Outcome dashboard
Running...Peak systemic damage
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Cascade index
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Population affected
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Annual GDP at risk
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Sector damage over time
No baseline savedOverall risk level
Day 00
Low
Choose a country, initial shock, area type, severity, preparedness, reserves, trust, response package and model sensitivity. The simulator runs a deterministic multi-sector cascade model and shows how risks spread through critical systems over time.
The model treats civilisation as a network of critical systems. Each shock applies direct pressure to several sectors. Damage then propagates along dependency links: for example, energy damage harms logistics, logistics harms food and health, cyber damage harms finance and governance, and governance damage slows recovery.
Country profiles are compact illustrative risk vectors: population, GDP size, resilience, health capacity, urbanisation, digital dependence, food import exposure, energy import exposure, climate exposure, conflict exposure, inequality and debt stress. Sliders let you override local readiness, social trust and reserves.
For each sector and day, new damage is approximately:
previous damage + direct shock + cascade inflow + tipping pressure - recovery capacity
Direct shock is shaped by shock type, severity, duration, global synchronisation, area exposure and sector vulnerability. Cascade inflow is the weighted sum of damage in connected sectors. Recovery is driven by country resilience, readiness, social trust, international support, reserves, response delay and selected interventions.
This is a scenario simulator, not a forecast. It does not ingest live intelligence, commodity prices, epidemiological feeds, satellite data or classified infrastructure data. It is best used for exploring relative dynamics, tipping points, intervention trade-offs and communication of systemic risk.
For real policy work, connect the same engine to live country data, historical disaster losses, commodity prices, ACLED/GDELT conflict indicators, health-system occupancy, grid status, cyber incident feeds and supply-chain data.