Adaptive Resilience Assessment and AI-Based Decision Support for Evolving Complex Systems

Complex systems evolve as technologies, infrastructures, policies, operational processes, and resource conditions change over time. Their resilience therefore cannot be assessed adequately using analytical representations that remain fixed while the systems and the threats affecting them continue to develop. This study introduces an adaptive approach for modelling, analyzing, and comparing changing systems and for supporting resilience-oriented decisions with artificial intelligence. The approach brings together semantic modelling, flexible system representations, agent-based simulation, disruption and scenario analysis, integrated assessment, and AI-supported exploration of alternative configurations. Semantic descriptions of system components, resources, interactions, processes, hazards, and adaptation options provide a common basis for integrating models from different disciplines and for extending them as new information or requirements emerge. The resulting analytical environment enables alternative system designs and adaptation strategies to be evaluated systematically across different disruption scenarios using resilience and sustainability indicators. ... mehrIts application is demonstrated for the allocation of strategic reserves in a food supply chain exposed to drought conditions associated with climate change. The example shows how alternative planning options can be generated, simulated, assessed, and compared within a consistent analytical workflow. The study provides a methodological basis for adaptive resilience assessment and AI-based decision support in complex systems undergoing continuous transformation.