Separate weighting from option scoring. Rooted in Keeney and Raiffa’s 1976 multi-attribute utility theory, the matrix reduces affective bias by making both steps explicit. Compare technology architectures, suppliers or investment programmes against weighted criteria to support transparent, defensible decisions.
Based on: Klassische Entscheidungstheorie
The Decision Matrix (formally known as Multi-Criteria Decision Analysis, or MCDA) is an axiomatic evaluation methodology designed to rank competing strategic alternatives against divergent criteria. Codified within modern decision analysis by Ralph Keeney and Howard Raiffa (1976), the framework neutralizes affective intuition, political posturing, and recency bias. By separating criteria weighting from option scoring, it establishes an auditable quantitative baseline for high-stakes governance.
The methodology executes via a rigorous five-step algorithmic sequence:
1. Alternative Definition: Formulating mutually exclusive, feasible strategic courses of action.
2. Criteria Architecture: Isolating performance dimensions across commercial viability, implementation friction, strategic alignment, and risk vectors.
3. Weight Allocation: Distributing percentage weights across criteria totaling 100%, executed prior to evaluating specific options to avoid confirmation bias.
4. Standardized Scoring: Grading each candidate option against each criterion utilizing an invariant scale (e.g., 1 to 10), accompanied by qualitative justifications.
5. Utility Computation: Calculating the aggregate weighted score for each alternative to derive an empirical ranking.
In executive committee settings, decision matrices are routinely corrupted: stakeholders retroactively manipulate weights to validate pre-ordained political choices. The Brainstorm Reactor framework enforces structural integrity:
A decision matrix is fundamentally constrained by the completeness of its criteria set. It cannot detect tail risks omitted from the model. Furthermore, calculating weighted sums across non-linear trade-offs can obscure catastrophic vulnerabilities in an otherwise high-scoring option.
Primary applications include enterprise infrastructure platform selection, RFP vendor scoring, portfolio project prioritization, and corporate location selection.
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