Relational Update Function:
 After each interaction, agents recompute R_{ij}(t+1) using updated values of V_k, weighted by w_k, and consider adaptive decay or reinforcement over time.
Zone Reclassification:
 Based on the updated R_{ij}(t+1), the dyadic relation is reclassified into one of the six zones. This classification influences subsequent behavior (e.g., more cooperation in Green, more evasion in Red).
Stochastic Noise and Ambiguity Injection:
 Realistic uncertainty is introduced through stochastic variations in perception, emotional interpretation, or strategic misalignment.
4. Experimental Scenarios
Simulations can test a wide range of hypotheses. Examples include:
Trust Network Stability:
 What proportion of relationships remain in the Green or White zones after X iterations under various initial trust distributions?
Resilience under Betrayal Shock:
 How does a network react when a key agent (e.g., a central node) defects or betrays trust?
Strategic Manipulation vs. Collaboration:
 Do agents adopting flexible, mixed-zone tactics outperform rigidly cooperative or defensive agents in long-term network utility?
Emergence of Social Subzones or Factions:
 Observe how agent clusters form based on shared history and zone alignment, and whether polarization (e.g., sustained Red--Black bifurcation) can emerge.
5. Metrics and Outputs for Evaluation
Simulation runs are analyzed using both quantitative and qualitative indicators:
Zone Distribution Entropy: Measures heterogeneity and order in the system.
Relational Volatility Index (RVI): Captures rate of change in zone status per dyad per unit time.
Strategic Efficiency Ratio: Compares utility outcomes of different agent strategy profiles (cooperative, manipulative, evasive, forgiving).
Stability of Clusters: Tracks persistence of subnetwork formations and their relational composition.
6. Implications of Simulation Results
Simulation findings offer parameter sensitivity analyses, revealing which w_k or C values most influence system dynamics.
Validate or revise zone thresholds, especially under extreme or noisy conditions.
Guide the design of AI agents in complex environments (e.g., social robotics, adaptive mediators), allowing real-time relational recalibration.
Support translation of theory into policy simulations (e.g., conflict negotiation tools, strategic HR planning, diplomatic advisory systems).
In sum, computational agent-based simulations provide a synthetic laboratory where the Six-Zone Relational Model can be stress-tested, falsified, optimized, and contextualized---paving the way for both theoretical refinement and real-world deployment.
C. Real-life Application Testing (e.g., in HR, coaching, trauma-informed world)
