To rigorously test the dynamic, adaptive properties of the Six-Zone Relational Model, Computational Agent-Based Simulations (ABS) offer a critical pathway. These simulations allow researchers to model heterogeneous agents interacting under varying rulesets, emotional constraints, and relational strategies across time, enabling observation of emergent macro-dynamics from micro-level behaviors.
1. Simulation Objective and Relevance
The Six-Zone model presumes that social actors:
Evaluate relational trust and threat through multi-variable functions.
Modify behavior strategically based on zone classification.
Are embedded in systems where mutual feedback and recursive adaptation shape long-term trajectories.
Agent-Based Simulations serve to:
Validate theoretical parameters (e.g., weighting coefficients, zone thresholds).
Observe how macro-patterns (e.g., social cohesion, fragmentation, polarizations) emerge from micro-level adaptive responses.
Evaluate strategic maneuverability and resilience of relational strategies under various perturbations.
2. Agent Architecture and Parameters
Each simulated agent is equipped with:
Cognitive Module: Implements bounded rationality with adaptive heuristics (drawing from Simon's models).
Emotional State Vector: Influences perception of threat/trust (modulating V_k variables).
Relational Memory: Stores past interactions and informs updating of the relational scoring function R_{ij}(t).
Behavioral Policy Engine: Determines tactical response (e.g., engage, avoid, retaliate, reconcile) based on current zone classification.
3. Interaction Rules and Dynamics
Agents operate in simulated environments (e.g., workplace, political field, family network) and engage in iterative interactions with the following mechanisms:
