Trustworthy statistical modeling is an emerging challenge in agent-based modeling (ABM). However, typical characteristics of ABM such as the potential for high numbers of entities and parameters, interdependent relations between entities, several layers of effects, and emergent social phenomena challenge this process. In particular, aggregated outcomes emerging from individual agent interactions are, at least partly, difficult to measure. This might impede the statistical modeling process and thus the formulation of trustable conclusions. For this reason, we introduce structural equation modeling (SEM) as a promising statistical modeling method to analyze the behavior of ABMs. SEM allows for the estimation and evaluation of highly networked systems by explicating interactions between types of agents, measuring emergent phenomena, and identifying output patterns of simulation models. Overall, these contributions foster the credibility and trustworthiness of ABMs, and also support the communication and understanding of simulation models' behavior and their output.
