This work introduces a novel interdisciplinary framework for observing and evaluating social interactions not through behavioral outcomes or persuasion efficacy, but through circulation-oriented safety indicators: autonomy, recovery, and reversibility. Unlike existing approaches in social influence modeling, behavioral prediction, or optimization-based intervention, this framework is explicitly designed to avoid control, ranking, or manipulation. Instead, it focuses on identifying conditions under which individuals and groups retain the capacity to recover to their own baseline states after interaction. The proposed model integrates:- A state-based representation of individual autonomy and load,- Interaction-level indicators of lock-in and irreversibility,- A two-layer disclosure design separating public safety signals from NDA-protected mathematical formulations,- And an experimental protocol suitable for ethically approved small-group studies. We believe this framework is particularly relevant to interdisciplinary audiences concerned with human–AI interaction, social resilience, institutional safety, and the ethical limits of influence technologies. All measurements are non-invasive, consent-based, and designed for interpretability rather than optimization. Mathematical formulations are used solely for state estimation and safety detection, and are not employed for behavioral control or outcome maximization.
Hinano Kimura (Sat,) studied this question.