What characterizes a 'stochastic model'?

A stochastic model is characterized by its incorporation of randomness and unpredictability. This means that the model recognizes that not all factors influencing the outcome are deterministic; instead, it includes elements of chance or probabilistic behavior. In practical applications, stochastic models are often used to simulate systems or processes that inherently involve uncertainty, allowing for a more realistic representation of potential behaviors or outcomes over time.

In contrast to deterministic models, which produce the same output every time given the same set of inputs, stochastic models can yield differing results across multiple simulations, even if the inputs remain constant. This variance arises from the random components built into the model, making it a valuable tool for analyzing complex systems where multiple factors can lead to various outcomes.

By recognizing the inherent unpredictability of certain processes, stochastic models provide insights that can help in decision-making, risk assessment, and forecasting in various fields such as finance, engineering, and environmental science.