows in common, and a coordinator’s problem emerges that is tractable. We used it to characterize Common information based Markov perfect equilibria for linear-Gaussian games (2014) and for finite games (2014), and to design incentives in Dynamic incentive design in multi-stage linear-Gaussian games with asymmetric information (2014).
Communication is not free
If agents can talk, which links actually matter? In Sketching for Elimination of Communication Links in LQG Teams (2021) and Communication Link Elimination in Static LQG Teams (2018) we ask when a communication channel can be removed with negligible loss — a direct handle on the trade-off between coordination and bandwidth.
Why it matters
Any system where autonomy is distributed lives here: you cannot centralize every decision, so agents must act locally toward a global goal. The results say when that is possible, how to structure the reasoning (around common information), and which communication is worth its cost.
What it means for multi-agent AI
We are building multi-agent systems at speed — swarms of robots, fleets of autonomous vehicles, and now teams of LLM-based agents collaborating on tasks. Every one of them faces the team-theory questions in disguise:
- What is the common knowledge the agents can coordinate around, and what stays private?
- How much communication is actually necessary, versus expensive overhead?
- Does a coherent joint strategy even exist under their information structure?
Decades before “agentic AI” was a phrase, decentralized control was working out its foundations. As we hand more collective decisions to machines that each see only part of the picture, that foundation is exactly what keeps the picture coherent.
Papers behind this post: On the existence of optimal policies for a class of static and sequential dynamic teams (2015) · Existence of Team-Optimal Solutions in Static Teams with Common Information (2020) · Existence of Team-Optimal Strategies in Teams with Countable Observation Spaces (2021) · Common information based Markov perfect equilibria for linear-Gaussian games (2014) · The topology of information on the space of probability measures over Polish spaces (2014) · Sketching for Elimination of Communication Links in LQG Teams (2021). See the Publications page.