Relational Viability and Constraint Inflation in Adaptive Systems

This work presents a relational framework for understanding persistence in adaptive systems under changing conditions. Rather than treating constraints as external or fundamental structures, constraints are operationalized as emergent indicators of relational mismatch between system form and environmental demands. Using agent-based simulations, the study identifies three distinct regimes of relational viability—stable, adaptive, and chaotic—characterized by quantified phase transitions in response to environmental change rates. Results show that constraint inflation reliably precedes loss of viability and functions as an early-warning diagnostic, while healthy systems exhibit graceful degradation rather than abrupt collapse. Exploration is shown to confer no advantage in slowly changing environments but becomes essential once environmental change exceeds adaptive capacity, revealing a clear exploration premium beyond critical thresholds. Catastrophic failure occurs only under rigidity, where adaptability itself is removed, producing pathological constraint inflation. The framework reframes evolution, adaptation, and collapse as processes of maintaining relational fit rather than optimizing toward fixed outcomes. By treating constraints as proportional stress signals rather than governing laws, this approach provides a unified, quantitative model applicable across biological, organizational, and cognitive systems.