DISCRETE EVENT SIMULATION · TRAINING
Discrete Event Simulation for Training: model repeatable practice where performance and outcomes can be reviewed, compare scenarios, and inspect persistent state and autonomous-agent behavior in Epic Simulation.
Represent systems as state changes triggered by events, queues and resource availability. For training, the model focuses on repeatable practice where performance and outcomes can be reviewed.
Start with explicit assumptions, change one or more conditions, preserve state history, and compare why trajectories diverge.
Also inspect agent choices, resource changes, constraints, feedback loops and second-order effects.
A simulation is a model of possibilities, not a guarantee or proof of a real-world outcome.
It applies discrete event simulation to repeatable practice where performance and outcomes can be reviewed.
event timing, queue length, resource utilization, cycle time.
No. A simulation explores modeled possibilities under explicit assumptions; it does not prove what will happen in the real world.