Economic Analysis: Theory and Practice
 

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Applying the non-numeric data statistics methods to analyze objects: The Moscow Oblast municipality case

Vol. 17, Iss. 10, OCTOBER 2018

PDF  Article PDF Version

Received: 30 July 2018

Received in revised form: 15 August 2018

Accepted: 28 August 2018

Available online: 31 October 2018

Subject Heading: MATHEMATICAL METHODS AND MODELS

JEL Classification: C02, С82, O18, R11

Pages: 1962–1980

https://doi.org/10.24891/ea.17.10.1962

Lychagina T.A. Joint Institute for Nuclear Research, Dubna, Moscow Oblast, Russian Federation
lychagina@jinr.ru

ORCID id: not available

Pakhomova E.A. Dubna State University, Dubna, Moscow Oblast, Russian Federation
pakhomova.ea@phystech.edu

ORCID id: not available

Rozhkova O.V. Dubna State University, Dubna, Moscow Oblast, Russian Federation
olga_r2006@mail.ru

ORCID id: not available

Starostin E.A. Dubna State University, Dubna, Moscow Oblast, Russian Federation
starostinudjin1@mail.ru

ORCID id: not available

Subject The article addresses the estimation of social and economic activity of territories as parties to competitive relationships.
Objectives The aim is to construct an integral index to assess the degree of area's attractiveness based on non-numeric data statistics methods.
Methods The choice of research techniques is dictated by the specifics of socio-economic problems. To find solutions often involves non-numeric data that are highly dependent on the scope of activities and subjective estimation. We employ expert estimations and the fuzzy set theory tools using the results of the survey for the Dubna State University graduates.
Results We selected socio-economic indicators for the Moscow Oblast for 2015–2016 and developed a sequential algorithm that includes stages of processing the expert opinions through paired comparison, rationing, ranking, system of weights determination, membership function construction and integral index calculation for each year. The obtained result shows that for the two analyzed years the integral indices of the Lyubertsy region's attractiveness were not high.
Conclusions The findings confirm the current ambiguous socio-economic situation in the region, which indicates the consistency of results obtained with the help of fuzzy set tools. The materials and conclusions of the study are an example of implementing new information technologies to manage the region. This expands the boundaries of practical use of the proposed tools.

Keywords: expert estimation, pairwise comparison, Kemeny median, fuzzy set, integral index

References:

  1. Kleiner G.B. [Mathematical modeling in economics and economic theory]. Ekonomika i matematicheskie metody = Economics and Mathematical Methods, 2001, vol. 37, no. 3, pp. 111–127. (In Russ.)
  2. Orlov A.I. Nechislovaya statistika [Non-numeric data statistics]. Moscow, MZ-Press Publ., 2004, 513 p.
  3. Zadeh L. Ponyatie lingvisticheskoi peremennoi i ee primenenie k prinyatiyu priblizhennykh reshenii [The Concept of a Linguistic Variable and Its Application to Approximate Reasoning]. Moscow, Mir Publ., 1976, 167 p.
  4. Kaufmann A. Vvedenie v teoriyu nechetkikh mnozhestv [Introduction à la théorie des sous-ensembles flous: à lusage des ingénieurs]. Moscow, Radio i svyaz' Publ., 1982, 432 p.
  5. Orlovskii S.A. Problemy prinyatiya reshenii pri nechetkoi iskhodnoi informatsii [Problems of decision-making with fuzzy initial information]. Moscow, Radio i svyaz' Publ., 1981, 286 p.
  6. Piegat A. Nechetkoe modelirovanie i upravlenie [Fuzzy Modeling and Control]. Moscow, BINOM. Laboratoriya znanii Publ., 2013, 798 p.
  7. Pakhomova E.A., Kharcheva T.S., Sharkova T.S. [A comprehensive analysis of the socio-economic situation of municipal districts on the basis of economic-mathematical tools: Evidence from the Moscow oblast]. Natsional'nye interesy: prioritety i bezopasnost' = National Interests: Priorities and Security, 2016, vol. 12, iss. 9, pp. 4–17. URL: Link (In Russ.)
  8. Yarushkina N.G. Osnovy teorii nechttkikh i gibridnykh sistem [Fundamentals of the theory of fuzzy and hybrid systems]. Moscow, Finansy i statistika Publ., 2004, 320 p.
  9. Altunin A.E., Semukhin M.V. Modeli i algoritmy prinyatiya reshenii v nechetkikh usloviyakh [Models and algorithms of decision-making in fuzzy environment]. Tyumen, TSU Publ., 2000, 352 p.
  10. Bondarenko P.V., Fokina E.A., Trukhlyaeva A.A. [Application of the theory of fuzzy sets for assessment of the quality of life population of the region]. Fundamental'nye issledovaniya = Fundamental Research, 2015, no. 11-5, pp. 967–971. (In Russ.)
  11. Kemeny J., Snell L.J. Kiberneticheskoe modelirovanie: nekotorye prilozheniya [Mathematical Models in the Social Sciences]. Moscow, Sovetskoe radio Publ., 1972, 192 p.
  12. Biryuleva E.P., Lychagina T.A., Pakhomova E.A., Chudina E.V. [Methods of applied statistics to solve the problems of High School on the example of the University of Dubna and the Moscow region]. Audit i finansovyi analiz = Audit and Financial Analysis, 2009, no. 4, pp. 115–148. (In Russ.)
  13. Gol'ts G.G., Gol'ts G.A., Kartavenko G.G. [Methods to convert an array of socio-economic indicators at the federal and regional level]. Izvestiya RAN. Seriya Geograficheskaya, 2008, no. 2, pp. 13–26. (In Russ.)
  14. Shtovba S.D. Vvedenie v teoriyu nechetkikh mnozhestv i nechetkuyu logiku [Introduction to the theory of fuzzy sets and the fuzzy logic]. Vinnitsa, UNIVERSUM-Vinnitsa Publ., 2001, 756 p.
  15. Fishburn P. Teoriya poleznosti dlya prinyatiya reshenii [Utility Theory for Decision Making]. Moscow, Nauka Publ., 1978, 352 p.
  16. Borisov A.N., Alekseev A.V., Merkur'eva G.V. Obrabotka nechetkoi informatsii v sistemakh prinyatiya reshenii [Processing the fuzzy information in decision making systems]. Moscow, Radio i svyaz' Publ., 1989, 304 p.
  17. Diligenskii N.V., Dymova L.G., Sevast'yanov P.V. Nechetkoe modelirovanie i mnogokriterial'naya optimizatsiya proizvodstvennykh sistem v usloviyakh neopredelennosti: tekhnologiya, ekonomika, ekologiya [Fuzzy modeling and multicriteria optimization of production systems in conditions of uncertainty: Technology, economy, ecology]. Moscow, Mashinostroenie Publ., 2004, 397 p.
  18. Pakhomova E.A. Metodologicheskie osnovy vliyaniya vuza na effektivnost' regional'nogo razvitiya [Methodological framework for assessing impact of a higher school on effectiveness of regional development]. Moscow, MEILER Publ., 2010, 725 p.
  19. Averkin A.N., Batyrshin I.Z., Blishun A.F. Nechetkie mnozhestva v modelyakh upravleniya i iskusstvennogo intellekta [Fuzzy sets in models of management and artificial intelligence]. Moscow, Nauka Publ., 1986, 312 p.
  20. Getmantsev A.A., Somina I.V. [Fuzzy set theory as a mathematical assessment innovation capacity of enterprises]. Sovremennye problemy nauki i obrazovaniya, 2013, no. 5. (In Russ.) URL: Link
  21. Nedosekin A.O. Nechetkie mnozhestva i finansovyi menedzhment [Fuzzy sets and financial management]. Moscow, Audit i finansovyi analiz Publ., 2003, 184 p.
  22. Rotshtein A.P., Katel'nikov D.I. [Identification of nonlinear dependence by a fuzzy knowledge base]. Kibernetika i sistemnyi analiz = Cybernetics and Systems Analysis, 1998, no. 5, pp. 53–61. (In Russ.)

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