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Non-linear Programming Approach to Solve Bi-matrix Games with Payoffs Represented by I-fuzzy Numbers

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Abstract

The aim of this paper is to develop a new methodology for solving bi-matrix games with payoffs of Atanassov’s intuitionistic fuzzy (I-fuzzy) numbers. In this methodology, we define the concepts of I-fuzzy numbers, the value-index and ambiguity-index, and develop a difference-index based ranking method. Hereby the parameterized non-linear programming models are derived from a pair of auxiliary I-fuzzy mathematical programming models, which are used to determine solutions of bi-matrix games with payoffs represented by I-fuzzy numbers. Validity and applicability of the models and method proposed in this paper are illustrated with a practical example.

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Acknowledgments

This research was supported by the Key Program of National Natural Science Foundation of China (No. 71231003), the National Natural Science Foundation of China (No. 71171055) and the Social Science Planning Project of Fujian province (No. 2014C132).

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Correspondence to Wei Fei or Deng-Feng Li.

Appendices

Appendix 1: Proof of Theorem 1

According to Eqs. (5) and (7), for any \(\alpha \in [0,1]\), we have \((\rho \tilde{A} + \tilde{A}^{{\prime }} )^{\alpha } = \rho \tilde{A}^{\alpha } + \tilde{A}^{{{\prime }\alpha }}\). Hence, we have

$$\begin{aligned} L^{\alpha } (\rho \tilde{A} + \tilde{A}^{\prime}) + R^{\alpha } (\rho \tilde{A} + \tilde{A}^{\prime}) & = L^{\alpha } (\rho \tilde{A}) + L^{\alpha } (\tilde{A}^{\prime}) + R^{\alpha } (\rho \tilde{A}) + R^{\alpha } (\tilde{A}^{\prime}) \\ \, & = \rho (L^{\alpha } (\tilde{A}) + R^{\alpha } (\tilde{A})) + L^{\alpha } (\tilde{A}^{\prime}) + R^{\alpha } (\tilde{A}^{\prime}). \\ \end{aligned}$$

Combining with Eq. (9), we have

$$\begin{aligned} V_{\mu } (\rho \tilde{A} + \tilde{A}^{\prime}) & = \rho \int_{0}^{1} {[(L^{\alpha } (\tilde{A}) + R^{\alpha } (\tilde{A}))/2]f(\alpha ){\text{d}}\alpha } + \int_{0}^{1} {[(L^{\alpha } (\tilde{A}^{\prime}) + R^{\alpha } (\tilde{A}^{\prime}))/2]f(\alpha ){\text{d}}\alpha } \\ \, & = \rho V_{\mu } (w,\tilde{A}) + V_{\mu } (w,\tilde{A}^{\prime}), \\ \end{aligned}$$

i.e. \(V_{\mu } (\rho \tilde{A} + \tilde{A}^{\prime}) = \rho V_{\mu } (\tilde{A}) + V_{\mu } (\tilde{A}^{\prime})\).

For any \(\beta \in [0,1]\), it easily follows from Eqs. (6) and (8) that \((\rho \tilde{A} + \tilde{A}^{\prime})_{\beta } = \rho \tilde{A}_{\beta } + \tilde{A}^{\prime}_{\beta }\). According to Eq. (10), we can similarly prove that \(V_{\upsilon } (\rho \tilde{A} + \tilde{A}^{\prime}) = \rho V_{\upsilon } (\tilde{A}) + V_{\upsilon } (\tilde{A}^{\prime})\).

For any \(\alpha \in [0,1]\), if \(\rho \ge 0\), it is easily derived from Eqs. (5) and (7) that

$$\begin{aligned} R^{\alpha } (\rho \tilde{A} + \tilde{A}^{\prime}) - L^{\alpha } (\rho \tilde{A} + \tilde{A}^{\prime}) & = R^{\alpha } (\rho \tilde{A}) - L^{\alpha } (\rho \tilde{A}) + R^{\alpha } (\tilde{A}^{\prime}) - L^{\alpha } (\tilde{A}^{\prime}) \\ \, & = \rho (R^{\alpha } (\tilde{A}) - L^{\alpha } (\tilde{A})) + R^{\alpha } (\tilde{A}^{\prime}) - L^{\alpha } (\tilde{A}^{\prime}). \\ \end{aligned}$$

Then, combining with Eq. (11), we have

$$\begin{aligned} W_{\mu } (\rho \tilde{A} + \tilde{A}^{{\prime }} ) & = \rho \int_{0}^{1} {(R^{\alpha } (\tilde{A}) - L^{\alpha } (\tilde{A}))f(\alpha ){\text{d}}\alpha } + \int_{0}^{1} {(R^{\alpha } (\tilde{A}^{{\prime }} ) - L^{\alpha } (\tilde{A}^{{\prime }} ))f(\alpha ){\text{d}}\alpha } \\ \, & = \rho W_{\mu } (\tilde{A}) + W_{\mu } (\tilde{A}^{{\prime }} ). \\ \end{aligned}$$

Likewise, if ρ < 0, then \(R^{\alpha } (\rho \tilde{A} + \tilde{A}^{{\prime }} ) - L^{\alpha } (\rho \tilde{A} + \tilde{A}^{{\prime }} ) = \rho (L^{\alpha } (\tilde{A}) - R^{\alpha } (\tilde{A})) + R^{\alpha } (\tilde{A}^{{\prime }} ) - L^{\alpha } (\tilde{A}^{{\prime }} )\). Hereby, we have

$$\begin{aligned} W_{\mu } (\rho \tilde{A} + \tilde{A}^{{\prime }} ) & = \rho \int_{0}^{1} {(L^{\alpha } (\tilde{A}) - R^{\alpha } (\tilde{A}))f(\alpha ){\text{d}}\alpha } + \int_{0}^{1} {(R^{\alpha } (\tilde{A}^{{\prime }} ) - L^{\alpha } (\tilde{A}^{{\prime }} ))f(\alpha ){\text{d}}\alpha } \\ \, & = \rho W_{\mu } (\tilde{A}) + W_{\mu } (\tilde{A}^{{\prime }} ). \\ \end{aligned}$$

Therefore, we have proven that \(W_{\mu } (\rho \tilde{A} + \tilde{A}^{\prime}) = \rho W_{\mu } (\tilde{A}) + W_{\mu } (\tilde{A}^{\prime})\) for any \(\rho \in {\text{R}}\).

Similarly, according to Eqs. (6), (8) and (12), we can prove that \(W_{\upsilon } (u,\rho \tilde{A} + \tilde{A}^{\prime}) = \rho W_{\upsilon } (u,\tilde{A}) + W_{\upsilon } (u,\tilde{A}^{\prime}).\)

Appendix 2: Proof of Theorem 2

According to Theorem 1, it is derived from Eq. (13) that

$$\begin{aligned}& V_{\lambda } (\rho \tilde{A} + \tilde{A}^{\prime}) = \lambda V_{\upsilon } (\rho \tilde{A} + \tilde{A}^{\prime}) + (1 - \lambda )V_{\mu } (\rho \tilde{A} + \tilde{A}^{\prime}) \hfill \\ \, =& \rho [\lambda V_{\upsilon } (\tilde{A}) + (1 - \lambda )V_{\mu } (\tilde{A})] + \lambda V_{\upsilon } (\tilde{A}^{\prime}) + (1 - \lambda )V_{\mu } (\tilde{A}^{\prime}) = \rho V_{\lambda } (\tilde{A}) + V_{\lambda } (\tilde{A}^{\prime}) \hfill \\ \end{aligned}$$

i.e. \(V_{\lambda } (\rho \tilde{A} + \tilde{A}^{{\prime }} ) = \rho V_{\lambda } (\tilde{A}) + V_{\lambda } (\tilde{A}^{{\prime }} ).\)

Likewise, according to Theorem 1 and Eq. (14), we can prove that \(W_{\lambda } (\rho \tilde{A} + \tilde{A}^{{\prime }} ) = \rho W_{\lambda } (\tilde{A}) + W_{\lambda } (\tilde{A}^{{\prime }} ).\)

2.1 Proof of Theorem 3

According to Theorem 2, it is derived from Eq. (15) that

$$\begin{aligned} D_{\lambda } (\rho \tilde{A} + \tilde{A}^{{\prime }} ) = V_{\lambda } (\rho \tilde{A} + \tilde{A}^{{\prime }} ) - W_{\lambda } (\rho \tilde{A} + \tilde{A}^{{\prime }} ) = (\rho V_{\lambda } (\tilde{A}) + V_{\lambda } (\tilde{A}^{{\prime }} )) - (\rho W_{\lambda } (\tilde{A}) + W_{\lambda } (\tilde{A}^{{\prime }} )) \\ \, = \rho (V_{\lambda } (\tilde{A}) - W_{\lambda } (\tilde{A})) + (V_{\lambda } (\tilde{A}^{{\prime }} ) - W_{\lambda } (\tilde{A}^{{\prime }} )) = \rho D_{\lambda } (\tilde{A}) + D_{\lambda } (\tilde{A}^{{\prime }} ). \\ \end{aligned}$$

Thus, we have completed the proof of Theorem 3.

Appendix 3: Proof of Theorem 4

(P1) For any I-fuzzy number \(\tilde{A}\), it directly follows from Eq. (15) that \(D_{\lambda } (\tilde{A}) \ge D_{\lambda } (\tilde{A})\) for any \(\lambda \in [0,1]\). Hereby, according to Definition 1, we have \(\tilde{A} \ge_{\text{IF}} \tilde{A}\).

(P2) For any I-fuzzy numbers \(\tilde{A}\) and \(\tilde{A}^{{\prime }}\), according to Definition 1, we have \(D_{\lambda } (\tilde{A}) \ge D_{\lambda } (\tilde{A}^{\prime})\) and \(D_{\lambda } (\tilde{A}^{{\prime }} ) \ge D_{\lambda } (\tilde{A})\) for any \(\lambda \in [0,1]\). Thus, \(D_{\lambda } (\tilde{A}) = D_{\lambda } (\tilde{A}^{{\prime }} )\). Hereby, we have proven that \(\tilde{A} \,=\,_{\text{IF}} \tilde{A}^{{\prime }}\).

(P3) For any I-fuzzy numbers \(\tilde{A}\), \(\tilde{A}^{{\prime }}\) and \(\tilde{A}^{{{\prime \prime }}}\), according to Definition 1, we have \(D_{\lambda } (\tilde{A}) \ge D_{\lambda } (\tilde{A}^{{\prime }} )\) and \(D_{\lambda } (\tilde{A}^{{\prime }} ) \ge D_{\lambda } (\tilde{A}^{{\prime \prime }} )\) for any \(\lambda \in [0,1]\). Hence, \(D_{\lambda } (\tilde{A}) \ge D_{\lambda } (\tilde{A}^{\prime\prime})\). Therefore, we have proven that \(\tilde{A} \,\ge\,_{\text{IF}}\;\tilde{A}^{{\prime \prime }}\).

(P4) It can be easily seen from Eqs. (9)–(15) that the difference-indices of I-fuzzy numbers \(\tilde{A}\) and \(\tilde{A}^{{\prime }}\) are completely determined by themselves. Thus, the ranking order of \(\tilde{A}\) and \(\tilde{A}^{{\prime }}\) completely depends on \(D_{\lambda } (\tilde{A})\) and \(D_{\lambda } (\tilde{A}^{{\prime }} )\), which have nothing to do with the other I-fuzzy numbers under comparison. Therefore, we have proven that \(\tilde{A} >_{\text{IF}}\;\tilde{A}^{\prime}\) on \(F_{1}\) if and only if \(\tilde{A} >_{\text{IF}}\;\tilde{A}^{\prime}\) on \(F_{2}\).

(P5) It is derived from Eqs. (9)–(10), we can obtain

$$V_{\mu } (\tilde{A}) = \int_{0}^{1} {[(L^{\alpha } (\tilde{A}) + R^{\alpha } (\tilde{A}))/2]f(\alpha ){\text{d}}\alpha } \ge \int_{0}^{1} {2\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{2} } \,\alpha {\text{d}}\alpha = \underline{a}$$

and \(V_{\mu } (\tilde{A}') = \int_{0}^{1} {[(L^{\alpha } (\tilde{B}) + R^{\alpha } (\tilde{B}))/2]f(\alpha ){\text{d}}\alpha } \le \int_{0}^{1} {2\bar{a}'} \,\alpha {\text{d}}\alpha = \bar{a}'\)

Combining with \(\sup p(\tilde{A}) > \sup \sup p(\tilde{A}^{\prime})\), it directly follows that \(V_{\mu } (\tilde{A}) > V_{\mu } (\tilde{A}^{\prime})\).

Similarly, it follows that \(V_{\upsilon } (\tilde{A}) = \int_{0}^{1} {[(L_{\beta } (\tilde{A}) + R_{\beta } (\tilde{A}))/2]g(\beta ){\text{d}}\beta } \ge \int_{0}^{1} {2\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a} } \,\alpha {\text{d}}\alpha = \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}\) and \(V_{\upsilon } (\tilde{A}^{{\prime }} ) = \int_{0}^{1} {[(L_{\beta } (\tilde{B}) + R_{\beta } (\tilde{B}))/2]g(\beta ){\text{d}}\beta } \le \int_{0}^{1} {2\bar{a}^{{\prime }} } \,\alpha {\text{d}}\alpha = \bar{a}^{{\prime }}\). Combining with \(\sup p(\tilde{A}) > \sup \sup p(\tilde{A}^{\prime})\), it directly follows that \(V_{\upsilon } (\tilde{A}) > V_{\upsilon } (\tilde{A}^{'} )\). Therefore, \(\lambda V_{\upsilon } (\tilde{A}) + (1 - \lambda )V_{\mu } (\tilde{A}) > \lambda V_{\upsilon } (\tilde{A}') + (1 - \lambda )V_{\mu } (\tilde{A}')\), i.e. \(V_{\lambda } (\tilde{A}) > V_{\lambda } (\tilde{A}^{\prime})\).

In a similar way, we have \(\lambda W_{\mu } (\tilde{A}) + (1 - \lambda )W_{\upsilon } (\tilde{A}) > \lambda W_{\mu } (\tilde{A}') + (1 - \lambda )W_{\upsilon } (\tilde{A}')\), i.e. \(W_{\lambda } (\tilde{A}) > W_{\lambda } (\tilde{A}^{\prime})\).

According to Definition 1, for any \(\lambda \in [0,1]\), we have \(D_{\lambda } (\tilde{A}) > D_{\lambda } (\tilde{A}^{\prime})\) if and only if \(\tilde{A}\) is larger than \(\tilde{A}^{{\prime }}\), i.e. \(V(\tilde{A},\lambda ) - W(\tilde{A},\lambda ) > V(\tilde{A}^{{\prime }} ,\lambda ) - W(\tilde{A}^{{\prime }} ,\lambda )\). Hence, \(\tilde{A} >_{\text{IF}}\;\tilde{A}^{\prime}\).

For instance, taking \(f(\alpha ) = \alpha\) (\(\alpha \in [0,1]\)) and \(g(\beta ) = 1 - \beta\) (\(\beta \in [0,1]\)), by using Eqs. (9)–(15), the difference-indexes of any I-fuzzy number \(\tilde{A}\) can be obtained as follows:

$$\begin{aligned} D_{\lambda } (\tilde{A}) & = V(\tilde{A},\lambda ) - W(\tilde{A},\lambda ) = [\lambda V_{\upsilon } (\tilde{A}) + (1 - \lambda )V_{\mu } (\tilde{A})] - [\lambda W_{\mu } (\tilde{A}) + (1 - \lambda )W_{\upsilon } (\tilde{A})] \\ \, & = {{[(3\lambda - 2)\bar{a}_{2} + (6\lambda - 4)a_{2r} + (4 - 2\lambda )a_{2l} + (2 - \lambda )\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{2} )]} \mathord{\left/ {\vphantom {{[(3\lambda - 2)\bar{a}_{2} + (6\lambda - 4)a_{2r} + (4 - 2\lambda )a_{2l} + (2 - \lambda )\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{2} )]} {12}}} \right. \kern-0pt} {12}} \\ \, & \quad + {{[(1 - 3\lambda )\bar{a}_{1} + (2 - 6\lambda )a_{1r} + (2 + 2\lambda )a_{1l} + (1 + \lambda )\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{1} )]} \mathord{\left/ {\vphantom {{[(1 - 3\lambda )\bar{a}_{1} + (2 - 6\lambda )a_{1r} + (2 + 2\lambda )a_{1l} + (1 + \lambda )\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{1} )]} {12}}} \right. \kern-0pt} {12}}, \\ \end{aligned}$$
(23)

where \(\underline{a}_{2} \le \underline{a}_{1} \le a_{2l} \le a_{1l} \le a_{1r} \le a_{2r} \le \bar{a}_{1} \le \bar{a}_{2}\). If \(\sup p(\tilde{A}) > \sup \sup p(\tilde{A}^{\prime})\), i.e. \(\underline{a} '_{1} \le a'_{2l} \le a'_{1l} \le\) \(a'_{1r} \le a'_{2r} \le \bar{a}'_{1} \le \bar{a}_{2} ' < \underline{a}_{2} \le \underline{a}_{1} \le a_{2l} \le a_{1l} \le a_{1r} \le a_{2r} \le \bar{a}_{1} \le \bar{a}_{2}\), then it follows from Eq. (23) that

$$\begin{aligned} D_{\lambda } (\tilde{A}) - D_{\lambda } (\tilde{A}') > {{(3\lambda - 2)(\bar{a}_{2} - \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{2} ) + (6\lambda - 4)(a_{2r} - \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{2} ) + (4 - 2\lambda )(a_{2l} - \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{2} )]} \mathord{\left/ {\vphantom {{(3\lambda - 2)(\bar{a}_{2} - \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{2} ) + (6\lambda - 4)(a_{2r} - \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{2} ) + (4 - 2\lambda )(a_{2l} - \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{2} )]} {12}}} \right. \kern-0pt} {12}} \\ & + [{{(1 - 3\lambda )(\bar{a}_{1} - \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{2} ) + (2 - 6\lambda )(a_{1r} - \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{2} ) + (2 + 2\lambda )(a_{1l} - \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{2} ) + (1 + \lambda )(\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{1} - \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{2} )]} \mathord{\left/ {\vphantom {{(1 - 3\lambda )(\bar{a}_{1} - \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{2} ) + (2 - 6\lambda )(a_{1r} - \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{2} ) + (2 + 2\lambda )(a_{1l} - \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{2} ) + (1 + \lambda )(\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{1} - \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{2} )]} {12}}} \right. \kern-0pt} {12}} \\ & \ge {{[(7\lambda - 2)(a_{2l} - \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{2} ) + (6 - 6\lambda )(\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{1} - \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{2} )]} \mathord{\left/ {\vphantom {{[(7\lambda - 2)(a_{2l} - \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{2} ) + (6 - 6\lambda )(\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{1} - \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{2} )]} {12}}} \right. \kern-0pt} {12}} \\ & \ge (\lambda + 4)(\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{1} - \underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{a}_{2} ) \ge 0 \\ \end{aligned}.$$

Therefore, we have proven that if \(\sup p(\tilde{A}) > \sup \sup p(\tilde{A}^{\prime})\), then \(\tilde{A} >_{\text{IF}} \tilde{A}^{\prime}\).

(P6) In the same way to that of (P3), for any \(\lambda \in [0,1]\), it follows from Definition 1 that

$$D_{\lambda } (\tilde{A}) \ge D_{\lambda } (\tilde{A}^{\prime}) .$$
(24)

Combining with Theorem 1, we have \(D_{\lambda } (\tilde{A} + \tilde{A}^{\prime\prime}) = D_{\lambda } (\tilde{A}) +\) \(D_{\lambda } (\tilde{A}^{\prime\prime}) \ge D_{\lambda } (\tilde{A}^{\prime}) + D_{\lambda } (\tilde{A}^{\prime\prime}){ = }D_{\lambda } (\tilde{A}^{\prime}{ + }\tilde{A}^{\prime\prime})\), i.e.

$$D_{\lambda } (\tilde{A} + \tilde{A}^{\prime\prime}) \ge D_{\lambda } (\tilde{A}^{\prime}{ + }\tilde{A}^{\prime\prime}) .$$
(25)

Hence, we have \(\tilde{A} + \tilde{A}^{\prime\prime} \,\ge\,_{\text{IF}}\;\tilde{A}^{\prime} + \tilde{A}^{\prime\prime}\).

(P6′) Eq. (24) is a strictly inequality due to \(\tilde{A}\, >\,_{\text{IF}}\;\tilde{A}^{\prime}\). Thus, Eq. (25) is also a strictly inequality. According to Definition 1, we have proven that \(\tilde{A} + \tilde{A}^{{\prime \prime }}>_{\text{IF}}\;\tilde{A}^{{\prime }} + \tilde{A}^{{\prime \prime }}\).

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Yang, J., Fei, W. & Li, DF. Non-linear Programming Approach to Solve Bi-matrix Games with Payoffs Represented by I-fuzzy Numbers. Int. J. Fuzzy Syst. 18, 492–503 (2016). https://doi.org/10.1007/s40815-015-0052-1

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