Total Relational Score Rij(t)R_{ij}(t):
Rij(t)=0.10+0.04+0.20+0.005+0.030.09+0.05=0.335R_{ij}(t) = 0.10 + 0.04 + 0.20 + 0.005 + 0.03 - 0.09 + 0.05 = \mathbf{0.335}
Zone Classification:
 At 0.335, this interaction also resides in the Yellow Zone, but with qualitatively different signals: the score is being artificially inflated due to competence masking coercion. With growing opacity and misuse of leverage, the system may soon downgrade to Red Zone.
These examples illustrate how relational intelligence can be formalized, tracked over time, and used to anticipate behavioral risks, opportunities for repair, and latent toxic dynamics. The model supports dynamic simulation, relational diagnostics, and even ethical AI-human alignment calibration.
Appendix C. Algorithmic Pseudocode (for Practical Applications)
This section provides a high-level pseudocode to illustrate the operational logic of the relational scoring model, including its dynamic classification into relational zones and temporal updating mechanisms. The pseudocode is structured for ease of adaptation into multiple programming environments (e.g., Python, JavaScript, R).
1. Initialize Entities and Parameters
DEFINE AGENTS = {A1, A2, ..., An}
DEFINE VARIABLES = {Trust_Consistency, Emotional_Reciprocity, Strategic_Opacity,
          Historical_Repair, Behavioral_Volatility, Situational_Leverage}
DEFINE WEIGHTS = {w1, w2, w3, w4, w5, w6}
