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Intelligent Systems and Applications pp 109–132Cite as

Using Fuzzy PROMETHEE to Select Countries for Developmental Aid

Part of the Studies in Computational Intelligence book series (SCI,volume 650)

Abstract

Wealthy nations continue to demonstrate their unwavering support to improving conditions and the general well-being of poor countries in spite of the recent economic crises. However, as developmental aid relatively shrinks, both Aid donors and recipient countries have shown keen interest in methodologies used in evaluating developmental assistance programs. Evaluation of aid programs is seen as a complex task mainly because of the several non-aid factors that tend to affect overall outcomes. Adding to the complexity are the subjective sets of criteria used in Aid evaluations programs. This paper proposes a two stage framework of fuzzy TOPSIS and sensitivity analysis to demonstrate how aid-recipient countries can be evaluated to deepen transparency, fairness, value for money and sustainability of such aid programs. Using the Organisation for Economic Co-operation and Development (OECD) set of subjective criteria for evaluating aid programs; a numerical example pre-defined by linguistic terms parameterized by triangular fuzzy numbers is provided to evaluate aid programs. Fuzzy PROMETHEE is used in the first stage to evaluate and rank aid-recipients followed by a comparative analysis with Fuzzy VIKOR and Fuzzy TOPSIS to ascertain an accurateness of the method used. A sensitivity analysis is further added that anticipates possible influences from lobbyists and examines the effect of that bias in expert ratings on the evaluation process. The result shows a framework that can be employed in evaluating aid effectiveness of recipient-countries.

Keywords

  • Developmental aid programs
  • Fuzzy set theory
  • Organization for Economic Cooperation and Development (OECD)
  • Fuzzy PROMETHEE
  • Fuzzy VIKOR
  • Fuzzy TOPAIA
  • Fuzzy MCDM
  • Evaluation
  • Sensitivity analysis

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References

  1. OECD International Development Statistics. OECD Press, France (2014)

    Google Scholar 

  2. Kharas, K.: Measuring Aid Effectiveness Effectively: A Quality of Official Development Assistance Index. Brookings Institution Press, Washington (2011). Available at: http://www.brookings.edu/research/opinions/2011/07/26-aid-effectiveness-kharas. Accessed 10 December 2014

  3. Howes, S.: A framework for understanding aid effectiveness determinants, strategies and tradeoffs. Asia Pac. Policy Stud. 1(1), 58–72 (2014)

    CrossRef  MathSciNet  Google Scholar 

  4. Dalgaard, C.J., Hansen, H.: Evaluating Aid Effectiveness in the Aggregate: A Critical Assessment of the Evidence. University Library of Munich, Germany (2010)

    Google Scholar 

  5. Deaton, A.: Instruments, randomization, and learning about development. J. Econ. Lit. 424–455 (2010)

    Google Scholar 

  6. Arndt, C., Jones, S., Tarp, F.: Aid, growth, and development: have we come full circle? J. Glob. Dev. 1(2) (2010)

    Google Scholar 

  7. Doucouliagos, H., Paldam, M.: The aid effectiveness literature: the sad results of 40 years of research. J. Econ. Surv. 23(3), 433–461 (2009)

    CrossRef  Google Scholar 

  8. Minoiu, C., Reddy, S.G.: Development aid and economic growth: a positive long-run relation. Q. Rev. Econ. Financ. 50(1), 27–39 (2010)

    CrossRef  Google Scholar 

  9. Morrison, K.M.: As the World Bank turns: determinants of IDA lending in the cold war and after. Bus. Pol. 13(2) (2011)

    Google Scholar 

  10. Claessens, S., Cassimon, D., Van Campenhout, B.: Evidence on changes in aid allocation criteria. World Bank Econ. Rev. 23(2), 185–208 (2009)

    CrossRef  Google Scholar 

  11. Zadeh, A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    CrossRef  MathSciNet  MATH  Google Scholar 

  12. Afful-Dadzie, E., Nabareseh, S., Afful-Dadzie, A., Oplatková, Z.K.: A fuzzy TOPSIS framework for selecting fragile states for support facility. Quality and Quantity, pp. 1–21 (2014). doi:10.1007/s11135-014-0062-3

    Google Scholar 

  13. Krohling, R.A., Campanharo, V.C.: Fuzzy TOPSIS for group decision making: a case study for accidents with oil spill in the sea. Expert Syst. Appl. 38(4), 4190–4197 (2011)

    CrossRef  Google Scholar 

  14. Vincke, J.P., Brans, Ph: A preference ranking organization method. The PROMETHEE method for MCDM. Manag. Sci. 31(6), 647–656 (1985)

    CrossRef  MathSciNet  MATH  Google Scholar 

  15. Ying-Hsiu, C., Tien-Chin, W., Chao-Yen, W.: Strategic decisions using the fuzzy PROMETHEE for IS outsourcing. Expert Syst. Appl. 38(10), 13216–13222 (2011)

    CrossRef  Google Scholar 

  16. Amaral, T.M., Ana, P.C.C.: Improving decision-making and management of hospital resources: An application of the PROMETHEE II method in an Emergency Department. Oper. Res. Health Care 3(1), 1–6 (2014)

    CrossRef  Google Scholar 

  17. Elevli, B.: Logistics freight center locations decision by using Fuzzy-PROMETHEE. Transport 29(4), 412–418 (2014)

    CrossRef  Google Scholar 

  18. Yi, P., Kou, G., Li, J.: A fuzzy promethee approach for mining customer reviews in chinese. Arab. J. Sci. Eng. 39(6), 5245–5252 (2014)

    CrossRef  Google Scholar 

  19. Shadman, R.M., Rahimi, S., Beglou, M.J.: PROMETHEE II and fuzzy AHP: an enhanced GIS-based landslide susceptibility mapping. Nat. Hazards 73(1), 77–95 (2014)

    CrossRef  Google Scholar 

  20. Xiaojuan, T., Liu, X., Wang, L.: An improved PROMETHEE II method based on Axiomatic Fuzzy Sets. Neural Comput. Appl. 25(7–8), 1675–1683 (2014)

    Google Scholar 

  21. Ting-Yu, C.: A PROMETHEE-based outranking method for multiple criteria decision analysis with interval type-2 fuzzy sets. Soft Comput. 18(5), 923–940 (2014)

    CrossRef  MATH  Google Scholar 

  22. Sonia, H., Halouani, N..: Hesitant-fuzzy-promethee method. In: 2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO), pp. 1–6. IEEE, New York (2013)

    Google Scholar 

  23. Wei-xiang, L., Li, B.: An extension of the Promethee II method based on generalized fuzzy numbers. Expert Syst. Appl. 37(7), 5314–5319 (2010)

    CrossRef  Google Scholar 

  24. Afful-Dadzie, A., Afful-Dadzie, E., Nabareseh, S., Oplatková, Z.K.: Tracking progress of African Peer Review Mechanism (APRM) using fuzzy comprehensive evaluation method. Kybernetes 43(8), 1193–1208 (2014)

    CrossRef  Google Scholar 

  25. Yoon, K.P., Hwang, C.L.: Multiple Attribute Decision Making: An Introduction, vol. 104. Sage Publications, New York (1995)

    Google Scholar 

  26. Afful-Dadzie, E., Nabareseh, S., Oplatková, Z. K., Klímek, P.: Model for assessing quality of online health information: a fuzzy VIKOR based method. J. Multi-Criteria Decis. Anal. (2015)

    Google Scholar 

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Acknowledgments

This work was supported by GACR P103/15/06700S, NPU I No. MSMT-7778/2014, CEBIA-Tech No. CZ.1.05/2.1.00/03.0089. It was also supported by Internal Grant Agency of TBU under the project Nos. IGA/FAI/2015/054, IGA/FaME/2016/019 and IGA/FaME/2015/023.

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Authors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlin, Czech Republic

    Eric Afful-Dadzie & Zuzana Komínková Oplatková

  2. Faculty of Management and Economics, Tomas Bata University in Zlin, Zlin, Czech Republic

    Stephen Nabareseh & Peter Klimek

Authors
  1. Eric Afful-Dadzie
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  2. Stephen Nabareseh
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  3. Zuzana Komínková Oplatková
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  4. Peter Klimek
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Corresponding author

Correspondence to Stephen Nabareseh .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Engineering, University of Ulster at Jordanstown, Newtownabbey, United Kingdom

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Rahul Bhatia

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Afful-Dadzie, E., Nabareseh, S., Oplatková, Z.K., Klimek, P. (2016). Using Fuzzy PROMETHEE to Select Countries for Developmental Aid. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. Studies in Computational Intelligence, vol 650. Springer, Cham. https://doi.org/10.1007/978-3-319-33386-1_6

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  • DOI: https://doi.org/10.1007/978-3-319-33386-1_6

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