Skip to main content
Log in

Marginalization in random permutation set theory: from the cooperative game perspective

  • Original Paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

Random permutation set theory (RPST), as an uncertainty modeling approach considering underlying ordered information, is proposed based on permutation set recently. Different from Dempster–Shafer Theory (DST), RPST extends the event space from power sets to permutation sets and thus can distinguish weights between ordered sets of the same cardinality. In this paper, we use an n-player cooperative game to interpret RPST and propose an OWA operator-based method to calculate the marginalizations of Permutation Mass Function (PerMF). In addition to reflecting the ordered information of the permutation events, the proposed method also relates the Probability Mass Function (ProbMF), the Basic Probability Assignment (BPA) and the PerMF from the perspective of random sets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Availability of data and materials

All data and materials generated or analyzed during this study are included in this article.

Code availability

The code of the current study is available from the corresponding author on reasonable request.

Notes

  1. They can transform to each other by invertible matrices.

  2. The mass function is all positive, while the unique function may be negative. However, this does not affect the mathematical results.

References

  1. Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. In: Classic Works of the Dempster–Shafer Theory of Belief Functions, pp. 57– 72. Springer, Berlin ( 2008)

  2. Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976). https://doi.org/10.1515/9780691214696

  3. Liu, Z.-G., Liu, Y., Dezert, J., Cuzzolin, F.: Evidence combination based on credal belief redistribution for pattern classification. IEEE Trans. Fuzzy Syst. 28(4), 618–631 (2020)

    Article  Google Scholar 

  4. Huang, L., Liu, Z., Pan, Q., Dezert, J.: Evidential combination of augmented multi-source of information based on domain adaptation. Sci. China Inf. Sci. 63(11), 210203–114 (2020)

  5. Fei, L., Wang, Y.: An optimization model for rescuer assignments under an uncertain environment by using Dempster–Shafer theory. Knowl.-Based Syst. 10-10162022109680 (2022)

  6. Wen, T., Gao, Q., Chen, Y.-w., Cheong, K.H.: Exploring the vulnerability of transportation networks by entropy: a case study of Asia–Europe maritime transportation network. Reliab. Eng. Syst. Saf. 108578 (2022)

  7. Zhao, J., Cheong, K.H.: Obfuscating community structure in complex network with evolutionary divide-and-conquer strategy. IEEE Trans. Evolut. Comput. (2023). https://doi.org/10.1109/TEVC.2023.3242051

  8. Tao, R., Liu, Z., Cai, R., Cheong, K.H.: A dynamic group MCDM model with intuitionistic fuzzy set: perspective of alternative queuing method. Inf. Sci. 555, 85–103 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  9. Lai, J.W., Cheong, K.H.: A comprehensive framework for preference aggregation Parrondo’s paradox. Chaos: Interdiscipl. J. Nonlinear Sci. 32(10), 103107 (2022)

    Article  MathSciNet  Google Scholar 

  10. Liang, Y., Ju, Y., Qin, J., Pedrycz, W.: Multi-granular linguistic distribution evidential reasoning method for renewable energy project risk assessment. Inf. Fus. 65, 147–164 (2021)

    Article  Google Scholar 

  11. Smets, P., Kennes, R.: The transferable belief model. Artif. intell. 66(2), 191–234 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  12. Liu, Z., Pan, Q., Dezert, J., Han, J.-W., He, Y.: Classifier fusion with contextual reliability evaluation. IEEE Trans. Cybern. 48(5), 1605–1618 (2017)

    Article  Google Scholar 

  13. Huang, L.-Q., Liu, Z.-G., Dezert, J.: Cross-domain pattern classification with distribution adaptation based on evidence theory. IEEE Trans. Cybern. (2021). https://doi.org/10.1109/TCYB.2021.3133890

  14. Nguyen, H.T.: On random sets and belief functions. In: Classic Works of the Dempster–Shafer Theory of Belief Functions, pp. 105– 116. Springer, Berlin (2008)

  15. Smarandache, F., Dezert, J.: Advances and applications of DSMT for information fusion (collected works) 2 (2006)

  16. Xiao, F.: CEQD: a complex mass function to predict interference effects. IEEE Trans. Cybern. 10-110920203040770 (2021)

  17. Xiao, F., Pedrycz, W.: Negation of the quantum mass function for multisource quantum information fusion with its application to pattern classification. IEEE Trans. Pattern Anal. Mach. Intell. 10-110920223167045 (2022)

  18. Pan, L., Deng, Y.: A new complex evidence theory. Inf. Sci. 608, 251–261 (2022)

    Article  Google Scholar 

  19. Deng, X., Jiang, W.: A framework for the fusion of non-exclusive and incomplete information on the basis of D number theory. Appl. Intell. 1010071048902203960 (2022)

  20. Barhoumi, S., Kallel, I.K., Bouhamed, S.A., Bossé, E., Solaiman, B.: Generation of fuzzy evidence numbers for the evaluation of uncertainty measures. In: 2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), pp. 1– 6 ( 2020). IEEE

  21. Deng, Y.: Random permutation set. Int. J. Comput. Commun. Control 17(1), 4542 (2022). https://doi.org/10.15837/ijccc.2022.1.4542

    Article  MathSciNet  Google Scholar 

  22. Deng, J., Deng, Y.: Maximum entropy of random permutation set. Soft. Comput. 26(21), 11265–11275 (2022)

    Article  Google Scholar 

  23. Chen, L., Deng, Y., Cheong, K.H.: The distance of random permutation set. Inf. Sci. 628, 226–239 (2023)

    Article  Google Scholar 

  24. Cobb, B.R., Shenoy, P.P.: On the plausibility transformation method for translating belief function models to probability models. Int. J. Approx. Reason. 41(3), 314–330 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  25. Zhou, Q., Huang, Y., Deng, Y.: Belief evolution network-based probability transformation and fusion. Comput. Ind. Eng. 108750 (2022)

  26. Smets, P.: Decision making in the TBM: the necessity of the pignistic transformation. Int. J. Approx. Reason. 38(2), 133–147 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  27. Yager, R.R.: On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Trans. Syst. Man Cybern. 18(1), 183–190 (1988)

    Article  MATH  Google Scholar 

  28. Han, D., Dezert, J., Duan, Z.: Evaluation of probability transformations of belief functions for decision making. IEEE Trans. Syst., Man, Cybern.: Syst. 46(1), 93–108 (2015)

    Article  Google Scholar 

  29. Abellán, J., Klir, G.J.: Additivity of uncertainty measures on credal sets. Int. J. Gen. Syst. 34(6), 691–713 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  30. Pichon, F., Dubois, D., Denoeux, T.: Relevance and truthfulness in information correction and fusion. Int. J. Approx. Reason. 53(2), 159–175 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  31. Yang, Y., Han, D.: A new distance-based total uncertainty measure in the theory of belief functions. Knowl.-Based Syst. 94, 114–123 (2016)

    Article  Google Scholar 

  32. Deng, X., Jiang, W.: A framework for the fusion of non-exclusive and incomplete information on the basis of d number theory. Appl. Intell. 1–24 (2022)

  33. Deng, X., Xue, S., Jiang, W.: A novel quantum model of mass function for uncertain information fusion. Inf. Fus. 89, 619–631 (2023)

    Article  Google Scholar 

  34. Yaghlane, B.B., Smets, P., Mellouli, K.: Belief function independence: I. The marginal case. Int. J. Approx. Reason. 29(1), 47–70 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  35. Pichon, F.: Canonical decomposition of belief functions based on Teugels’ representation of the multivariate Bernoulli distribution. Inf. Sci. 428, 76–104 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  36. Shapley, L.: Quota solutions op n-person games1. Edited by Emil Artin and Marston Morse, 343 (1953)

  37. Koh, J.M., Cheong, K.H.: New doubly-anomalous Parrondo’s games suggest emergent sustainability and inequality. Nonlinear Dyn. 96(1), 257–266 (2019)

    Article  MATH  Google Scholar 

  38. Cheong, K.H., Wen, T., Benler, S., Koh, J.M., Koonin, E.V.: Alternating lysis and lysogeny is a winning strategy in bacteriophages due to Parrondo’s paradox. Proc. Natl. Acad. Sci. 119(13), 2115145119 (2022). https://doi.org/10.1073/pnas.2115145119

    Article  Google Scholar 

  39. Yager, R.R.: On the entropy of fuzzy measures. IEEE Trans. Fuzzy Syst. 8(4), 453–461 (2000)

    Article  MathSciNet  Google Scholar 

  40. Yager, R.R.: On using the Shapley value to approximate the Choquet integral in cases of uncertain arguments. IEEE Trans. Fuzzy Syst. 26(3), 1303–1310 (2017)

    Article  Google Scholar 

  41. Zhou, L., Cui, H., Huang, C., Kang, B., Zhang, J.: Counter deception in belief functions using Shapley value methodology. Int. J. Fuzzy Syst. 24(1), 340–354 (2022)

    Article  Google Scholar 

  42. Fujita, H., Gaeta, A., Loia, V., Orciuoli, F.: Hypotheses analysis and assessment in counterterrorism activities: a method based on OWA and fuzzy probabilistic rough sets. IEEE Trans. Fuzzy Syst. 28(5), 831–845 (2019)

    Article  Google Scholar 

  43. Wu, X., Liao, H., Pedrycz, W.: Probabilistic linguistic term set with interval uncertainty. IEEE Trans. Fuzzy Syst. 29(11), 3532–3545 (2020)

    Article  Google Scholar 

  44. Mi, X., Lv, T., Tian, Y., Kang, B.: Multi-sensor data fusion based on soft likelihood functions and OWA aggregation and its application in target recognition system. ISA Trans. 112, 137–149 (2021)

    Article  Google Scholar 

  45. Torra, V.: Andness directedness for operators of the OWA and WOWA families. Fuzzy Sets Syst. 414, 28–37 (2021)

  46. O’Hagan, M.: Aggregating template or rule antecedents in real-time expert systems with fuzzy set logic. In: 22nd Asilomar Conference on Signals, Systems and Computers, vol. 2, pp. 681– 689 (1988). IEEE

  47. Filev, D., Yager, R.R.: Analytic properties of maximum entropy OWA operators. Inf. Sci. 85(1–3), 11–27 (1995)

    Article  MATH  Google Scholar 

  48. Dubois, D., Prade, H., Sandri, S.: On possibility/probability transformations. In: Fuzzy Logic, pp. 103– 112. Springer, Berlin ( 1993)

  49. Yang, J.-B., Xu, D.-L.: Evidential reasoning rule for evidence combination. Artif. Intell. 205, 1–29 (2013)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

The authors are grateful for the guidance of master’s candidate, Jixiang Deng, and PhD candidate, Lipeng Pan, in the Information Fusion and Intelligent Systems Laboratory.

Funding

The work was partially supported by National Natural Science Foundation of China (Grant No. 61973332).

Author information

Authors and Affiliations

Authors

Contributions

Qianli Zhou took part in conceptualization, methodology, formal analysis, investigation, writing—original draft, writing—review & editing. Ye Cui involved in data experiment, formal analysis, writing—review & editing. Zhen Li took part in validation, writing—review & editing. Yong Deng involved in validation, resources, supervision, funding acquisition.

Corresponding author

Correspondence to Yong Deng.

Ethics declarations

Conflict of interest

All the authors certify that there is no conflict of interest with any individual or organization for this work.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Consent for publication

The participant has consented to the submission of the case report to the journal.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, Q., Cui, Y., Li, Z. et al. Marginalization in random permutation set theory: from the cooperative game perspective. Nonlinear Dyn 111, 13125–13141 (2023). https://doi.org/10.1007/s11071-023-08506-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-023-08506-7

Keywords

Navigation