A time and space complexity analysis of model integration

• Published in: Proceedings - Winter Simulation Conference pp. 1644 - 1651
• Main Author: North, Michael J.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2014
• Subjects: Analytical models; Clustering algorithms; Complexity theory; Computational modeling; Data models; Mathematical model; Software
• ISSN: 0891-7736
• DOI: 10.1109/WSC.2014.7020015

The computational study of complex systems increasingly requires model integration. The drivers include a growing interest in leveraging accepted legacy models, an intensifying pressure to reduce development costs by reusing models, and expanding user requirements that are best met by combining different modeling methods. There have been many published successes including supporting theory, conceptual frameworks, software tools, and case studies. Nonetheless, on an empirical basis, the published work suggests that correctly specifying model integration strategies remains challenging. This naturally raises a question that has not yet been answered in the literature, namely `what is the computational difficulty of model integration?' This paper's contribution is to address this question with a time and space complexity analysis that concludes that deep model integration with proven correctness is both NP-complete and PSPACE-complete and that reducing this complexity requires sacrificing correctness proofs in favor of guidance from both subject matter experts and modeling specialists.