OWA operators in the insurance industry

István Á. Harmati ,Norbert Kovács ,Dávid Fülep ,Krisztián Koppány

doi:10.24294/jipd8015

Home - JIPD - Volume 8 - Issue 13

Submit to JIPD

Journal Browser

Volume | Year

Issue

• Current lssue

View All

News and Announcements

View All

OWA operators in the insurance industry

István Á. Harmati

Norbert Kovács

Dávid Fülep

Krisztián Koppány

Journal of Infrastructure Policy and Development 2024, 8(13), 8015; https://doi.org/10.24294/jipd8015

Submitted：15 Jul 2024

Accepted：26 Sept 2024

Published：08 Nov 2024

Download PDF(435.53KB)

XML

Abstract

In this paper, we examine a possible application of ordered weighted average (OWA for short) aggregation operators in the insurance industry. Aggregation operators are essential tools in decision-making when a single value is needed instead of a couple of features. Information aggregation necessarily leads to information loss, at least to a specific extent. Whether we concentrate on extreme values or middle terms, there can be cases when the most important piece of the puzzle is missing. Although the simple or weighted mean considers all the values there is a drawback: the values get the same weight regardless of their magnitude. One possible solution to this issue is the application of the so-called Ordered Weighted Averaging (OWA) operators. This is a broad class of aggregation methods, including the previously mentioned average as a special case. Moreover, using a proper parameter (the so-called orness) one can express the risk awareness of the decision-maker. Using real-life statistical data, we provide a simple model of the decision-making process of insurance companies. The model offers a decision-supporting tool for companies.

Keywords

References

Beliakov, G., James, S. (2011). Induced Ordered Weighted Averaging Operators. In: Yager, R.R., et al., (ed) Recent developments in the OWA operators; Studies in Fuzziness and Soft Computing 265. Springer-Verlag, pp. 29-47, https://doi.org/10.1007/978-3-642-17910-5_3

Belles-Sampera, J., Merigó, J. M., Guillén, M., and Santolino, M. (2013). The connection between distortion risk measures and ordered weighted averaging operators. Insurance: Mathematics and Economics, 52(2), pp. 411-420. https://doi.org/10.1016/j.insmatheco.2013.02.008

Benati, S., and Conde, E. (2024). A robust ordered weighted averaging loss model for portfolio optimization. Computers & Operations Research, 167, 106666. https://doi.org/10.1016/j.cor.2024.106666

Bueno, I., Carrasco, R. A., Ureña, R., and Herrera-Viedma, E. (2019). Application of an opinion consensus aggregation model based on OWA operators to the recommendation of tourist sites. Procedia Computer Science, 162, pp. 539-546. https://doi.org/10.1016/j.procs.2019.12.021

Carlsson, C., and Fullér, R. (2018). Maximal entropy and minimal variability OWA operator weights: a short survey of recent developments. Soft Computing Applications for Group Decision-Making and Consensus Modeling, pp. 187-199. https://doi.org/10.1007/978-3-319-60207-3_12

Casanovas, M., Torres-Martinez, A., and Merigo, J. M. (2016). Decision making in reinsurance with induced OWA operators and Minkowski distances. Cybernetics and Systems, 47(6), pp. 460-477. https://doi.org/10.1080/01969722.2016.1206767

Casanovas, M., Torres-Martínez, A., and Merigó, J. M. (2020). Multi-person and multi-criteria decision making with the induced probabilistic ordered weighted average distance. Soft Computing, 24(2), 1435-1446. https://doi.org/10.1007/s00500-019-03977-6

Chen, M., and Ye, Y. (2024). Multiple large shareholders, earnings management, and operating risk: Empirical evidence from China. Journal of Infrastructure, Policy and Development, 8(5), 3955. https://doi.org/10.24294/jipd.v8i5.3955

Cheng, R.; Zhu, R.; Tian, Y.; Kang, B.; Zhang, J.(2023) A multi-criteria group decision-making method based on OWA aggregation operator and Z-numbers. Soft Computing 27, pp. 1439–1455, https://doi.org/10.1007/s00500-022-07667-8

Figuerola-Wischke, A., and 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), pp. 956-975, https://doi.org/10.3846/tede.2024.20763

Figuerola-Wischke, A., Gil-Lafuente, A. M., and Merigó, J. M. (2023). OWA Operators in Pensions. In Artificial Intelligence in Control and Decision-making Systems: Dedicated to Professor Janusz Kacprzyk, pp. 267-292. Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-25759-9_13

Figuerola-Wischke, A., Merigó, J. M., Gil-Lafuente, A. M., and Boria-Reverter, J. (2024). A Bibliometric Review of the Ordered Weighted Averaging Operator. Mathematics, 12(7), 1053, https://doi.org/10.3390/math12071053

Filev, D., and Yager, R. R. (1998). On the issue of obtaining OWA operator weights. Fuzzy sets and systems, 94(2), pp. 157-169. https://doi.org/10.1016/S0165-0114(96)00254-0

Fullér, R., and Majlender, P. (2001). An analytic approach for obtaining maximal entropy OWA operator weights. Fuzzy sets and Systems, 124(1), pp. 53-57. https://doi.org/10.1016/S0165-0114(01)00007-0

Fullér, R., and Majlender, P. (2003). On obtaining minimal variability OWA operator weights. Fuzzy sets and systems, 136(2), pp. 203-215. https://doi.org/10.1016/S0165-0114(02)00267-1

Gupta, S., Chaudhari, S., Joshi, G., and Yağan, O. (2021). Multi-armed bandits with correlated arms. IEEE Transactions on Information Theory, 67(10), pp. 6711-6732. https://doi.org/10.1109/TIT.2021.3081508

Hamidoğlu, A. (2021). A novel one target game model in the life insurance market. International Journal of Management Science and Engineering Management, 16(3), pp. 221-228, https://doi.org/10.1080/17509653.2021.1941370

Harmati, I. Á., Fullér, R., and Felde, I. (2022). On stability of maximal entropy OWA operator weights. Fuzzy Sets and Systems, 448, pp. 145-156, https://doi.org/10.1016/j.fss.2022.01.003

Jiang, Y., and Tu, Q. (2023). Research on the Risk Management of Shantytown Renovation Project Based on Grey Clustering Method. In E3S Web of Conferences (Vol. 439, p. 02004). EDP Sciences. https://doi.org/10.1051/e3sconf/202343902004

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, pp. 16–30. https://doi.org/10.1109/MCI.2018.2881641

Kim, M. J., and Lim, A. E. (2016). Robust multiarmed bandit problems. Management Science, 62(1), pp. 264-285. https://doi.org/10.1287/mnsc.2015.2153

Kishor, A., Singh, A. K., and Pal, N. R. (2013). Orness measure of OWA operators: a new approach. IEEE Transactions on Fuzzy Systems, 22(4), pp. 1039-1045. https://doi.org/10.1109/TFUZZ.2013.2282299

Ma, Y., Ji, Y., Qu, D., Zhang, X., and Wang, L. (2024). Maximum expert consensus model with uncertain adjustment costs for social network group decision making. Information Fusion, 108, 102403. https://doi.org/10.1016/j.inffus.2024.102403

Merigó, J. M. (2011). A unified model between the weighted average and the induced OWA operator. Expert Systems with Applications, 38(9), 11560-11572. https://doi.org/10.1016/j.eswa.2011.03.034

Pachêco Gomes, I., and Wolf, D. F. (2024). Driving Style Recognition Using Interval Type-2 Fuzzy Inference System and Multiple Experts Decision-Making. International Journal of Fuzzy Systems, 26(2), pp. 553-571. https://doi.org/10.1007/s40815-023-01616-9

Renaud, J., Levrat, E., and Fonteix, C. (2008). Weights determination of OWA operators by parametric identification. Mathematics and Computers in Simulation, 77(5-6), pp. 499-511. https://doi.org/10.1016/j.matcom.2007.11.024

Seong-Min, K., and Byung-Soo, K. (2024). Optimal model for selection of material with low emission of indoor air pollutants. Journal of Infrastructure, Policy and Development, 8(1), 2545. https://doi.org/10.24294/jipd.v8i1.2545

Shapiro, A. F. (2004). Fuzzy logic in insurance. Insurance: Mathematics and Economics, 35(2), pp. 399-424. https://doi.org/10.1016/j.insmatheco.2004.07.010

Srivastava, V., Kishor, A., and Singh, A. K. (2023). Novel optimistic and pessimistic family of OWA operator with constant orness. International Journal of Approximate Reasoning, 161, 109006. https://doi.org/10.1016/j.ijar.2023.109006

Torra, V. (2000). The WOWA operator and the interpolation function W*: Chen and Otto's interpolation method revisited. Fuzzy Sets and Systems, 113(3), pp. 389-396. https://doi.org/10.1016/S0165-0114(98)00040-2

Van Tran, H., Tran, A. V., Ho, N. Q. A., and Pham, D. N. (2024). Factors influencing the decision to use rooftop solar power systems in Vietnam. Journal of Infrastructure, Policy and Development, 8(6), 4631. https://doi.org/10.24294/jipd.v8i6.4631

Vizuete-Luciano, E., Merigo, J. M., Gil-Lafuente, A. M., and Boria-Reverter, S. (2015). Decision making in the assignment process by using the Hungarian algorithm with OWA operators. Technological and Economic Development of Economy, 21(5), pp. 684-704. https://doi.org/10.3846/20294913.2015.1056275

Wang, Y. M., and Parkan, C. (2005). A minimax disparity approach for obtaining OWA operator weights. Information Sciences, 175(1-2), pp. 20-29. https://doi.org/10.1016/j.ins.2004.09.003

Xie, J.;Wu, B.; Zou,W. (2024) Ordered weighted utility distance operators and their applications in group decision-making. Applied Soft Computing, 150, 111016., https://doi.org/10.1016/j.asoc.2023.111016

Xu, Z. (2005). An overview of methods for determining OWA weights. International journal of intelligent systems, 20(8), pp. 843-865. https://doi.org/10.1002/int.20097

Yager, R.R. (1988), On Ordered Weighted Averaging Aggregation Operators in Multi-criteria Decision Making. IEEE Transactions on Systems and Man Cybernetics, 18, pp. 183–190

Yager, R.R. (1996), Quantifier Guided Aggregation Using OWA Operators. International Journal of Intelligent Systems, 11, pp. 49-73, https://doi.org/10.1002/(SICI)1098-111X(199601)11:1<49::AID-INT3>3.0.CO;2-Z

Yager, R.R. (1998), Including Importances in OWA Aggregations Using Fuzzy Systems Modelling. IEEE Transactions on Fuzzy Systems, 6(2), pp. 286-294, https://doi.org/10.1109/91.669028

Zheng, T., Chen, H., and Yang, X. (2023). Entropy and probability based Fuzzy Induced Ordered Weighted Averaging operator. Journal of Intelligent & Fuzzy Systems, 44(3), pp. 4949-4962. https://doi.org/10.3233/JIFS-222241

Previous
article in this issue

Next
article in this issue