Marcos Flávio Silveira Vasconcelos Dangelo

This paper introduces the Minimum Uncertainty Method (MinUn-M), a new approach for constructing composite indicators that integrate subjective expert opinions with objective, data-driven techniques to analyze multidimensional phenomena that are not measurable by a single indicator. The MinUn-M method provides a stable composite indicator in which variations in the model inputs do not lead to significant changes in the model outputs, ensuring greater reliability in representing multidimensional phenomena and facilitating informed decision-making by managers and policymakers. The methods also minimize incompatibilities between subjective and objective weights, thereby promoting alignment between theory and practice by treating the expert’s opinions as constraints on the objective function that minimizes the uncertainty of the composite indicator ranks. Through two case studies, the MinUn-M method is applied to address the challenges of measuring and correlating the child well-being and health systems in low- and middle-income countries, as well as child well-being in Brazilian states. The results demonstrate superior robustness and precision compared to traditional weighting methods (e.g., equal weights, factor analysis, entropy). The main methodological contribution is the balance between objectivity and subjectivity in allocating weights, providing practical tools for public management and future research on constructing multidimensional indicators.

This study presents MGDM-Sy, a multicriteria group decision-making system that supports heterogeneous preference elicitation within a unified computational environment. The system allows evaluators participating in the same decision problem to express their preferences using different preference elicitation formats, including ordering of alternatives, direct scoring, and pairwise comparison. Heterogeneous preference representations are automatically transformed into a common score representation suitable for aggregation. For pairwise comparisons, MGDM-Sy incorporates a consistency-improvement mechanism that assists decision-makers in identifying and correcting inconsistent judgments while preserving the original assessments as much as possible. The system also performs consensus analysis at both the individual and group levels and generates decision-support reports. In addition, MGDM-Sy incorporates two methodological contributions: the Card Sorting method, which combines ordering and pairwise comparison while allowing the assignment of zero importance to alternatives, and the Consensus Degree-Enhancement tool, which improves group consensus by filtering assessments according to their individual consensus degree. The system is illustrated through two multicriteria group decision-making applications: (i) the prioritization of regions for agrivoltaic system deployment and (ii) the weighting of the criteria composing the Cost of Doing Business Index. The results demonstrate the applicability of the proposed framework and show that the Consensus Degree-Enhancement tool increases the group consensus degree while preserving representative assessments that would otherwise be unnecessarily excluded.

Composite indicators are widely used to represent multidimensional phenomena; however, existing weighting methods typically optimize statistical properties without explicitly incorporating construct validity into the estimation process. This study proposes Max-explanation with multiple constraints, a theory-driven optimization framework that estimates aggregation weights by maximizing adherence to a conceptually significant variable while simultaneously enforcing construct-validity criteria, including information transfer and construct non-dominance. Variables whose empirical relationships are inconsistent with their theoretical polarity are identified as noise and excluded before optimization. The proposed method is applied to construct composite indicators of present and potential economic development for the 27 Brazilian states. Sensitivity and robustness analyses demonstrate that the method performs best when the input variables provide complementary rather than redundant information. As intercorrelation among the variables increases, the weighting degrees of freedom available for optimization progressively decrease, reducing the attainable adherence to the conceptually significant variable and eventually preventing feasible solutions under highly redundant correlation structures. Compared with conventional weighting methods, the proposed framework produces composite indicators with stronger conceptual representation while explicitly controlling construct dominance and information transfer. The proposed optimization framework is readily applicable to other multidimensional phenomena whenever a conceptually significant reference variable is available.

Objective: We aimed to investigate the effects of traditional acupuncture combined with auricular acupressure on quality of life in patients with head and neck squamous cell carcinoma undergoing radiotherapy. Study Design: This is a two-arm, parallel, randomized clinical trial with blinded outcome assessment. The study population comprised 107 patients (55 without intervention and 52 with intervention). Data were collected at Dilson Godinho Hospital in Brazil from March 2017 to June 2018, and all patients provided informed consent and were registered under the UTN U1111-1204-8410/RBR-10v5gcdk. Patients in the intervention group received traditional and auricular acupressure in weekly sessions during their radiotherapy treatment, and patients in the control group received no acupuncture. In addition, quality-of-life assessment data were collected using the WHOQOL-BREF instrument. Results: Analysis of the scores obtained in the WHOQOL-BREF before and after radiotherapy demonstrated that the use of traditional and auricular acupressure had a positive impact on physical, psychological, social, environmental, and overall quality of life. Conclusions: Our findings suggest benefits and provide a context for patients and physicians to decide if acupuncture is a desirable treatment option. However, further studies are required to understand the therapy’s effectiveness better.

Composite indicators are mathematical tools that assist sociologists, geographers, environmentalists, and urban planners in understanding and analyzing complex phenomena. Composite indicators also attract significant interest among researchers in operations research, who seek to improve and develop new methods for their construction. This study aligns with this latter line of research by proposing a new method for constructing composite indicators. The so-called Subjective Benefit-of-the-Doubt (S-BoD) was developed to address the uncertainty about the conceptual importance of sub-indicators in the data-driven definition of their respective weights. This logic ensures the weights adapt to the maturity of the theoretical-conceptual framework for the multidimensional phenomenon. On the one hand, absolute consensus among experts signals a mature theoretical-conceptual framework that prevents the BoD from modifying the weights. On the other hand, absolute disagreement among experts signals the presence of incipient theoretical-conceptual frameworks, which allow for full weight fluctuations, corresponding to the operationalization of the classic BoD algorithm. The results of applying the method show that the S-BoD outperforms BoD in multidimensional governance by better distinguishing units, extracting information, explaining GDP per capita, and generating scores without outliers, resulting in a composite governance indicator with greater discriminatory, informational, and explanatory power. Simulations confirm this superiority, except for the proportion of outliers, which is specific to governance data.

Interest in child health and well-being is growing, and both are multidimensional. Composite indicators (CIs) are useful tools for analyzing their relationships. This study examines the correlation between the CI of child well-being and that of public health system performance and proposes a weighting scheme for constructing CIs. Among schemes, entropy weighting yielded the strongest correlation. Uncertainty analysis revealed that country rankings with entropy weighting are the most stable, demonstrating robust CI. Strong explanatory power indicates that the CIs correlate with GDP, validating their compatibility with a key reference variable. High discriminant power confirms that CIs provide informational diversity, helping decision-makers distinguish between countries. The analysis shows that better health system performance is associated with higher child well-being, reinforcing the role of public health systems in promoting sustainable development and child well-being.

Bruising in cattle carcasses is both an economic and animal welfare concern. Using over 18.6 million carcass records from ten Brazilian states, we combined Self-Organizing Maps (SOM) with Extreme Gradient Boosting (XGBoost) to predict bruising across six primal cuts and to estimate its physical and financial consequences. Models incorporating SOM-based clustering outperformed those without, improving recall for bruised carcasses from 36% to 84% in the round cut. A linear model confirmed that each additional bruise reduced carcass weight by 0.40 kg (p < 0.001). When translated into operational terms, bruising caused an average loss of 0.41 kg per carcass (approx USD 1.05) or USD 3.84 per metric ton of beef carcass produced. These findings demonstrate the systemic nature of bruising and provide the beef industry with robust predictive tools and quantifiable indicators to reduce losses and enhance animal welfare.