Stochasticity refers to the inclusion of randomness or uncertainty in the model. Randomness can be introduced through probabilistic processes, random events, or noisy behaviors of agents. Stochasticity acknowledges that real-world systems often exhibit inherent variability and unpredictability, which can be essential to capture the dynamics and outcomes of the model.
What processes are modeled by assuming they are random or partly random?
Here, describe where and how stochastic processes—those driven by pseudorandom numbers— are used in the model. While some ABMs base most of their processes on random events, others can produce highly variable results with no stochasticity at all. In general, stochastic processes are used when we want some part of a model to have variation (among entities, over time, etc.) but we do not want to model the mechanisms that cause the variability. It may be critical for a model to include how weather affects some system such as the electric power grid, but we certainly do not want to add the enormous complexity of predicting weather to the model; instead, we simply model the timing and duration of weather events as stochastic processes. One common use of stochasticity in ABMs is to insert variability in initial conditions: when we create our agents (and other entities) at the start of a simulation (Element 5, below) we do not want them to be identical, so we use pseudorandom number distributions to set the initial values of some state variables. A second common use is to simplify submodels by assuming they are partly stochastic. Assuming that an agent dies if a random number between 0.0 and 1.0 is greater than its survival probability is a very common example: we do not want to represent all the detail of when agents die. A third use of stochasticity is modeling agent behaviors in a way that causes the model agents to use different alternatives with the same frequency as real agents have been observed to. For example, a sociological model could use a stochastic process to model the age at which people marry, comparing random numbers to the marriage rates observed in real people. This approach would impose the observed marriage rates on the simulated population. Describe: • Which processes are modeled as stochastic, using pseudorandom number distributions to determine the outcome. • Why stochasticity was used in each such process. Often, the reason is simply to make the process variable without having to model the causes of variability; or the reason could be to make model events or behaviors occur with a specified frequency.
In ITC evaluation model: