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Probabilistic multiplicative unbalanced linguistic term set and its application in matrix games

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Abstract

Probabilistic linguistic term sets (PLTSs) are suitable for enunciating evaluators’ complex linguistic perceptions more accurately within the intricate qualitative setting. Usually, the PLTS is based on a balanced concept and does not serve as a good information representation for an unbalanced qualitative concept. Therefore, to reflect experts’ distinct preferences and uncertainties, this paper proposes a new PLTS called the probabilistic multiplicative unbalanced linguistic term set (PM-ULTS), where both probabilities and non-uniform spacing of the linguistic labels are considered simultaneously. Afterwards, we put forward specific operational laws for the newly constructed PLTS to preserve the resultant linguistic labels and corresponding probability information. Some elementary aggregation operators beneficial in aggregating probabilistic linguistic information in decision-making problems are also constructed, and their excellent properties are addressed. Furthermore, based on the proposed concept, this study initiates the design of a unified two-person linguistic matrix game model with the new PLTS as a parameter. It addresses the imprecise information by the information measure function. Such a two-player probabilistic unbalanced linguistic matrix game is considered a convenient technique for multiple decision scenarios. Additionally, the proposed game model involves a re-translation process to convert the output back into the original probabilistic unbalanced linguistic domain without information loss, thereby escalating the interpretability of the game model compared with other existing uncertain matrix game methodologies. Finally, we discuss the significance of the proposed methodology and concept to question its validity and usefulness by presenting suitable examples.

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References

  1. Arfi B (2006) Linguistic fuzzy-logic game theory. J Confl Resolut 50(1):28–57

    Google Scholar 

  2. Arfi B (2006) Linguistic fuzzy-logic social game of cooperation. Ration Soc 18:471–537

    Google Scholar 

  3. Barron EN (2009) Game theory: an introduction. Wiley, New Jersy

    MATH  Google Scholar 

  4. Bector CR, Chandra S (2005) Fuzzy mathematical programming and fuzzy matrix games. Springer, Heidelberg

    MATH  Google Scholar 

  5. Bector CR, Chandra S, Vidyottama V (2004) Duality in linear programming with fuzzy parameters and matrix games with fuzzy payoffs. Fuzzy Sets Syst 146:253–269

    MATH  Google Scholar 

  6. Bai C, Ren Z, Shuang S et al (2018) Interval-valued probabilistic linguistic term sets in multi-criteria group decision making. Int J Intell Syst 33(6):1301–1321

    Google Scholar 

  7. Chen YW, Larbani M (2006) Two-person zero-sum game approach for fuzzy multiple attribute decision making problems. Fuzzy Sets Syst 157(1):34–51

    MathSciNet  MATH  Google Scholar 

  8. Chen J, Hai W, Xu Z (2019) Uncertain probabilistic linguistic term sets in group decision making. Int J Fuzzy Syst 21(3):1241–1258

    MathSciNet  Google Scholar 

  9. Cabrerizo FJ, Pérez IJ, Herrera-Viedma E (2010) Managing the consensus in group decision making in an unbalanced fuzzy linguistic context with incomplete information. Knowl Based Syst 23:169–181

    Google Scholar 

  10. Dubois D, Prade H (1980) Fuzzy sets and systems. Theory and applications. Mathematics in sciences and engineering series, vol 144. Academic Press, New York

    MATH  Google Scholar 

  11. Dong Y, Xu Y, Yu S (2009) Computing the numerical scale of the linguistic term set for the 2-tuple fuzzy linguistic representation model. IEEE Trans Fuzzy Syst 17(6):1366–1378

    Google Scholar 

  12. Dong Y, Li CC, Herrera F (2016) Connecting the linguistic hierarchy and the numerical scale for the 2-tuple linguistic model and its use to deal with hesitant unbalanced linguistic information. Inf Sci 367:259–278

    MATH  Google Scholar 

  13. Dong YC, Li CC, Herrera F (2015) An optimization-based approach to adjusting unbalanced linguistic preference relations to obtain a required consistency level. Inf Sci 292:27–38

    MathSciNet  MATH  Google Scholar 

  14. Dong YC, Li CC, Xu Y, Gu X (2015) Consensus-based group decision making under multi-granular unbalanced 2-tuple linguistic preference relations. Group Decis Negot 24:217–242

    Google Scholar 

  15. Dong YC, Wu YZ, Zhang HJ, Zhang GG (2015) Multi-granular unbalanced linguistic distribution assessments with interval symbolic proportions. Knowl Based Syst 82:139–151

    Google Scholar 

  16. Fernández FR, Puerto J, Zafra MJ (2002) Cores of stochastic cooperative games. Int Game Theory Rev 4(3):265–280

    MathSciNet  MATH  Google Scholar 

  17. Feng X, Liu Q, Wei C (2019) Probabilistic linguistic QUALIFLEX approach with possibility degree comparison. J Intell Fuzzy Syst 36(1):710–730

    Google Scholar 

  18. Gou XJ, Xu ZS (2016) Novel basic operational laws for linguistic terms, hesitant fuzzy linguistic term sets and probabilistic linguistic term sets. Inf Sci 372:407–427

    Google Scholar 

  19. Han B, Tao Z, Chen H, Zhou L, Liu J (2020) A new computational model based on Archimedean copula for probabilistic unbalanced linguistic term set and its application to multiple attribute group decision making. Comput Ind Eng 140:106–264

    Google Scholar 

  20. Han B, Chen HY, Zhu JM, Liu JP (2018) An approach to linguistic multiple attribute decision-making based on unbalanced linguistic generalized Heronian mean aggregation operator. Comput Intell Neurosci. https://doi.org/10.1155/2018/1404067

    Article  Google Scholar 

  21. Han B, Tao ZF, Chen HY, Zhou LG (2018) Intuitionistic unbalanced linguistic generalized multiple attribute group decision making and its application to green products selection. Math Probl Eng 1–24

  22. Herrera F, Herrera-Viedma E, Martínez L (2008) A fuzzy linguistic methodology to deal with unbalanced linguistic term sets. IEEE Trans Fuzzy Syst 16(2):354–370

    Google Scholar 

  23. Isern D, Marin L, Valls A, Moreno A (2010) The unbalanced linguistic ordered weighted averaging operator. In: IEEE International conference on fuzzy systems (FUZZ), p 1–8

  24. Jiang L, Liu HB, Cai JF (2015) The power average operator for unbalanced linguistic term sets. Inf Fusion 22:85–94

    Google Scholar 

  25. Liao HC, Jiang LS, Xu ZS, Xu JP, Herrera F (2017) A probabilistic linguistic linear programming method in hesitant qualitative multiple criteria decision making. Inf Sci 415–416:341–355

    Google Scholar 

  26. Liao HC, Jiang L, Lev B et al (2019) Novel operations of PLTSs based on the disparity degrees of linguistic terms and their use in designing the probabilistic linguistic ELECTRE III method. Appl Soft Comput 80:450–464

    Google Scholar 

  27. Liu P, Li Y (2019) Multi-attribute decision making method based on generalized Maclaurin symmetric mean aggregation operators for probabilistic linguistic information. Comput Ind Eng 131:282–294

    Google Scholar 

  28. Liu PD, Teng F (2019) Probabilistic linguistic TODIM method for selecting products through online product reviews. Inf Sci 485:441–455

    Google Scholar 

  29. Liu HB, Jiang L, Xu ZS (2018) Entropy measures of probabilistic linguistic term sets. Int J Comput Intell Syst 11:45–57

    Google Scholar 

  30. Lin M, Chen Z, Liao HC et al (2019) ELECTRE II method to deal with probabilistic linguistic term sets and its application to edge computing. Nonlinear Dyn 96(3):2125–2143

    MATH  Google Scholar 

  31. Lin MW, Wang HB, Xu ZS, Yao ZQ, Huang JL (2018) Clustering algorithms based on correlation coefficients for probabilistic linguistic term sets. Int J Intell Syst 33:2402–2424

    Google Scholar 

  32. Lin M, Chen Z, Xu Z, Gou X, Herrera F (2021) Score function based on concentration degree for probabilistic linguistic term sets: an application to TOPSIS and VIKOR. Inf Sci 551:270–290

    MathSciNet  Google Scholar 

  33. Lin M, Xu Z et al (2018) Multi-attribute group decision-making under probabilistic uncertain linguistic environment. J Oper Res Soc 69(2):157–170

    Google Scholar 

  34. Li CC, Dong Y, Herrera F, Herrera-Viedma E, Martínez L (2017) Personalized individual semantics in computing with words for supporting linguistic group decision making. An application on consensus reaching. Inf Fusion 33:29–40

    Google Scholar 

  35. Li CC, Rodríguez RM, Herrera-Viedma E, Martínez L, Dong YC, Herrera F (2018) Personalized individual semantics based on consistency in hesitant linguistic group decision making with comparative linguistic expressions. Knowl Based Syst 145:156–165

    Google Scholar 

  36. Li DF, Nan JX, Zhang MJ (2012) Interval programming models for matrix games with interval payoffs. Optim Methods Softw 27(1):1–16

    MathSciNet  MATH  Google Scholar 

  37. Li DF (2011) Linear programming approach to solve interval-valued matrix games. Omega 39(6):655–666

    Google Scholar 

  38. Ma Z, Zhu J, Chen Y (2019) A probabilistic linguistic group decision-making method from a reliability perspective based on evidential reasoning. IEEE Trans Syst Man Cybern Syst. https://doi.org/10.1109/TSMC.2018.2815716

    Article  Google Scholar 

  39. Mata F, Pérez LG, Chiclana F, Herrera-Viedma E (2015) Aggregation of unbalanced fuzzy linguistic information in decision problems based on Type-1 OWA operator. In: IEEE international conference on in fuzzy systems (FUZZ-IEEE), p 1–6

  40. Meng D, Pei Z (2013) On weighted unbalanced linguistic aggregation operators in group decision making. Inf Sci 223:31–41

    MathSciNet  MATH  Google Scholar 

  41. Mao XB, Wu M, Dong JY, Wan SP, Jin Z (2019) A new method for probabilistic linguistic multi-attribute group decision making: application to the selection of financial technologies. Appl Soft Comput 77:155–175

    Google Scholar 

  42. Mi X, Liao H, Zeng XJ, Xu Z (2021) The two-person and zero-sum matrix game with probabilistic linguistic information. Inf Sci 570:487–499

    MathSciNet  Google Scholar 

  43. Malhotra T, Gupta A (2020) A new 2-tuple linguistic approach for unbalanced linguistic term sets. IEEE Trans Fuzzy Syst 29:2158–2168

    Google Scholar 

  44. Malhotra T, Gupta A, Singh A (2019) Methodology for interval-valued matrix games with 2-tuple fuzzy linguistic information. In: International conference on numerical computations: theory and algorithms, Springer, Cham, p 154–168

  45. Martinez L, Ruan D, Herrera F, Herrera-Viedma E, Wang PP (2009) Linguistic decision making: tools and applications. Inf Sci 179(14):2297–2298

    Google Scholar 

  46. Nan JX, Li DF (2014) Linear programming technique for solving interval-valued constraint matrix games. J Ind Manag Optim 10(4):1059–1070

    MathSciNet  MATH  Google Scholar 

  47. Nishizaki I, Sakawa M (2001) Fuzzy and multiobjective games for conflict resolution. Physica-Verlag, Heidelberg

    MATH  Google Scholar 

  48. Pang Q, Wang H, Xu Z (2016) Probabilistic linguistic term sets in multi-attribute group decision making. Inf Sci 369:128–143

    Google Scholar 

  49. Pei Z, Zheng L (2017) New unbalanced linguistic scale sets: the linguistic information representations and applications. Comput Ind Eng 105:377–390

    Google Scholar 

  50. Suijs J, Borm P, De Waegenaere A (1998) Stochastic cooperative games in insurance. Insurance 22:209–228

    MathSciNet  MATH  Google Scholar 

  51. Suijs J, Borm PP, De Waegenaere A, Tijs S (1999) Cooperative games with stochastic payoffs. Eur J Oper Res 113:193–205

    MATH  Google Scholar 

  52. Singh A, Gupta A, Mehra A (2020) Matrix games with 2-tuple linguistic information. Ann Oper Res 287(2):895–910

    MathSciNet  MATH  Google Scholar 

  53. Singh A, Gupta A (2018) Matrix games with interval-valued 2-tuple linguistic information. Games 9(3):62

    MathSciNet  MATH  Google Scholar 

  54. Song Y, Li G (2019) A large-scale group decision-making with incomplete multi-granular probabilistic linguistic term sets and its application in sustainable supplier selection. J Oper Res Soc 70(5):827–841

    Google Scholar 

  55. Sengupta JK (1989) A portfolio decisions as games. Int J Syst Sci 20(8):1323–1334

    MathSciNet  MATH  Google Scholar 

  56. Tang M, Long Y, Liao H, Xu Z (2019) Inclusion measures of probabilistic linguistic term sets and their application in classifying cities in the Economic Zone of Chengdu Plain. Appl Soft Comput 82:105–572

    Google Scholar 

  57. Von Neumann J, Morgenstern O (1953) Theory of games and economic behavior, 3rd edn. Princeton University Press, Princeton

    MATH  Google Scholar 

  58. Wu XL, Liao HC, Xu ZS et al (2018) Probabilistic linguistic multimoora: a multicriteria decision making method based on the probabilistic linguistic expectation function and the improved borda rule. IEEE Trans Fuzzy Syst 26(6):3688–3702

    Google Scholar 

  59. Wu XL, Liao HC (2018) An approach to quality function deployment based on probabilistic linguistic term sets and ORESTE method for multi-expert multi-criteria decision making. Inf Fusion 43:13–26

    Google Scholar 

  60. Wu XL, Liao HC (2019) A consensus-based probabilistic linguistic gained and lost dominance score method. Eur J Oper Res 272(3):1017–1027

    MathSciNet  MATH  Google Scholar 

  61. Wang JH, Hao J (2006) A new version of 2-tuple fuzzy linguistic representation model for computing with words. IEEE Trans Fuzzy Syst 14(3):435–445

    Google Scholar 

  62. Wang B, Liang J, Qian Y, Dang C (2015) A normalized numerical scaling method for the unbalanced multi-granular linguistic sets. Int J Uncertain Fuzziness Knowl Based Syst 23(2):221–243

    MathSciNet  MATH  Google Scholar 

  63. Wang H, Xu Z, Zeng XJ (2018) Hesitant fuzzy linguistic term sets for linguistic decision making: current developments, issues and challenges. Inf Fusion 43:1–12

    Google Scholar 

  64. Xu ZS, He Y, Wang X (2019) An overview of probabilistic-based expressions for qualitative decision-making: techniques, comparisons and developments. Int J Mach Learn Cybern 10(6):1513–1528

    Google Scholar 

  65. Yu W, Zhang H, Li B (2019) Operators and comparisons of probabilistic linguistic term sets. Int J Intell Syst 34(7):1476–1504

    Google Scholar 

  66. Zhang YX, Xu ZS, Liao HC (2017) A consensus process for group decision making with probabilistic linguistic preference relations. Inf Sci 414:260–275

    MATH  Google Scholar 

  67. Zhang Y, Xu Z, Wang H, Liao H (2016) Consistency-based risk assessment with probabilistic linguistic preference relation. Appl Soft Comput 49:817–833

    Google Scholar 

  68. Zhang XL (2018) A novel probabilistic linguistic approach for large-scale group decision making with incomplete weight information. Int J Fuzzy Syst 20(7):2245–2256

    Google Scholar 

  69. Zhang XL, Xing XM (2017) Probabilistic linguistic VIKOR method to evaluate green supply chain initiatives. Sustainability 9(7):1231

    Google Scholar 

  70. Zhai Y, Xu Z, Liao H (2016) Probabilistic linguistic vector-term set and its application in group decision making with multi-granular linguistic information. Appl Soft Comput 49:801–816

    Google Scholar 

  71. Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning-I. Inf Sci 8(3):199–249

    MathSciNet  MATH  Google Scholar 

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Correspondence to Anjana Gupta.

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Malhotra, T., Gupta, A. Probabilistic multiplicative unbalanced linguistic term set and its application in matrix games. Int. J. Mach. Learn. & Cyber. 14, 1253–1283 (2023). https://doi.org/10.1007/s13042-022-01697-2

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