Frank Schweitzer's paper defines resilience as trade-off between robustness and adaptivity

A new paper by Frank Schweitzer, published on arXiv (2607.25458), offers a rigorous framework for understanding resilience in complex systems, with direct implications for AI-driven organizations. Schweitzer distinguishes between two fundamental dynamics. The first involves a clear separation between periods of normal operation and phases of rapid breakdown followed by slow recovery. The second applies to volatile systems where these phases are intertwined, making resilience an emergent property arising from agent interactions. Crucially, breakdowns are often self-inflicted: psychological biases impair situation awareness, leading to incorrect expectations. Through positive feedback loops, the failure of a few elements cascades into a system-wide collapse. Yet the same feedback mechanisms can be harnessed to drive recovery.

To model these dynamics, Schweitzer advocates a data-driven approach that combines agent-based models with modern AI tools, knowledge graphs, and large-scale repositories. This allows practitioners to simulate how resilience emerges from local interactions and to identify the trade-off between robustness and adaptivity. A key finding is that maximizing performance often directly undermines resilience. Instead of trying to reconstruct past conditions, second-order solutions that transform the system's structure are more promising. For professionals building or managing AI-powered platforms, the paper provides a theoretical blueprint for designing systems that can withstand shocks without sacrificing long-term viability.