Greco, Salvatore and Ishizaka, Alessio and Tasiou, Menelaos and Torrisi, Gianpiero (2019): The Ordinal Input for Cardinal Output Approach of Non-compensatory Composite Indicators: The PROMETHEE Scoring Method.
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Abstract
Despite serious threats as to their soundness, the adoption of composite indicators is constantly growing alongside their popularity, especially when it comes to their adoption in policy-making exercises. This study presents a robust non-compensatory approach to construct composite indicators mainly based, at least with respect to the basic ideas, on the classic Borda scoring procedure. The non- compensatory indicators we are proposing can be seen as aggregation of ordinal non-compensatory preferences between considered units supplying a numerical cardinal comprehensive evaluation. For this reason we define our methodology, the ordinal input for cardinal output non-compensatory approach for composite indicators. To take into account hesitation, imprecision and ill-determination in defining preference relations with respect to the elementary indices, we adopt the PROMETHEE methods, whose net flow score can be seen as an extension to the fuzzy preferences of the Borda score. Moreover, we systematically deal with robustness of the results with respect to weighting and parameters such as indifference and preference thresholds, permitting to define preference relations of elementary indices. In this regard, we couple PROMETHEE methods with the recently proposed σ−μ approach, which permits to explore the whole domain of feasible preference parameters mentioned above, giving a synthetic representation of the distribution of the values assumed by the composite indicators in terms of mean, μ, and standard deviation, σ. μ and σ are also used to define a comprehensive overall composite indicator. Finally, we enrich the results of this analysis with a set of graphical visualizations based on principal component analysis applied to the PROMETHEE methods with the GAIA technique, providing better understanding of the outcomes of our approach. To illustrate its assets, we provide a case study of inclusive development evaluation, based on the data of the homonymous report produced by the World Economic Forum.
Item Type: | MPRA Paper |
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Original Title: | The Ordinal Input for Cardinal Output Approach of Non-compensatory Composite Indicators: The PROMETHEE Scoring Method |
Language: | English |
Keywords: | Multiple-Criteria Analysis, Composite Indicators, Non-compensatory Aggregation, PROMETHEE method, Inclusive Development Index |
Subjects: | C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C43 - Index Numbers and Aggregation C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C44 - Operations Research ; Statistical Decision Theory F - International Economics > F6 - Economic Impacts of Globalization > F63 - Economic Development |
Item ID: | 95816 |
Depositing User: | Dr Menelaos Tasiou |
Date Deposited: | 12 Sep 2019 11:42 |
Last Modified: | 26 Sep 2019 19:00 |
References: | Angilella, S., Catalfo, P., Corrente, S., Giarlotta, A., Greco, S., and Rizzo, M. (2018). Robust sustainable development assessment with composite indices aggregating interacting dimensions: The hierarchical-SMAA-Choquet integral approach. Knowledge-Based Systems, 158:136–153. Angilella, S., Corrente, S., and Greco, S. (2015). Stochastic multiobjective acceptability analysis for the choquet integral preference model and the scale construction problem. European Journal of Operational Research, 240(1):172–182. Angilella, S., Corrente, S., Greco, S., and Słowiński, R. (2016). Robust ordinal regression and stochastic multiobjective acceptability analysis in multiple criteria hierarchy process for the choquet integral preference model. Omega, 63:154–169. Antanasijević, D., Pocajt, V., Ristić, M., and Perić-Grujić, A. (2017). A differential multi-criteria analysis for the assessment of sustainability performance of european countries: Beyond country ranking. Journal of Cleaner Production, 165:213–220. Arcidiacono, S. G., Corrente, S., and Greco, S. (2018). GAIA-SMAA-PROMETHEE for a hierarchy of interacting criteria. European Journal of Operational Research, 270(2):606–624. Arrow, K. J. and Raynaud, H. (1986). Social choice and multicriterion decision-making. MIT Press, Cambridge. Artzner, P., Delbaen, F., Eber, J.-M., and Heath, D. (1999). Coherent measures of risk. Mathematical finance, 9(3):203–228. Attardi, R., Cerreta, M., Sannicandro, V., and Torre, C. M. (2018). Non-compensatory composite indicators for the evaluation of urban planning policy: The land-use policy efficiency index (lupei). European Journal of Operational Research, 264(2):491–507. Bandura, R. (2011). Composite Indicators and Rankings: Inventory 2011. Technical report, New York: Office of Development Studies, United Nations Development Programme (UNDP). Bell, D. E. (1982). Regret in decision making under uncertainty. Operations research, 30(5):961–981. Billaut, J. C., Bouyssou, D., and Vincke, P. (2010). Should you believe in the Shanghai ranking?, Scientometrics, 84(1):237–263. Black, D. (1976). Partial justification of the Borda count. Public Choice, 28(1):1–15. Booysen, F. (2002). An overview and evaluation of composite indices of development. Social Indicators Research, 59(2):115–151. Borda, J. C. (1781). Histoire de I’Academie Royale des Sciences. 1781. Bouyssou, D. (1986). Some remarks on the notion of compensation in mcdm. European Journal of Operational Research, 26(1):150–160. Bouyssou, D. and Vansnick, J.-C. (1986). Noncompensatory and generalized noncompensatory preference structures. Theory and decision, 21(3):251–266. Brans, J.-P. and De Smet, Y. (2016). PROMETHEE Methods. In Greco, S., Ehrgott, M., and Figueira, J., editors, Multiple Criteria Decision Analysis: State of the Art Surveys, pages 187–219. Springer New York, New York, NY. Brans, J.-P. and Mareschal, B. (1995). The PROMETHEE VI procedure: how to differentiate hard from soft multicriteria problems. Journal of Decision Systems, 4(3):213–223. Brans, J. P. and Vincke, P. (1985). Note—A Preference Ranking Organisation Method. Management Science, 31(6):647–656. Brans, J.-P., Vincke, P., and Mareschal, B. (1986). How to select and how to rank projects: The PROMETHEE method. European journal of operational research, 24(2):228–238. Burgass, M. J., Halpern, B. S., Nicholson, E., and Milner-Gulland, E. J. (2017). Navigating uncertainty in environmental composite indicators. Ecological Indicators, 75:268–278. Corrente, S., Figueira, J. R., and Greco, S. (2014). The SMAA-PROMETHEE method. European Journal of Operational Research, 239(2):514–522. Corrente, S., Greco, S., and Słowiński, R. (2019). Robust Ranking of Universities Evaluated by Hierarchical and Interacting Criteria. In Huber, S., Geiger, M., and de Almeida, A., editors, Multiple Criteria Decision Making and Aiding. International Series in Operations Research & Management Science, chapter 5, pages 145–192. Springer, Cham. Costanza, R., Hart, M., Posner, S., and Talberth, J. (2009). Beyond GDP: The Need for New Measures of Progress. Pardee Center for the Study of the Longer-Range Future, Boston. Dasgupta, P. and Weale, M. (1992). On measuring the quality of life. World development, 20(1):119–131. De Mare, G., Granata, M. F., and Nesticò, A. (2015). Weak and strong compensation for the prioritization of public investments: multidimensional analysis for pools. Sustainability, 7(12):16022–16038. Figueira, J. R., Greco, S., Roy, B., and Slowinski, R. (2013). An Overview of ELECTRE Methods and their Recent Extensions. Journal of Multi-Criteria Decision Analysis, 20(1-2):61–85. Figueira, J. R., Mousseau, V., and Roy, B. (2016). ELECTRE methods. In Greco, S., Ehrgott, M., and Figueira, J., editors, Multiple criteria decision analysis: State of the art surveys, pages 155–185. Springer. Fishburn, P. C. (1974). Lexicographic orders, utilities and decision rules: A survey. Management science, 20(11):1442–1471. Fishburn, P. C. (1975). Axioms for lexicographic preferences. The Review of Economic Studies, 42(3):415–419. Fishburn, P. C. (1976). Noncompensatory preferences. Synthese, 33(1):393–403. Fishburn, P. C. (2013). The foundations of expected utility, volume 31. Springer Science & Business Media. Greco, S., Ehrgott, M., and Figueira, J. (2016). Multiple Criteria Decision Analysis: State of the Art Surveys. International Series in Operations Research & Management Science. 2nd edition, New York: Springer. Greco, S., Ishizaka, A., Matarazzo, B., and Torrisi, G. (2018). Stochastic multi-attribute acceptability analysis (SMAA): an application to the ranking of Italian regions, Regional Studies, 52(4), pp.585-600. Greco, S., Ishizaka, A., Resce, G., and Torrisi, G. (2017). Is the grass always greener on the other side of the fence? composite index of well-being taking into account the local relative appreciations in better life index, MPRA paper 82718. Greco, S., Ishizaka, A., Tasiou, M., and Torrisi, G. (2019a). On the methodological framework of composite indices: A review of the issues of weighting, aggregation, and robustness. Social Indicators Research, 141(1):61–94. Greco, S., Ishizaka, A., Tasiou, M., and Torrisi, G. (2019b). Sigma-mu efficiency analysis: A methodology for evaluating units through composite indicators. European Journal of Operational Research, 278(3):942–960. Grupp, H. and Schubert, T. (2010). Review and new evidence on composite innovation indicators for evaluating national performance. Research Policy, 39(1):67–78. Hubinont, J.-P. (2016). SMAA-GAIA: a complementary tool of the SMAA-PROMETHEE method. International Journal of Multicriteria Decision Making, 6(3):237–246. Ishizaka, A. and Nemery, P. (2013). Multi-Criteria Decision Analysis: Methods and Software. Chichester, United Kingdom: John Wiley & Sons. Ishizaka, A., Siraj, S., and Nemery, P. (2016). Which energy mix for the uk (united kingdom)? an evolutive descriptive mapping with the integrated gaia (graphical analysis for interactive aid)–ahp (analytic hierarchy process) visualization tool. Energy, 95:602–611. Jorion, P. (2000). Value at risk: The new benchmark for managing financial risk. McGraw–Hill, New York. Kahneman, D. and Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47:263–291. Kahneman, D. and Tversky, A. (1984). Choices, values, and frames. American Psychologist, 39:341–350. Kubiszewski, I., Costanza, R., Franco, C., Lawn, P., Talberth, J., Jackson, T., and Aylmer, C. (2013). Beyond gdp: Measuring and achieving global genuine progress. Ecological Economics, 93:57–68. Kuznets, S. (1934). National income, 1929-1932. In National Income, 1929-1932, pages 1–12. NBER. Lahdelma, R., Hokkanen, J., and Salminen, P. (1998). SMAA - Stochastic multiobjective acceptability analysis. European Journal of Operational Research, 106(1):137–143. Lahdelma, R. and Salminen, P. (2001). SMAA-2 : Stochastic Multicriteria Acceptability Analysis for Group Decision Making. Operations Research, 49(3):444–454. Loomes, G. and Sugden, R. (1982). Regret theory: An alternative theory of rational choice under uncertainty. The Economic Journal, 92(368):805–824. Marchant, T. (1998). Cardinality and the Borda score. European Journal of Operational Research, 108(2):464–472. Marchant, T. (2000). Does the borda rule provide more than a ranking? Social Choice and Welfare, 17(3):381–391. Mareschal, B. and Brans, J.-P. (1988). Geometrical representations for MCDA. European Journal of Operational Research, 34(1):69 – 77. Mazziotta, M. and Pareto, A. (2016). On a Generalized Non-compensatory Composite Index for Measuring Socio-economic Phenomena. Social Indicators Research, 127(3):983–1003. Munda, G. (2007). Social multi-criteria evaluation. Springer-Verlag, Heidelberg, New York. Munda, G. (2012). Choosing Aggregation Rules for Composite Indicators. Social Indicators Research, 109(3):337–354. Munda, G. and Nardo, M. (2009). Noncompensatory/nonlinear composite indicators for ranking countries: a defensible setting. Applied Economics, 41(12):1513–1523. Nitzan, S. and Rubinstein, A. (1981). A further characterization of Borda ranking method. Public choice, 36(1):153–158. OECD (2008). Handbook on Constructing Composite Indicators: Methodology and User Guide. OECD Publishing, Paris. Özerol, G. and Karasakal, E. (2008). A parallel between regret theory and outranking methods for multicriteria decision making under imprecise information. Theory and Decision, 65(1):45–70. Paruolo, P., Saisana, M., and Saltelli, A. (2013). Ratings and rankings: voodoo or science? Journal of the Royal Statistical Society: Series A (Statistics in Society), 176(3):609–634. Plott, C. R., Little, J. T., and Parks, R. P. (1975). Individual choice when objects have" ordinal" properties. The Review of Economic Studies, 42(3):403–413. Roberts, F. S. (1985). Measurement Theory. Cambridge University Press. Rosić, M., Pešić, D., Kukić, D., Antić, B., and Božović, M. (2017). Method for selection of optimal road safety composite index with examples from DEA and TOPSIS method. Accident Analysis & Prevention, 98:277–286. Roy, B. (1990). The outranking approach and the foundations of electre methods. In Readings in multiple criteria decision aid, pages 155–183. Springer. Saisana, M., Saltelli, A., and Tarantola, S. (2005). Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators. Journal of the Royal Statistical Society. Series A: Statistics in Society, 168(2):307–323. Samans, R., Blanke, J., Corrigan, G., and Drzeniek, M. (2017). The inclusive growth and development report 2017. In Geneva: World Economic Forum. Seiford, L. M. and Zhu, J. (2003). Context-dependent Data Envelopment Analysis: Measuring attrac- tiveness and progress. Omega, 31(5):397–408. Stiglitz, J., Sen, A. K., and Fitoussi, J.-P. (2009). The measurement of economic performance and social progress revisited: Reflections and Overview. Commission on the Measurement of Economic Performance and Social Progress, Paris. Tervonen, T. and Lahdelma, R. (2007). Implementing stochastic multicriteria acceptability analysis. European Journal of Operational Research, 178(2):500–513. Tversky, A. and Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481):453–458. Zhang, L. and Zhou, P. (2018). A non-compensatory composite indicator approach to assessing low- carbon performance, European Journal of Operational Research, 270(1), pp.352-361. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/95816 |