Empiricism in Nonlinear Adaptive Systems

This project develops a minimal empirical framework for studying nonlinear adaptive systems in financial markets, where equilibrium and linear econometric assumptions systematically fail. In reflexive systems, agents’ expectations recursively shape the environment that informs those same expectations, causing large models and high precision to obscure generative structure. I combine minimal nonlinear models with structural lenses—targeted diagnostics that isolate feedback mechanisms under conditions of stress. Across speculative bubbles, futures market reversibility, and organizational hierarchy, I show that extreme events and regime shifts function as empirical microscopes for hidden structure. The framework replaces prediction with structural diagnosis and equilibrium optimization with stability analysis.