Transparency of information is crucial in bounded rationality environments, as originally posited by Herbert Simon. The Information Transparency variable evaluates the availability, clarity, and consistency of communication. Low transparency fosters suspicion, misinterpretation, and manipulation, while high transparency promotes epistemic trust and efficient strategy alignment. This variable is particularly salient in institutional or digital contexts, where agents often rely on indirect signals or filtered data.
8. Responsiveness to Repair
Finally, the Responsiveness to Repair variable assesses the willingness and ability of agents to address breakdowns in the relationship through apology, restitution, or behavioral change. It draws from studies on apology efficacy, reconciliation, and conflict mediation. This variable plays a crucial role in moderating the long-term trajectory of relationships, allowing recovery from yellow or even red zones if other conditions (e.g., trust, alignment) are sufficiently restored. High responsiveness is often a key indicator of relational maturity and systemic resilience.
Synthesis and Interaction
These eight variables collectively form a multi-dimensional assessment space, where relational states are neither fixed nor binary but dynamically responsive to internal and external forces. The assigned weights reflect both empirical priorities and theoretical interdependence. Importantly, variables interact nonlinearly: a strong betrayal event may override moderate strategic alignment; conversely, high emotional reciprocity may cushion the impact of third-party distortions. The model's design, therefore, reflects the inherent complexity, adaptivity, and strategic fluidity of real-world human relationships.
Constants and Tuning Parameters
CC: A relational baseline constant calibrated to account for cultural, contextual, or structural predispositions toward trust or suspicion. In some high-trust environments, C>0C > 0; in high-conflict settings, C<0C < 0.
Time Sensitivity (tt): All variables are considered time-dependent. In high-volatility contexts, recent events may be weighted more heavily via an exponential decay factor t\delta^t for temporal discounting.
Weight Justification Framework
The weights wkw_k are not static and should be dynamically adapted based on:
1. Empirical Sensitivity: Variables with greater predictive power in longitudinal data should receive higher weights.
2. Contextual Priority: In trauma recovery models, V2V_2 (Betrayal Memory) may dominate; in diplomacy, V4V_4 (Strategic Alignment) may be primary.
3. Agent Typology: Certain agents (e.g., AI vs. human, introvert vs. extrovert) may encode or respond to signals with different salience functions.
4. Cultural Calibration: Relational values may differ across collectivist vs. individualist settings; thus, trust signals may require distinct thresholds.
Multi-Dimensional Integrity
