A family of formal methods and procedures for evaluating, comparing and ranking alternatives by aggregating their performance across multiple, often conflicting criteria through a specified preference model (for example: weighted sum/value functions, outranking, multi‑attribute utility, or AHP), typically including normalization, weighting, and sensitivity analysis.

Principle

Any multi‑criteria evaluation requires an explicit mapping from heterogeneous criterion measurements to a common preference scale and a decision rule; the chosen mapping and rule embody normative assumptions that determine the outcome.

Demonstration

Illustrative scenario: A client must choose a façade system. Engineers score alternatives on thermal performance, cost, durability and aesthetics. Using a weighted sum yields one preferred system; applying an outranking method that emphasizes minimum performance thresholds may produce a different preferred system. A sensitivity analysis shows which weights change the choice.

Misapplication

Directly summing raw criterion values with different units or scales without normalization; treating rank orders as cardinal scores; eliciting weights without documenting stakeholder preferences; or assuming method choice is neutral rather than normative.

Consequence

MCDA makes trade‑offs and implicit value judgments explicit, supports structured stakeholder involvement and robustness checks, and can change decisions by revealing which criteria or assumptions drive outcomes; however, method and weight choices can materially alter results.

Reversal

When objectives are deeply uncertain, highly probabilistic, or when decisions aim to satisfy regulatory constraints rather than optimize trade‑offs, robust decision‑making or scenario‑based approaches may be preferable to standard MCDA aggregation.

Boundary

Clearly within: selection among design alternatives judged on several conflicting, measurable criteria. Boundary case: criteria with strong interdependence or where social welfare functions are required. Clearly outside: single‑criterion optimization or purely qualitative deliberation without quantification.

Semantic Tension

Normative transparency ↔ Method dependence: MCDA forces explicit value choices but the analytical framing and aggregation rule embed normative judgments that may conflict with stakeholder plurality.

Synthesis

MCDA is not a single algorithm but a framework family that makes preference structure explicit: it turns measurement into decision only when one accepts the normalization, weighting and aggregation conventions used.