Simulating Border Conflict and Proxy War

A Systems Approach Using Agent-Based, Network, and System Dynamics Models

Modern border conflicts rarely resemble conventional wars.
They are persistent, indirect, and system-driven, involving multiple actors, informal resource flows, and adaptive behaviors rather than open military confrontation.

Conflicts similar to those seen in parts of Southeast Asia—and many other regions globally—are best understood not as isolated incidents, but as complex systems.

This article explains which simulation approaches are most suitable for modeling border tensions, proxy dynamics, and indirect conflict—without focusing on tactical or military detail.

1. From Incidents to Systems

Traditional analysis often asks:

“Who won the clash?”

Systems-oriented simulation asks instead:

“Why does tension persist—or gradually decline—over time?”

In this view, border incidents are outputs of an underlying system, not root causes.

Conceptual Model

Tension (T)
= f(Resources, Decisions, Legitimacy, Cooperation)

A simple directional form:

T = αR + βA − γL − δC

Where:

R (Resources): funding, logistics, informal support
A (Actions): decisions made by actors on the ground
L (Legitimacy): perceived authority and public trust
C (Cooperation): cross-border and institutional coordination

High resources and aggressive actions, combined with low legitimacy and cooperation, naturally increase tension—without requiring escalation orders.

2. Agent-Based Simulation: Modeling Adaptive Actors

Agent-Based Simulation (ABS) models each participant as an autonomous decision-maker rather than a scripted unit.

Typical agents include:

State institutions
Proxy or non-state groups
Intermediaries and facilitators
Local communities
Enforcement bodies

Diagram: Agent-Based Perspective

graph TD
    State["State Institutions"]
    Proxy["Proxy / Non-State Actors"]
    Broker["Intermediaries"]
    Community["Local Communities"]
    Enforcement["Law Enforcement"]

    State --> Enforcement
    Enforcement --> Broker
    Broker --> Proxy
    Proxy --> Community
    Community --> State

Decision Logic (Intuitive Form)

Decision = Benefit − Risk − Cost

If perceived benefits outweigh risk and cost, behavior continues—even under pressure.

This explains why proxy dynamics tend to adapt rather than disappear.

3. Network Simulation: The Core of Indirect Conflict

Proxy conflicts are sustained through networks, not formations.

What matters most is not weaponry, but resource flow efficiency.

Diagram: Resource Flow Network

flowchart LR
    Funding["Informal Funding Sources"]
    Broker["Intermediaries"]
    Routes["Logistics Routes"]
    Capacity["Operational Capacity"]
    Interdiction["State Interdiction"]

    Funding --> Broker
    Broker --> Routes
    Routes --> Capacity
    Interdiction -. disruption .-> Routes

Capability Flow Model

K = M × E × (1 − I)

Where:

M (Money): available funding
E (Efficiency): network adaptability
I (Interdiction rate): disruption effectiveness

Increasing interdiction alone is insufficient if networks rapidly adapt and reroute.

4. System Dynamics: Understanding Long-Term Policy Effects

System Dynamics captures feedback loops that unfold over months or years.

Diagram: Feedback Loop

graph LR
    Enforcement["Enforcement Pressure ↑"]
    Cost["Network Cost ↑"]
    Profit["Potential Returns ↑"]
    Incentive["Incentives ↑"]
    Adaptation["Adaptation ↑"]

    Enforcement --> Cost
    Cost --> Profit
    Profit --> Incentive
    Incentive --> Adaptation
    Adaptation --> Enforcement

A simplified stock-flow relationship:

ΔResources / Δt = Revenue − Losses

If revenue growth outpaces enforcement losses, the system stabilizes rather than collapses.

5. Border Incidents as System Outputs

In this framework, border incidents are emergent outcomes, not direct control variables.

Incident Rate
= f(Network Capacity, Agent Decisions, Local Context)

This implies:

Tactical escalation does not guarantee fewer incidents
Structural interventions often have more durable effects

6. Practical Applications

This simulation approach is well suited for:

Policy testing before real-world implementation
Evaluating cross-border cooperation scenarios
Risk assessment without operational escalation
Supporting evidence-based decision-making

It is not a war-planning tool, but a conflict management and prevention framework.

Conclusion

Modern border conflicts and proxy wars are not driven by battlefield superiority alone.
They emerge from interacting systems of incentives, networks, and legitimacy.

By combining: