Challenges and promises of self-adaptive simulation models

The notion of (self-)adaptation, according to J. Holland (1975), describes the process of iterative refinement of an entity's behavior or structure to improve its performance in its environment. We argue that the potential role of (self-)adaptive simulation models has not been sufficiently acknowledged in the past. A focus on adaptive simulation will likely propel methodological advances in modeling and simulation and digital twinning, increasing its impact on solving urgent real-world problems. Therefore, we will review methods for including adaptation as part of or within simulation models and consequently discuss how simulation models themselves may become the subject of adaptation within and across simulation studies. We will identify different motivations for and types of adaptations within simulation studies by analyzing a family of simulation models. The need to automatically conduct various types of adaptations increases with the importance of online adaptations, e.g. as encountered in digital twins. Further methodological developments in unambiguously representing and intelligently processing the various knowledge sources used in simulation studies, domain-specific languages, analysis methods, self-adaptive software, and, last but not least, model learning are needed. Combining the adaptation within and of simulation models, we arrive at the vision of self-adaptive models, which are less subject to adaptation but become the central actors in their adaptation.