Karim C Abbaspour, Ario Saeid Vaghefi, Abbas Mirmashhouri, Nazanin Abbaspour, Markus Leippold, Mory Ghomshei
The model yields empirically testable signatures, including pronounced timescale separation between AI and human entropy trajectories, a non-zero human entropy plateau, and bounded fluctuations whose variance increases near the stability boundary. Systems that enforce symmetric entropy minimization are predicted to become rigid or fragile under distributional shift. By contrast, systems operating within the AUR regime are predicted to maintain predictive reliability while preserving exploratory variability, contextual adaptation, and value-sensitive human judgment.
INTRODUCTION: Human-AI interaction is commonly framed as a problem of uniformly minimizing uncertainty across the joint system. We challenge this assumption by proposing Asymmetric Uncertainty Regulation (AUR), a dynamical framework in which stable and adaptive collaboration requires directional rather than symmetric uncertainty regulation.
METHODS: Human-AI systems are modeled as coupled entropy dynamics. The artificial subsystem rapidly contracts predictive entropy through statistical inference and optimization, whereas the human subsystem maintains bounded but persistently non-zero entropy. System stability is characterized by a positive stability margin that prevents both symmetric entropy collapse and runaway instability. The framework is conceptually operationalized through illustrative applications in nutritional counseling, clinical diagnosis under novelty, and criminal investigation.
RESULTS: The model yields empirically testable signatures, including pronounced timescale separation between AI and human entropy trajectories, a non-zero human entropy plateau, and bounded fluctuations whose variance increases near the stability boundary. Systems that enforce symmetric entropy minimization are predicted to become rigid or fragile under distributional shift. By contrast, systems operating within the AUR regime are predicted to maintain predictive reliability while preserving exploratory variability, contextual adaptation, and value-sensitive human judgment.
DISCUSSION: AUR reframes uncertainty as a structured resource rather than a deficiency to be uniformly eliminated. The illustrative applications suggest how reliable AI prediction can be combined with human flexibility and normative judgment, although they constitute conceptual operationalizations rather than empirical validations. By preserving an asymmetry between AI certainty and bounded human uncertainty, AUR offers a principled foundation for designing human-AI systems that augment rather than erode human agency.