Choosing Aggregation Rules for Composite Indicators
By Giuseppe Munda. 2012
90 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/aa6a63b3d3cdca3a04dab54d7177b05b4b667ed9
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Alessio Ishizaka
- Menelaos Tasiou
- Gianpiero Torrisi
- Michael Gonzalez
- Salvatore Greco
- R. Słowiński
- Miłosz Kadziński
- Marco Cinelli
In Management Science and Operations Research
- Marco Cinelli
- R. Słowiński
- Salvatore Greco
- Miłosz Kadziński
- Michael Gonzalez
- Alessio Ishizaka
- Gianpiero Torrisi
- Menelaos Tasiou
Citations in the corpus
- On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness (2018, methodology): “A ‘satisfying’ one is for the ranking of alternatives to be obtained according to the maximum likelihood principle,27 which essentially chooses as the final ranking the one with the ‘maximum pair-wise support’ (Munda 2012, p. 345).”
- How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy (2020, methodology): “The compensation level can be driven differently according to the aggregation algorithm used by the MCDA method [78,126].”