While classical trade theory (e.g., Ricardian or Heckscher-Ohlin models) and game-theoretic frameworks (e.g., Prisoner's Dilemma or repeated games) have advanced our understanding of trade cooperation and conflict, they typically assume rational behavior, fixed preferences, and transparent strategies. However, modern trade conflicts---such as U.S.-China technology decoupling, rare-earth export controls, or vaccine nationalism---are deeply embedded in strategic ambiguity, long-term relational memory, and asymmetric zone shifts. The Relational Zone Economics (RZE) framework provides a dynamic lens to simulate how trade conflicts escalate, de-escalate, and reconfigure alliances based not only on payoff but on zone positions and transformations.
2. Simulation Design
a. Agent Structure:
Nation-states or trade blocs (e.g., U.S., China, EU, ASEAN) as principal agents.
Export-import sectors (e.g., semiconductors, agriculture, pharmaceuticals) as relational sub-layers influencing aggregate zone states.
Optional: Shadow actors (e.g., lobbyists, regulatory bodies, cyber networks) as stochastic forces triggering hidden zone shifts.
b. Simulation Environment:
Trade relationships are mapped via dyadic zone matrices Zij(t)Z_{ij}(t) updated at each round based on observed trade behavior, policy announcements, and indirect signaling.
Each product or sector can have a local zone, allowing for multi-sectoral asymmetries within a bilateral trade relation.
c. Time Dynamics:
Simulation spans T=200T = 200 time steps (interpreted as months or quarters).
Agent behavior follows a mixed strategy integrating payoff analysis, zone memory, and strategic re-zoning heuristics.
3. Relational Dynamics Modeled
Each agent:
Evaluates current zone and trajectory of each partner in relation to national economic-security goals:
Zij(t){White,Green,Yellow,Red,Black,Jernih}Z_{ij}(t) \in \{White, Green, Yellow, Red, Black, Jernih\}
Assigns weighted sectoral stakes wkw_k based on exposure, dependence, and political salience.
Makes decisions to:
Impose tariffs or subsidies (Red-zone behavior),
Signal openness through agreements (Green-zone shift),
Withhold strategic goods or patents (Black-zone escalation),
Or propose multilateral frameworks (Jernih reorientation).
Updates relational payoff function:
 Rij(t+1)=kwkVk,ij(t)+Iij(t)+R_{ij}(t+1) = \sum_{k} w_k \cdot V_{k,ij}(t) + \lambda \cdot I_{ij}(t) + \epsilon
 where \epsilon incorporates uncertainty or exogenous shocks.
4. Performance Metrics Captured
Trade flow volatility and long-term welfare implications.
Zone volatility index: Frequency and intensity of shifts between cooperative and conflictual zones.
Relational betrayal cost: Reduction in future trade potential due to Red/Black behavior.
Alliance asymmetry coefficient: How differing zone alignments with third parties (e.g., India-US vs. China-Russia) affect conflict persistence.
Effectiveness of Jernih strategies: Can long-term visions and relational repair outperform short-term retaliation?
5. Key Findings (Illustrative)
Initial Yellow zones with history of cooperation are more prone to stabilization than those that evolved from earlier Black zones.
A single Black-zone action (e.g., rare-earth export ban) by a strategic partner leads to multi-sector zone regression across unrelated sectors, highlighting spillover effects of betrayal.
Green-to-Yellow transitions often precede formal conflict, suggesting early-warning indicators can be built from zone trajectories.
Jernih-positioned agents, such as smaller trade partners (e.g., Singapore or Switzerland), play critical roles as relational buffers or credibility anchors in larger conflicts.
Zone-based negotiation sequencing (e.g., de-escalating low-sensitivity sectors first) significantly increases odds of conflict resolution versus comprehensive, all-or-nothing treaties.
6. Policy and Strategic Implications
