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An Empirical Comparison of Rank-Based Surrogate Weights in Additive Multiattribute Decision Analysis

Many methods for creating surrogate swing weights based only on the rank order of the attributes are proposed to avoid the cost and effort of eliciting weights in multiattribute decision analysis. We explore empirically how well eight different methods perform based on a large sample of real-world elicited weights. We use the Euclidean distance from the elicited weights to judge the quality of the surrogate weights as well as three other metrics. The sum reciprocal method gives results, on average, statistically closest to the elicited weights for all metrics used. The equal ratio method using a fixed ratio of 0.716 performs just as well on three of the metrics. The rank sum method, the simplest and one of the oldest methods, performs generally next best. The rank order centroid method, which does well in simulation studies, performs relatively poorly in this evaluation using real-world data.

Ketersediaan

Informasi Detail

Judul Seri

DECISION ANALYSIS; Vol.20 No.1 March 2023

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 55-72

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MULTIPLE-ATTRIBUTE DECISION ANALYSIS
ATTRIBUTE WEIGHTS
SURROGATE WEIGHTS
RANK SUM
RANK EXPONENT
RANK RECIPROCAL
SUM RECIPROCAL
EQUAL RATIO
RANK ORDER CENTROID
LINEAR WITH VARIABLE SIOPE
RANK ORDER DISTRBUTION

Info Detail Spesifik

https://doi.org/10.1287/deca.2022.0456

Pernyataan Tanggungjawab

Roger Chapman Burk, Richard M. Nehring

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