Article
Version 1
Preserved in Portico This version is not peer-reviewed
Discrete Event Systems Theory for Fast Stochastic Simulation via Tree Expansion
Version 1
: Received: 17 January 2024 / Approved: 18 January 2024 / Online: 18 January 2024 (14:04:42 CET)
A peer-reviewed article of this Preprint also exists.
Zeigler, B.P. Discrete Event Systems Theory for Fast Stochastic Simulation via Tree Expansion. Systems 2024, 12, 80. Zeigler, B.P. Discrete Event Systems Theory for Fast Stochastic Simulation via Tree Expansion. Systems 2024, 12, 80.
Abstract
Paratemporal methods based on tree expansion have proven to be effective in efficiently generating trajectories of stochastic systems. However, combinatorial explosion of branching arising from multiple choice points presents a major hurdle that must be overcome to implement such techniques. In this paper we tackle this scalability problem by developing a systems theory-based framework covering both conventional and proposed tree expansion algorithms for speeding up discrete event system stochastic simulations while preserving desired accuracy. An example is discussed to illustrate the tree expansion framework in which a discrete event system specification (DEVS) Markov stochastic model takes the form of a tree isomorphic to a free monoid over the branching alphabet. We derive the computation times for baseline, non-merging, and merging tree expansion algorithms to compute the distribution of output values at any given depth. The results show the remarkable reduction from exponential to polynomial dependence on depth effectuated by node merging. We relate these results to the similarly reduced computation time of binomial coefficients underlying Pascal’s triangle. Finally, we discuss application of tree expansion to estimating temporal distributions in stochastic simulations involving serial and parallel compositions with potential real world use cases.
Keywords
modeling and simulation; paratemporal methods; tree expansion; systems theory; stochastic simulation; computation complexity; temporal distributions; serial and parallel compositions; DEVS; Markov systems
Subject
Computer Science and Mathematics, Other
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment
