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Home›Decision-making›UTA* — Additive utility disaggregation from reference ranking (revised UTA)
MCDMRankingcrisp

UTA* — Additive utility disaggregation from reference ranking (revised UTA)

UTASTAR (UTA* — Additive utility disaggregation from reference ranking (revised UTA)) is a ranking multi-criteria decision-making (MCDM) method introduced by Siskos, Y., Yannacopoulos, D. in 1985. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

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UTASTAR
AHPANPBWMBWM-BAYESIANCCSDCILOSCIMASCRITIC

When to use it

UTA* — Additive utility disaggregation from reference ranking (revised UTA)

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
  • Assumes full compensation — a strong score on one criterion can offset a weak score on another.

Sources

  1. Siskos, Y., Yannacopoulos, D. (1985). UTASTAR: An ordinal regression method for building additive value functions. Investigación Operativa link ↗

How to cite this page

ScholarGate. (2026, June 2). UTA* — Additive utility disaggregation from reference ranking (revised UTA). ScholarGate. https://scholargate.app/en/decision-making/utastar

Related methods

AHPANPBWMBWM-BAYESIANCCSDCILOSCIMASCRITIC

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Similar methods

UTASTOCHASTIC-UTAUTADISMAUTWASPASRANK-REVERSALTOPSISEVAMIX

Related reference concepts

Decision Support SystemsDecision MakingWeighted ScoresCriteria for Decision-Making under Risk and UncertaintyMultidimensional ScalingDecision Making Skills

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

ScholarGate — UTASTAR (UTA* — Additive utility disaggregation from reference ranking (revised UTA)). Retrieved 2026-07-21 from https://scholargate.app/en/decision-making/utastar · Dataset: https://doi.org/10.5281/zenodo.20539026
Quick facts
Originator
Siskos, Y., Yannacopoulos, D.
Subfamily
Ranking
Year
1985
Type
Additive utility disaggregation — LP from ordinal reference judgements
Value Space
crisp
Uncertainty
None
Compensation
full
Rank Reversal
No
Related methods
AHPANPBWMBWM-BAYESIANCCSDCILOSCIMASCRITIC
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