Objective:
 Ensure the model's cultural generalizability and adaptability to diverse socio-relational contexts.
Methodology:
Conduct ethnographic fieldwork in culturally diverse relational systems (e.g., hierarchical vs. egalitarian, collectivist vs. individualist).
Use semi-structured interviews and relational mapping tasks to trace local expressions of zone dynamics.
Compare empirical thresholds and cultural norms in zone classification (e.g., what constitutes "Red" in Japan vs. Brazil).
Expected Outcome:
Cultural sensitivity of zone thresholds and relational cues.
Refinement of model parameters for contextual calibration.
Foundation for developing localized relational AI systems that respect cultural variability.
5. Machine Learning-Based Behavioral Prediction
Objective:
 Use historical behavioral data to predict future relational zone transitions and validate the scoring algorithm's predictive power.
Methodology:
Input features: behavioral logs, emotional tone analysis, interaction frequency, decision patterns.
Target label: Zone classification at time t+1.
Use time-series ML models (e.g., LSTMs, Transformer-based relational predictors).
Measure performance with AUC, F1-score, and precision in zone transition prediction.
Expected Outcome:
High predictive accuracy supports validity of model's computational structure.
Error analysis offers insights into non-modeled variables or unexpected transitions (e.g., Black-to-Green due to abrupt forgiveness).
Inform tuning of adaptive feedback mechanisms in future AI applications.
In conclusion, validation of the Six-Zone Relational Model demands a multi-method, multi-level, and transdisciplinary approach, ensuring that the formal model not only withstands theoretical scrutiny but also proves empirically grounded, ecologically valid, and cross-culturally reliable.
B. Computational Agent-Based Simulations
