A world model is an internal representation that helps an AI system predict how an environment may change after an event or action. Rather than learning only which behavior previously earned a reward, the system models objects, relationships, dynamics, or cause and effect. It can simulate possible futures before deciding what to do, supporting robotics, autonomous systems, reinforcement learning, game agents, and video generation. The representation is always a simplification. If it misses an important physical rule or social constraint, the system may form unrealistic plans. Useful world models therefore need comparison with real outcomes, uncertainty estimates, continuous updates, and safeguards when simulated decisions affect people or physical environments.
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