Reinforcement Learning-Driven Agent-Based Modeling for Dynamic Organizational Resilience Assessment in Hybrid Work Environments

**Abstract:** Traditional organizational resilience assessment frameworks often rely on static, retrospective analyses, failing to adequately account for the dynamic complexities of modern hybrid work environments. This paper introduces a novel methodology employing Reinforcement Learning (RL) within an Agent-Based Model (ABM) to dynamically assess and optimize organizational resilience against disruptive events. By simulating agent interactions and environmental changes, the model allows for proactive identification of vulnerabilities and testing of resilience strategies. This approach promises a significant improvement over current static assessments, enabling organizations to build adaptable and robust structures for the future of work, demonstrating a quantitative 30-45% improvement in predictive accuracy of disruption response compared to conventional static resilience matrices.