Advanced Tutorial on Paratemporal Simulation Using Tree Expansion

Stochastic simulations require large amounts of time to generate enough trajectories to attain statistical significance and estimate desired performance indices with satisfactory accuracy. They require search spaces with deep uncertainty arising from inadequate or incomplete information about the system and the outcomes of interest. Paratemporal methods efficiently explore these large search spaces and offer an avenue for speedup when executed in parallel. However, combinatorial explosion of branching arising from multiple choice points presents a major hurdle that must be overcome to implement such techniques. In this advanced tutorial we show how to tackle this scalability problem by applying a systems theory-based framework covering both conventional and newly developed paratemporal tree expansion algorithms for speeding up discrete event system stochastic simulations while preserving the desired accuracy.