Neardeterministic

Neardeterministic is an adjective used to describe systems or processes whose behavior is almost deterministic, with a small probability of deviation caused by randomness, noise, or nondeterminism. In practice, neardeterministic models behave like deterministic ones for most executions, while occasional events may lead to different outcomes.

In computer science, neardeterministic algorithms are those that produce the correct or intended result with high probability, and whose running time is close to that of their deterministic counterparts. Randomness is typically used to break symmetry, reduce contention, or explore a search space, but the probability of deviation from the expected path is kept small and often controllable. Analysis often focuses on the probability of failure or the expected performance, with guarantees that hold with high probability as input size grows.

Contexts where neardeterministic behavior is discussed include randomized algorithms that are correct with high probability, probabilistic data structures, and certain distributed protocols where randomization reduces deadlock and contention. Neardeterministic behavior is also examined in stochastic optimization and simulation, where models include noise or random perturbations but results remain stable under typical conditions.

See also: deterministic, randomized algorithm, Las Vegas algorithm, Monte Carlo algorithm, probabilistic method, stochastic process.