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FUZZY ECONOMIC REVIEW

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

CHANGING STRATEGIES USING FUZZY SETS RELATIONS – A DIAGNOSTICS APPROACH

The traditional Second Order Change (SOC) model contains a set of equations that allow us to determine the possible strategy change using probability. In this article, we propose a simpler heuristic model, based on fuzzy numbers, to diagnose the organization’s personnel, and determine whether the strategy needs to be changed. The proposal starts with the diagnostics provided by experts in favor or against the new strategy. These experts are included in the SOC model through fuzzy set relations that indicate the preference to the current strategy. That strategy decreases tension and avoids early changes as a tactical strategy. Finally, we must prove there is a significant SOC using the Hamming distance.

Additional Info

  • Authors 1 R. Chavez. Facultad de Químico Farmacobiología, ININEE. Email: pintachavez@gmail.com
  • Authors 2 F. González. Facultad de Ciencias Contables y Administrativas, Universidad Michoacana, Mexico, Email: fsantoyo@umich.mx
  • Authors 3 B. Flores. Facultad de Ciencias Contables y Administrativas, Universidad Michoacana, Mexico, Email: betyf@umich.mx
  • Authors 4 J.J. Flores. División de Estudios de Posgrado, Facultad de Ingeniería Eléctrica, Universidad de Michoacana, Mexico, Email: juanf@umich.mx
  • review FER_V22_N2_2017_P3.pdf
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BONFERRONI DISTANCES WITH HYBRID WEIGHTED DISTANCE AND IMMEDIATE WEIGHTED DISTANCE

The aim of the paper is to develop new aggregation operators using Bonferroni means, ordered weighted averaging (OWA) operators and some measures of distance. We introduce the Bonferroni Hybrid-weighted distance (BON-HWD), and Bonferroni distances with OWA operators and weighted averages (BON-IWOWAD). The main advantages of using these operators are that they allow the consideration of different aggregations contexts to be considered and multiple-comparison between each argument and distance measures in the same formulation. We develop a mathematical application to show the versatility of new models. Finally, this new group of family distances can be used in a wide range of management and economic fields.

Additional Info

  • Authors 1 F. Blanco-Mesa. School of Business Administration, Faculty of Economic and Administrative Science, Pedagogical and Technological University of Colombia, North Central Avenue 39-115, 150001, Tunja, Boyacá, Colombia
  • Authors 2 J.M. Merigó. Department of Management Control and Information Systems, School of Economics and Business, University of Chile, Av. Diagonal Paraguay 257, 8330015, Santiago Chile.
  • review FER_V22_N2_2017_P2.pdf
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FUGA, A FUZZY GREEDY ALGORITHM FOR REDISTRICTING IN MEXICO

Redistricting is the redrawing of the boundaries of legislative districts for electoral purposes in such a way that the generated districts fulfill federal and state requirements such as contiguity, population equality and compactness. Redistricting is a multi-objective problem which has been proved to be NP-hard. In Mexico, the redistricting process has been done using an aggregation function, considering a weighted sum of the objectives. However, if different weighting factors are used then a set of diverse, high quality solutions can be generated and a new problem arises: which solution should be implemented? In this paper we propose a novel alternative, called FuGA, to select the best solution for the redistricting problem using a fuzzyfication of the objective function. The proposed algorithm was applied in a real case, and its solutions were compared with those produced by VIKOR, a well-known algorithm for decision making. FuGA showed a better performance since it was able to avoid the selection of dominated solutions.

Additional Info

  • Authors 1 S.G. de-los-Cobos-Silva. Universidad Autónoma Metropolitana-Iztapalapa, Departamento de Ingeniería Eléctrica. Av. San Rafael Atlixco 186, Col. Vicentina, Del. Iztapalapa, México D.F., C.P. 09340, cobos@xanum.uam.mx
  • Authors 2 M. A. Gutiérrez-Andrade. Universidad Autónoma Metropolitana-Iztapalapa, Departamento de Ingeniería Eléctrica. Av. San Rafael Atlixco 186, Col. Vicentina, Del. Iztapalapa, México D.F., C.P. 09340, gamma@xanum.uam.mx
  • Authors 3 E. A. Rincón-García. Universidad Autónoma Metropolitana-Azcapotzalco, Departamento de Sistemas, Av. San Pablo 180, Colonia Reynosa Tamaulipas, Del. Azcapotzalco, México D.F., C.P. 02200, rigaeral@correo.azc.uam.mx
  • Authors 4 R. A. Mora-Gutiérrez. Universidad Autónoma Metropolitana-Azcapotzalco, Departamento de Sistemas, Av. San Pablo 180, Colonia Reynosa Tamaulipas, Del. Azcapotzalco, México D.F., C.P. 02200, mgra@correo.azc.uam.mx
  • Authors 5 P. Lara-Velázquez. Universidad Autónoma Metropolitana-Iztapalapa, Departamento de Ingeniería Eléctrica. Av. San Rafael Atlixco 186, Col. Vicentina, Del. Iztapalapa, México D.F., C.P. 09340, plara@xanum.uam.mx
  • Authors 6 A. Ponsich. Universidad Autónoma Metropolitana-Azcapotzalco, Departamento de Sistemas, Av. San Pablo 180, Colonia Reynosa Tamaulipas, Del. Azcapotzalco, México D.F., C.P. 02200, aspo@correo.azc.uam.mx
  • review FER_V22_N2_2017_P1.pdf
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Similarity classifier with weighted ordered weighted averaging operator

In this paper we present a similarity-based classifier that utilizes a weighted ordered weighted averaging (WOWA) operator in the aggregation of infor-mation. The aggregation process used in the WOWA operator is studied and tested with five different Regular Increasing Monotonic (RIM) weight generators or quantifiers. The proposed approach is tested with five real-world data sets. For comparison purposes the obtained results are compared to results from two previously introduced classifiers.

The proposed new classifier showed comparatively improved performance over for all studied data sets. The results indicate that there are benefits in using a WOWA operator in similarity classifiers.

 

Additional Info

  • Authors 1 O. Kurama. School of engineering sciences, Lappeenranta University of Technology, P.O.
  • Authors 2 P. Luukka. Dept. of Mathematics, Makerere University, P.O. Box 7062, Kampala, Uganda.
  • Authors 3 M. Collan. School of Business and Management, Lappeenranta University of Technology, P.O
  • review FER_V21_N2_2016_P5.pdf
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Real option approach for comparing lifetime costs of alternative diabetes type i treatment methods

This paper compares with cash flow simulation real option valuation method the costs of treating diabetes type 1 (T1D) with the commonly used multiple daily injection (MDI) method and the novel method of combining insulin pump and continuous glucose monitoring (CSII + CGM) into a hybrid closed-loop system. Both direct and indirect costs of T1D treatment are considered. Daily basic treatment costs are twice as high with CSII + CGM in comparison with the MDI method. However, overall lifetime costs with CSII + CGM are approximately only one third in comparison with the MDI method. This is due to expensive indirect complication costs related to insufficient therapeutic control of many diabetics using MDI, while advanced CSII+CGM users can avoid these complications nearly completely. At the same time, quality of life of type 1 diabetics improves significantly when using advanced CSII+CGM systems.

 

Additional Info

  • Authors 1 Tero Haahtela. Department of Industrial Engineering and Management, Aalto University. PO Box 15500, FI- 00076, Aalto, Finland.
  • review FER_V21_N2_2016_P4.pdf
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Mixed models for risk aversion, optimal saving, and prudence

The models of this paper refer to mixed risk situations: one parameter is a fuzzy number and the other is a random variable. Three notions of mixed expected utility are proposed as a mathematical basis of these models. The results of the paper describe risk aversion and prudence of an agent in front of a risk situation with mixed parameters and the changes of optimal saving as an effect of mixed risk.

 

Additional Info

  • Authors 1 Irina Georgescu. Academy of Economic Studies.
  • Authors 2 Jani Kinnunen. Institute for Advanced Management Systems Research.
  • review FER_V21_N2_2016_P3.pdf
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Fuzzy grouping variables in economic analysis. a pilot study of a verification of a normative model for r&d alliances

Many of the investments decisions facing with uncertainty can be characterized as real options problems. There is evidence of deviation from the predictions derived using such normative models. The proposed research sheds light on the importance of integrating normative models with experimental methods in order to predict and explain such cognitive limitations, in the particular context of R&D alliances. The focus is on appropriate validation of such models on experimental data. We propose a simple design starting from a real options model dealing with alliance timing decisions. We present the decision makers with risky choices formulated as abstract gambling decisions in order to assess their risk propensity and to validate the normative predictions of the model. This paper introduces the basic principles of the use of fuzzy grouping variables in economic analysis. On the survey data gathered to validate the predictive power of the presented model we show that fuzzy sets can be effectively used to partition the experimental data into fuzzy subsets for model verification (e.g. when subgroups cannot be defined in a crisp way). We compare the validation of the model on a full data set with a “refocused” validation on a fuzzy subset of the original sample.

 

Additional Info

  • Authors 1 A. Morreale. School of Business and Management, Lappeenranta University of Technology Skinnarilankatu 34, 53850 Lappeenranta, Finland.
  • Authors 2 J. Stoklasa. Palacký University, Olomouc, Faculty of Arts, Department of Applied Economics Křížkovského 8, 771 47 Olomouc, Czech Republic.
  • Authors 3 T. Talášek. Palacký University, Olomouc, Faculty of Arts, Department of Applied Economics Křížkovského 8, 771 47 Olomouc, Czech Republic.
  • review FER_V21_N2_2016_P2.pdf
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Simulation decomposition: new approach for better simulation analysis of multi-variable investment projects

This paper presents a new method to enhance simulation-based analysis of complex investments that contain multi-variable uncertainty. The method is called “simulation decomposition”. Typically the result of simulation-based investment analysis is in the form of histogram distributions - here we propose a method for first classifying the possible outcomes of selected uncertain variables into states and then using combinations of the created states in the decomposition of the simulated distribution into a number of sub-distributions. The sub-distributions that can be matched to state-combinations of the variables contain relevant actionable information that helps managers in decision-making with regards to the studied investments.

A numerical illustration of a renewable energy investment is used to demonstrate the usability, the enhanced analytical power, and the intuitively understandable benefits that can be reached by using the simulation decomposition method. The proposed method is generally usable and can be utilized independent of the investment context.

 

Additional Info

  • Authors 1 M. Kozlova. School of Business and Management, Lappeenranta University of Technology, Skinnarilankatu 34, 53850 Lappeenranta, Finland.
  • Authors 2 M. Collan. School of Business and Management, Lappeenranta University of Technology, Skinnarilankatu 34, 53850 Lappeenranta, Finland.
  • Authors 3 P. Luukka. School of Business and Management, Lappeenranta University of Technology, Skinnarilankatu 34, 53850 Lappeenranta, Finland.
  • review FER_V21_N2_2016_P1.pdf
Read more...
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