Team Theory Foundations

10 canonical team-theory primitives for cooperative multi-agent decision problems — agents share a payoff but each sees a different slice of the world. Game theory handles strategic conflict; decision theory handles solo choice under uncertainty; team theory handles the regime in between, which is exactly where subagents and orchestrated AI agents operate.

The field was founded by Jacob Marschak (1955) and formalized by Roy Radner (1962). It is the formal basis for: when to centralize a decision, what each agent must observe, when communication pays for itself, and why decentralized teams can be optimal even with free communication channels. Modern multi-agent reinforcement learning (Dec-POMDPs, MARL) is its computational descendant.

When to Apply

Apply team-theory when:

• Multiple agents with shared payoff but partitioned observations (the canonical subagent setting)
• Designing what each subagent sees vs. what is centralized
• Choosing between centralized orchestrator, decentralized swarm, and hierarchical structures
• Costing communication: is it worth the latency / token / coordination overhead?
• Building agent teams where role specialization matters (each agent owns a different observation lane)
• Multi-agent RL or Dec-POMDP problem framing