Quantifying Calm

Modern analytics systems measure efficiency with remarkable precision, yet they rarely account for cognitive ease, contextual load, or the human experience of decision-making. This paper introduces a conceptual framework for privacy-preserving behavioral analytics an approach that emphasizes awareness, consent, and architectural restraint as core design principles. By modeling directional calm within local-first simulations and expiring all synthetic data immediately after use, the framework demonstrates how precision and privacy can coexist without deeper collection
or surveillance.

All examples are statically defined and purely synthetic. This work contributes to emerging discussions in applied AI, human-centered design, and privacy-preserving system architecture. It is part of a three-paper Research Edition trilogy examining ethical system design, reflective AI,
and behavioral measurement under privacy-first constraints.