neighbourhood-adaptive Ordered Weighted Averaging
LOCAL-OWA (neighbourhood-adaptive Ordered Weighted Averaging) is a ranking multi-criteria decision-making (MCDM) method introduced by Malczewski, J.; Liu, X. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
Key highlights
- Follows a transparent, reproducible computational procedure that can be audited step by step.
- Handles multiple criteria of differing scales and units within a single decision matrix.
Intuition
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How it works
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When to use it
V(A^{lo}_i) reflects criterion performance, the local importance of each criterion within the alternative's neighbourhood (range-sensitivity), and the global risk attitude encoded in λ. The score amplifies LOCAL-WLC: an alternative that excels on the criterion that is most locally variable AND most favoured by the order weights (rank-1 with high λ_1) gains a disproportionate boost. Always inspect the local weight table (G.local_weights), the range-ratio table (G.range_ratios), and the ORness scalar to understand what drove the ranking.
Strengths & limitations
- Follows a transparent, reproducible computational procedure that can be audited step by step.
- Handles multiple criteria of differing scales and units within a single decision matrix.
- May exhibit rank reversal when alternatives are added to or removed from the set.
Common pitfalls
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Sources
- 1.Malczewski, J., Liu, X. (2014). Local ordered weighted averaging in GIS-based multicriteria analysis. Annals of GIS
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Cite this page
ScholarGate. (2026, June 2). LOCAL-OWA. ScholarGate. https://scholargate.app/decision-making/local-owa