Markov chains are mathematical models that describe a sequence of possible events where the probability of each event depends only on the state attained in the previous event. This statistical concept characterizes a system that transitions between states with memoryless properties, meaning future states are conditionally independent of past states given the present state. They are widely applied in fields requiring the modeling of sequential data and probabilistic outcomes. The state transitions are governed by a set of probabilities.
Context
While not directly a blockchain term, Markov chains find application in analyzing certain aspects of cryptocurrency networks, such as transaction propagation or consensus mechanism probabilities. News reports occasionally reference their use in academic research for modeling network behavior or predicting price movements, though with caveats regarding market efficiency. A key debate involves the extent to which such probabilistic models can accurately predict complex, emergent behaviors in decentralized systems. Their utility often relates to understanding the statistical properties of network operations.
This research introduces a novel methodology and taxonomy for formally analyzing blockchain consensus algorithm liveness against malicious attacks, ensuring robust system progress.
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