🤖 AI Summary
This study addresses a critical limitation in existing simulation credibility assessment approaches, which predominantly focus on individual models and thus fail to capture the reliability of complex, multi-model architectures. Moving beyond the single-model evaluation paradigm, this work redefines trustworthiness at the architectural level and proposes a multidimensional framework that integrates sensitivity analysis, expert knowledge, explainable artificial intelligence, and complex network modeling. Through a systematic comparison of diverse methodologies across dimensions such as methodological rigor, generalizability, and computational resource demands, the research offers both theoretical foundations and practical guidance for constructing high-assurance simulation architectures.
📝 Abstract
Credibility of a simulation model is an important topic. Several approaches try to quantify the credibility of simulation. However, models are mostly assembled within a simulation architecture. Can the credibility of a simulation architecture be assessed based on the credibility of the models that comprise it? This paper aims to address this issue by providing an overview of the current state of the art in the field of assembly credibility. It will compare sensitivity analysis techniques, qualitative analysis by experts, explainability in AI, and networks. Finally, an assessment of the proposed approaches, based on criteria such as rigor, generalization, and resource requirements, will reveal the strengths and weaknesses of each approach.