The Constraint Collapse Framework (CCF): A Predictive Model of Perception, Energy, and Emergent Behavior in Humans and AI

The Constraint Collapse Framework (CCF) is a predictive model explaining the emergence and fragility of intelligent behavior in humans and AI systems. CCF integrates three interacting components—perception, energy, and constraints—to describe when systems succeed or fail. Emergent behavior occurs when perceptual capacity expands faster than constraints can stabilize and regulate it. Large language models (LLMs) serve as controlled empirical tests of the framework, demonstrating predictable patterns of capability and failure. Historical examples (e.g., evolution, industrial scaling) and bounded microcosms (e.g., poker) illustrate consistent patterns of constraint-driven emergence. CCF offers a falsifiable structure for designing and analyzing future intelligent systems.