Stability and Bifurcation Analysis of Discrete Dynamical Systems 2021View this Special Issue
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Longfei Wei, Haiwei Wang, Jing Wang, Jialong Hou, "Dynamics and Stability Analysis of a Stackelberg Mixed Duopoly Game with Price Competition in Insurance Market", Discrete Dynamics in Nature and Society, vol. 2021, Article ID 3985367, 18 pages, 2021. https://doi.org/10.1155/2021/3985367
Dynamics and Stability Analysis of a Stackelberg Mixed Duopoly Game with Price Competition in Insurance Market
This paper investigates the dynamical behaviors of a Stackelberg mixed duopoly game with price competition in the insurance market, involving one state-owned public insurance company and one private insurance company. We study and compare the stability conditions for the Nash equilibrium points of two sequential-move games, public leadership, and private leadership games. Numerical simulations present complicated dynamic behaviors. It is shown that the Nash equilibrium becomes unstable as the price adjustment speed increases, and the system eventually becomes chaotic via flip bifurcation. Moreover, the time-delayed feedback control is used to force the system back to stability.
The insurance market in most countries has shown typical characteristics of an oligopoly market. Most of the existing literature on oligopoly games in the insurance market concentrates on static games, while less on dynamic games. The competition among oligarchs in the insurance market is mainly reflected in price competition. One of the most famous price game models is the Bertrand model. For a long time, the complex dynamics in Bertrand oligopoly games have been researched widely. For instance, Zhang and Ma  investigated a nonlinear Bertrand game of insurance market wherein one of the oligarchs made the decision only with bounded rationality without delay, while the other part made the delayed decision with one period and two periods. Xu and Ma  established a price game model with delay based on the insurance market and discussed the existence and stability of equilibrium points. Ahmed et al.  analyzed the dynamic behaviors of a differentiated Bertrand duopoly game, in which boundedly rational players apply a gradient adjustment mechanism to update their price in each period. Ma and Si  introduced a continuous Bertrand duopoly game model with a two-stage delay and investigated the influence of delay parameters on the dynamic characteristics of the system. Zhao  studied the dynamic properties of a Bertrand game model with three oligarchs in which enterprises have heterogeneous expectations. Askar and Al-khedhairi  introduced two different Bertrand duopoly models where the first one is the competition of price in which each player wants to maximize its relative profit, and the second model is the classic Bertrand competition in which the players want to maximize their profits. There is still some literature which studies the Bertrand game theory and the complexity of the dynamical system; see [7–12]. The above price game models assume that firms play simultaneous-move games; however, the competition among oligarchs in the real market is mostly a dynamic game or sequential game. It is well known that the most classical sequential game is the Stackelberg game. Shi et al.  proposed a price-Stackelberg duopoly game model with boundedly rational players and studied the complex dynamical behaviors. Shah et al.  applied the Stackelberg game with stochastic demand for the vendor-retailer system. Wang  investigated a manufacturer-Stackelberg game in a price competition supply chain under a fuzzy decision environment. Xiao et al.  analyzed a nonlinear two-dimensional duopoly Stackelberg game, including two types of heterogeneous players which are bounded rational players and adaptable players. Other interesting works that can be used to extend the applications of such economic games have been reported in [17–20].
Most countries have state-owned public firms that have a substantial influence on their market competitors. The competition in mixed oligopolies, in which a state-owned public firm competes against a private firm, is widespread in the real market. Such mixed oligopolies occur in various industries, such as automobile, postal services, hospitals, education, banking, and insurance . Analysis of mixed oligopolies can be dated to 1966 when Merrill and Schneider first put forward the assumption that a public firm maximizes welfare (consumer surplus plus firm profits), while private firms maximize profits . Some discussions of the mixed oligopolies were presented in [23–25]. In related works, there are few studies on Stackelberg mixed oligopolies. For instance, Wang and Mukherjee  showed welfare under different numbers of private firms under the assumption of a public firm as the Stackelberg leader and private firms as Stackelberg followers. Wang and Lee  examined the influence of the order of the firms’ moves on the social efficiency with foreign ownership and free entry in a mixed oligopoly market. Tao et al.  studied and compared total welfare in Stackelberg mixed duopolies when either the public firm or the private firm acts as the leader. Gelves and Heywood  compared the merger between the public leader and the private follower with unilateral privatization of the public leader. Hirose and Matsumura  compared welfare and profit under price and quantity competition in Stackelberg mixed duopolies, wherein a state-owned public firm competes against a private firm. These studies mainly focus on quantity competition, less on price competition, and all of them discuss directly the decisions of competitors in Nash equilibrium, and their dynamics have not been studied. However, the dynamic adjustment process converging to Nash equilibrium, and the stability of Nash equilibrium are important in the real market.
In this paper, we pay attention to a Stackelberg mixed duopoly game of price competition between a state-owned public insurance company and a private insurance company in the insurance market and study how this duopoly game evolves to different Nash equilibriums in two sequential-move games, public leadership and private leadership games. Simulations of the complicated dynamic behaviors and chaos control are presented, and the welfare and profit in Nash equilibrium are also discussed.
The rest of this paper is organized as follows: in Section 2, the Stackelberg mixed duopoly game model with price competition is briefly described. In Section 3, the existence of equilibrium points, the instability of bounded equilibrium, and the local stability conditions of Nash equilibrium in two sequential-move cases are analyzed. In Section 4, numerical simulations are used to show the dynamic features of the game, including bifurcation diagram, maximum Lyapunov exponents, phase portrait, and sensitive dependence on initial conditions. The comparison of welfare and profit in Nash equilibrium under two sequential-move cases is also shown by the figures in this section. In Section 5, time-delayed feedback control is used to suppress the appearance of chaotic behavior for the proposed system. Finally, the paper is concluded in Section 6.
2. The Model
We consider the dynamical behaviors of a duopoly price game in the insurance market with two insurance companies (ICs), labeled by . We assume is a state-owned public insurance company, while is a private insurance company, and the two ICs use different decisional mechanisms. Each chooses a nonnegative real number , which is the price of its own product. The strategy profile results in a corresponding market quantity demanded . We adopt a standard duopoly model with differentiated products and linear demand [30, 31]. The utility function of the representative consumer is given by
Parameters and are positive constants and measures the degree of horizontal differentiation, where a smaller indicates a larger degree of insurance product differentiation. Then, the inverse demand functions are
According to equation (2), the direct demand functions can be given by
We denote the marginal cost of with a positive constant , assuming . In addition, we assume that is sufficiently large and that is not too large to assure interior solutions in the following games. Since is a public firm, its payoff is the domestic social surplus (welfare) [21, 28] and is given by
For the convenience of expression, equation (4) can be simplified to the following form:and is a private firm and its payoff is its own profit:
In this study, we consider two sequential-move games, public leadership and private leadership games. To construct and investigate the dynamic characteristics of the two games, we assume that both ICs are bounded rational, in which the players modify their price decisions dynamically according to the marginal payoff . When () is the leader (follower), , the dynamic system for two ICs has the following form:where and are positive parameters known as the speed of price adjustment and . At , the leader takes the lead in determining the price . Then, the follower chooses to maximize its own payoff after observing , which can be seen in the second equation of system (7).
3. Equilibrium Points and Local Stability
3.1. Public Leadership
In this case, we analyze a Stackelberg model in which () is the leader (follower). At , the leader takes the lead in determining the price on the basis of marginal payoff. The marginal payoff of is
Then, we can get
The follower has an advantage over the leader. has known the current price of when it chooses its price . Hence, the price of at period is determined by its own price of period and ’s price of period . Then, the marginal payoff of is given by
Then, we have
Thus, the duopoly game can be described by a discrete-time dynamic map as follows:
When the market structure is stable enough at time , are approximately equal to . Setting and in equation (12), we can get the following equilibria:where
The points , , and are known as boundary equilibrium points while is the Nash equilibrium point. For the stake of economic significance, the equilibrium points should be nonnegative. It is easily concluded that , , and are all positive according to the conditions that , and are positive parameters, , and . represents that every IC has no price; and represent the monopolies and , respectively; and represents both ICs competing in a duopoly game with equilibrium prices of and .
To analyze the local stability of the equilibrium points, we consider the Jacobian matrix of system (12), which can be given bywhere
Lemma 1. Suppose the Jacobian matrix (15) at any fixed point has two eigenvalues and , then(i)If and , then is locally asymptotically stable and is called an attracting node(ii)If and , then is an unstable repelling node(iii)If and (or and ), then is an unstable saddle point(iv)If and (or and ), then is a nonhyperbolic point
Theorem 1. The boundary equilibrium is an unstable repelling node.
Proof. At , the Jacobian matrix takes the form:The eigenvalues of are given byThey are both greater than 1, so the point is an unstable repelling node.
Theorem 2. The boundary equilibrium is unstable. More precisely, we have the following:(i)If , then is a saddle point(ii)If , then is a nonhyperbolic point(iii)If , then is a repelling node
Proof. At , the Jacobian matrix becomeswhereThe eigenvalues of are given by is always satisfied, so is unstable. Simple calculations show that if , if , and if . This concludes the proof.
Theorem 3. The boundary equilibrium is unstable. More precisely, we have the following:(i)If , then is a saddle point(ii)If , then is a nonhyperbolic point(iii)If , then is a repelling node
Proof. At , the Jacobian matrix isThe eigenvalues of are given by is always satisfied, so is unstable. Simple calculations show that if , if , and if . This concludes the proof.
The boundary equilibrium points correspond to the situation of one or both ICs going bankrupt. That is, the duopoly market becomes a monopoly, or both of the ICs are out of the insurance market at the same time. However, from an economic point of view, we should pay more attention to the situation of the duopoly market. Hence, we are more interested in investigating the local stability properties of the Nash equilibrium point . The Jacobian matrix at takes the form:The characteristic equation iswhere is the trace and is the determinant, which are given byThen, we havewhich indicates that the eigenvalues are real. According to Jury conditions , the necessary and sufficient conditions for the local stability of are as follows:The above conditions are, respectively, equivalent toClearly, the condition (ii) is always satisfied. Then, the following result can be obtained from the derivation of conditions (i) and (iii).
Theorem 4. The Nash equilibrium point is asymptotically locally stable if
Proof. Inequality (29) can be rewritten asInequality (31) can be modified asInequality (34) holds if and only if the following two conditions are satisfied:orIt is obvious that the first condition implies inequality (33). On the other hand, inequality (33) is impossible in the -plane determined by the second condition. This concludes the proof.
Condition (32) defines a stability region in the plane of the price adjustment speed (see Figure 1(a)). The boundary intersects the axes and at points and , respectively, whose coordinates areSimple calculations show that if or , one of the absolute values of eigenvalues is equal to 1. Inequalities (29) and (31) define a bounded region of stability beyond which a flip bifurcation and a Neimark–Sacker bifurcation occur, respectively [33, 36]. According to Theorem 4, we can get that the Nash equilibrium point loses its stability only via flip bifurcation when one or both values of and are greater than the boundary values of the stability region.
According to the expressions of the coordinate value of the boundary points, we can clearly find out the effects of the changing values of parameters , and on the stability region, respectively, but it is difficult to directly observe how parameter affects the stability regions from the expression. By computer work on the stability conditions for four cases , the stability regions in the -plane are numerically obtained and are plotted in Figure 1(a). We can see that increasing reduces the stability region, and the stability of system (12) is more sensitive to . If is relatively lower, even if is relatively higher, system (12) is stable.
3.2. Private Leadership
When () is the leader (follower), we can write system (7) as
Setting and in equation (38), we have the following equilibria:where
Points , , and are boundary equilibrium points and is the unique Nash equilibrium point. It is clear that , , and are all positive according to the conditions that , and are positive parameters, , and .
The Jacobian matrix of system (38) can be given bywhere
Theorem 5. The boundary equilibrium is an unstable repelling node.
Proof. The Jacobian matrix (41) at the point takes the form:The eigenvalues of are given byIt is clear that they are both greater than 1, so the point is an unstable repelling node.
Theorem 6. The boundary equilibrium is unstable. More precisely, we have the following:(i)If , then is a saddle point(ii)If , then is a nonhyperbolic point(iii)If , then is a repelling node
Proof. At , the Jacobian matrix becomesThe eigenvalues of are given by is always satisfied, so is unstable. Simple calculations show that if , if , and if . This concludes the proof.
Theorem 7. The boundary equilibrium is unstable. More precisely, we have the following:(i)If , then is a saddle point(ii)If , then is a nonhyperbolic point(iii)If , then is a repelling node
Proof. At , the Jacobian matrix isThe eigenvalues of are given by is always satisfied, so is unstable. Simple calculations show that if , if , and if . This concludes the proof.
Next, we investigate the local stability properties of the Nash equilibrium point . The Jacobian matrix at the point takes the form:The trace and determinant of areThen, we haveIt indicates that the eigenvalues are real. According to Jury conditions, the necessary and sufficient conditions for the local stability of can be given byClearly, condition (ii) is always satisfied. According to conditions (i) and (iii), we have the following result.
Theorem 8. The Nash equilibrium point is asymptotically locally stable if
Proof. Inequality (52) can be rewritten asInequality (54) can be modified asWe complete the proof by imitating the discussions in Theorem 4.
The stability region for the Nash equilibrium point is defined by the inequalities in condition (55). The boundary curve intersects the axes and at points and , respectively, whose coordinates areSimple calculations show that if or , one of the absolute values of eigenvalues is equal to 1. According to Theorem 8, we can get that the Nash equilibrium point is stable inside the stability region, and loses its stability through flip bifurcation. By computer work on the stability conditions for four cases , the stability regions of Nash equilibrium point in the -plane are shown in Figure 1(b). Comparing Figures 1(a) and 1(b), we see that when the parameter is close to zero, the stability regions of Nash equilibrium points and are similar, but the difference between them becomes larger with the increase of the parameter .
4. Numerical Simulation
In this section, we perform some numerical simulations for the complex dynamical behaviors of systems (12) and (38) and show how the systems evolve under different levels of parameters. Such simulations include a bifurcation diagram, maximum Lyapunov exponents, phase portrait, and sensitive dependence on initial conditions to further study the unpredictable behavior of the game. In all numerical simulations, parameters , , , , and are commonly set as , , , , and . We perform numerical simulations for the following two situations, respectively.
4.1. Public Leadership
In a real insurance market, the demand function and their marginal costs are relatively certain, so the price adjustment speed is regarded as an important strategy for ICs to pursue profit maximization. In this case, we show by numerical simulations how system (12) evolves under different levels of the parameter , the price adjustment speed of . We fix the parameter , and the bifurcation diagram with respect to the fact that parameter is plotted in Figure 2(a). It shows that the equilibrium point begins stable; increasing the value of gives the appearance of a stable 2-cycle period through flip bifurcation, then increasing the value of further shows a sequence of period-doubling bifurcations followed by cycles with high periodicity; and chaotic scenario occurs in the end. The corresponding maximum Lyapunov exponents are plotted in Figure 2(b) to show bifurcation and chaos, where positive values show the chaotic behaviors.
The observations from Figure 2(a) tell that system (12) becomes unstable through the period-doubling bifurcation when the parameter takes suitable values. About the case in Figure 2, five two-dimension phase portraits for different values of are shown in Figure 3, which give a more detailed description of the orbits of system (12). The phase portraits show a flip bifurcation process, where chaos occurs when takes its value big enough, and strange attractors can be seen in the fifth phase portrait in Figure 3. The strange attractor reflects the complexity of ICs’ dynamic price competition in chaos.
The sensitivity to initial conditions is also one of the important characteristics of chaos. Figure 4 reflects the case that the initial state ranges from 0.1785 to 0.1786 with other parameters keeping fixed. Figures 4(a) and 4(b) show the orbits of ’s price and ’s price, respectively, where the blue ones (labeled by superscript (1)) start from the initial point and the red ones (labeled by superscript (2)) start from the initial point . After a series of iterations, great impacts will emerge in both ICs’ prices, even though the initial price of alters a little.
4.2. Private Leadership
In this case, the numerical simulations show the effect of the parameter and the price adjustment speed of , on the stability of system (38). Figures 5(a) and 5(b) show the bifurcation diagram with respect to and the corresponding maximum Lyapunov exponents of system (38), respectively. Figure 6 shows five situations of phase portraits with different of system (38). We can see system (38) loses its stability through flip bifurcation, and chaotic attractors occur after the accumulation of a period-doubling cascade. The results demonstrate that the insurance market is stable for relatively small values of , and a faster adjustment speed is disadvantageous for system (38) to keep the stability. Figure 7 shows the sensitive dependence on the initial conditions of system (38) when and . Figures 7(a) and 7(b) show the orbits of ’s price and ’s price, respectively, where the blue ones (labeled by superscript (1)) start from the initial point and the red ones (labeled by superscript (2)) start from the initial point . We can see that the difference between the orbits with slightly deviated initial values builds up rapidly after a series of iterations, although their states are indistinguishable at the beginning.
4.3. The Comparison of Welfare and Profit for Two Games
We now compare the welfare and profit levels for the above two sequential-move games (public leadership and private leadership). Keeping fixed , , , , and , Figure 8(a) shows the values of the welfare of , and Figure 8(b) shows the values of the profit of . The values of the welfare and profit in the Nash equilibrium state of both games are marked, where the red points refer to the welfare and profit at the Nash equilibrium point of the game (12), and the blue points refer to the welfare and profit at the Nash equilibrium point of the game (38). We can see that the values of the welfare and profit at the two Nash equilibrium points are positive, where is greater than and is less than . The results show that an IC, whether public or private, is more profitable in the Nash equilibrium state when it is the leader in the price competition game.
5. Chaos Control
As can be seen from the above numerical simulations that price adjustment speeds have a great influence on the stability of systems (12) and (38), the dynamical behaviors of both two systems will be chaotic when the parameters fail to locate in the stable region. In practical application, the appearance of chaos is not expected, we hope that the occurrence of chaos can be avoided or controlled, and the insurance market can be controlled to a balance when it runs irregularly. In this section, we use the time-delayed feedback control method to control the chaotic phenomenon [37–40]. Similar to the fourth section, we divide the following two cases for numerical simulations.
5.1. Public Leadership
We modify the equations of system (12) by inserting the control action , where is the controlling coefficient. Then, the controlled system can be given bywhich can be rewritten as
Figure 2(a) shows that chaotic behavior of the original system (12) occurs when parameter values are fixed as . By a similar approach in Section 3 to get the stability conditions (i)-(iii) for the original system (12), we can get that all the eigenvalues of the matrix (61) will lie within the unit circle provided that , when the other parameters take above values. In other words, when , the controlled system (60) will be asymptotically locally stable. This result can be numerically shown by Figure 9.
Figure 9(a) is the bifurcation diagram with respect to , where we see that, with the value of increasing, the system is gradually controlled from the chaotic state, 8, 4, 2-period bifurcation to a stable state. Figure 9(b) shows the stable behaviors of the orbits of the controlled system (60) beginning from the initial state for different levels of . We can see that the larger the feedback value is, the faster the system tends to be stable.
5.2. Private Leadership
Adding control action to system (38) and simplifying the system, we get the following form of the controlled system: