Inferring Dependency Graphs for Agent-based Models Using Aspect-oriented Programming

Kreikemeyer, Justin N. and Köster, Till and Uhrmacher, Adelinde M. and Warnke, Tom (2021) Inferring Dependency Graphs for Agent-based Models Using Aspect-oriented Programming. In: Winter Simulation Conference (WSC 2021), 13-16 Dec 2021, Phoenix, Arizona, USA. Proceedings, published by IEEE Press, pp. 1-12.

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Population-based CTMC models can generally be executed efficiently with stochastic simulation algorithms (SSAs). However, the heterogeneity in agent-based models poses a challenge for SSAs. To allow for an efficient simulation, we take SSAs that exploit dependency graphs for population-based models and adapt them to agent-based models. We integrate our approach with object-oriented frameworks for agent-based simulation by detecting dependencies via aspect-oriented programming (AOP). This way, modelers can implement models without manually recording dependency information, while still executing the models with efficient, dependency-aware SSAs. We provide an open-source implementation of our approach for the framework MASON, showing significant speedups in model execution.

Item Type: Conference or Workshop Item (Paper)
Additional Information: DOI: 10.1109/WSC52266.2021.9715293, Article No. 196
Projects: MoSiLLDe