OWA operators in the insurance industry
Vol 8, Issue 13, 2024
VIEWS - 24 (Abstract) 13 (PDF)
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
Full Text:
PDFReferences
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
DOI: https://doi.org/10.24294/jipd8015
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 István Á. Harmati, Norbert Kovács, Dávid Fülep, Krisztián Koppány
License URL: https://creativecommons.org/licenses/by/4.0/
This site is licensed under a Creative Commons Attribution 4.0 International License.