01
What is what-if scenario analysis?
It is a method for changing one assumption and comparing how the scenario path, actor reactions, and decision risks may change.
- Baseline and variant
- Controlled assumption change
- Comparable report review
What-if scenario analysis
What-If Scenario Analysis AI
MiroFish supports what-if scenario analysis when the team wants to change one assumption and inspect how the simulated path changes. Use the same source packet, compare graph and report differences, then ask follow-up questions about evidence gaps and validation checks. This keeps the exercise bounded enough for human review.
Not a statistically representative survey, customer panel, or deterministic prediction.
Live decision rehearsal
Multi-role reaction path
R1 · Baselinereaction
Defines the original decision, actors, constraints, and report path.
R2 · What-if variantreaction
Changes one assumption such as timing, price, message, actor, or channel.
R3 · Graph reviewreaction
Shows which relationships, conflicts, or pressure points changed.
R4 · Follow-upreview
Explains why the report changed and which claims need validation.
Variant signalChanged assumption, graph delta, report delta, validation task
Direct answers
What should teams understand before they simulate?
Start with the evidence needed for the decision. Use simulation to expose uncertainty, not to hide it behind generated volume.
02
What can MiroFish compare?
MiroFish can compare bounded variants around timing, message, price, launch sequence, market entry, competitor moves, or policy framing.
- Scenario graph changes
- Prediction report differences
- Follow-up questions about evidence
03
What makes a what-if run useful?
A useful run keeps sources and actors stable, changes one assumption, and produces a clear validation task after the report.
- One variable at a time
- Inspectable assumptions
- Human validation plan
Five-step workflow
How does the simulation move from evidence to action?
Every step leaves something inspectable: the source, the actor assumptions, the reaction path, or the next human check.
- 1
Create the baseline
Upload the source packet and run the first scenario.
- 2
Choose one assumption
Change timing, pricing, message, actor behavior, channel, or constraint.
- 3
Run the variant
Generate simulation rounds and read the new prediction report.
- 4
Compare graph and report
Inspect what changed in actors, relationships, reactions, and claims.
- 5
Ask follow-up questions
Question why the path changed and what evidence would confirm or reject it.
What should the report give your team?
Useful output makes the next decision or validation step more specific.
- Baseline and what-if scenario comparison
- Graph changes across assumptions
- Report differences and fragile claims
- Validation questions for real-world decision review
What can this simulation not establish?
These boundaries apply even when the output looks detailed or consistent.
- Scenario simulation produces hypotheses, reaction paths, assumptions, and validation questions, not a deterministic prediction.
- MiroFish is not a spreadsheet sensitivity model, financial forecast engine, statistical survey, or representative customer panel.
- Generated actors, graph relationships, report claims, and follow-up answers should be reviewed against the source packet and real-world evidence.
- Validate consequential decisions with domain experts, customer or stakeholder evidence, market data, experiments, legal review, or accountable human judgment.
Frequently asked questions
What else should teams know?
Can MiroFish replace scenario planning workshops?
No. MiroFish can prepare and stress-test scenario thinking by turning source material into a graph, simulation rounds, a report, and follow-up questions, but accountable planning remains human.
What should teams upload before scenario analysis?
Use a focused source packet: decision memo, market context, product or policy brief, competitor evidence, stakeholder notes, constraints, assumptions, and the question to test.
What does the graph add to scenario analysis?
The graph makes actors, relationships, knowledge, constraints, and pressure points visible before the report becomes persuasive, so teams can review assumptions earlier.
How should teams use follow-up questions?
Use follow-up questions to inspect why the report reached a conclusion, which actors matter, what evidence is weak, and which real-world checks should come next.
Evidence → actors → reactions → review
Rehearse the decision before the market makes it expensive.
Bring the current evidence, a bounded question, and the assumptions your team is willing to challenge.
Related methods and scenarios
Scenario Analysis Tool
Compare strategic scenarios through evidence-grounded actors, assumptions, reaction paths, and review tasks.
What-If Scenario Analysis Examples
See examples for pricing, launch, market entry, policy, public reaction, and competitive response scenarios.
Predictive Simulation
Explore plausible futures while keeping assumptions and validation needs visible.