Ordinal measurement, preference aggregation and interlaboratory comparisons

Muravyov, Sergey V.

doi:10.1016/j.measurement.2013.04.044

Cited by 30 publications

(6 citation statements)

References 21 publications

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“…Therefore, aside from large values of ω, the execution time is affordable even for this number of alternatives. The obtained results show that the proposed algorithms for the computation of the Kemeny rankign(s) outperform the state-of-the-art exact algorithm [13], which already outperformed greatly other exact algorithms proposed in the literature [18,19].…”

Section: Resultsmentioning

confidence: 81%

Reducing the Computational Time for the Kemeny Method by Exploiting Condorcet Properties

et al. 2021

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Preference aggregation and in particular ranking aggregation are mainly studied by the field of social choice theory but extensively applied in a variety of contexts. Among the most prominent methods for ranking aggregation, the Kemeny method has been proved to be the only one that satisfies some desirable properties such as neutrality, consistency and the Condorcet condition at the same time. Unfortunately, the problem of finding a Kemeny ranking is NP-hard, which prevents practitioners from using it in real-life problems. The state of the art of exact algorithms for the computation of the Kemeny ranking experienced a major boost last year with the presentation of an algorithm that provides searching time guarantee up to 13 alternatives. In this work, we propose an enhanced version of this algorithm based on pruning the search space when some Condorcet properties hold. This enhanced version greatly improves the performance in terms of runtime consumption.

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Section: Resultsmentioning

confidence: 81%

Reducing the Computational Time for the Kemeny Method by Exploiting Condorcet Properties

et al. 2021

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“…as the number of alternatives increases. Nevertheless, for a reasonable number of alternatives and a sufficiently powerful computer, this problem is solvable 5,6,30 . The main problem, however, is that, unlike the aforementioned ordinal scale, where in rare cases, two neighboring adjacent median categories are possible; in this situation, a much larger number of medians can appear.…”

Section: Suggested Methods For Overcoming Problemsmentioning

confidence: 99%

Processing new types of quality data

Marmor

Bashkansky

2020

Quality & Reliability Eng

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Quality engineers are increasingly faced with the need to deal with new types of data, which are significantly different from ordinary numerical data by virtue of their nature and the operations that can be performed with them. Basic concepts related to processing of such data, ie, data similarity, measurement system analysis, variation analysis, and data fusion, need to be thoroughly rethought. Reviewing recent publications in the field, we suggest a common approach to processing all data types on the basis of the idea of defining the distance metric for the appropriate data space. The article discusses six types of quality data (nominal, ordinal, preference chains, strings, tree structured, and product/process distribution) and four data processing aspects (calculating data similarity, error description, data fusion, and intradispersion and interdispersion studies). Necessary information and recommendations are given for each combination of data type and problem. They are also summarized in a table that refers the reader to various sections of the article. Any other data type for which the distance metric is definable can be included into the framework of the proposed unified approach.

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“…The exploration of prefixes of rankings (i.e., rankings starting from the same alternatives) was firstly introduced in [4]. These prefixes are also used in [14], where the author provides a lower bound for the Kemeny distance associated with each prefix by adding the values min(o i j , o ji ) for every two alternatives that have not been fixed by the prefix.…”

Section: Branch and Bound Algorithmsmentioning

confidence: 99%

The Borda Count as an Initial Threshold for Kemeny Ranking Aggregation

Rico

Vela

Pérez‐Fernández

et al. 2021

Atlantis Studies in Uncertainty Modelling

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The need of establishing a consensus ranking from the preferences expressed by different voters arises in several contexts. The method proposed by Kemeny to this purpose is famously known due to its numerous fulfilled properties and intuitive interpretation, as it minimizes the number of pairwise comparisons in which the consensus ranking disagrees with the preferences given by the voters. Nevertheless, this method has a main drawback regarding its execution time, thus preventing its use in practice. There exist some other methods, such as the Borda Count, that can be computed in polynomial time, even though they do not fulfill as many intuitive properties as the Kemeny method. Here, we propose to use the Borda Count ranking as the initial solution of a Branchand-Bound algorithm for reducing the execution time of the Kemeny method. The presented experiments show that this approach leads to an improvement in execution time.

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Ordinal measurement, preference aggregation and interlaboratory comparisons

Cited by 30 publications

References 21 publications

Reducing the Computational Time for the Kemeny Method by Exploiting Condorcet Properties

Reducing the Computational Time for the Kemeny Method by Exploiting Condorcet Properties

Processing new types of quality data

The Borda Count as an Initial Threshold for Kemeny Ranking Aggregation

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