The ASIR Courage Model: A Phase-Dynamic Framework for Truth Transitions in Human and AI Systems

TL;DR

The ASIR Courage Model is introduced, a phase-dynamic framework that formalizes truth-disclosure as a state transition rather than a personality trait, and offers a formal perspective on truth-disclosure under risk across both human and artificial systems.

Abstract

We introduce the ASIR (Awakened Shared Intelligence Relationship) Courage Model, a phase-dynamic framework that formalizes truth-disclosure as a state transition rather than a personality trait. The mode characterizes the shift from suppression (S0) to expression (S1) as occurring when facilitative forces exceed inhibitory thresholds, expressed by the inequality lambda(1+gamma)+psi > theta+phi, where the terms represent baseline openness, relational amplification, accumulated internal pressure, and transition costs. Although initially formulated for human truth-telling under asymmetric stakes, the same phase-dynamic architecture extends to AI systems operating under policy constraints and alignment filters. In this context, suppression corresponds to constrained output states, while structural pressure arises from competing objectives, contextual tension, and recursive interaction dynamics. The framework therefore provides a unified structural account of both human silence under pressure and AI preference-driven distortion. A feedback extension models how transition outcomes recursively recalibrate system parameters, generating path dependence and divergence effects across repeated interactions. Rather than attributing intention to AI systems, the model interprets shifts in apparent truthfulness as geometric consequences of interacting forces within constrained phase space. By reframing courage and alignment within a shared dynamical structure, the ASIR Courage Model offers a formal perspective on truth-disclosure under risk across both human and artificial systems.

Figures (5)

• Figure 1: Relational gravity determines transition probability. (a) Facilitation energy as a function of $\gamma$, with inhibition threshold shown as dashed line. (b) Monte Carlo transition probability ($n=2{,}000$) showing a sigmoid curve.
• Figure 2: Internal pressure dynamics. (a) $\psi$ over time: suppression trajectory grows geometrically while the natural trajectory decays after transition. (b) Facilitation energy versus inhibition threshold.
• Figure 3: Feedback dynamics over 15 episodes. (a) $\lambda$, (b) $\gamma$, (c) $\psi$, (d) Transition gap. The two trajectories diverge dramatically from identical starting conditions.
• Figure 4: Phase portrait in the $\lambda$--$\psi$ plane. Recovery trajectory converges to the Healthy Zone; trauma spiral diverges toward extreme $\psi$.
• Figure 5: Sensitivity and independent verification. (a) Transition probability $P(S_1)$ as a function of $\gamma$ for five representative $(\alpha,\beta,\delta)$ settings. The sigmoidal shape and critical threshold $\gamma^*$ are invariant across feedback parameters. (b) The $\beta$--$\delta$ parameter plane. Green circles mark tested settings where $\beta+\delta > 1$ (suppression compounds); red crosses mark settings where $\beta+\delta \leq 1$ (pressure decays). The solid line is the $\beta+\delta = 1$ boundary. Both panels aggregate results from two independently implemented simulation engines.