Not all systems behave in the same way, so analysts use different types of models to represent them accurately. Models can be classified based on how they handle time, randomness, and state changes.

• Discrete vs. Continuous In discrete models, changes to the system happen at specific, separate points in time (like customers arriving at a bank). In continuous models, the system’s state variables change constantly over time (like the level of water filling a dam).
• Stochastic vs. Deterministic Deterministic systems are those that are not affected by randomness; given a starting condition, the output will always be the same. Stochastic systems, however, are affected by randomness, which means their output is a random variable that can differ each time the simulation is run.
• Static vs. Dynamic Static models are not affected by the passage of time. A good example is a Monte Carlo simulation, which uses random sampling to understand risks or outcomes. Dynamic models, on the other hand, represent systems that change and evolve over time.

Regardless of the type of model you build, ensuring it is a correct and accurate representation of the real system is paramount.