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Article Contents

The effect of caputo fractional difference operator on a novel game theory model

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  • It is well-known that fractional-order discrete-time systems have a major advantage over their integer-order counterparts, because they can better describe the memory characteristics and the historical dependence of the underlying physical phenomenon. This paper presents a novel fractional-order triopoly game with bounded rationality, where three firms producing differentiated products compete over a common market. The proposed game theory model consists of three fractional-order difference equations and is characterized by eight equilibria, including the Nash fixed point. When suitable values for the fractional order are considered, the stability of the Nash equilibrium is lost via a Neimark-Sacker bifurcation or via a flip bifurcation. As a consequence, a number of chaotic attractors appear in the system dynamics, indicating that the behaviour of the economic model becomes unpredictable, independently of the actions of the considered firm. The presence of chaos is confirmed via both the computation of the maximum Lyapunov exponent and the 0-1 test. Finally, an entropy algorithm is used to measure the complexity of the proposed game theory model.

    Mathematics Subject Classification: 37-00, 91A25, 26A33, 44A55, 65P20, 37A35.

    Citation:

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  • Figure 1.  The phase portraits of game (8) with parameter values $ \alpha = 2, \, \varepsilon_1 = 1.042811791, \, \varepsilon _{2} = 1.1, \varepsilon _{3} = 1.1, \zeta_{1} = 0.4, \zeta_{2} = 0.8, \zeta_{3} = 0.1, \gamma _{1} = 0.07, \gamma _{2} = 0.03, \gamma _{3} = 0.4 $ for different fractional order values: (a) $ \nu = 1, (b)\, \nu = 0.9, (c) \, \nu = 0.865, (d)\, \nu = 0.81 $

    Figure 2.  (a) Bifurcation diagram versus $ \nu $ when $ \alpha = 2, \varepsilon _{1} = 1.042811791, \varepsilon _{2} = 1.1, \varepsilon _{3} = 1.1, \zeta_{1} = 0.4, \zeta_{2} = 0.8, \zeta_{3} = 0.1, \gamma _{1} = 0.07, \gamma _{2} = 0.03, \gamma _{3} = 0.4 $.(b) The maximum Lyapunov exponents with respect to $ \nu $ corresponding to (a)

    Figure 3.  (a) Bifurcation diagram versus $ \varepsilon _1 $ with order $ \nu = 0.985 $ when $ \alpha = 2, \varepsilon _{2} = 1.1, \varepsilon _{3} = 1.1, \zeta_{1} = 0.4, \zeta_{2} = 0.8, \zeta_{3} = 0.1, \gamma _{1} = 0.07, \gamma _{2} = 0.03, \gamma _{3} = 0.4 $. (b) Bifurcation diagram versus $ \varepsilon _{1} $ with order $ \nu = 0.972. $

    Figure 4.  (a) Bifurcation diagram versus $ \nu $ when $ \alpha = 2, \varepsilon _{1} = 1.44, \varepsilon _{2} = 1.1, \varepsilon _{3} = 1.1, \zeta_{1} = 0.4, \zeta_{2} = 0.8, \zeta_{3} = 0.1, \gamma _{1} = 0.07, \gamma _{2} = 0.03, \gamma _{3} = 0.4 $. (b) The maximum Lyapunov exponents with respect to $ \nu $ corresponding to (a)

    Figure 5.  Chaotic attractor of the proposed game with $\nu =0.98$ and for $\alpha =2,% \varepsilon _{1}=1.44,\varepsilon _{2}=1.1,\varepsilon _{3}=1.1,\zeta_{1}=0.4,\zeta_{2}=0.8,\zeta_{3}=0.1,\gamma _{1}=0.07, \gamma _{2}=0.03,\gamma _{3}=0.4$.

    Figure 6.  Periodic attractor of the proposed game with $ \nu = 0.975 $ and for $ \alpha = 2, \varepsilon _{1} = 1.44,\varepsilon _{2} = 1.1,\varepsilon _{3} = 1.1,\zeta_{1} = 0.4,\zeta_{2} = 0.8,\zeta_{3} = 0.1,\gamma _{1} = 0.07, \gamma _{2} = 0.03,\gamma _{3} = 0.4 $

    Figure 7.  Chaotic attractor of the proposed game with $ \nu = 0.96 $ and for $ \alpha = 2, \varepsilon _{1} = 1.44, \varepsilon _{2} = 1.1, \varepsilon _{3} = 1.1, \zeta_{1} = 0.4, \zeta_{2} = 0.8, \zeta_{3} = 0.1, \gamma _{1} = 0.07, \gamma _{2} = 0.03, \gamma _{3} = 0.4 $

    Figure 8.  (a) Bifurcation diagram versus $ \varepsilon_1 $ with order $ \nu = 1 $ when $ \alpha = 1, \varepsilon _{2} = 0.9, \varepsilon _{3} = 0.9, \zeta_{1} = 0.4, \zeta_{2} = 0.8, \zeta_{3} = 0.1, \gamma _{1} = 0.07, \gamma _{2} = 0.03, \gamma _{3} = 0.4 $ (b) Bifurcation diagram versus $ \varepsilon _{1} $ with order $ \nu = 0.7635. $

    Figure 9.  Periodic attractor of the proposed game with $ \nu = 0.7635 $ for $ \varepsilon_1 = 2.6 $ and $ \alpha = 1, \varepsilon _{2} = 0.9,\varepsilon _{3} = 0.9,\zeta_{1} = 0.4,\zeta_{2} = 0.8,\zeta_{3} = 0.1, \gamma _{1} = 0.07, \gamma _{2} = 0.03, \gamma _{3} = 0.4 $

    Figure 10.  Chaotic attractor of the proposed game with $ \nu = 0.7635 $ for $ \varepsilon_1 = 2.9 $ and $ \alpha = 1, \varepsilon _{2} = 0.9,\varepsilon _{3} = 0.9,\zeta_{1} = 0.4,\zeta_{2} = 0.8,\zeta_{3} = 0.1, \gamma _{1} = 0.07, \gamma _{2} = 0.03, \gamma _{3} = 0.4 $

    Figure 11.  0-1 test: regular dynamics of the translation components $ (p, q) $ of the Cournot game (8) for $ \alpha = 2 $, $ \varepsilon _{2} = 1.1, \varepsilon _{3} = 1.1, \zeta_{1} = 0.4, \zeta_{2} = 0.8, \zeta_{3} = 0.1, \gamma _{1} = 0.07, \gamma _{2} = 0.03, \gamma _{3} = 0.4 $ with fractional order $ \nu = 0.865 $

    Figure 12.  0-1 test: regular dynamics of the translation components $ (p, q) $ of the Cournot game (8) for $ \alpha = 2, \varepsilon _{2} = 1.1, \varepsilon _{3} = 1.1, \zeta_{1} = 0.4, \zeta_{2} = 0.8, \zeta_{3} = 0.1, \gamma _{1} = 0.07, \gamma _{2} = 0.03, \gamma _{3} = 0.4 $ with fractional order $ \nu = 0.7635 $

    Figure 13.  ApEn of the game model (8) vesrus $ \nu$

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