Markov Chains: How Random Transitions Build Big Systems—Like Diamonds Power XXL
Markov Chains formalize the behavior of systems where transitions between states occur probabilistically, generating predictable order from randomness. At their core, these chains embody a memoryless property: the future state depends only on the current state, not the sequence of events leading there. This elegant simplicity allows complex systems—from atomic lattices to large-scale networks—to emerge through repeated small transitions, revealing deep patterns in nature and computation.
Core Concept: Memoryless Transitions and Predictable PatternsA Markov Chain is defined by a set of states and transition probabilities between them, encoded in a transition matrix where each entry reflects the likelihood of moving from one state to another. The defining memoryless property ensures that no historical path is needed to predict the next state—only the present.