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Home›Decision-making›neighbourhood-adaptive Ordered Weighted Averaging
MCDMRankingcrisp

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.

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LOCAL-OWA
AHPANPBWMCRITICENTROPYFUCOMMERECSWARA

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

Strengths
  • 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.
Limitations
  • May exhibit rank reversal when alternatives are added to or removed from the set.

Sources

  1. Malczewski, J., Liu, X. (2014). Local ordered weighted averaging in GIS-based multicriteria analysis. Annals of GIS DOI: 10.1080/19475683.2014.904439 ↗

How to cite this page

ScholarGate. (2026, June 2). neighbourhood-adaptive Ordered Weighted Averaging. ScholarGate. https://scholargate.app/en/decision-making/local-owa

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Similar methods

LOCAL-WLCOWASOWAPROXIMITY-WLCROUGH-VIKORWASPASL2T-VIKORLMAW

Related reference concepts

Decision Support SystemsDecision MakingWeighted ScoresBayesian Model AveragingMultidimensional ScalingQuadratic Discriminant Analysis

Spotted an issue on this page? Report or suggest a fix →

ScholarGate — LOCAL-OWA (neighbourhood-adaptive Ordered Weighted Averaging). Retrieved 2026-07-20 from https://scholargate.app/en/decision-making/local-owa · Dataset: https://doi.org/10.5281/zenodo.20539026
Quick facts
Originator
Malczewski, J.; Liu, X.
Subfamily
Ranking
Year
2014
Type
Range-sensitive neighbourhood-local OWA — criterion weights w^q k scale with local criterion variance within each spatial neighbourhood; order weights λ k remain global, encoding a single risk attitude applied everywhere
Value Space
crisp
Uncertainty
None
Compensation
partial
Rank Reversal
Yes
Related methods
AHPANPBWMCRITICENTROPYFUCOMMERECSWARA
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