Modeling and simulation of large-scale Systems: a systematic comparison of modeling paradigms

TL;DR

This paper investigates the challenges and preferences in large-scale system modeling, highlighting expert opinions on the suitability of acausal versus causal modeling paradigms through surveys and analytic hierarchy process analysis.

Contribution

It provides a systematic comparison of modeling paradigms for large-scale systems based on expert surveys and prioritization methods.

Findings

Experts favor acausal modeling for large-scale systems

Causal modeling is considered less suitable for large-scale systems

The study offers insights into research needs and challenges in system modeling

Abstract

A trend across most areas where simulation-driven development is used is the ever increasing size and complexity of the systems under consideration, pushing established methods of modeling and simulation towards their limits. This paper complements existing surveys on large-scale modeling and simulation of physical systems by conducting expert surveys. We conducted a two-stage empirical survey in order to investigate research needs, current challenges as well as promising modeling and simulation paradigms. Furthermore, we applied the analytic hierarchy process method to prioritise the strengths and weakness of different modeling paradigms. The results of this study show that experts consider acausal modeling techniques to be suitable for modeling large scale systems, while causal techniques are considered less suitable.