Evaluation of government investment using nested probabilistic linguistic preference relations based on graph theory
Abstract
Government investment, as a major government function, is closely related to national development and economic growth. It plays a key role to maximize the benefits of this fund, which requires the government to choose the optimal investment plan. Considering the complex and uncertain decision-making environment, we propose the nested probabilistic linguistic preference relation (NPLPR) based on the nested probabilistic linguistic term sets (NPLTSs), to express preference information from the qualitative and quantitative angle. According to graph theory, we define a consistency index and an acceptable consistency of NPLPR to measure the additive consistency. Based on which, we establish a novel algorithm for unacceptable consistent NPLPR to meet the acceptable consistency. Finally, projects in government investment are evaluated by the proposed decision-making method, and some comparative analyses, discussions, and implications are provided from three angles. This study provides a new perspective for scholars to make scientific and rational decisions with the help of technological and economic development in various fields.
Keyword : government investment, nested probabilistic linguistic term sets, nested probabilistic linguistic preference relation, consistency check, graph theory, cognitive decision-making
How to Cite
Wang, X., Xu, Z., & Li, H. (2022). Evaluation of government investment using nested probabilistic linguistic preference relations based on graph theory. Technological and Economic Development of Economy, 28(3), 831–853. https://doi.org/10.3846/tede.2022.16444
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Dahooie, J. H., Hosseini Dehshiri, S. J., Banaitis, A., & Binkyte-Veliene, A. (2020). Identifying and prioritizing cost reduction solutions in the supply chain by integrating value engineering and gray multi-criteria decision-making. Technological and Economic Development of Economy, 26(6), 1311–1338. https://doi.org/10.3846/tede.2020.13534
Dong, Y. C., Xu, Y. F., & Li, H. (2008). On consistency measures of linguistic preference relations. European Journal of Operational Research, 189(2), 430–444. https://doi.org/10.1016/j.ejor.2007.06.013
Dong, Y. C., Xu, Y. F., Li, H. Y., & Feng, B. (2010). The OWA-based consensus operator under linguistic representation models using position indexes. European Journal of Operational Research, 203(2), 455–463. https://doi.org/10.1016/j.ejor.2009.08.013
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Leeper, E. M., Walker, T. B., & Yang, S. C. S. (2010). Government investment and fiscal stimulus. Journal of Monetary Economics, 57(8), 1000–1012. https://doi.org/10.1016/j.jmoneco.2010.09.002
Liao, H. C., Xu, Z. S., & Zeng, X. J. (2014). Distance and similarity measures for hesitant fuzzy linguistic term sets and their application in multi-criteria decision making. Information Sciences, 271, 125–142. https://doi.org/10.1016/j.ins.2014.02.125
Liao, H. C., Xu, Z. S., & Zeng, X. J. (2015a). Hesitant fuzzy linguistic VIKOR method and its application in qualitative multiple criteria decision making. IEEE Transactions on Fuzzy Systems, 23(5), 1343–1355. https://doi.org/10.1109/TFUZZ.2014.2360556
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Mi, X. M., Liao, H. C., Liao, Y., Lin, Q., Lev, B., & Al-Barakati, A. (2020). Green suppler selection by an integrated method with stochastic acceptability analysis and MULTIMOORA. Technological and Economic Development of Economy, 26(3), 549–572. https://doi.org/10.3846/tede.2020.11964
Pang, Q., Wang, H., & Xu, Z. S. (2016). Probabilistic linguistic term sets in multi-attribute group decision making. Information Science, 369, 128–143. https://doi.org/10.1016/j.ins.2016.06.021
Rodríguez, R. M., Martínez, L., & Herrera, F. (2012). Hesitant Fuzzy linguistic term sets for decision making. IEEE Transactions on Fuzzy Systems, 20(1), 109–119. https://doi.org/10.1109/TFUZZ.2011.2170076
Rodríguez, R. M., Martínez, L., & Herrera, F. (2013). A group decision making model dealing with comparative linguistic expressions based on hesitant fuzzy linguistic term sets. Information Sciences, 241, 28–42. https://doi.org/10.1016/j.ins.2013.04.006
Torra, V. (2010). Hesitant fuzzy sets. International Journal of Inrelligent Systems, 25(6), 529–539. https://doi.org/10.1002/int.20418
Wang, J. H., & Hao, J. Y. (2006). A new version of 2-tuple fuzzy linguistic representation model for computing with words. IEEE Transactions on Fuzzy Systems, 14(3), 435–445. https://doi.org/10.1109/TFUZZ.2006.876337
Wang, X. (1997). An investigation into relations between some transitivity related concepts. Fuzzy Sets and Systems, 89(2), 257–262. https://doi.org/10.1016/S0165-0114(96)00104-2
Wang, X. X., Xu, Z. S., & Gou, X. J. (2019a). Nested probabilistic-numerical linguistic term sets in two-stage multi-attribute group decision making. Applied Intelligence, 49(7), 2582–2602. https://doi.org/10.1007/s10489-018-1392-y
Wang, X. X., Xu, Z. S., Gou, X. J., & Trajković, L. (2020). Tracking a maneuvering target by multiple sensors using extended kalman filter with nested probabilistic-numerical linguistic information. IEEE Transaction on Fuzzy Systems, 28(2), 346–360. https://doi.org/10.1109/TFUZZ.2019.2906577
Wang, X. X., Xu, Z. S., Gou, X. J., & Xu, M. (2019b). Distance and similarity measures for nested probabilistic-numerical linguistic term sets applied to evaluation of medical treatment. International Journal of Fuzzy System, 21(5), 1306–1329. https://doi.org/10.1007/s40815-019-00625-x
Wang, X. X., Xu, Z. S., Wen, Q., & Li, H. H. (2021). A multidimensional decision with nested probabilistic linguistic term sets and its application in corporate investment. Economic Research-Ekonomska Istrazivanja. https://doi.org/10.1080/1331677X.2021.1875255
Wang, Y. M., & Xin, L. (2020). The impact of China’s trade with economies participating in the Belt and Road Initiative on the ecological total factor energy efficiency of China’s logistics industry. Journal of Cleaner Production, 276, 124196. https://doi.org/10.1016/j.jclepro.2020.124196
Wu, Z. B., & Xu, J. P. (2016). Managing consistency and consensus in group decision making with hesitant fuzzy linguistic preference relations. Omega-International Journal of Management Science, 65, 28–40. https://doi.org/10.1016/j.omega.2015.12.005
Xu, Z. S. (2004). A method based on linguistic aggregation operators for group decision making with linguistic preference relations. Information Sciences, 166(1–4), 19–30. https://doi.org/10.1016/j.ins.2003.10.006
Xu, Z. S. (2005). Deviation measures of linguistic preference relations in group decision making. Omega-International Journal of Management Science, 33(3), 249–254. https://doi.org/10.1016/j.omega.2004.04.008
Xu, Z. S. (2012). Linguistic decision making: Theory and methods. Springer-Verlag. https://doi.org/10.1007/978-3-642-29440-2
Yang, S. Q., Zhou, C. M., & Chen, Y. C. (2021). Do topic consistency and linguistic style similarity affect online review helpfulness? An elaboration likelihood model perspective. Information Proceeding and Management, 58(3), 102521. https://doi.org/10.1016/j.ipm.2021.102521
Yao, X., Yasmeen, R., Hussain, J., & Shah, W. U. (2021). The repercussions of financial development and corruption on energy efficiency and ecological footprint: Evidence from BRICS and next 11 countries. Energy, 223, 120063. https://doi.org/10.1016/j.energy.2021.120063
Zadeh, L. A. (1975). Concept of a linguistic variable and its application to approximate reasoning. Information Sciences, 8(4), 301–357. https://doi.org/10.1016/0020-0255(75)90046-8
Zhang, G. Q., Dong, Y. C., & Xu, Y. F. (2014). Consistency and consensus measures for linguistic preference relations based on distribution assessments. Information Fusion, 17, 46–55. https://doi.org/10.1016/j.inffus.2012.01.006
Zhang, Z., Kou, X. Y., & Dong, Q. X. (2018). Additive consistency analysis and improvement for hesitant fuzzy preference relations. Expert Systems with Applications, 98, 118–128. https://doi.org/10.1016/j.eswa.2018.01.016
Zhao, J. B., Zhu, H., & Li, H. (2019). 2-Dimension linguistic PROMETHEE methods for multiple attribute decision making. Expert Systems with Applications, 127, 97–108. https://doi.org/10.1016/j.eswa.2019.02.034
Bai, C. Z., Zhang, R., Qian, L. X., & Wu, Y. N. (2017). Comparisons of probabilistic linguistic term sets for multi-criteria decision making. Knowledge-based Systems, 119, 284–291. https://doi.org/10.1016/j.knosys.2016.12.020
Ban, A. J., Ban, O. J., Bogdan, V., Popa, D. C. S., & Tuse, D. (2020). Performance evaluation model of Romanian manufacturing listed companies by fuzzy AHP and TOPSIS. Technological and Economic Development of Economy, 26(4), 808–836. https://doi.org/10.3846/tede.2020.12367
Boffey, T. B. (1982). Graph theory in operations research. Palgrave. https://doi.org/10.1007/978-1-349-16675-6
Chuang, Y. C., Hu, S. K., Liou, J. J. H., & Tzeng, W. S. (2020). A data-driven made model for personnel selection and improvement. Technological and Economic Development of Economy, 26(4), 751–784. https://doi.org/10.3846/tede.2020.12366
Dahooie, J. H., Hosseini Dehshiri, S. J., Banaitis, A., & Binkyte-Veliene, A. (2020). Identifying and prioritizing cost reduction solutions in the supply chain by integrating value engineering and gray multi-criteria decision-making. Technological and Economic Development of Economy, 26(6), 1311–1338. https://doi.org/10.3846/tede.2020.13534
Dong, Y. C., Xu, Y. F., & Li, H. (2008). On consistency measures of linguistic preference relations. European Journal of Operational Research, 189(2), 430–444. https://doi.org/10.1016/j.ejor.2007.06.013
Dong, Y. C., Xu, Y. F., Li, H. Y., & Feng, B. (2010). The OWA-based consensus operator under linguistic representation models using position indexes. European Journal of Operational Research, 203(2), 455–463. https://doi.org/10.1016/j.ejor.2009.08.013
Geng, J. B., Du, Y. J., Ji, Q., & Zhang, D. Y. (2021). Modeling return and volatility spillover networks of global new energy companies. Renewable and Sustainable Energy Review, 135, 110214. https://doi.org/10.1016/j.rser.2020.110214
Gou, X. J., Liao, H. C., Xu, Z. S., & Herrera, F. (2017). Double hierarchy hesitant fuzzy linguistic term set and MULTIMOORA method: A case of study to evaluate the implementation status of haze controlling measures. Information Fusion, 38, 22–34. https://doi.org/10.1016/j.inffus.2017.02.008
Herrera, F., & Martinez, L. (2000). A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Transactions on Fuzzy Systems, 8(6), 746–752. https://doi.org/10.1109/91.890332
Herrera-Viedma, E., Herrera, F., Chiclana, F., & Luque, M. (2004). Some issues on consistency of fuzzy preference relations. European Journal of Operational Research, 154(1), 98–109. https://doi.org/10.1016/S0377-2217(02)00725-7
Leeper, E. M., Walker, T. B., & Yang, S. C. S. (2010). Government investment and fiscal stimulus. Journal of Monetary Economics, 57(8), 1000–1012. https://doi.org/10.1016/j.jmoneco.2010.09.002
Liao, H. C., Xu, Z. S., & Zeng, X. J. (2014). Distance and similarity measures for hesitant fuzzy linguistic term sets and their application in multi-criteria decision making. Information Sciences, 271, 125–142. https://doi.org/10.1016/j.ins.2014.02.125
Liao, H. C., Xu, Z. S., & Zeng, X. J. (2015a). Hesitant fuzzy linguistic VIKOR method and its application in qualitative multiple criteria decision making. IEEE Transactions on Fuzzy Systems, 23(5), 1343–1355. https://doi.org/10.1109/TFUZZ.2014.2360556
Liao, H. C., Xu, Z. S., Zeng, X. J., & Merigo, J. M. (2015b). Qualitative decision making with correlation coefficients of hesitant fuzzy linguistic term sets. Knowledge-based Systems, 76, 127–138. https://doi.org/10.1016/j.knosys.2014.12.009
Meng, F. Y., Tang, J., & Zhang, S. L. (2019). Inverval linguistic fuzzy decision making in perspective of preference relations. Technological and Economic Development of Economy, 25(5), 998–1015. https://doi.org/10.3846/tede.2019.10548
Mi, X. M., Liao, H. C., Liao, Y., Lin, Q., Lev, B., & Al-Barakati, A. (2020). Green suppler selection by an integrated method with stochastic acceptability analysis and MULTIMOORA. Technological and Economic Development of Economy, 26(3), 549–572. https://doi.org/10.3846/tede.2020.11964
Pang, Q., Wang, H., & Xu, Z. S. (2016). Probabilistic linguistic term sets in multi-attribute group decision making. Information Science, 369, 128–143. https://doi.org/10.1016/j.ins.2016.06.021
Rodríguez, R. M., Martínez, L., & Herrera, F. (2012). Hesitant Fuzzy linguistic term sets for decision making. IEEE Transactions on Fuzzy Systems, 20(1), 109–119. https://doi.org/10.1109/TFUZZ.2011.2170076
Rodríguez, R. M., Martínez, L., & Herrera, F. (2013). A group decision making model dealing with comparative linguistic expressions based on hesitant fuzzy linguistic term sets. Information Sciences, 241, 28–42. https://doi.org/10.1016/j.ins.2013.04.006
Torra, V. (2010). Hesitant fuzzy sets. International Journal of Inrelligent Systems, 25(6), 529–539. https://doi.org/10.1002/int.20418
Wang, J. H., & Hao, J. Y. (2006). A new version of 2-tuple fuzzy linguistic representation model for computing with words. IEEE Transactions on Fuzzy Systems, 14(3), 435–445. https://doi.org/10.1109/TFUZZ.2006.876337
Wang, X. (1997). An investigation into relations between some transitivity related concepts. Fuzzy Sets and Systems, 89(2), 257–262. https://doi.org/10.1016/S0165-0114(96)00104-2
Wang, X. X., Xu, Z. S., & Gou, X. J. (2019a). Nested probabilistic-numerical linguistic term sets in two-stage multi-attribute group decision making. Applied Intelligence, 49(7), 2582–2602. https://doi.org/10.1007/s10489-018-1392-y
Wang, X. X., Xu, Z. S., Gou, X. J., & Trajković, L. (2020). Tracking a maneuvering target by multiple sensors using extended kalman filter with nested probabilistic-numerical linguistic information. IEEE Transaction on Fuzzy Systems, 28(2), 346–360. https://doi.org/10.1109/TFUZZ.2019.2906577
Wang, X. X., Xu, Z. S., Gou, X. J., & Xu, M. (2019b). Distance and similarity measures for nested probabilistic-numerical linguistic term sets applied to evaluation of medical treatment. International Journal of Fuzzy System, 21(5), 1306–1329. https://doi.org/10.1007/s40815-019-00625-x
Wang, X. X., Xu, Z. S., Wen, Q., & Li, H. H. (2021). A multidimensional decision with nested probabilistic linguistic term sets and its application in corporate investment. Economic Research-Ekonomska Istrazivanja. https://doi.org/10.1080/1331677X.2021.1875255
Wang, Y. M., & Xin, L. (2020). The impact of China’s trade with economies participating in the Belt and Road Initiative on the ecological total factor energy efficiency of China’s logistics industry. Journal of Cleaner Production, 276, 124196. https://doi.org/10.1016/j.jclepro.2020.124196
Wu, Z. B., & Xu, J. P. (2016). Managing consistency and consensus in group decision making with hesitant fuzzy linguistic preference relations. Omega-International Journal of Management Science, 65, 28–40. https://doi.org/10.1016/j.omega.2015.12.005
Xu, Z. S. (2004). A method based on linguistic aggregation operators for group decision making with linguistic preference relations. Information Sciences, 166(1–4), 19–30. https://doi.org/10.1016/j.ins.2003.10.006
Xu, Z. S. (2005). Deviation measures of linguistic preference relations in group decision making. Omega-International Journal of Management Science, 33(3), 249–254. https://doi.org/10.1016/j.omega.2004.04.008
Xu, Z. S. (2012). Linguistic decision making: Theory and methods. Springer-Verlag. https://doi.org/10.1007/978-3-642-29440-2
Yang, S. Q., Zhou, C. M., & Chen, Y. C. (2021). Do topic consistency and linguistic style similarity affect online review helpfulness? An elaboration likelihood model perspective. Information Proceeding and Management, 58(3), 102521. https://doi.org/10.1016/j.ipm.2021.102521
Yao, X., Yasmeen, R., Hussain, J., & Shah, W. U. (2021). The repercussions of financial development and corruption on energy efficiency and ecological footprint: Evidence from BRICS and next 11 countries. Energy, 223, 120063. https://doi.org/10.1016/j.energy.2021.120063
Zadeh, L. A. (1975). Concept of a linguistic variable and its application to approximate reasoning. Information Sciences, 8(4), 301–357. https://doi.org/10.1016/0020-0255(75)90046-8
Zhang, G. Q., Dong, Y. C., & Xu, Y. F. (2014). Consistency and consensus measures for linguistic preference relations based on distribution assessments. Information Fusion, 17, 46–55. https://doi.org/10.1016/j.inffus.2012.01.006
Zhang, Z., Kou, X. Y., & Dong, Q. X. (2018). Additive consistency analysis and improvement for hesitant fuzzy preference relations. Expert Systems with Applications, 98, 118–128. https://doi.org/10.1016/j.eswa.2018.01.016
Zhao, J. B., Zhu, H., & Li, H. (2019). 2-Dimension linguistic PROMETHEE methods for multiple attribute decision making. Expert Systems with Applications, 127, 97–108. https://doi.org/10.1016/j.eswa.2019.02.034