Difference Between Deterministic And Stochastic Models


Deterministic and stochastic models are two distinct approaches used in various fields, including mathematics, physics, economics, and engineering, to understand and predict phenomena.

A deterministic model is a mathematical model in which outcomes are precisely determined through known relationships among states and events without any room for random variation. In such models, a given input will always produce the same output.

The mathematical characteristics are known in this case. None of them is random, and each problem has just one set of specified values as well as one answer or solution. The unknown components in a deterministic model are external to the model. It deals with the definitive outcomes as opposed to random results and doesn’t make allowances for error.

A stochastic model is a tool for estimating probability distributions of potential outcomes by allowing for random variation in one or more inputs over time. The random variation is usually based on fluctuations observed in historical data for a selected period using standard time-series techniques.

The model generates a large number of answers, estimates, and outcomes, much like adding variables to a difficult maths problem to see how they affect the solution. The identical procedure is then done several times in different settings.


Basically, a model is considered to be deterministic when it has a set of known inputs, necessarily tied to a single set of possible outputs. Deterministic systems are usually modeled analytically, except when there is a very large number of variables involved.

For example, a phenomena with deterministic characteristics could be modeled by the following function:


For each possible input the model generates only one possible output.

In stochastic models, however, the associated uncertainty is incorporated. The variables respond to a specific probability distribution and there is not only one output for each possible input. Instead, there is a distribution of potential solutions associated with a given probability. Stochastic events are unpredictable due to the influence of these random variables.

This sort of modeling is quite useful in engineering applications. Imagine you are about to decide whether or not to start an oil drilling operation which is financially viable only with crude oil prices above X dollars. Since oil prices are stochastic processes in discrete time (which take only discrete values due to the limited measurement scale), then it’s reasonable to work with probabilities to estimate the odds of your engineering project be truly profitable.

Key Differences

Nature of Uncertainty

  • Deterministic Models: These models assume that the future is entirely determined by the initial conditions and the system’s parameters. They do not account for randomness or uncertainty in the system.
  • Stochastic Models: Stochastic models incorporate randomness and uncertainty into the system. They acknowledge that future outcomes are influenced by both the initial conditions and probabilistic factors.


  • Deterministic Models: Since deterministic models rely solely on initial conditions and fixed parameters, their predictions are often more precise and deterministic. Given the same initial conditions, they will always produce the same outcomes.
  • Stochastic Models: Stochastic models generate probabilistic predictions due to the inclusion of random variables. They provide a range of possible outcomes along with associated probabilities, making them inherently less predictable than deterministic models.

Mathematical Representation

  • Deterministic Models: These models are usually represented using differential equations, algebraic equations, or other deterministic mathematical relationships.
  • Stochastic Models: Stochastic models involve the incorporation of random variables and probabilities. They are often represented using stochastic differential equations, Markov chains, or other probabilistic techniques.

Assumption about Reality

  • Deterministic Models: These models assume a perfect understanding of the system, without any external influences or randomness affecting the outcomes.
  • Stochastic Models: Stochastic models acknowledge that real-world systems are subject to inherent randomness and external influences that can lead to variations in outcomes.

Application Areas

  • Deterministic Models: These models are commonly used when the system being studied is relatively stable and deterministic, such as in classical mechanics or engineering systems with well-defined parameters.
  • Stochastic Models: Stochastic models are employed when there is inherent variability or randomness in the system, such as in financial markets, epidemiology, and environmental modeling.


  • Deterministic Models: These models are often simpler to analyze and solve since they do not involve probabilistic calculations and random variables.
  • Stochastic Models: Stochastic models can be more complex to analyze due to the incorporation of randomness. They often require advanced mathematical techniques and simulations to capture the range of possible outcomes.