A Scenario Model Can Be Precise and Still Be Incomplete
A scenario table can look thorough and still miss a plausible outcome. Check an AI-generated base/bull/bear analysis for omitted scenarios and correlated assumptions.
Who this is for
Investment analysts and researchers — Analysts reviewing an AI-generated base/bull/bear scenario model before it informs a position or a decision
The problem
A base case, a bull case, and a bear case, each with a clean probability weight — a scenario analysis can look thorough while quietly treating its key drivers as independent when they actually move together, or omitting a scenario entirely because nothing in the source material suggested it. The structure looks complete. Whether it actually covers the plausible range of outcomes is a separate question the structure alone can't answer.
How ConvergePanel helps
ConvergePanel checks an AI-generated scenario model across five models: are the drivers behind each scenario actually independent, does the probability weighting have a stated basis, and is there a plausible scenario the analysis left out entirely. Where models flag the same missing dependency or omitted scenario, that's the gap to close before the analysis is used.
How they compare
| Scenario | Assumptions | Missing Dependency | Contradictory Evidence | Model Disagreement | Reviewer Conclusion |
|---|---|---|---|---|---|
| Bull case | Volume grows 15% and price holds flat | Treats volume and pricing power as independent when a competitor response to volume gains is plausible | Prior cycle showed competitors cutting price when share shifted quickly | 3 of 5 models flagged the volume/pricing correlation unprompted | Rebuild the bull case with a linked volume-pricing sensitivity, not two independent assumptions |
| Base and bear cases only | No scenario modeled for a supply-chain disruption | N/A — the gap is an omitted scenario, not a flawed one | Company's own risk disclosures name single-supplier dependency | 4 of 5 models noted the missing scenario when asked what wasn't modeled | Add a supply-disruption scenario with its own probability weight before treating the range as complete |
How it works
- 1List the stated scenarios and the key driver behind each one
- 2Check whether drivers assumed to move independently are actually correlated
- 3Check the probability weighting for a stated basis, not just an assigned number
- 4Identify sensitivities the model treats as fixed but that could plausibly vary
- 5Consider what scenario is plausible but wasn't modeled at all
- 6Run the scenario set through ConvergePanel across five models
- 7Flag correlated-assumption errors and omitted scenarios for review
Use cases
- Checking whether a bull case assumes two drivers that can't both hold at once
- Verifying that scenario probability weights have a stated rationale
- Identifying a plausible downside scenario the analysis never modeled
- Auditing an AI-generated scenario table before it supports a decision
Twelve things a scenario table can quietly get wrong
- Base case — is it genuinely the most likely path, or just the middle number
- Bull case — what specifically has to go right, named explicitly
- Bear case — what specifically has to go wrong, named explicitly
- Probability weight — does it have a stated basis or just an assigned number
- Key driver — the single factor each scenario actually depends on
- Dependency — whether that driver is really independent of the others
- Sensitivity — how much the conclusion moves if the driver shifts modestly
- Nonlinear risk — whether a small input change could produce an outsized outcome change
- Correlated assumptions — drivers that move together being modeled as if they don't
- Omitted scenario — a plausible path with no row in the table at all
- Terminal condition — what happens beyond the model's explicit forecast window
- Decision implication — what actually changes based on which scenario proves closest to true
A complete-looking table isn't the same as a complete range
Three rows and three probability weights that sum to 100% create the visual impression of thoroughness regardless of whether the three scenarios actually bracket the plausible outcome space. The structure is easy to produce; checking whether it's the right structure takes more work, and that work is exactly what a scenario validation step is for.
Frequently asked questions
How do I know if two assumptions are wrongly treated as independent?
Check whether a real-world mechanism would link them — a company gaining volume by cutting price, for instance, links volume and price directly. If the model varies one without adjusting the other, that's a correlated-assumption error worth flagging.
What if the omitted scenario seems unlikely?
Unlikely isn't the same as implausible — a low-probability scenario still belongs in the table with an honest weight, rather than being excluded because it's inconvenient or wasn't suggested by the source material.
Does a probability-weighted average of scenarios give a reliable expected value?
Only as reliable as the scenarios and weights feeding it. A probability-weighted average of three incomplete scenarios produces a precise-looking number built on an incomplete input set.
How many scenarios are enough?
There's no fixed number — three well-differentiated scenarios that actually bracket the plausible range beat five scenarios that are all variations on the same underlying assumption.
Can ConvergePanel assign the correct probability weights to each scenario?
No. It can compare how models characterize the scenario set and flag missing dependencies or omitted scenarios — assigning defensible probability weights is a judgment call for a qualified financial professional.
Explore related pages
ConvergePanel provides AI-assisted verification for informational purposes only. Not forensic analysis. Not legal evidence.
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