Markov Property

The Markov property is a central assumption in probability theory and sequential decision problems. It states that the future of a system can be predicted from its current state alone, without needing the entire past history. Reinforcement learning, Markov decision processes, and many time-dependent models are built on this logic. Although real-world systems are not always perfectly Markovian, this assumption makes complex processes much more manageable. For that reason, the Markov property is a powerful abstraction for handling uncertainty.