Cause-effect graphs have been applied in non agent-based simulations, where they are used to model chained causal relations between input parameters and system behaviour measured by appropriate indicators. This can be useful for the analysis and interpretation of simulations. However, multi-agent simulations shift the paradigm of chained causal relations to multiple levels of detail and abstraction. Thus, conventional cause-effect graphs need to be extended to capture the hierarchical structure of causal relations in multi-agent models. In this paper, we present a graphical modelling method that we call Multi-Agent Modelling Notation (MAMN), with which global aspects of the simulation as well as detailed interior mechanisms of agent behaviour can be described. We give proof of concept by showing how the logic that connects individual agent behaviour to global outcomes in a previously published simulation model can be expressed in a concise diagrammatic form. This provides understanding into what drives the model behaviour without having to study source code. We go on to discuss benefits and limitations as well as new opportunities that arise from this type of model analysis.
Nguyen, J., Powers, S., Urquhart, N., Farrenkopf, T., & Guckert, M. (2022). Multi-Agent Modelling Notation (MAMN): A multi-layered graphical modelling notation for agent-based simulations. In PRIMA 2022: Principles and Practice of Multi-Agent Systems - 24th International Conference, Valencia, Spain, November 16–18, 2022, Proceedings (640-649). https://doi.org/10.1007/978-3-031-21203-1_42