Multi-criteria classification of reward collaboration proposals

Sant’Anna, Annibal Parracho; Gavião, Luiz Octávio; Sant’Anna, Tiago Lezan

doi:10.1080/24725854.2023.2173368

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…In the last stage, the probabilities of each alternative being the best or worst in each criterion are combined in different ways, providing different points of view for decision-making. For instance, PMax and PMin can be composed by axis (progressiveconservative/optimist-pessimist) [32], by principles of concentration/dilution [33], by Choquet capacities [34], by the Gini index [35], by classes [2], and associated with other methods [36]. Automation of the complete CPP procedure is available with open access in the "CPP" package [37].…”

Section: The Basics Of Cppmentioning

confidence: 99%

Composition of Probabilistic Preferences in Multicriteria Problems With Variables Measured in Likert Scales and Fitted by Empirical Distributions

Gavião¹,

Sant’Anna²,

Lima³

et al. 2023

Preprint

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Multicriteria decision aid requires a database as a decision matrix, in which two or more alternatives are evaluated according to two or more variables selected as decision criteria. Several problems of this nature use measures by Likert scales. Depending on the method, parameters from these data (e.g. means, modes or medians) are required for calculations. This parameterization of data in ordinal scales has fueled controversy for decades between authors who favor mathematical/statistical rigor and argue against the procedure, stating that ordinal scales should not be parameterized, and scientists from other areas who have shown gains from the process that compensate for this relaxation. The aim of this article is to demonstrate the advantages of the Composition of Probabilistic Preferences (CPP) method in multicriteria problems with data from Likert scales. The CPP is capable of allaying the protests raised and obtaining more accurate results than descriptive statistics or parametric models can bring. The proposed algorithm in R-code involves the use of CPP with empirical distributions and fitting histograms of data measured by Likert scales. Two case studies with simulated datasets having peculiar characteristics and a real case illustrate the advantages of the CPP.

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Section: The Basics Of Cppmentioning

confidence: 99%

Composition of Probabilistic Preferences in Multicriteria Problems With Variables Measured in Likert Scales and Fitted by Empirical Distributions

Gavião¹,

Sant’Anna²,

Lima³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Section: The Basics Of Cppmentioning

confidence: 99%

Composition of Probabilistic Preferences in Multicriteria Problems with Variables Measured in Likert Scales and Fitted by Empirical Distributions

Gavião¹,

Sant’Anna

Lima

et al. 2023

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The aim of this article is to demonstrate the advantages of the Composition of Probabilistic Preferences method in multicriteria problems with data from Likert scales. Multicriteria decision aids require a database as a decision matrix, in which two or more alternatives are evaluated according to two or more variables selected as decision criteria. Several problems of this nature use measures by Likert scales. Depending on the method, parameters from these data (e.g., means, modes or medians) are required for calculations. This parameterization of data in ordinal scales has fueled controversy for decades between authors who favor mathematical/statistical rigor and argue against the procedure, stating that ordinal scales should not be parameterized, and scientists from other areas who have shown gains from the process that compensates for this relaxation. The Composition of Probabilistic Preferences can allay the protests raised and obtain more accurate results than descriptive statistics or parametric models can bring. The proposed algorithm in R-code involves the use of probabilities with empirical distributions and fitting histograms of data measured by Likert scales. Two case studies with simulated datasets having peculiar characteristics and a real case illustrate the advantages of the Composition of Probabilistic Preferences.

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“…In [25], a model to address the problem of classifying rewarded collaboration proposals, as defined in the Anti-crime Law, is developed. Each proposal is evaluated separately and allocated in one item from an ordered list of predetermined classes.…”

Section: Introductionmentioning

confidence: 99%

A Multicriteria Standard to Rank Plea Bargain Proposals

Sant’Anna

Gavião²,

Sant’Anna

2023

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This article presents a model for the comparison of plea bargain proposals. The use of the model increases the possibility of the satisfactory development of the negotiation of rewarded collaboration agreements recently permitted under Brazilian law. A novelty in the model is the objective consideration of society’s interest in adequately punishing defendants whose guilt can be proven. To allow for the inclusion of this element, a multicriteria approach that adds the criteria representing the prosecution’s aims to the criteria regarding the accused’s positions is adopted. The importance of the criteria is derived without direct criteria weighting. A novel joint treatment to criteria collinearity and interaction is developed, which enables the model to accommodate any number of defendants, proposals, and criteria. The framework so developed enhances transparency and encourages collaboration. By assigning a new meaning to the plea bargain, it is able to bring about the necessary shift in cultural standards that can lead to the effective weakening of criminal organizations.

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Multi-criteria classification of reward collaboration proposals

Cited by 3 publications

References 22 publications

Composition of Probabilistic Preferences in Multicriteria Problems With Variables Measured in Likert Scales and Fitted by Empirical Distributions

Composition of Probabilistic Preferences in Multicriteria Problems With Variables Measured in Likert Scales and Fitted by Empirical Distributions

Composition of Probabilistic Preferences in Multicriteria Problems with Variables Measured in Likert Scales and Fitted by Empirical Distributions

A Multicriteria Standard to Rank Plea Bargain Proposals

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