The simulation model you choose will depend upon the System you are trying to understand.
#Deterministic vs Stochastic
A deterministic model is a model whose behavior is entirely predictable. Given a set of inputs, the model will always result in a unique set of outputs.
A stochastic or probabilistic model is a model that has random variables as inputs, and consequently its outputs are random.
For example: In a donut shop simulation, with a deterministic model, we would assume that a new customer arrives every 5 minutes and an employee takes 2 minutes to serve a customer. In a stochastic/probabilistic model we would assume that the arrival times and the serving time follows some random variables like the normal distribution.
#Static vs Dynamic
Simulation models that represent the system only at a particular point in time are called static. This type of simulations are often called [[Monte Carlo Simulation in Python | Monte Carlo simulations]].
Dynamic simulation models represent systems that evolve over time. The simulation of the donut shop during its working hours is an example of a dynamic model.
#Discrete vs Continuous
Dynamic simulations can be further categorized into discrete or continuous.
In Discrete simulation models, the variables of interest change only at a discrete set of points in time. The number of people queuing in the donut shop is an example of a discrete simulation. The number of customer changes only when a new customer arrives or when a customer has been served.
In Continuous simulation models, the variables of interest change continuously over time.
e.g. a simulation model for a car journey where the interest is on the speed of the car throughout the journey.
System