Skip to main content
Log in

An interval-valued Pythagorean prioritized operator-based game theoretical framework with its applications in multicriteria group decision making

  • Soft Computing Techniques: Applications and Challenges
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Multicriteria decision-making process explicitly evaluates multiple conflicting criteria in decision making. The conventional decision-making approaches assumed that each agent is independent, but the reality is that each agent aims to maximize personal benefit which causes a negative influence on other agents’ behaviors in a real-world competitive environment. In our study, we proposed an interval-valued Pythagorean prioritized operator-based game theoretical framework to mitigate the cross-influence problem. The proposed framework considers both prioritized levels among various criteria and decision makers within five stages. Notably, the interval-valued Pythagorean fuzzy sets are supposed to express the uncertainty of experts, and the game theories are applied to optimize the combination of strategies in interactive situations. Additionally, we also provided illustrative examples to address the application of our proposed framework. In summary, we provided a human-inspired framework to represent the behavior of group decision making in the interactive environment, which is potential to simulate the process of realistic humans thinking.

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

Similar content being viewed by others

References

  1. Liao H, Xu Z, Zeng XJ, Xu DL (2016) An enhanced consensus reaching process in group decision making with intuitionistic fuzzy preference relations. Inf Sci 329(C):274–286

    Article  Google Scholar 

  2. Zhang X, Mahadevan S (2017) A game theoretic approach to network reliability assessment. IEEE Trans Reliab PP(99):1–18

    Google Scholar 

  3. Zhang X, Mahadevan S (2018) A bio-inspired approach to traffic network equilibrium assignment problem. IEEE Trans Cybern 48(4):1304–1315

    Article  Google Scholar 

  4. Kannan D, Khodaverdi R, Olfat L, Jafarian A, Diabat A (2013) Integrated fuzzy multi criteria decision making method and multi-objective programming approach for supplier selection and order allocation in a green supply chain. J Clean Prod 47(9):355–367

    Article  Google Scholar 

  5. Jiang W, Wei B, Liu X, Li XY, Zheng H (2018) Intuitionistic fuzzy power aggregation operator based on entropy and its application in decision making. Int J Intell Syst 33(1):49–67

    Article  Google Scholar 

  6. Cao Z, Lin C-T (2018) Inherent fuzzy entropy for the improvement of eeg complexity evaluation. IEEE Trans Fuzzy Syst 2(26):1032–1035

    Article  Google Scholar 

  7. Sayadi MK, Heydari M, Shahanaghi K (2009) Extension of vikor method for decision making problem with interval numbers. Appl Math Model 33(5):2257–2262

    Article  MathSciNet  Google Scholar 

  8. Lin C-T, Ding W, Cao Z (2018) Deep neuro-cognitive co-evolution for fuzzy attribute reduction by quantum leaping PSO with nearest-neighbor memeplexes. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2018.2834390

    Article  Google Scholar 

  9. Wu S-L, Zehong JC, Wang Y-K, Huang C-S, King J-T, Chen S-A, Lu S-W, Lin C-T, Liu Y-T, Chuang C-H (2017) Eeg-based brain-computer interfaces: a novel neurotechnology and computational intelligence method. IEEE Syst Man Cybern Mag 3(4):16–26

    Article  Google Scholar 

  10. Liu F, Qin Y, Pedrycz W, Zhang WG (2018) A group decision making model based on an inconsistency index of interval multiplicative reciprocal matrices. Knowl Based Syst 145:67–76

    Article  Google Scholar 

  11. Zeshui X, Yager RR (2012) Dynamic intuitionistic fuzzy multi-attribute decision making. Int J Approx Reason 48(1):246–262

    MATH  Google Scholar 

  12. Mousavi SM, Foroozesh N, Gitinavard H, Vahdani B (2018) Solving group decision-making problems in manufacturing systems by an uncertain compromise ranking method. Int J Appl Decis Sci 11(1):55

    Google Scholar 

  13. Morente-Molinera JA, Kou G, Peng Y, Torres-Albero C, Herrera-Viedma E (2018) Analysing discussions in social networks using group decision making methods and sentiment analysis. Inf Sci 447:157–168

    Article  Google Scholar 

  14. Kang B, Chhipi-Shrestha G, Deng Y, Mori J, Hewage K, Sadiq R (2017) Development of a predictive model for \(Clostridium~difficile\) infection incidence in hospitals using Gaussian mixture model and Dempster–Shafer theory. Stoch Environ Res Risk Assess 32(6):1743–1758

    Article  Google Scholar 

  15. Dempster AP (1967) Upper and lower probabilities induced by a multivalued mapping. Ann Math Stat 38(2):325–339

    Article  MathSciNet  Google Scholar 

  16. Zhang L, Ding LY, Wu XG, Skibniewski MJ (2017) An improved Dempster–Shafer approach to construction safety risk perception. Knowl Based Syst 132:30–46

    Article  Google Scholar 

  17. Zhang L, Chen HY, Li HX, Wu XG, Skibniewski MJ (2018) Perceiving interactions and dynamics of safety leadership in construction projects. Saf Sci 106:66–78

    Article  Google Scholar 

  18. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  Google Scholar 

  19. Liang D, Zeshui X, Liu D, Yao W (2018) Method for three-way decisions using ideal topsis solutions at Pythagorean fuzzy information. Inf Sci 435:282–295

    Article  MathSciNet  Google Scholar 

  20. Yager RR, Abbasov AM (2013) Pythagorean membership grades, complex numbers, and decision making. Int J Intell Syst 28(5):436–452

    Article  Google Scholar 

  21. Chen T-Y (2014) A prioritized aggregation operator-based approach to multiple criteria decision making using interval-valued intuitionistic fuzzy sets: a comparative perspective. Inf Sci 281:97–112

    Article  MathSciNet  Google Scholar 

  22. Nash J (1951) Non-cooperative games. Ann Math 54(2):286–295

    Article  MathSciNet  Google Scholar 

  23. Zeshui X (2007) Intuitionistic preference relations and their application in group decision making. Inf Sci Int J 177(11):2363–2379

    MathSciNet  MATH  Google Scholar 

  24. Yager RR (2008) Prioritized aggregation operators. Int J Approx Reason 48(1):263–274

    Article  MathSciNet  Google Scholar 

  25. Cabrerizo FJ, Morente-Molinera JA, Pedrycz W, Taghavi A, Herrera-Viedma E (2018) Granulating linguistic information in decision making under consensus and consistency. Expert Syst Appl 99:83–92

    Article  Google Scholar 

  26. Li X, Jusup M, Wang Z, Li H, Shi L, Podobnik B, Stanley HE, Havlin S, Boccaletti S (2017) Punishment diminishes the benefits of network reciprocity in social dilemma experiments. Proc Natl Acad Sci 115(1):30–35

    Article  Google Scholar 

  27. Wang Z, Xia C-Y, Meloni S, Zhou C-S, Moreno Y (2013) Impact of social punishment on cooperative behavior in complex networks. Sci Rep 3:3055

    Article  Google Scholar 

  28. Wang Z, Andrews MA, Wu Z-X, Wang L, Bauch CT (2015) Coupled disease–behavior dynamics on complex networks: a review. Phys Life Rev 15:1–29

    Article  Google Scholar 

  29. Deng XY, Zhang ZP, Deng Y, Liu Q, Chang S (2016) Self-adaptive win-stay-lose-shift reference selection mechanism promotes cooperation on a square lattice. Appl Math Comput 284:322–331

    MathSciNet  MATH  Google Scholar 

  30. Nash JF (1950) Equilibrium points in \(n\)-person games. Proc Natl Acad Sci USA 36(1):48

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The authors are grateful to anonymous reviewers for their useful comments and suggestions on improving this paper.

Funding

The work is partially supported by National Natural Science Foundation of China (Grant Nos. 61573290, 61503237) and National Undergraduate Training Program for Innovation and Entrepreneurship (Grant No. 201810635012).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Yong Deng or Zehong Cao.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights statement

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

Additional information

Publisher's Note

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

Appendix

Appendix

Interval-valued Pythagorean fuzzy prioritized average operators are defined as follows:

Definition 4

Let \(a_i=\{\langle s_{\theta (a_i)},[\mu _{\tilde{P}}^L(a_i), \; \mu _{\tilde{P}}^U(a_i)]\), \([v_{\tilde{P}}^L(a_i), v_{\tilde{P}}^U(a_i)]\rangle \}\)\((i=1, 2, 3, \ldots , n)\) be a collection of IVPFSs, then their aggregated, where \([\mu _{\tilde{P}}^L(a_i), \; \mu _{\tilde{P}}^U(a_i)]\subset [0,1], [v_{\tilde{P}}^L(a_i)\), \(v_{\tilde{P}}^U(a_i)] \subset [0,1]\) and let IVPFPWA \(V^n\rightarrow V\). if:

$$\begin{aligned}&{{\mathrm{IVPFPWA}}}\left( a_1, a_2, a_3, \ldots , a_n\right) \\&\quad =\frac{T_1}{\sum _{i=1}^nT_i}a_1 \oplus \frac{T_2}{\sum _{i=1}^nT_i}a_2 \oplus , \ldots , \oplus \frac{T_n}{\sum _{i=1}^nT_i}a_n \end{aligned}$$

The interval-valued Pythagorean fuzzy prioritized weighted average operator is abbreviated as IVPFPWA with \(T_i=\prod _{j=1}^{i-1}S(a_j)\)\((i= 2, \ldots , n)\), \(T_1=1\) and \(S(a_j)\) is the score of IVPFS a.

We could obtain the Theorem 1 based on the operations of IVPFSs described in Preliminary.

Theorem 1

Let\(a_i=\{\langle s_{\theta (a_i)},[\mu _{\tilde{P}}^L(a_i), \; \mu _{\tilde{P}}^U(a_i)]\), \(\quad [v_{\tilde{P}}^L(a_i)\), \(\quad v_{\tilde{P}}^U(a_i)]\rangle \}\)\((i=1, 2, 3, \ldots , n)\) be a collection of IVPFSs, then their aggregated, IVPFPWA is

$$\begin{aligned}&{\mathrm{IVPFPWA}}\left( a_1, a_2, a_3, \ldots , a_n\right) \\&\quad =\left( \left\langle \left[ \sqrt{1-\prod \limits _{i=1}^n\left( 1-\mu _p^L(a_i)^2\right) ^\frac{T_i}{\sum _{i=1}^nT_i}}, \right. \right. \right. \\&\qquad \left. \sqrt{1-\prod \limits _{i=1}^n\left( 1-\mu _p^U(a_i)^2\right) ^\frac{T_i}{\sum _{i=1}^nT_i}} \right] ,\\&\qquad \left. \left. \left[ \prod \limits _{i=1}^n\left( v_p^L(a_i)\right) ^\frac{T_i}{\sum _{i=1}^nT_i}, \prod \limits _{i=1}^n\left( v_p^U(a_i)\right) ^\frac{T_i}{\sum _{i=1}^nT_i} \right] \right\rangle \right) \end{aligned}$$

where\(T_i=\prod _{j=1}^{i-1}S(a_j)\)\((i= 2, \ldots , n)\), \(T_1=1\) andS(a) is the score of IVPFSa.

Proof

In the following, we prove the first result follows quickly from Definition 2 and Theorem 1:

$$\begin{aligned}&{\mathrm{IVPFPWA}}\left( a_1, a_2, a_3, \ldots , a_n\right) \\&\quad =\frac{T_1}{\sum _{i=1}^nT_i}a_1 \oplus \frac{T_2}{\sum _{i=1}^nT_i}a_2 \oplus , \ldots , \oplus \frac{T_n}{\sum _{i=1}^nT_i}a_n\\&\quad =\left( \left\langle \left[ \sqrt{1-\prod \limits _{i=1}^n\left( 1-\mu _p^L(a_i)^2\right) ^\frac{T_i}{\sum _{i=1}^nT_i}},\right. \right. \right. \\&\qquad \left. \sqrt{1-\prod \limits _{i=1}^n\left( 1-\mu _p^U(a_i)^2\right) ^\frac{T_i}{\sum _{i=1}^nT_i}} \right] , \\&\qquad \left. \left. \left[ \prod \limits _{i=1}^n\left( v_p^L(a_i)\right) ^\frac{T_i}{\sum _{i=1}^nT_i}, \prod \limits _{i=1}^n\left( v_p^U(a_i)\right) ^\frac{T_i}{\sum _{i=1}^nT_i} \right] \right\rangle \right) \end{aligned}$$

by using mathematical induction on n:

  1. (1)

    For n = 2, then

    $$\begin{aligned}&{\mathrm{IVPFPWA}}\left( a_1, a_2\right) =\frac{T_1}{\sum _{i=1}^2T_i}a_1+\frac{T_2}{\sum _{i=1}^2T_i}a_2 \\&\quad \frac{T_1}{\sum _{i=1}^2T_i}a_1=\left( \left\langle \left[ \sqrt{1-\left( 1-\mu _p^L(a_1)^2\right) ^\frac{T_1}{\sum _{i=1}^2T_i}},\right. \right. \sqrt{1-\left( 1-\mu _p^U(a_1)^2\right) ^\frac{T_1}{\sum _{i=1}^2T_i}} \right] , \\&\qquad \left. \left. \left[ \left( v_p^L(a_1)\right) ^\frac{T_1}{\sum _{i=1}^2T_i}, \left( v_p^U(a_1)\right) ^\frac{T_1}{\sum _{i=1}^2T_i} \right] \right\rangle \right) \frac{T_2}{\sum _{i=1}^2T_i}a_2=\left( \left\langle \left[ \sqrt{1-\left( 1-\mu _p^L(a_2)^2\right) ^\frac{T_2}{\sum _{i=1}^2T_i}},\right. \right. \right. \\&\qquad \left. \sqrt{1-\left( 1-\mu _p^U(a_2)^2\right) ^\frac{T_2}{\sum _{i=1}^2T_i}} \right] , \left. \left. \left[ \left( v_p^L(a_1)\right) ^\frac{T_2}{\sum _{i=1}^2T_i}, \left( v_p^U(a_2)\right) ^\frac{T_2}{\sum _{i=1}^2T_i} \right] \right\rangle \right) \\&{\mathrm{IVPFPWA}}\left( a_1, a_2\right) = \left( \left\langle \left[ \sqrt{1-\left( 1-\mu _p^L(a_1)^2\right) ^\frac{T_1}{\sum _{i=1}^2T_i}+1-\left( 1-\mu _p^L(a_2)^2\right) ^\frac{T_2}{\sum _{i=1}^2T_i}, \left( 1-\left( 1-\mu _p^L(a_1)^2\right) ^\frac{T_1}{\sum _{i=1}^2T_i}\right) \left( 1-\left( 1-\mu _p^L(a_2)^2\right) ^\frac{T_2}{\sum _{i=1}^2T_i}\right) },\right. \right. \right. \\&\quad \quad \left. \sqrt{1-\left( 1-\mu _p^U(a_1)^2\right) ^\frac{T_1}{\sum _{i=1}^2T_i}+1-\left( 1-\mu _p^U(a_2)^2\right) ^\frac{T_2}{\sum _{i=1}^2T_i}, \left( 1-\left( 1-\mu _p^U(a_1)^2\right) ^\frac{T_1}{\sum _{i=1}^2T_i}\right) \left( 1-\left( 1-\mu _p^U(a_2)^2\right) ^\frac{T_2}{\sum _{i=1}^2T_i}\right) }\right] , \left[ \left( v_p^L(a_1)\right) ^\frac{T_1}{\sum _{i=1}^2T_i}\left( v_p^L(a_2)\right) ^\frac{T_2}{\sum _{i=1}^2T_i},\right. \\&\quad \quad \left. \left. \left. \left( v_p^U(a_1)\right) ^\frac{T_1}{\sum _{i=1}^2T_i}\left( v_p^U(a_2)\right) ^\frac{T_2}{\sum _{i=1}^2T_i}\right] \right\rangle \right) \\&\quad =\left( \left\langle \left[ \sqrt{1-\left( 1-\mu _p^L(a_1)^2\right) ^\frac{T_1}{\sum _{i=1}^2T_i}\left( 1-\mu _p^L(a_2)^2\right) ^\frac{T_2}{\sum _{i=1}^2T_i}},\right. \right. \right. \\&\qquad \left. \sqrt{1-\left( 1-\mu _p^U(a_1)^2\right) ^\frac{T_1}{\sum _{i=1}^2T_i}\left( 1-\mu _p^U(a_2)^2\right) ^\frac{T_2}{\sum _{i=1}^2T_i}}\right] , \\&\quad \quad \left[ \left( v_p^L(a_1)\right) ^\frac{T_1}{\sum _{i=1}^2T_i}\left( v_p^L(a_2)\right) ^\frac{T_2}{\sum _{i=1}^2T_i},\right. \\&\quad \quad \left. \left. \left. \left( v_p^U(a_1)\right) ^\frac{T_1}{\sum _{i=1}^2T_i}\left( v_p^U(a_2)\right) ^\frac{T_2}{\sum _{i=1}^2T_i}\right] \right\rangle \right) \\ \end{aligned}$$
  2. (2)

    We suppose it holds for \(n=k\), that is

    $$\begin{aligned}&{\mathrm{IVPFPWA}}\left( a_1, a_2, a_3, \ldots , a_k\right) \\&\quad =\frac{T_1}{\sum _{i=1}^kT_i}a_1 \oplus \frac{T_2}{\sum _{i=1}^kT_i}a_2 \oplus , \ldots , \oplus \frac{T_n}{\sum _{i=1}^kT_i}a_n\\&\quad =\left( \left\langle \left[ \sqrt{1-\prod \limits _{i=1}^k\left( 1-\mu _p^L(a_i)^2\right) ^\frac{T_i}{\sum _{i=1}^kT_i}},\right. \right. \right. \\&\qquad \left. \sqrt{1-\prod \limits _{i=1}^k\left( 1-\mu _p^U(a_i)^2\right) ^\frac{T_i}{\sum _{i=1}^kT_i}} \right] , \\&\qquad \left. \left. \left[ \prod \limits _{i=1}^k\left( v_p^L(a_i)\right) ^\frac{T_i}{\sum _{i=1}^kT_i}, \prod \limits _{i=1}^k\left( v_p^U(a_i)\right) ^\frac{T_i}{\sum _{i=1}^kT_i} \right] \right\rangle \right) \end{aligned}$$
  3. (3)

    We suppose it holds for \(n=k+1\), we have

    $$\begin{aligned}&\frac{T_{k+1}}{\sum _{i=1}^{k+1}T_i}a_{k+1}=\left( \left\langle \left[ \sqrt{1-\left( 1-\mu _p^L\left( a_{k+1}\right) ^2\right) ^\frac{T_{k+1}}{\sum _{i=1}^{k+1}T_i}},\right. \right. \right. \\&\quad \quad \left. \sqrt{1-\left( 1-\mu _p^U\left( a_{k+1}\right) ^2\right) ^\frac{T_2}{\sum _{i=1}^{k+1}T_{k+1}}}\right] , \\&\quad \quad \left. \left. \left[ \left( v_p^L(a_{k+1})\right) ^\frac{T_{k+1}}{\sum _{i=1}^{k+1}T_{k+1}}, \left( v_p^U(a_{k+1})\right) ^\frac{T_{k+1}}{\sum _{i=1}^{k+1}T_{k+1}} \right] \right\rangle \right) \\&{\mathrm{IVPFPWA}}\left( a_1, a_2, a_3, \ldots , a_{k+1}\right) \\&\quad = \left( \left\langle \left[ \sqrt{1-\prod \limits _{i=1}^k\left( 1-\mu _p^L(a_i)^2\right) ^\frac{T_i}{\sum _{i=1}^kT_i}+1-\left( 1-\mu _p^U(a_{k+1})^2\right) ^\frac{T_{k+1}}{\sum _{i=1}^{k+1}T_i}-\left( 1-\prod \limits _{i=1}^k\left( 1-\mu _p^L(a_i)^2\right) ^\frac{T_i}{\sum _{i=1}^kT_i}\right) \left( 1-\left( 1-\mu _p^L(a_{k+1})^2\right) ^\frac{T_{k+1}}{\sum _{i=1}^{k+1}T_i}\right) },\right. \right. \right. \\&\quad\quad \left. \sqrt{1-\prod \limits _{i=1}^k(1-\mu _p^U(a_i)^2)^\frac{T_i}{\sum _{i=1}^kT_i}+1-(1-\mu _p^U(a_{k+1})^2)^\frac{T_{k+1}}{\sum _{i=1}^{k+1}T_i}-(1-\prod \limits _{i=1}^k(1-\mu _p^U(a_i)^2)^\frac{T_i}{\sum _{i=1}^kT_i})(1-(1-\mu _p^U(a_{k+1})^2)^\frac{T_{k+1}}{\sum _{i=1}^{k+1}T_i})}\right] \\&\quad \quad \left[ \left( v_P^L(a_{k+1})\right) ^\frac{T_{k+1}}{\sum _{i=1}^{k+1}T_i}\prod \limits _{i=1}^k\left( \mu _p^L(a_i)\right) ^\frac{T_i}{\sum _{i=1}^kT_i},\right. \\&\quad \quad \left. \left. \left. \left( v_P^U(a_{k+1})\right) ^\frac{T_{k+1}}{\sum _{i=1}^{k+1}T_i}\prod \limits _{i=1}^k\left( \mu _p^U(a_i)\right) ^\frac{T_i}{\sum _{i=1}^kT_i}\right] \right\rangle \right) \\&{\mathrm{IVPFPWA}}\left( a_1, a_2, a_3, \ldots , a_{k+1}\right) \\&\quad =\frac{T_1}{\sum _{i=1}^{k+1}T_i}a_1 \oplus \frac{T_2}{\sum _{i=1}^{k+1}T_i}a_2 \oplus , \ldots , \oplus \frac{T_n}{\sum _{i=1}^{k+1}T_i}a_n\\&\quad =\left( \left\langle \left[ \sqrt{1-\prod \limits _{i=1}^{k+1}\left( 1-\mu _p^L(a_i)^2\right) ^\frac{T_i}{\sum _{i=1}^{k+1}T_i}},\right. \right. \right. \\&\quad \quad \left. \sqrt{1-\prod \limits _{i=1}^{k+1}\left( 1-\mu _p^U(a_i)^2\right) ^\frac{T_i}{\sum _{i=1}^{k+1}T_i}} \right] , \\&\quad \quad \left. \left. \left[ \prod \limits _{i=1}^{k+1}\left( v_p^L(a_i)\right) ^\frac{T_i}{\sum _{i=1}^{k+1}T_i}, \prod \limits _{i=1}^{k+1}\left( v_p^U(a_i)\right) ^\frac{T_i}{\sum _{i=1}^{k+1}T_i} \right] \right\rangle \right) \end{aligned}$$

\(\square\)

Theorem 2

(Idempotency) Let\(a_i=\{\langle s_{\theta (a_i)},[\mu _{\tilde{P}}^L(a_i), \; \mu _{\tilde{P}}^U(a_i)]\), \(\quad [v_{\tilde{P}}^L(a_i)\), \(\quad v_{\tilde{P}}^U(a_i)]\rangle \}\)\((i=1, 2, 3, \ldots , n)\) be a collection of IVPFSs, if all\(a_i\)\((i=1,2,3, \ldots , n))\) are equal (\(a_i=a\)), then:

$$\begin{aligned}&{\mathrm{IVPFPWA}}\left( a_1, a_2, a_3, \ldots , a_n\right) =a\\&{\mathrm{IVPFPWA}}\left( a_1, a_2, a_3, \ldots , a_n\right) \\&\quad =\frac{T_1}{\sum _{i=1}^nT_i}a_1 \oplus \frac{T_2}{\sum _{i=1}^nT_i}a_2 \oplus , \ldots , \oplus \frac{T_n}{\sum _{i=1}^nT_i}a_n \\&\quad =\frac{T_1}{\sum _{i=1}^nT_i}a \oplus \frac{T_2}{\sum _{i=1}^nT_i}a \oplus , \ldots , \oplus \frac{T_n}{\sum _{i=1}^nT_i}a\\&\quad =\frac{\sum _{i=1}^nT_i}{\sum _{i=1}^nT_i}a=a \end{aligned}$$

Corollary 1

If \(a_i=\{\langle s_{\theta (a_i)},[\mu _{\tilde{P}}^L(a_i), \; \mu _{\tilde{P}}^U(a_i)]\), \([v_{\tilde{P}}^L(a_i)\), \(v_{\tilde{P}}^U(a_i)]\rangle \}\)\((i=1, 2, 3, \ldots , n)\) be a collection of IVPFSs, if all \(a_i\)\((i=1,2,3, \ldots , n))=a^*=([1,1], [0,0])\), then:

$$\begin{aligned}&{\mathrm{IVPFPWA}}\left( a_1, a_2, a_3, \ldots , a_n\right) \\&\quad ={\mathrm{IVPFPWA}}\left( a^*, a^*, a^*, \ldots , a^*\right) =([1,1], [0,0]) \end{aligned}$$

After the aggregating, it is also the largest IVPFS.

Proof

In the similar way showed above:

$$\begin{aligned} \begin{aligned}&{\mathrm{IVPFPWA}}\left( a_1, a_2, a_3, \ldots , a_n\right) \\&\quad =\frac{T_1}{\sum _{i=1}^nT_i}a_1 \oplus \frac{T_2}{\sum _{i=1}^nT_i}a_2 \oplus , \ldots , \oplus \frac{T_n}{\sum _{i=1}^nT_i}a_n \\&\quad =\frac{T_1}{\sum _{i=1}^nT_i}a^* \oplus \frac{T_2}{\sum _{i=1}^nT_i}a^* \oplus , \ldots , \oplus \frac{T_n}{\sum _{i=1}^nT_i}a^*\\&\quad =\frac{\sum _{i=1}^nT_i}{\sum _{i=1}^nT_i}a^*=a^* \end{aligned} \end{aligned}$$

Corollary 2

If \(a_i=\{\langle s_{\theta (a_i)},[\mu _{\tilde{P}}^L(a_i), \; \mu _{\tilde{P}}^U(a_i)], \; [v_{\tilde{P}}^L(a_i)\), \(v_{\tilde{P}}^U(a_i)]\rangle \}\)\((i=1, 2, 3, \ldots , n)\) be a collection of IVPFSs, if all\(a_i\)\((i=1,2,3, \ldots , n))=a_*=([0,0], [1,1])\), then:

$$\begin{aligned}&{\mathrm{IVPFPWA}}\left( a_1, a_2, a_3, \ldots , a_n\right) \\&\quad ={\mathrm{IVPFPWA}}\left( a_*=, a_*=, a_*=, \ldots , a_*=\right) \\&\quad =([0,0], [1,1]) \end{aligned}$$

After the aggregating, it is also the smallest IVPFS.

Proof

Since \(a_1=([0,0], [1,1])\), then we have the score function:

$$\begin{aligned} S(a_1)=0 \end{aligned}$$

Since:

$$\begin{aligned} T_i=\prod \limits _{k=1}^{i-1}S(a_k)\,(i=2, 3, \ldots , n)\hbox { and }T_1=1 \end{aligned}$$

We have

$$\begin{aligned} T_i&= \prod \limits _{k=1}^{i-1}S(a_k)=S(a_1)\times S(a_2)\times S(a_3)\times \cdots S(a_{i-1})\\&\quad =0\times S(a_2)\times S(a_3)\times \cdots S(a_{i-1})\\&\quad =0\,(i=2, 3, \ldots , n) \end{aligned}$$

Thus, \(\sum _{i=1}^nT_i=1\)

$$\begin{aligned} \begin{aligned}&{\mathrm{IVPFPWA}}\left( a_1, a_2, a_3, \ldots , a_n\right) \\&\quad =\frac{T_1}{\sum _{i=1}^nT_i}a_1 \oplus \frac{T_2}{\sum _{i=1}^nT_i}a_2 \oplus , \ldots , \oplus \frac{T_n}{\sum _{i=1}^nT_i}a_n \\&\quad =\frac{1}{1}a_1 \oplus \frac{0}{1}a_2 \oplus , \ldots , \oplus \frac{0}{1}a_n=\frac{1}{1}a_1=([0,0], [1,1]) \end{aligned} \end{aligned}$$

This reveals that when the criteria owning the highest priority has the smallest IVPFS, then any other criteria could not compensate it even they are all satisfied. \(\square\)

Theorem 3

(Boundary) If\(a_i=\{\langle s_{\theta (a_i)},[\mu _{\tilde{P}}^L(a_i), \; \mu _{\tilde{P}}^U(a_i)], \; [v_{\tilde{P}}^L(a_i)\), \(\quad v_{\tilde{P}}^U(a_i)]\rangle \}\)\((i=1, 2, 3, \ldots , n)\) be a collection of IVPFSs, and \(S(a_i)\) is the scores of IVPFS\(a_i\):

$$\begin{aligned} a_*&= \left( \left[ \min _i{\mu _{\tilde{P}}^L(a_i)}, \min _i{\mu _{\tilde{P}}^U(a_i)}\right] ,\left[ \max _i{v_{\tilde{P}}^L(a_i)}, \max _i{v_{\tilde{P}}^U(a_i)}\right] \right) \\ a^*&= \left( \left[ \max _i{\mu _{\tilde{P}}^L(a_i)}, \max _i{\mu _{\tilde{P}}^U(a_i)}\right] ,\left[ \min _i{v_{\tilde{P}}^L(a_i)}, \min _i{v_{\tilde{P}}^U(a_i)}\right] \right) \end{aligned}$$

Then, \(a_*\le {{IVPFPWA}}(a_1, a_2, a_3, \ldots , a_n)\le a^*\)

Proof

Since \(\min _i{\mu _{\tilde{P}}^L(a_i)}\le \mu _{\tilde{P}}^L(a_i)\le \max _i{\mu _{\tilde{P}}^L(a_i)}\), \(\min _i{\mu _{\tilde{P}}^U(a_i)}\le \mu _{\tilde{P}}^U(a_i)\le \max _i{\mu _{\tilde{P}}^U(a_i)}\), \(\min _i{v_{\tilde{P}}^L(a_i)}\le v_{\tilde{P}}^L(a_i)\le \max _i{v_{\tilde{P}}^L(a_i)}\), \(\min _i{v_{\tilde{P}}^U(a_i)}\le v_{\tilde{P}}^U(a_i)\le \max _i{v_{\tilde{P}}^U(a_i)}\)

$$\begin{aligned} \prod \limits _{i=1}^n\left( 1-\mu _p^L(a_i)^2\right) ^\frac{T_i}{\sum _{i=1}^nT_i}\ge & {} \prod \limits _{i=1}^n\left( 1-\max _i\left( \mu _p^L(a_i)^2\right) \right) ^\frac{T_i}{\sum _{i=1}^nT_i}\\&= 1-\max _i\left( \mu _p^L(a_i)^2\right) \end{aligned}$$

And then

$$\begin{aligned} 1-\prod \limits _{i=1}^n\left( 1-\mu _p^L(a_i)^2\right) ^\frac{T_i}{\sum _{i=1}^nT_i} \le \max _i\left( \mu _p^L(a_i)^2\right) \end{aligned}$$

In the similar way, we have:

$$\begin{aligned} 1-\prod \limits _{i=1}^n\left( 1-\mu _p^L(a_i)^2\right) ^\frac{T_i}{\sum _{i=1}^nT_i}\ge & {} \min _i\left( \mu _p^L(a_i)^2\right) , \quad \\ 1-\prod \limits _{i=1}^n\left( 1-\mu _p^L(b_i)^2\right) ^\frac{T_i}{\sum _{i=1}^nT_i}\ge & {} \min _i\left( \mu _p^L(b_i)^2\right) \end{aligned}$$

and

$$\begin{aligned}&\prod \limits _{i=1}^k\left( \min _i\left( v_p^L(a_i)\right) \right) ^\frac{T_i}{\sum _{i=1}^kT_i}\\&\quad \le \prod \limits _{i=1}^k\left( v_p^L(a_i)\right) ^\frac{T_i}{\sum _{i=1}^kT_i}\le \prod \limits _{i=1}^k\left( \max _i\left( v_p^L(a_i)\right) \right) ^\frac{T_i}{\sum _{i=1}^kT_i}\\&\prod \limits _{i=1}^k\left( \min _i\left( v_p^U(a_i)\right) \right) ^\frac{T_i}{\sum _{i=1}^kT_i}\\&\quad \le \prod \limits _{i=1}^k\left( v_p^U(a_i)\right) ^\frac{T_i}{\sum _{i=1}^kT_i}\le \prod \limits _{i=1}^k\left( \max _i\left( v_p^U(a_i)\right) \right) ^\frac{T_i}{\sum _{i=1}^kT_i}\\&{\mathrm{IVPFPWA}}\left( a_1, a_2, a_3, \ldots , a_n\right) \\&\quad =a={\left\{ \left\langle s_{\theta (a_i)},\left[ \mu _{\tilde{P}}^L(a_i), \quad \mu _{\tilde{P}}^U(a_i)\right] , \quad \left[ v_{\tilde{P}}^L(a_i), \quad v_{\tilde{P}}^U(a_i)\right] \right\rangle \right\} } \end{aligned}$$

then

$$\begin{aligned} S(a)&= \frac{\big [2+\left( \mu _P^L\right) ^2+\left( \mu _P^U\right) ^2-\left( v_P^L\right) ^2-\left( v_P^U\right) ^2\big ]}{4}\\\le & {} \frac{\max _i\left( \mu _p^L(a_i)^2\right) +\max _i\left( \mu _p^U(a_i)^2\right) -\min _i\left( v_p^L(a_i)^2\right) - \min _i\left( v_p^U(a_i)^2\right) }{4}\\&= S\left( a^*\right) \\ S(a)&= \frac{\big [2+\left( \mu _P^L\right) ^2+\left( \mu _P^U\right) ^2-\left( v_P^L\right) ^2-\left( v_P^U\right) ^2\big ]}{4}\\\ge & {} \frac{\min _i\left( \mu _p^L(a_i)^2\right) +\min _i\left( \mu _p^U(a_i)^2\right) -\max _i\left( v_p^L(a_i)^2\right) - \max _i\left( v_p^U(a_i)^2\right) }{4}\\&= S\left( a_*\right) \end{aligned}$$

If \(S(a_*)< S(a)< S(a^*)\) we could conclude that

$$\begin{aligned} S\left( a_*\right)< {\mathrm{IVPFPWA}}\left( a_1, a_2, a_3, \ldots , a_n\right) < S\left( a^*\right) \end{aligned}$$

Otherwise, we have \(S(a)=S(a^*)\):

$$\begin{aligned} S(a)&= \frac{\big [2+\left( \mu _P^L\right) ^2+\left( \mu _P^U\right) ^2-\left( v_P^L\right) ^2-\left( v_P^U\right) ^2\big ]}{4}\\&= \frac{\max _i\left( \mu _p^L(a_i)^2\right) +\max _i\left( \mu _p^U(a_i)^2\right) -\min _i\left( v_p^L(a_i)^2\right) - \min _i\left( v_p^U(a_i)^2\right) }{4}\\&= S(a^*) \end{aligned}$$

Then, we have

$$\begin{aligned} \mu _{\tilde{P}}^L(a_i)&= \max _i{\mu _{\tilde{P}}^L(a_i)}, \mu _{\tilde{P}}^U(a_i)= \max _i{\mu _{\tilde{P}}^U(a_i)}, v_{\tilde{P}}^L(a_i)\\&= \min _i{v_{\tilde{P}}^L(a_i)}, v_{\tilde{P}}^U(a_i)=\min _i{v_{\tilde{P}}^U(a_i)} \end{aligned}$$

Thus,

$$\begin{aligned} h(a)&= \frac{\big [\left( \mu _P^L\right) ^2+\left( \mu _P^U\right) ^2+\left( v_P^L\right) ^2+\left( v_P^U\right) ^2\big ]}{2}\\&= \frac{\max _i\left( \mu _p^L(a_i)^2\right) +\max _i\left( \mu _p^U(a_i)^2\right) +\min _i\left( v_p^L(a_i)^2\right) + \min _i\left( v_p^U(a_i)^2\right) }{2}\\&= h\left( a^*\right) \end{aligned}$$

Then, we have

$$\begin{aligned} {\mathrm{IVPFPWA}}\left( a_1, a_2, a_3, \ldots , a_n\right) =a^* \end{aligned}$$

On the other hand, if \(S(a)=S(a_*)\):

$$\begin{aligned} S(a)&= \frac{\big [2+\left( \mu _P^L\right) ^2+\left( \mu _P^U\right) ^2-\left( v_P^L\right) ^2-\left( v_P^U\right) ^2\big ]}{4}\\&= \frac{\min _i\left( \mu _p^L(a_i)^2\right) +\min _i\left( \mu _p^U(a_i)^2\right) -\max _i\left( v_p^L(a_i)^2\right) - \max _i\left( v_p^U(a_i)^2\right) }{4}\\&= S\left( a_*\right) \end{aligned}$$

Then we have:

$$\begin{aligned} \mu _{\tilde{P}}^L(a_i)&= \min _i{\mu _{\tilde{P}}^L(a_i)}, \mu _{\tilde{P}}^U(a_i)= \min _i{\mu _{\tilde{P}}^U(a_i)}, v_{\tilde{P}}^L(a_i)=\\&= \max _i{v_{\tilde{P}}^L(a_i)}, v_{\tilde{P}}^U(a_i)=\max _i{v_{\tilde{P}}^U(a_i)} \end{aligned}$$

Therefore

$$\begin{aligned} h(a)&= \frac{\big [\left( \mu _P^L\right) ^2+\left( \mu _P^U\right) ^2+\left( v_P^L\right) ^2+\left( v_P^U\right) ^2\big ]}{2}\\&= \frac{\min _i\left( \mu _p^L(a_i)^2\right) +\min _i\left( \mu _p^U(a_i)^2\right) +\max _i\left( v_p^L(a_i)^2\right) + \max _i\left( v_p^U(a_i)^2\right) }{2}\\&= h\left( a_*\right) \end{aligned}$$
$$\begin{aligned} {\mathrm{IVPFPWA}}\left( a_1, a_2, a_3, \ldots , a_n\right) =a_* \end{aligned}$$

\(\square\)

Definition 5

Let \(a_i=\{\langle s_{\theta (a_i)},[\mu _{\tilde{P}}^L(a_i), \; \mu _{\tilde{P}}^U(a_i)], \; [v_{\tilde{P}}^L(a_i)\), \(v_{\tilde{P}}^U(a_i)]\rangle \}\)\((i=1, 2, 3, \ldots , n)\) be a collection of IVPFSs, then their aggregated, where \([\mu _{\tilde{P}}^L(a_i), \; \mu _{\tilde{P}}^U(a_i)]\subset [0,1]\), \([v_{\tilde{P}}^L(a_i), v_{\tilde{P}}^U(a_i)] \subset [0,1]\) and let IVPFPWG \(V^n\rightarrow V\). If

$$\begin{aligned} \begin{aligned}&{\mathrm{IVPFPWG}}\left( a_1, a_2, a_3, \ldots , a_n\right) \\&\quad =a_1^{\frac{T_1}{\sum _{i=1}^nT_i}} \otimes a_2^{\frac{T_2}{\sum _{i=1}^nT_i}} \otimes , \ldots , \otimes a_n^{\frac{T_n}{\sum _{i=1}^nT_i}} \end{aligned} \end{aligned}$$

The interval-valued Pythagorean fuzzy prioritized weighted geometric operator is abbreviated as IVPFPWG with \(T_i=\prod _{j=1}^{i-1}S(a_j)\)\((i= 2, \ldots , n)\), \(T_1=1\) and \(S(a_j)\) is the score of IVPFS a.

Theorem 4

Let\(a_i=\{\langle s_{\theta (a_i)},[\mu _{\tilde{P}}^L(a_i), \; \mu _{\tilde{P}}^U(a_i)], \; [v_{\tilde{P}}^L(a_i)\), \(v_{\tilde{P}}^U(a_i)]\rangle \}\)\((i=1, 2, 3, \ldots , n)\) be a collection of IVPFSs, then their aggregated value is still an IVPFS by using IVPFPWG operator and:

$$\begin{aligned}&{\mathrm{IVPFPWG}}\left( a_1, a_2, a_3, \ldots , a_n\right) \\&\quad =\left( \left\langle \left[ \prod \limits _{i=1}^n(\mu _p^L(a_i))^\frac{T_i}{\sum _{i=1}^nT_i}, \prod \limits _{i=1}^n(\mu _p^U(a_i))^\frac{T_i}{\sum _{i=1}^nT_i} \right] ,\right. \right. \\&\qquad \left[ \sqrt{1-\prod \limits _{i=1}^n(1-v_p^L(a_i)^2)^\frac{T_i}{\sum _{i=1}^nT_i}},\right. \\&\quad \quad \left. \left. \left. \sqrt{1-\prod \limits _{i=1}^n(1-v_p^U(a_i)^2)^\frac{T_i}{\sum _{i=1}^nT_i}} \right] \right\rangle \right) \end{aligned}$$

where \(T_i=\prod _{j=1}^{i-1}S(a_j)\)\((i= 2, \ldots , n)\), \(T_1=1\) and S(a) is the score of IVPFSa.

Proof

The proof of Theorem 4 is similar to Theorem 1. \(\square\)

Theorem 5

(Idempotency) Let\(a_i=\{\langle s_{\theta (a_i)},[\mu _{\tilde{P}}^L(a_i), \; \mu _{\tilde{P}}^U(a_i)]\), \([v_{\tilde{P}}^L(a_i), \; v_{\tilde{P}}^U(a_i)]\rangle \}\)\((i=1, 2, 3, \ldots , n)\) be a collection of IVPFSs, if all\(a_i\)\((i=1,2,3, \ldots , n))\) are equal (\(a_i=a\)), then

$$\begin{aligned} {\mathrm{IVPFPWG}}\left( a_1, a_2, a_3, \ldots , a_n\right) =a \end{aligned}$$

Proof

The proof of Theorem 5 is similar to Theorem 2. \(\square\)

Theorem 6

(Boundary) If\(a_i=\{\langle s_{\theta (a_i)},[\mu _{\tilde{P}}^L(a_i), \; \mu _{\tilde{P}}^U(a_i)]\), \([v_{\tilde{P}}^L(a_i), \; v_{\tilde{P}}^U(a_i)]\rangle \}\)\((i=1, 2, 3, \ldots , n)\) be a collection of IVPFSs, and\(S(a_i)\) is the scores of IVPFS\(a_i\):

$$\begin{aligned} a_*&= \left( \left[ \min _i{\mu _{\tilde{P}}^L(a_i)}, \min _i{\mu _{\tilde{P}}^U(a_i)}\right] ,\left[ \max _i{v_{\tilde{P}}^L(a_i)}, \max _i{v_{\tilde{P}}^U(a_i)}\right] \right) \\ a^*&= \left( \left[ \max _i{\mu _{\tilde{P}}^L(a_i)}, \max _i{\mu _{\tilde{P}}^U(a_i)}\right] ,\left[ \min _i{v_{\tilde{P}}^L(a_i)}, \min _i{v_{\tilde{P}}^U(a_i)}\right] \right) \end{aligned}$$

Then \(a_*\le {{IVPFPWG}}(a_1, a_2, a_3, \ldots , a_n)\le a^*\).

Proof

The proof of Theorem 6 is similar to Theorem 3. \(\square\)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Han, Y., Deng, Y., Cao, Z. et al. An interval-valued Pythagorean prioritized operator-based game theoretical framework with its applications in multicriteria group decision making. Neural Comput & Applic 32, 7641–7659 (2020). https://doi.org/10.1007/s00521-019-04014-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04014-1

Keywords

Navigation