Scenario-Based Risk Modeling

Monte Carlo produces probabilistic distributions. Scenario-based modeling produces specific narratives — detailed descriptions of how a risk event unfolds, what cascading effects it triggers, and what the total cost is when the story ends. Scenarios are Monte Carlo's complement: Monte Carlo tells you the probability of a $1M loss. Scenarios tell you the story of how a $1M loss happens. The story is what makes the number real to stakeholders who think in narratives, not distributions.

1. Best Case Scenario The risk materializes but containment is fast, impact is limited, and recovery is complete. The best case is not zero loss — the risk has materialized. It is the minimum plausible loss given that the event occurred. This scenario calibrates the lower bound of the loss distribution.
2. Expected Case Scenario The risk materializes with typical timing, typical scope, and typical response effectiveness. This is the most likely outcome — not the worst, not the best, but the one that historical data and expert judgment suggest is most probable. The expected case drives budget planning.
3. Worst Reasonable Case The risk materializes at the worst plausible timing, with cascading secondary effects, and with response complications. This is not the theoretical maximum — it is the worst outcome that a reasonable professional would consider plausible. The worst reasonable case drives insurance coverage and reserve allocation. [RECOMMEND]: The worst reasonable case should map to the 95th percentile of the Monte Carlo distribution.