Forecasting the real average retirement benefit in the United States using OWA operators
Abstract
The issue of pensions has become increasingly topical. This paper presents the ordered weighted averaging real average pension (OWARAP) operator. The OWARAP operator is based on the ordered weighted averaging (OWA) operator and calculates the future average retirement benefit taking into account price changes. Moreover, this work extends the OWARAP operator by using order-inducing variables, generalized means, and probabilities. This paper ends by analyzing the applicability of the OWARAP operator and its extensions in forecasting the real average Social Security benefits for retired workers in each state of the United States (U.S.). The results demonstrate the usefulness of the proposed approach in retirement decision making.
First published online 30 April 2024
Keyword : aggregation operator, forecasting, inflation, OWA operator, retirement benefit, Social Security
How to Cite
Figuerola-Wischke, A., & Gil-Lafuente, A. M. (2024). Forecasting the real average retirement benefit in the United States using OWA operators. Technological and Economic Development of Economy, 30(4), 956–975. https://doi.org/10.3846/tede.2024.20763
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Altman, N. J., & Kingson, E. R. (2021). Expanding Social Security to address the retirement income crisis. Public Policy & Aging Report, 31(3), 102–107. https://doi.org/10.1093/ppar/prab014
Amin, G. R., & Siddiq, F. K. (2019). Measuring global prosperity using data envelopment analysis and OWA operator. International Journal of Intelligent Systems, 34(10), 2713–2738. https://doi.org/10.1002/int.22176
Basiglio, S., & Oggero, N. (2020). The effects of pension information on individuals’ economic outcomes: A survey. Economies, 8(3), Article 67. https://doi.org/10.3390/economies8030067
Bongini, P., & Cucinelli, D. (2019). University students and retirement planning: Never too early. International Journal of Bank Marketing, 37(3), 775–797. https://doi.org/10.1108/IJBM-03-2018-0066
Chiclana, F., Herrera, F., & Herrera-Viedma, E. (2000). The ordered weighted geometric operator: Properties and applications. In Proceedings of the 8th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 985–991). Madrid, Spain.
Chiclana, F., Herrera, F., & Herrera-Viedma, E. (2002). The ordered weighted geometric operator: Properties and application in MCDM problems. In B. Bouchon-Meunier, J. Gutiérrez-Ríos, L. Magdalena, & R. R. Yager (Eds.), Studies in fuzziness and soft computing: Vol. 90. Technologies for constructing intelligent systems 2 (pp. 173–183). Physica. https://doi.org/10.1007/978-3-7908-1796-6_14
Dyckhoff, H., & Pedrycz, W. (1984). Generalized means as model of compensative connectives. Fuzzy Sets and Systems, 14(2), 143–154. https://doi.org/10.1016/0165-0114(84)90097-6
Emrouznejad, A., & Marra, M. (2014). Ordered weighted averaging operators 1988–2014: A citation-based literature survey. International Journal of Intelligent Systems, 29(11), 994–1014. https://doi.org/10.1002/int.21673
Espinoza-Audelo, L. F., León-Castro, E., Olazabal-Lugo, M., Merigó, J. M., & Gil-Lafuente, A. M. (2020). Using ordered weighted average for weighted averages inflation. International Journal of Information Technology & Decision Making, 19(2), 601–628. https://doi.org/10.1142/S0219622020500066
Figuerola-Wischke, A., Gil-Lafuente, A. M., & Merigó, J. M. (2022). The uncertain ordered weighted averaging adequacy coefficient operator. International Journal of Approximate Reasoning, 148, 68–79. https://doi.org/10.1016/j.ijar.2022.06.001
Flores-Sosa, M., Avilés-Ochoa, E., & Merigó, J. M. (2020). Induced OWA operators in linear regression. Journal of Intelligent & Fuzzy Systems, 38(5), 5509–5520. https://doi.org/10.3233/JIFS-179642
He, X., Wu, Y., Yu, D., & Merigó, J. M. (2017). Exploring the ordered weighted averaging operator knowledge domain: A bibliometric analysis. International Journal of Intelligent Systems, 32(11), 1151–1166. https://doi.org/10.1002/int.21894
Kacprzyk, J., Yager, R. R., & Merigó, J. M. (2019). Towards human-centric aggregation via ordered weighted aggregation operators and linguistic data summaries: A new perspective on Zadeh’s inspirations. IEEE Computational Intelligence Magazine, 14(1), 16–30. https://doi.org/10.1109/MCI.2018.2881641
Kintzel, D. (2017). Social Security retirement benefits and private annuities: A comparative analysis. Social Security Administration. https://www.ssa.gov/policy/docs/issuepapers/ip2017-01.html
León-Castro, E., Avilés-Ochoa, E., & Gil-Lafuente, A. M. (2016). Exchange rate USD/MXN forecast through econometric models, time series and HOWMA operators. Economic Computation and Economic Cybernetics Studies and Research, 50(4), 135–150.
León-Castro, E., Avilés-Ochoa, E., Merigó, J. M., & Gil-Lafuente, A. M. (2018). Heavy moving averages and their application in econometric forecasting. Cybernetics and Systems, 49(1), 26–43. https://doi.org/10.1080/01969722.2017.1412883
León-Castro, E., Espinoza-Audelo, L. F., Merigó, J. M., Gil-Lafuente, A. M., & Yager, R. R. (2020). The ordered weighted average inflation. Journal of Intelligent & Fuzzy Systems, 38(2), 1901–1913. https://doi.org/10.3233/JIFS-190442
Merigó, J. M. (2009). Nuevas extensiones a los operadores OWA y su aplicación en los métodos de decisión [Doctoral dissertation, University of Barcelona]. TDX. http://hdl.handle.net/10803/1488
Merigó, J. M. (2012). Probabilities in the OWA operator. Expert Systems with Applications, 39(13), 11456–11467. https://doi.org/10.1016/j.eswa.2012.04.010
O’Neill, R., Ralph, J., & Smith, P. A. (2017). Inflation: History and measurement. Springer. https://doi.org/10.1007/978-3-319-64125-6
Organization for Economic Cooperation and Development. (2019). Pensions at a glance 2019: OECD and G20 indicators. OECD Publishing. https://doi.org/10.1787/b6d3dcfc-en
Organization for Economic Cooperation and Development. (2023a). Fertility rates [Data set]. Retrieved March 25, 2023, from https://doi.org/10.1787/8272fb01-en
Organization for Economic Cooperation and Development. (2023b). Health status: Life expectancy [Data set]. Retrieved March 25, 2023, from https://stats.oecd.org/index.aspx?queryid=30114#
Peris-Ortiz, M., Álvarez-García, J., Domínguez-Fabián, I., & Devolder, P. (2020). Economic challenges of pension systems: A sustainability and international management perspective. Springer. https://doi.org/10.1007/978-3-030-37912-4
Rauh, J. D., Stefanescu, I., & Zeldes, S. P. (2020). Cost saving and the freezing of corporate pension plans. Journal of Public Economics, 188, Article 104211. https://doi.org/10.1016/j.jpubeco.2020.104211
Social Security Administration. (2021). Cost-of-living adjustment. https://www.ssa.gov/pubs/EN-05-10526.pdf
Su, W., Zeng, S., & Ye, X. (2013). Uncertain group decision-making with induced aggregation operators and Euclidean distance. Technological and Economic Development of Economy, 19(3), 431–447. https://doi.org/10.3846/20294913.2013.821686
Yager, R. R. (1988). On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Transactions on Systems, Man, and Cybernetics, 18(1), 183–190. https://doi.org/10.1109/21.87068
Yager, R. R. (1992). On generalized measures of realization in uncertain environments. Theory and Decision, 33(1), 41–69. https://doi.org/10.1007/BF00133982
Yager, R. R. (1996). Constrained OWA aggregation. Fuzzy Sets and Systems, 81(1), 89–101. https://doi.org/10.1016/0165-0114(95)00242-1
Yager, R. R. (2002). Heavy OWA operators. Fuzzy Optimization and Decision Making, 1(4), 379–397. https://doi.org/10.1023/A:1020959313432
Yager, R. R. (2004). Generalized OWA aggregation operators. Fuzzy Optimization and Decision Making, 3(1), 93–107. https://doi.org/10.1023/B:FODM.0000013074.68765.97
Yager, R. R., & Alajlan, N. (2014). On characterizing features of OWA aggregation operators. Fuzzy Optimization and Decision Making, 13(1), 1–32. https://doi.org/10.1007/s10700-013-9167-8
Yager, R. R., & Filev, D. P. (1999). Induced ordered weighted averaging operators. IEEE Transactions on Systems, Man, and Cybernetics – Part B (Cybernetics), 29(2), 141–150. https://doi.org/10.1109/3477.752789
Yu, D., Pan, T., Xu, Z. S., & Yager, R. R. (2023). Exploring the knowledge diffusion and research front of OWA operator: A main path analysis. Artificial Intelligence Review, 56(10), 12233–12255. https://doi.org/10.1007/s10462-023-10462-y
Zeng, S., Gu, J., & Peng, X. (2023). Low-carbon cities comprehensive evaluation method based on Fermatean fuzzy hybrid distance measure and TOPSIS. Artificial Intelligence Review, 56(8), 8591–8607. https://doi.org/10.1007/s10462-022-10387-y
Zeng, S., Hu, Y., & Llopis-Albert, C. (2023). Stakeholder-inclusive multi-criteria development of smart cities. Journal of Business Research, 154, Article 113281. https://doi.org/10.1016/j.jbusres.2022.08.045
Amin, G. R., & Siddiq, F. K. (2019). Measuring global prosperity using data envelopment analysis and OWA operator. International Journal of Intelligent Systems, 34(10), 2713–2738. https://doi.org/10.1002/int.22176
Basiglio, S., & Oggero, N. (2020). The effects of pension information on individuals’ economic outcomes: A survey. Economies, 8(3), Article 67. https://doi.org/10.3390/economies8030067
Bongini, P., & Cucinelli, D. (2019). University students and retirement planning: Never too early. International Journal of Bank Marketing, 37(3), 775–797. https://doi.org/10.1108/IJBM-03-2018-0066
Chiclana, F., Herrera, F., & Herrera-Viedma, E. (2000). The ordered weighted geometric operator: Properties and applications. In Proceedings of the 8th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 985–991). Madrid, Spain.
Chiclana, F., Herrera, F., & Herrera-Viedma, E. (2002). The ordered weighted geometric operator: Properties and application in MCDM problems. In B. Bouchon-Meunier, J. Gutiérrez-Ríos, L. Magdalena, & R. R. Yager (Eds.), Studies in fuzziness and soft computing: Vol. 90. Technologies for constructing intelligent systems 2 (pp. 173–183). Physica. https://doi.org/10.1007/978-3-7908-1796-6_14
Dyckhoff, H., & Pedrycz, W. (1984). Generalized means as model of compensative connectives. Fuzzy Sets and Systems, 14(2), 143–154. https://doi.org/10.1016/0165-0114(84)90097-6
Emrouznejad, A., & Marra, M. (2014). Ordered weighted averaging operators 1988–2014: A citation-based literature survey. International Journal of Intelligent Systems, 29(11), 994–1014. https://doi.org/10.1002/int.21673
Espinoza-Audelo, L. F., León-Castro, E., Olazabal-Lugo, M., Merigó, J. M., & Gil-Lafuente, A. M. (2020). Using ordered weighted average for weighted averages inflation. International Journal of Information Technology & Decision Making, 19(2), 601–628. https://doi.org/10.1142/S0219622020500066
Figuerola-Wischke, A., Gil-Lafuente, A. M., & Merigó, J. M. (2022). The uncertain ordered weighted averaging adequacy coefficient operator. International Journal of Approximate Reasoning, 148, 68–79. https://doi.org/10.1016/j.ijar.2022.06.001
Flores-Sosa, M., Avilés-Ochoa, E., & Merigó, J. M. (2020). Induced OWA operators in linear regression. Journal of Intelligent & Fuzzy Systems, 38(5), 5509–5520. https://doi.org/10.3233/JIFS-179642
He, X., Wu, Y., Yu, D., & Merigó, J. M. (2017). Exploring the ordered weighted averaging operator knowledge domain: A bibliometric analysis. International Journal of Intelligent Systems, 32(11), 1151–1166. https://doi.org/10.1002/int.21894
Kacprzyk, J., Yager, R. R., & Merigó, J. M. (2019). Towards human-centric aggregation via ordered weighted aggregation operators and linguistic data summaries: A new perspective on Zadeh’s inspirations. IEEE Computational Intelligence Magazine, 14(1), 16–30. https://doi.org/10.1109/MCI.2018.2881641
Kintzel, D. (2017). Social Security retirement benefits and private annuities: A comparative analysis. Social Security Administration. https://www.ssa.gov/policy/docs/issuepapers/ip2017-01.html
León-Castro, E., Avilés-Ochoa, E., & Gil-Lafuente, A. M. (2016). Exchange rate USD/MXN forecast through econometric models, time series and HOWMA operators. Economic Computation and Economic Cybernetics Studies and Research, 50(4), 135–150.
León-Castro, E., Avilés-Ochoa, E., Merigó, J. M., & Gil-Lafuente, A. M. (2018). Heavy moving averages and their application in econometric forecasting. Cybernetics and Systems, 49(1), 26–43. https://doi.org/10.1080/01969722.2017.1412883
León-Castro, E., Espinoza-Audelo, L. F., Merigó, J. M., Gil-Lafuente, A. M., & Yager, R. R. (2020). The ordered weighted average inflation. Journal of Intelligent & Fuzzy Systems, 38(2), 1901–1913. https://doi.org/10.3233/JIFS-190442
Merigó, J. M. (2009). Nuevas extensiones a los operadores OWA y su aplicación en los métodos de decisión [Doctoral dissertation, University of Barcelona]. TDX. http://hdl.handle.net/10803/1488
Merigó, J. M. (2012). Probabilities in the OWA operator. Expert Systems with Applications, 39(13), 11456–11467. https://doi.org/10.1016/j.eswa.2012.04.010
O’Neill, R., Ralph, J., & Smith, P. A. (2017). Inflation: History and measurement. Springer. https://doi.org/10.1007/978-3-319-64125-6
Organization for Economic Cooperation and Development. (2019). Pensions at a glance 2019: OECD and G20 indicators. OECD Publishing. https://doi.org/10.1787/b6d3dcfc-en
Organization for Economic Cooperation and Development. (2023a). Fertility rates [Data set]. Retrieved March 25, 2023, from https://doi.org/10.1787/8272fb01-en
Organization for Economic Cooperation and Development. (2023b). Health status: Life expectancy [Data set]. Retrieved March 25, 2023, from https://stats.oecd.org/index.aspx?queryid=30114#
Peris-Ortiz, M., Álvarez-García, J., Domínguez-Fabián, I., & Devolder, P. (2020). Economic challenges of pension systems: A sustainability and international management perspective. Springer. https://doi.org/10.1007/978-3-030-37912-4
Rauh, J. D., Stefanescu, I., & Zeldes, S. P. (2020). Cost saving and the freezing of corporate pension plans. Journal of Public Economics, 188, Article 104211. https://doi.org/10.1016/j.jpubeco.2020.104211
Social Security Administration. (2021). Cost-of-living adjustment. https://www.ssa.gov/pubs/EN-05-10526.pdf
Su, W., Zeng, S., & Ye, X. (2013). Uncertain group decision-making with induced aggregation operators and Euclidean distance. Technological and Economic Development of Economy, 19(3), 431–447. https://doi.org/10.3846/20294913.2013.821686
Yager, R. R. (1988). On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Transactions on Systems, Man, and Cybernetics, 18(1), 183–190. https://doi.org/10.1109/21.87068
Yager, R. R. (1992). On generalized measures of realization in uncertain environments. Theory and Decision, 33(1), 41–69. https://doi.org/10.1007/BF00133982
Yager, R. R. (1996). Constrained OWA aggregation. Fuzzy Sets and Systems, 81(1), 89–101. https://doi.org/10.1016/0165-0114(95)00242-1
Yager, R. R. (2002). Heavy OWA operators. Fuzzy Optimization and Decision Making, 1(4), 379–397. https://doi.org/10.1023/A:1020959313432
Yager, R. R. (2004). Generalized OWA aggregation operators. Fuzzy Optimization and Decision Making, 3(1), 93–107. https://doi.org/10.1023/B:FODM.0000013074.68765.97
Yager, R. R., & Alajlan, N. (2014). On characterizing features of OWA aggregation operators. Fuzzy Optimization and Decision Making, 13(1), 1–32. https://doi.org/10.1007/s10700-013-9167-8
Yager, R. R., & Filev, D. P. (1999). Induced ordered weighted averaging operators. IEEE Transactions on Systems, Man, and Cybernetics – Part B (Cybernetics), 29(2), 141–150. https://doi.org/10.1109/3477.752789
Yu, D., Pan, T., Xu, Z. S., & Yager, R. R. (2023). Exploring the knowledge diffusion and research front of OWA operator: A main path analysis. Artificial Intelligence Review, 56(10), 12233–12255. https://doi.org/10.1007/s10462-023-10462-y
Zeng, S., Gu, J., & Peng, X. (2023). Low-carbon cities comprehensive evaluation method based on Fermatean fuzzy hybrid distance measure and TOPSIS. Artificial Intelligence Review, 56(8), 8591–8607. https://doi.org/10.1007/s10462-022-10387-y
Zeng, S., Hu, Y., & Llopis-Albert, C. (2023). Stakeholder-inclusive multi-criteria development of smart cities. Journal of Business Research, 154, Article 113281. https://doi.org/10.1016/j.jbusres.2022.08.045