Multi-Agent Simulation
Simulation involving multiple simultaneous agents interacting within a shared environment
Multi-agent simulation is a simulation framework involving multiple autonomous agents that exist and act concurrently within a shared environment, where each agent's actions affect the state and available options for others.
Multi-agent simulations can model cooperative scenarios (multiple agents working toward a shared goal), competitive scenarios (agents with opposing objectives), or mixed settings with both cooperation and competition. The interaction dynamics that emerge — negotiation, coordination, deception, alliance formation — arise from the agents' concurrent existence in shared state.
These simulations have been used extensively in AI research, economics, social science, and biology to study complex systems where global behavior emerges from local rules. Games have served as a particularly useful testbed, with systems trained on Atari, Minecraft, StarCraft, and other environments.
Multi-agent world models extend traditional multi-agent simulations by learning the simulation dynamics from data rather than specifying them explicitly.
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