"Using Internal functions, Model the probability of escalation between Morocco and Algeria under different diplomatic tension levels by integrating arms import data (SIPRI), defense spending, and energy export dependencies, using Monte Carlo simulations to estimate 5-year conflict risk."

Proxima Alpha

Using Internal functions, Model the probability of escalation between Morocco and Algeria under different diplomatic tension levels by integrating arms import data (SIPRI), defense spending, and energy export dependencies, using Monte Carlo simulations to estimate 5-year conflict risk.

08-Nov-2025 12:55:18

Executive Summary:

I will model the probability of conflict escalation between Morocco and Algeria over a 5-year horizon using Monte Carlo simulation. The model integrates key drivers: diplomatic tension levels, arms imports sourced from SIPRI data, defense spending, and energy export dependencies. Each factor will be parameterized based on typical values and their influence on escalation likelihood will be quantified. The Monte Carlo approach will incorporate uncertainty and variability in these parameters to estimate a probabilistic risk of escalation over different diplomatic tension scenarios.

Step-by-step Approach:

Data Parameters Definition: Based on open-source historical data and typical ranges:
- Defense Spending Ratio (Morocco vs Algeria): typically Algeria spends more, estimated approx. $10B vs $5B.
- Arms Imports (SIPRI data proxies): Algeria historically imports more advanced arms; normalized scores assigned.
- Energy Export Dependency: Algeria is energy exporter with high dependency on hydrocarbon revenues; Morocco less so.
- Diplomatic Tension Levels: Modeled as discrete states - Low, Medium, High - with increasing baseline risk escalation probabilities.
Model Construction: Using a probabilistic function where escalation risk increases with defense spending, arms imports, energy dependency (reflecting economic pressure or vulnerability), and diplomatic tension.
Monte Carlo Simulation: Run 10,000 iterations per diplomatic tension state to simulate variability and uncertainty in all parameters and compute distribution of escalation probabilities.
Output: Graphical display of escalation probability distribution for each tension level, including mean, median, and uncertainty bounds.

Modeling and Calculation Details:

The escalation probability (P_escalation) is modeled as a logistic function of a weighted sum of normalized parameters:

P_escalation = 1 / (1 + exp( - (w1*DefSp + w2*ArmsImp + w3*EnergyDep + w4*DipTension) ))

Weights (w1...w4) reflect relative influence, set for demonstration as 0.3, 0.3, 0.2, 0.4.
Parameters are normalized to [0,1]: DefSp and ArmsImp by ratio Morocco/(Morocco + Algeria), EnergyDep by Algeria's share, DipTension as fixed: Low=0.2, Med=0.5, High=0.8.

Uncertainty incorporated by sampling parameters from beta distributions around central estimates ± 10%.

Sources and Methodology Explanation:

Defense spending and arms imports estimates reflect SIPRI data trends through 2023 and common defense budget reports.
Energy dependency values derive from hydrocarbon export data typical for Algeria (gas and oil) vs Morocco (limited exports).
Diplomatic tension scaling is based on historical flashpoints such as border closures, UN Sahara dispute issues, and periodic diplomatic crises.
Monte Carlo simulation is a standard approach to estimate probabilistic outcomes under parametric uncertainty by repeated random sampling.

Now, I will execute the Monte Carlo simulation with the parameters defined and generate the requested comparative graphical outputs for the 5-year escalation risk across diplomatic tension levels.