Scenario Analysis and What-If Simulation
Also known as: what-if analysis, what-if simulation, stress testing, scenario planning, Senaryo Analizi ve What-If Simülasyonu
Scenario analysis is a structured analytical approach that systematically compares system outputs across different combinations of uncertain input values. When paired with a quantitative model, it becomes a simulation — capable of stress-testing assumptions and projecting the range of plausible outcomes. Formalised in strategic planning by Peter Schwartz and Herman Kahn from the 1950s onward, the method is widely used in policy evaluation, business forecasting, financial risk assessment, and scientific model exploration.
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When to use it
Scenario analysis fits whenever the outcome of a decision or system depends on one or more variables whose future values are genuinely uncertain, and where a single-point forecast would be misleading. It is particularly valuable for strategic planning, policy evaluation, financial stress testing, and scientific model exploration. The method imposes no sample-size requirement and no distributional assumption on the data — it is model-driven rather than sample-driven. The only prerequisite is that a quantitative relationship between inputs and outputs can be specified, and that the scenarios are constructed with stakeholder input to ensure they are meaningful rather than arbitrary.
Strengths & limitations
- Requires no minimum sample size or distributional assumptions — purely model-driven.
- Forces explicit, structured thinking about uncertainty rather than hiding it in a single-point estimate.
- Highly flexible: applicable to any domain where inputs and outputs can be linked by a quantitative model.
- Results are transparent and interpretable to non-technical stakeholders, supporting participatory decision-making.
- Easily extended to probabilistic treatment via Monte Carlo distributions within each scenario.
- The quality of the analysis depends entirely on the realism of the scenario definitions; poorly chosen scenarios produce misleading results.
- A finite set of discrete scenarios cannot cover the full continuous uncertainty space — outcomes between scenarios are not observed.
- The method does not rank or select the best decision on its own; scenario results must be combined with a decision framework such as a decision tree or multi-criteria analysis.
- Constructing scenarios collaboratively with stakeholders is time-consuming and can introduce political or cognitive biases into the choice of scenarios.
Frequently asked
How many scenarios should I define?
Three scenarios — pessimistic, base, and optimistic — is the most common and practical structure. It is simple enough to communicate clearly to stakeholders while covering the main range of uncertainty. More scenarios can be added when specific intermediate states are important, but beyond five to six scenarios the results tend to become difficult to interpret and communicate.
What is the difference between scenario analysis and sensitivity analysis?
Sensitivity analysis varies one input at a time while holding all other inputs constant, measuring how much the output changes per unit change in a single variable. Scenario analysis simultaneously changes multiple inputs to represent coherent, internally consistent states of the world. Scenario analysis is therefore better for strategic questions about plausible futures, while sensitivity analysis is better for identifying which individual inputs matter most.
Do I need a computer model to run scenario analysis?
No — a simple spreadsheet formula is sufficient. Any quantitative relationship that maps inputs to outputs can serve as the model. The sophistication of the model should match the decision at hand; a revenue projection that multiplies demand by price is entirely valid as a scenario model.
How do I combine scenario results with a decision?
Scenario outputs are typically fed into a complementary decision framework. A decision tree can assign probabilities to scenarios and compute expected values. A multi-criteria analysis can score scenarios across multiple objectives. The scenario analysis itself does not select the best option — it provides the outcome estimates that a decision framework then uses.
Sources
- Goodwin, P. & Wright, G. (2014). Decision Analysis for Management Judgment (5th ed.). Wiley. ISBN: 978-1118173671
- Schwartz, P. (1991). The Art of the Long View. Currency Doubleday. ISBN: 978-0385267328
How to cite this page
ScholarGate. (2026, June 1). Scenario Analysis and What-If Simulation. ScholarGate. https://scholargate.app/en/simulation/scenario-analysis-simulation
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Global Sensitivity AnalysisSimulation↔ compare
- MONTE-CARLO-SIMULATIONDecision-making↔ compare
- SENSITIVITY-ANALYSISDecision-making↔ compare