Rij(t)=Rij(t1)++GainLoss,where >+R_{ij}(t) = R_{ij}(t-1) + \alpha^+ \cdot \text{Gain} - \alpha^- \cdot \text{Loss}, \quad \text{where } \alpha^- > \alpha^+
Such asymmetry reflects loss aversion and emotional inertia, empirically validated in behavioral economics and trauma studies. The model thus embeds irreversibility thresholds---it may be easier to slide into the red zone than to climb back to green.
3. Volatility Index and Micro-Aberrations
To detect instability or manipulation, the model incorporates a volatility index based on sudden fluctuations in variable values. This is crucial in identifying:
Strategic deception (e.g., abrupt trust signaling followed by betrayal),
Emotional dysregulation, and
Third-party manipulation.
Let:
ij(t)=1nk=1n(Vk,ij(t)Vij(t))2\sigma_{ij}(t) = \sqrt{\frac{1}{n} \sum_{k=1}^{n} (V_{k,ij}(t) - \bar{V}_{ij}(t))^2}
A high ij(t)\sigma_{ij}(t) may signal a deceptive or unstable relationship, even if the overall Rij(t)R_{ij}(t) remains in a "green" threshold temporarily. This dynamic foresight allows pre-emptive relational reclassification or strategic withdrawal.
4. Tactical Recalibration and Behavioral Weight Adjustment
Agents may respond to relational cues by reweighting the importance of specific variables. For example, after repeated betrayal, a person may shift their attention from emotional reciprocity to strategic alignment, prioritizing tactical over affective metrics. This internal shift is captured via:
wk(t+1)=fk(wk(t),k,Vk)w_k(t+1) = f_k(w_k(t), \eta_k, \Delta V_k)
where k\eta_k represents the agent's evolving strategic priorities or emotional sensitivities. This creates space for person-specific and culture-specific relational styles, allowing individualized evolution of trust calculus.
