Specialists’ knowledge and cognitive stress in making pairwise comparisons

Libório, Matheus Pereira; Ekel, Petr Iakovlevitch; Bernardes, Patrícia; Gomes, Luiz Flávio Autran Monteiro; Vieira, Douglas Alexandre Gomes

doi:10.1007/s12597-023-00689-2

Cited by 3 publications

(6 citation statements)

References 51 publications

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“…The core of SAW-Max-Entropy lies in its hybrid weighting scheme, which considers subjective information while objectively defining the weights. Hybrid weighting schemes, wherein expert opinion is considered in the objective definition of weights, are frequently used in several studies [49][50][51][52].…”

Section: Saw-max-entropy Methodsmentioning

confidence: 99%

“…The natural strategy to overcome this challenge is the application of so-called transformation functions [56][57][58][59]. This strategy enables experts to choose their preferred criteria/alternative assessment format, e.g., ordering, scoring, comparison, or budget allocation [60], giving them greater psychological comfort and reducing errors and inconsistencies in assessments [51].…”

Section: Application Example: Costs Of Doing Business In G20 Countriesmentioning

confidence: 99%

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The Use of Information Entropy and Expert Opinion in Maximizing the Discriminating Power of Composite Indicators

Libório,

Karagiannis,

Diniz

et al. 2024

Entropy

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This research offers a solution to a highly recognized and controversial problem within the composite indicator literature: sub-indicators weighting. The research proposes a novel hybrid weighting method that maximizes the discriminating power of the composite indicator with objectively defined weights. It considers the experts’ uncertainty concerning the conceptual importance of sub-indicators in the multidimensional phenomenon, setting maximum and minimum weights (constraints) in the optimization function. The hybrid weighting scheme, known as the SAW-Max-Entropy method, avoids attributing weights that are incompatible with the multidimensional phenomenon’s theoretical framework. At the same time, it reduces the influence of assessment errors and judgment biases on composite indicator scores. The research results show that the SAW-Max-Entropy weighting scheme achieves greater discriminating power than weighting schemes based on the Entropy Index, Expert Opinion, and Equal Weights. The SAW-Max-Entropy method has high application potential due to the increasing use of composite indicators across diverse areas of knowledge. Additionally, the method represents a robust response to the challenge of constructing composite indicators with superior discriminating power.

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Section: Saw-max-entropy Methodsmentioning

confidence: 99%

Section: Application Example: Costs Of Doing Business In G20 Countriesmentioning

confidence: 99%

The Use of Information Entropy and Expert Opinion in Maximizing the Discriminating Power of Composite Indicators

Libório,

Karagiannis,

Diniz

et al. 2024

Entropy

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“…The subsystems have a complete and independent character and can be utilized for solving the corresponding problems. For instance, five preference formats can be considered and processed: ordering vector, utility vector, fuzzy estimates vector, multiplicative preference relations matrices, and fuzzy preference relations matrices [8,66].…”

Section: Computing Systemmentioning

confidence: 99%

“…stance, five preference formats can be considered and processed: ordering vector, utility vector, fuzzy estimates vector, multiplicative preference relations matrices, and fuzzy preference relations matrices [8,66].…”

Section: Computing Systemmentioning

confidence: 99%

Multi-Criteria Decision under Uncertainty as Applied to Resource Allocation and Its Computing Implementation

Ekel,

Libório,

Ribeiro

et al. 2024

Mathematics

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This research addresses the problem of multi-objective resource allocation or resource deficits, offering robust answers to planning decisions that involve the elementary question: “How is it done?”. The solution to the problem is realized using the general scheme of multi-criteria decision-making in uncertain conditions. The bases of the proposed scheme are associated with the possibilistic approach, which involves the generalization of fuzzy sets from the classical approach to process the uncertainty of information to produce robust (non-dominated) solutions in multi-criteria analysis. Applying this general scheme makes it possible to reduce regions of decision uncertainty through the maximum use of available quantitative information. In the case where quantitative information analysis is insufficient to obtain a unique solution, the proposed approach presupposes the appropriation of qualitative data extracted from experts, who express their opinions considering their knowledge, experience, and intuition. The information on the qualitative character can be represented in diverse preference formats processed by transformation functions to provide homogeneous information for decision procedures used at the final decision stage. The presented results have been implemented within the system of multi-criteria decision-making under uncertain conditions described in the paper. Its functioning is illustrated by solving the typical problem in investment planning activities.

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“…The flexibility of using different preference expression formats has advantages and disadvantages for the assessment process [71]. On the one hand, experts can choose the most comfortable format to assess alternatives, reducing the cognitive stress of the assessment process.…”

Section: Preference Formatsmentioning

confidence: 99%

Fuzzy-Set-Based Multi-Attribute Decision-Making, Its Computing Implementation, and Applications

Ferreira,

Ribeiro,

Schuffner

et al. 2024

Axioms

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This paper reflects the results of research analyzing models of multi-attribute decision-making based on fuzzy preference relations. Questions of constructing the corresponding multi-attribute models to deal with quantitative information concomitantly with qualitative information based on experts’ knowledge are considered. Human preferences may be represented within the fuzzy preference relations and by applying diverse other preference formats. Considering this, so-called transformation functions reduce any preference format to fuzzy preference relations. This paper’s results can be applied independently or as part of a general approach to solving a wide class of problems with fuzzy coefficients, as well as within the framework of a general scheme of multi-criteria decision-making under conditions of uncertainty. The considered techniques for fuzzy preference modeling are directed at assessing, comparing, choosing, prioritizing, and/or ordering alternatives. These techniques have served to develop a computing system for multi-attribute decision-making. It has been implemented in the C# programming language, utilizing the “.NET” framework. The computing system allows one to represent decision-makers’ preferences in one of five preference formats. These formats and quantitative estimates are reduced to nonreciprocal fuzzy preference relations, providing homogeneous preference information for decision procedures. This paper’s results have a general character and were applied to analyze power engineering problems.

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Specialists’ knowledge and cognitive stress in making pairwise comparisons

Cited by 3 publications

References 51 publications

The Use of Information Entropy and Expert Opinion in Maximizing the Discriminating Power of Composite Indicators

The Use of Information Entropy and Expert Opinion in Maximizing the Discriminating Power of Composite Indicators

Multi-Criteria Decision under Uncertainty as Applied to Resource Allocation and Its Computing Implementation

Fuzzy-Set-Based Multi-Attribute Decision-Making, Its Computing Implementation, and Applications

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