Multi-objective Scenario Analysis — Evaluating alternative futures across multiple competing objectives
Also known as: MOSA, Multi-criteria scenario analysis, Multi-objective futures analysis, MO-scenario analysis
Multi-objective Scenario Analysis (MOSA) is a structured method that constructs a set of plausible future scenarios and evaluates each scenario against multiple competing objectives or criteria. By making trade-offs explicit across objectives and across possible futures, it supports strategic decisions where uncertainty about the future and conflicts between goals co-exist. It is widely applied in energy planning, climate adaptation, public policy, and corporate strategy.
Key highlights
- Makes value trade-offs explicit across both objectives and plausible futures, supporting transparent stakeholder deliberation.
- Identifies robust alternatives that perform acceptably across multiple scenarios rather than optimizing narrowly for one future.
- Combines qualitative scenario narratives with quantitative multi-criteria scoring, bridging strategic foresight and analytical rigor.
- Applicable to wicked problems where futures are deeply uncertain and objectives are contested.
- Flexible in choice of weighting method, aggregation rule, and robustness criterion.
Intuition
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How it works
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When to use it
Use MOSA when decisions must be made under deep uncertainty (futures cannot be assigned confident probabilities) and multiple conflicting objectives must be balanced simultaneously — common in infrastructure investment, climate policy, energy transition, and long-horizon strategic planning. It is particularly suited when stakeholders have divergent values and consensus requires making trade-offs transparent. Do not use MOSA when a single scenario is sufficiently certain, when only one objective matters, or when computational simplicity is required; in those cases standard single-objective scenario analysis or simple cost-benefit analysis may suffice.
Strengths & limitations
- Makes value trade-offs explicit across both objectives and plausible futures, supporting transparent stakeholder deliberation.
- Identifies robust alternatives that perform acceptably across multiple scenarios rather than optimizing narrowly for one future.
- Combines qualitative scenario narratives with quantitative multi-criteria scoring, bridging strategic foresight and analytical rigor.
- Applicable to wicked problems where futures are deeply uncertain and objectives are contested.
- Flexible in choice of weighting method, aggregation rule, and robustness criterion.
- Scenario construction is inherently subjective; poorly designed scenarios can mislead the analysis.
- The method's output is sensitive to the choice of objective weights, which may be contested among stakeholders.
- Aggregating cross-scenario performance into a single recommendation requires additional robustness criteria that introduce further assumptions.
- Scaling to large numbers of scenarios, objectives, and alternatives increases cognitive and computational burden.
Common pitfalls
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Applications
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Frequently asked
How is MOSA different from plain scenario analysis?
Standard scenario analysis describes what happens in each future but does not provide a principled way to choose between alternatives when values conflict. MOSA adds a multi-criteria layer that scores and compares alternatives across objectives within every scenario, making trade-offs explicit and enabling a structured recommendation.
Do scenarios in MOSA need probabilities assigned to them?
No. MOSA is designed for deep uncertainty, where credible probability assignments are not possible. Scenarios are treated as equally plausible alternative futures. If reliable probabilities are available, stochastic extensions such as stochastic scenario analysis or Monte Carlo simulation may be more appropriate.
How many scenarios and objectives are practical?
Typically two to five scenarios and three to eight objectives work well. Beyond five scenarios, the analysis becomes difficult to communicate; beyond eight objectives, weight elicitation and interpretation become burdensome. Clustering or hierarchical structuring of objectives can help manage larger sets.
What robustness criterion should be used to select the final recommendation?
Common choices include minimax regret (minimize the worst-case loss relative to the scenario-optimal alternative), satisficing (require all objectives to exceed an acceptability threshold in all scenarios), and stochastic dominance. The choice should reflect stakeholder risk attitude and be disclosed explicitly.
Can MOSA be combined with simulation models?
Yes. Scenario-specific performance scores are often generated by simulation models — for example, energy system models, transport models, or climate impact models. The MOSA framework then aggregates these model outputs across objectives and scenarios, adding the multi-criteria and robustness layers.
Sources
- 1.Stewart, T. J., French, S., & Rios, J. (2013). Integrating multicriteria decision analysis and scenario planning: Review and extension. Omega, 41(4), 679-688.
- 2.Schoemaker, P. J. H. (1995). Scenario planning: A tool for strategic thinking. Sloan Management Review, 36(2), 25-40.
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Cite this page
ScholarGate. (2026, June 3). Multi-objective Scenario Analysis. ScholarGate. https://scholargate.app/simulation/multi-objective-scenario-analysis