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Home›Decision-making›Election based on Relative Value Distances
MCDMRankingcrisp

Election based on Relative Value Distances

ERVD (Election based on Relative Value Distances) is a ranking multi-criteria decision-making (MCDM) method introduced by Shyur, H. J., Yin, L., Shih, H. S., Cheng, C. B. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

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ERVD
AHPBWMCRITICENTROPY

When to use it

φ_i ∈ [0,1]. Higher = better (closer to PIS). Behaviorally grounded: losses are weighted more heavily than gains (λ=2.25 default). Reference point strongly affects results — choose carefully.

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. Shyur, H. J., Yin, L., Shih, H. S., Cheng, C. B. (2015). A multiple criteria decision making method based on relative value distances. Foundations of Computing and Decision Sciences DOI: 10.1515/fcds-2015-0017 ↗

How to cite this page

ScholarGate. (2026, June 2). Election based on Relative Value Distances. ScholarGate. https://scholargate.app/en/decision-making/ervd

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AHPBWMCRITICENTROPY

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PIF-TODIMEVAMIXPROBIDTOPSISPIVWEDBACODASD-TOPSIS

Related reference concepts

Decision MakingDecision Support SystemsCriteria for Decision-Making under Risk and UncertaintyEvaluation CriteriaMultidimensional ScalingParticipative Decision Making

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

ScholarGate — ERVD (Election based on Relative Value Distances). Retrieved 2026-07-21 from https://scholargate.app/en/decision-making/ervd · Dataset: https://doi.org/10.5281/zenodo.20539026
Quick facts
Originator
Shyur, H. J., Yin, L., Shih, H. S., Cheng, C. B.
Subfamily
Ranking
Year
2015
Type
Prospect-theory value function with reference-point separation
Value Space
crisp
Uncertainty
None
Compensation
full
Rank Reversal
No
Related methods
AHPBWMCRITICENTROPY
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