WARGAME — STRATEGIC SCENARIO

Supply Chain Disruption: Mubadala

Document Classification: CONFIDENTIAL — Client Deliverable
Product Tier: F3 — Strategic Intelligence (W04 Wargaming Engine)
Report ID: WG-2026-08-SUPP-001
Date: August 25, 2026
Target: Mubadala (company)
Engine: WorldDuplicate 1000-branch Monte Carlo v3.1.0

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EXECUTIVE SUMMARY

Mubadala faces a Supply Chain Disruption scenario with a baseline Forreast Score of 50/100. A 500-branch Monte Carlo simulation converges to a mean of 49.2 (σ 14.8), with P(critical) = 1.4% and P(high) = 20.4%.

Convergence analysis (Gelman-Rubin R-hat = 1.002 across 4 chains) confirms the distribution is CONVERGED — stable. 5th-percentile tail risk: 25.6. System phase: fluctuation (near equilibrium — moderate fluctuation).

Red/Blue/Green adversarial debate verdict: HEDGE RECOMMENDED (net risk 53.0). Consensus: HEDGE RECOMMENDED. Net risk after Red-Blue-Green debate: 53.0. Red impact (9.6) vs Blue mitigation (5.8) → Red dominates. Green observes.

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SCENARIO PARAMETERS

Parameter	Value	Source
Scenario	Supply Chain Disruption	Forreast scenario library
Branches	500	WorldDuplicate Monte Carlo
Base score	50/100	Forreast Score (live)
Volatility	15.0pt	signal-derived
Signal shock	0.0pt	live signals
P(critical)	1.4%	Monte Carlo
Tail risk (P5)	25.6	Monte Carlo
R-hat	1.002 (converged)	4-chain Gelman-Rubin
Elapsed	0.336s	engine timing

Factor scores (from real signal mix where present):

Factor	Score (0-100)
supplier_concentration	79.0
logistics_diversity	75.0
geopolitical_exposure	59.0
inventory_buffer	32.0

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MONTE CARLO DISTRIBUTION

Statistic	Value
Mean	49.15
Median	48.69
Std	14.84
Min / Max	7.5 / 98.6
P5 / P25 / P75 / P95	25.6 / 39.7 / 59.5 / 73.8
P(critical ≥81)	1.4%
P(high ≥61)	20.4%
P(significant ≥41)	71.4%
Tail risk (P5)	25.6

Convergence: R-hat = 1.0015 across 4 chains × 200 branches. CONVERGED — results are stable.

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RED / BLUE / GREEN ADVERSARIAL DEBATE

Red Team (Adversary)

Attack vector: supplier_capture (exploits `inventory_buffer`, impact 9.6pt)

Red Team assesses that Mubadala's 'inventory_buffer' factor (score: 32) is the critical vulnerability. Attack vector: supplier_capture. Projected impact: 9.6 point degradation. Monte Carlo shows P(critical) = 1.4%, tail risk at 5th percentile = 25.6. Recommend aggressive exploitation of this axis.

Blue Team (Client Defense)

Intervention: diversify_suppliers (mitigation 5.8pt)

Blue Team counters: apply 'diversify_suppliers' (effect: -0.18) to harden 'inventory_buffer'. Projected mitigation: 5.8 points. Defense capability: reroute_logistics. Post-intervention Monte Carlo mean shifts from 49.2 to est. 31.2. Recommend immediate pre-positioning.

Green Team (Neutral / Exogenous)

Exogenous event: regulator_intervention (intervention probability 30%)

Green Team evaluates: exogenous event 'regulator_intervention' has 30% probability of materializing. No immediate green intervention. Balance assessment: 49.2 mean with 14.8 std. System near equilibrium.

Move-by-Move (3 turns)

Turn	Red action	Blue response	Green reaction	ΔScore
1	port_blockade	alternative_sourcing	regulator_intervention	-1.4
2	supplier_capture	alternative_sourcing	insurance_backstop	-3.0
3	port_blockade	alternative_sourcing	neutral_shipping	+2.8

Consensus: HEDGE RECOMMENDED

Consensus: HEDGE RECOMMENDED. Net risk after Red-Blue-Green debate: 53.0. Red impact (9.6) vs Blue mitigation (5.8) → Red dominates. Green observes.

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SENSITIVITY ANALYSIS (TORNADO)

Variable	Swing (pts)	Low → High
base_score	38.7	30 → 70 (30.0 → 68.6)
signal_shock	9.1	-5.0 → 5.0 (46.0 → 55.1)
volatility	1.7	18.75 → 7.5 (48.7 → 50.3)

Most sensitive variable: base_score (swing: 38.7 points). Model stability: UNSTABLE — high parameter sensitivity.

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SYSTEM DYNAMICS (STOCK/FLOW)

Final risk: 30.9 (initial 50) over 100 steps

Signals detected: 9 | Interventions applied: 9

Detection efficiency: 9.0% | Intervention efficiency: 100.0%

Equilibrium risk: 29.4

System dynamics: risk decreased from 50 to 30.9 over 100 steps. Detection rate: 9.0%. Intervention rate: 100.0%.

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ABNORMALITY DETECTION & BALANCE

Abnormality: Simulation diverges from reality by 0.8 points (0.1σ). Normal — model aligns with reality.

Balance: near equilibrium — moderate fluctuation (phase: fluctuation)

Meadows leverage points:

Leverage point	Level	Effect
Constants and parameters	12	Adjust signal thresholds and alert sensitivity.
Feedback loop delays	9	Reduce time between signal detection and response.
Structure of material flows	5	Redesign information flow between INT disciplines.
Rules of the system	3	Change authority levels and escalation triggers.
Goal of the system	1	Reframe from 'monitoring' to 'anticipatory positioning'.

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AGENT-BASED CASCADE (ABM)

- Agents: 50	Mean health: 0.142 (σ 0.147)
- Cascade events: 45	Target agent health: 0.525

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6-DIMENSION RISK SCORECARD

Dimension	Score (1-5)	Rationale
Exposure	4	Simulation std 14.8pt across 500 branches
Velocity	4	Supply Chain Disruption propagates in days-to-weeks
Severity	3	P(critical) 1.4%
Confidence	2	0 real signals underpinning the run
Reversibility	3	Interventions (scenario library) can reduce net risk
Contagion	3	P(high) 20.4% — cascade risk via ABM: 45 events
Composite Risk	19/30 — HIGH	Mean 49.2, σ 14.8, P(crit) 1.4%

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RECOMMENDED ACTIONS

Intervention ladder (by impact):

- `diversify_suppliers` — effect -0.18 on factor scores - `legal_restructure` — effect -0.15 on factor scores - `increase_inventory` — effect -0.12 on factor scores - `do_nothing` — effect +0.00 on factor scores

Blue Team priority: deploy `diversify_suppliers` with 5.8pt expected mitigation.

Model driver: `base_score` dominates outcomes (swing 38.7pt) — monitor it first.

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FALSIFICATION CONDITIONS

If the observed Forreast Score of Mubadala stays within 1σ of the simulated mean (49.2 ± 14.8) for 90 days, the model's predictive value is confirmed.

If R-hat exceeds 1.1 on the next run, the branch count (500) must be increased.

If real signal volume drops to zero and the run falls back to name-hash factors, the report is a hypothesis, not an evidence-based assessment — re-run after signal recovery.

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Generated by Forreast Intelligence — WorldDuplicate 1000-branch wargaming engine. Montis Sapientia, Fluminis Vis.