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FUZZY ECONOMIC REVIEW

ISSN (print) 1136-0593 · ISSN (online) 2445-4192

WELFARE SUBJECTIVE EVALUATION. APPLICATION TO HEALTH SYSTEM ACCESS

Luisa L. Lazzari. Universidad de Buenos Aires. Facultad de Ciencias Económicas. Instituto Interdisciplinario de Economía Política de Buenos Aires (IIEP - BAIRES). Ciudad de Buenos Aires, Argentina. E-mail: luisalazzari@gmail.com.

María José Fernandez. Universidad de Buenos Aires. Facultad de Ciencias Económicas. CONICET. (IIEP - BAIRES). Ciudad de Buenos Aires, Argentina. E-mail: mariajfernandez@economicas.uba.ar

Patricia Mouliá. Universidad de Buenos Aires. Facultad de Ciencias Económicas. CIMBAGE - IADCOM. Ciudad de Buenos Aires, Argentina. E-mail: pimoulia@hotmail.com

Subjective assessments of the quality of life are considered when measuring development. These measurements are built using population surveys where people are asked to define their situation on a qualitative scale. Information is obtained through simple surveys that capture subjective perceptions of access to certain goods. To formalize human ability to make decisions in uncertain environments, Zadeh (2011) defined Z-numbers, which provide a vague assessment and an idea of their reliability. Both can be expressed by linguistic variables. As perceptions of welfare dimensions and its indicators are heterogeneous and given that it is also interesting to value its impact, it is important to identify opinions and the frequency of those perceptions together. In this paper, it is presented a subjective valuation index of economic welfare with Z-information. To show how the model works, it is defined and calculated for one of the dimensions associated with household economic welfare: health system access. Identical procedure will be able to be extended to all the dimensions determined to measure wellbeing.

 

 

 

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A COMPARATIVE CLUSTERING MODEL THAT CONSIDERS FALSE POSITIVES AND FALSE NEGATIVES IN SOME SOCIOECONOMIC APPLICATIONS

D.E. Urueta-Hinojosa. Universidad Autónoma Metropolitana-Iztapalapa, Departamento de Ingeniería Eléctrica, Ciudad de México, México. E-mail: deurueta@xanum.uam.mx

P. Lara-Velázquez. Universidad Autónoma Metropolitana-Iztapalapa, Departamento de Ingeniería Eléctrica, Ciudad de México, México. E-mail: plara@xanum.uam.mx

M.A. Gutiérrez-Andrade. Universidad Autónoma Metropolitana-Iztapalapa, Departamento de Ingeniería Eléctrica, Ciudad de México, México. E-mail: gamma@xanum.uam.mx

S.G. De-los-Cobos-Silva. Universidad Autónoma Metropolitana-Iztapalapa, Departamento de Ingeniería Eléctrica, Ciudad de México, México. E-mail: cobos@xanum.uam.mx

E.A. Rincón-García. Universidad Autónoma Metropolitana-Azcapotzalco, Departamento de Sistemas, Ciudad de México, México. E-mail: rigaeral@correo.azc.uam.mx

R.A. Mora-Gutiérrez. Universidad Autónoma Metropolitana-Azcapotzalco, Departamento de Sistemas, Ciudad de México, México. E-mail: mgra@correo.azc.uam.mx

Unsupervised learning enables classifier models to be built quickly and inexpensively in comparison with supervised approaches because the labeling task is eliminated. On the other hand, to assess the quality of a classifier, the only parameter to consider is usually accuracy, treating incorrect predictions like if they had the same importance when in reality the consequences of diagnosing a healthy patient as sick (Type I Error), or diagnosing a sick patient as healthy (Type II Error) are different. That is why, depending on the application, it is preferable to avoid a specific type of error, even if the accuracy decreases. The present work shows a model based on clustering methods that take into account Type I and II Errors to solve medical and business instances using three techniques: k-means, Spectral and Gauss. Based on representative and well-studied datasets for socioeconomic applications, the results show that the accuracy of a model is not a conclusive parameter and to make a decision it is necessary to focus on errors in the confusion matrix which according to each specific instance, take a different meaning and significance. Our results and analysis are discussed to determine the best model for each case study. Finally, conclusions and limitations are analyzed.

 

 

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USING POSSIBILISTIC MOMENTS AND BI-OBJECTIVE OPTIMIZATION METAHEURISTICS TO COMPUTE PESSIMISTIC AND OPTIMISTIC EFFICIENT PORTFOLIOS / FRONTIERS FOR FUZZY-VALUED RETURNS OF RISKY ASSETS

Valise Georgescu. University of Craiova (Romania). Department of Statistics and Informatics, Email: vasile_georgescu@edu.ucv.ro.com

Andreea-Mirabela Stefan. University of Craiova (Romania). Department of Statistics and Informatics, Email: stefan.andreea.h3a@student.ucv.ro

Most of existing portfolio selection models are based on a probabilistic approach combined with optimization techniques. The uncertainty is equated with randomness. However, there are many non-probabilistic factors that affect the financial markets and a recent literature has increasingly been interested in modeling the fuzziness of portfolios returns. In this paper we consider the mean-variance portfolio optimization problem for LR-shaped fuzzy-valued returns of risky assets. Our approach is based upon Puri and Lalescu concept of fuzzy random variables and a suitable -metric on the Hilbert space of fuzzy variables whose outcomes have square-integrable support functions. The latter are defined with respect to the left and right spreads of the LR-shaped fuzzy-valued returns and can be used to obtain pessimistic and optimistic estimates of the expected returns and their corresponding covariance matrices, both with possibilistic interpretation. This allows formulating and solving three possibilistic bi-objective portfolio optimization problems, which result in pessimistic, optimistic and combined efficient possibilistic portfolios and their corresponding efficient possibilistic frontiers. A fourth problem, the classical Markowitz’s mean–variance one (defined with respect to crisp returns) is also considered as a benchmark. We employ a multi-objective metaheuristic (namely, ANSGAIII) to solve these four portfolio optimization problems for a universe of assets listed at Bucharest Stock Exchange.

 

 

 

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REPLY STRUCTURE AND PARTICIPATION IN ONLINE CONVERSATIONS ENABLED BY ARGUMENTATION PLATFORMS: A REAL WORLD EXPERIMENT OF COLLECTIVE DELIBERATION IN E-DEMOCRACY

Luca Iandoli. St John’s University, College of Professional Studies, New York, USA. E-mail: iandolil@stjohns.edu

Ivana Quinto. University of Naples Federico II, Department of Industrial Engineering, Naples, Italy. E-mail: ivana.quinto@unina.it,

Lorella Cannavacciuolo. University of Naples Federico II, Department of Industrial Engineering, Naples, Italy. E-mail: lorella.cannavacciuolo@unina.it

In this paper we report evidence from a collective deliberation experiment in which the supporters of a political party were asked to debate online about ways to reform the electoral law. We compared a forum with an argumentation platform, an online collaboration tool that supports the construction of a collective map representing the debate in terms of issues, proposals, pros and cons. We analyze the structural proprieties of the reply networks generated in the two conditions. Our findings show that forum generated more redundant ideas and highly central speakers, whereas the argumentation platform tested in this study favored viewing and rating of others’ posts, produced more arguments per idea, and promoted brokerage between users belonging to different subgroups.

 

 

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USING META-MODELS IN SIMULATION-BASED INVESTMENT ANALYSIS – STUDYING THE FINANCING MIX OF METAL MINING INVESTMENTS

Jyrki Savolainen. LUT University, School of Business and Management, Lappeenranta, Finland. Email: jyrki.savolainen@lut.fi

Mikael Collan. LUT University, School of Business and Management, Lappeenranta, Finland. Email: mikael.collan@lut.fi

 

This paper is the first documented research effort on how simple meta-models can be used in simulation-based investment analysis. Modern computers allow the construction and simulation of near real-world emulating models, often referred to as “digital twins”, that offer requisite variety to real world phenomena, such as an industrial investment. These models can be extremely complex and computationally demanding which reduces the scope of their practical applications. This is where meta-models can help. Meta-models are simple black-box models that are fitted with the input-output -combinations from more complex models to be able to approximate complex model behavior. As the simple meta-models are very fast to solve they may be used to explore much larger solution spaces with considerably higher speed and less computing power needed than the original models. We demonstrate how the meta-modeling approach can be used in the context of metal mining investment analysis that is originally conducted with a dynamic system model constructed based on a real-world metal mining investment. We show how two simple meta-models, a linear regression model and a regression-tree model, can be used in gaining insight about a suitable financing-mix for the said metal mining investment.

 

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SELECTION OF TOMATO CULTIVATION SYSTEMS UNDER SUSTAINABILITY CRITERIA

José M. Brotons. Miguel Hernández University, Estudios Económicos y Financiero, Spain. Email: jm.brotons@umh.es

José M. Cámara-Zapata. Miguel Hernández University, Departamento de Física Aplicada, Escuela Politécnica Superior de Orihuela, Spain. Email: jm.camara@umh.es

 

Greenhouse production is the most intensive plant production system, both in terms of yield and investment and inputs. There are different alternatives of farming systems among which producers can choose. To facilitate decision-making it is convenient to develop applications that analyze the alternatives from the point of view of sustainability. The analysis of the sustainability of greenhouse production must be carried out according to the criteria that affect all the processes that make up the agri-food value chain. In this work, the criteria and sub-criteria that influence the sustainability of greenhouse production are analyzed first. Next, the alternatives in the most important cultivation system in the production of greenhouse tomatoes in the Spanish Mediterranean Basin (soil, perlite and nutrient film technique, NFT) are evaluated. The work is carried out based on the opinion of ten experts from the integrated sectors of the agrifood value chain. The results indicate that the sustainability of greenhouse production increases in the order: soil < perlite < NFT due to a higher valuation of the latter alternative in relation to commercial, natural, human, material resources and management versus economic resources.

 

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BONFERRONI MEANS WITH THE INDUCED OWAWA OPERATOR

Ernesto Leon-Castro. Universidad Católica de la Santísima Concepción. Facultad de Ciencias Económicas y Administrativas, Concepción, Chile. Email: eleon@ucsc.cl

Fabio Blanco-Mesa. Universidad Pedagógica y Tecnológica de Colombia, Facultad de Ciencias Económicas y Administrativas, Tunja, Colombia. Email: fabio.blanco01@uptc.edu.co

José M. Merigó. University of Chile, Department of Management Control and Information Systems, School of Economics and Business, Santiago de Chile, Chile. Email: jmerigo@fen.uchile.cl

 

The induced ordered weighted average is an averaging aggregation operator that provides a parameterized family of aggregation operators between the minimum and the maximum. This paper presents a new operator that takes into the same formulation the IOWA operator and the Bonferroni means. This new operator is called Bonferroni Induced Ordered Weighted Averaging-Weighted Average (BON-IOWAWA) operator. The main advantage of this approach is the possibility of reordering the results according to complex ranking processes based on order inducing variables. The article also considers the applicability of the new approach in the decision-making process in the selection of investment.

 

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ON PERFORMANCE FEASIBILITY AND SERVICE QUALITY OF E-COMMERCE MARKETING BASED ON FUZZY LOGIC

Ridhima Mehta. Jawaharlal Nehru University, School of Computer and Systems Sciences. India. Email: mehtar1989@gmail.com

 

In the evolving era of digitization, the success of the modern e-commerce industry is measured by several key performance metrics. These performance indicators determine its efficiency and overall service quality in online business marketing. In this paper, we investigate the feasibility performance of e-commerce based on assessment of multiple qualitative parameters influencing the behaviour of online consumers. The attributes considered for evaluating the proficiency of e-commerce platforms and improving their service quality in the highly competitive global market include setup and operational cost, scalability, execution time, and resources accessibility. These multiple independent parameters with different units and range of values exhibit implicit imprecision and uncertainties, which can be effectively handled by multivariate fuzzy logic modeling. For a given dataset application of the presented fuzzy multi-criteria decision making framework, simulation results are used to compute the absolute error, root mean square error and normalized error in the estimation of fuzzy output variable of feasibility, thereby validating the proposed model. Furthermore, numerical results and analyses depict that our model offers substantial performance enhancement in terms of achieving higher accuracy than other estimation methodologies existing in literature with equivalent dataset size.

 

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