Agent-Based Resilience Analysis: A Domain-Agnostic Framework for System Degradation and Recovery Analysis

Abstract

Abstract. This article introduces a domain-agnostic simulation framework for evaluating the resilience and robustness of network-based systems subjected to degradation and partial recovery. Built upon a previously developed mutation-only genetic algorithm (GA) for network optimization, the framework integrates agent-based modeling to simulate full-lifecycle behavior. Functional, degradation, and recovery agents operate concurrently over time, enabling dynamic representation of system performance loss, cascading effects, and restoration efforts. The simulation evaluates ten optimized network configurations, including in-depth analysis of three representative cases (best overall, balanced, and vulnerable) to explore how structural design influences degradation response. Results demonstrate that GA-derived fitness and topological centrality measures fail to predict long-term resilience outcomes. In contrast, four new behavior-driven metrics introduced in this work (the normalized performance loss per unit degradation, robustness index, sustained functionality index, and structural integrity index) offer interpretable and transferable insights into system survivability. These metrics are shown to be independent of traditional indicators and capture both node-level sensitivity and system-wide adaptation over time. As a result, they provide a more complete foundation for resilience assessment in mission-critical systems. This study completes a three-part research effort aimed at advancing mission assurance by bridging design-time optimization with operational survivability analysis. Future work will explore cross-domain applications of the metrics, integration of resilience objectives into the GA process, and the extension of simulation dynamics to support probabilistic degradation, adaptive pathing, and intelligent repair strategies.