Abstract
The phenomenon of individual disguise is ubiquitous in the real world. Recent evidences show that peer punishment is successful to stabilize cooperation among selfish individuals. However, it is unclear whether peer punishment is still able to stabilize cooperation when individual disguise for escaping being punished is considered. In this paper, we thus introduce individual disguise of defectors into the public goods game with peer punishment and aim to explore how peer punishment influences the evolutionary dynamics of cooperation, defection, and punishment in this scenario. We consider both cases of infinite and finite populations. In infinite populations, by using replicator equations, we find that although low disguise cost can hinder public cooperation, peer punishment still plays a positive role in promoting the evolution of cooperation, no matter whether second-order punishment is considered or not. We further demonstrate that the larger fine on defectors or the smaller the cost of punishment, the easier to establish a state of full punishment. In addition, in finite populations we reveal that the population spends most of the time in the region of defection for low disguise cost, and the population spends most of the time in the region of cooperation for high disguise. When second-order punishment is not considered, the punishment strategy does not have evolutionary advantage, whereas when second-order punishment is considered, the population can evolve toward regime of punishment and spends most of the time in the monomorphic states with widespread punishment or cooperation.
Similar content being viewed by others
References
Axelrod R (1986) An evolutionary approach to norms. Am Polit Sci Rev 80:1095–1111
Bayer P, Brown JS, Stan̆ková K (2018) A two-phenotype model of immune evasion by cancer cells. J Theor Biol 455:191–204
Boyd R, Richerson PJ (1992) Punishment allows the evolution of cooperation (or anything else) in sizable groups. Ethol Sociobiol 13(3):171–195
Brandt H, Hauert C, Sigmund K (2006) Punishing and abstaining for public goods. Proc Natl Acad Sci USA 103(2):495–497
Broom M, Rychtář J (2018) Evolutionary games with sequential decisions and dollar auctions. Dyn Games Appl 8:211–231
Broom M, Johanis M, Rychtář J (2018) The effect of fight cost structure on fighting behaviour involving simultaneous decisions and variable investment levels. J Math Biol 76:457–482
Broom M, Pattni K, Rychtář J (2018) Generalized social dilemmas: the evolution of cooperation in populations with variable group size. Bull Math Biol 81:4643–4674
Cason TN, Gangadharan L (2015) Promoting cooperation in nonlinear social dilemmas through peer punishment. Exp Econ 18(1):66–88
Chen X, Szolnoki A (2018) Punishment and inspection for governing the commons in a feedback-evolving game. PLoS Comput Biol 14(7):e1006347
Chen X, Szolnoki A, Perc M (2014) Probabilistic sharing solves the problem of costly punishment. New J Phys 16:083016
Chen X, Sasaki T, Perc M (2015) Evolution of public cooperation in a monitored society with implicated punishment and within-group enforcement. Sci Rep 5:17050
Cressman R, Křivan V (2019) Bimatrix games that include interaction times alter the evolutionary outcome: the Owner–Intruder game. J Theor Biol 460:262–273
Cressman R, Tao Y (2014) The replicator equation and other game dynamics. Proc Natl Acad Sci USA 111:10810–10817
Cressman R, Halloway A, Mcnickle GG, Apaloo J, Brown JS, Vincent TL (2017) Unlimited niche packing in a Lotka–Volterra competition game. Theor Popul Biol 116:1–17
Dawes RM (1980) Social dilemmas. Annu Rev Psychol 31:169–193
De Weerd H, Verbrugge R (2011) Evolution of altruistic punishment in heterogeneous populations. J Theor Biol 290:88–103
Fehr E, Fischbacher U (2004) Third-party punishment and social norms. Evol Hum Behav 25(2):63–87
Fehr E, Gächter S (2002) Altruistic punishment in humans. Nature 415(6868):137–140
Fowler JH (2005) Altruistic punishment and the origin of cooperation. Proc Natl Acad Sci USA 102(19):7047–7049
Fudenberg D, Imhof LA (2006) Imitation processes with small mutations. J Econ Theory 131(1):251–262
Gajewski TF, Schreiber H, Fu YX (2013) Innate and adaptive immune cells in the tumor microenvironment. Nat Immunol 14:1014–1022
Gintis H (2000) Game theory evolving. Princeton University Press, Princeton
Hardin G (1968) The tragedy of the commons. Science 162(3859):1243–1248
Hauert C (2010) Replicator dynamics of reward and reputation in public goods games. J Theor Biol 267(1):22–28
Hauert C, De Monte S, Hofbauer J, Sigmund K (2002) Replicator dynamics for optional public good games. J Theor Biol 218:187–194
Hauert C, Traulsen A, née Brandt HDS, Nowak MA, Sigmund K (2008) Public goods with punishment and abstaining in finite and infinite populations. Biol Theor 3(2):114–122
He N, Chen X, Szolnoki A (2019) Central governance based on monitoring and reporting solves the collective-risk social dilemma. Appl Math Comput 347:334–341
Helbing D, Szolnoki A, Perc M, Szabó G (2010) Evolutionary establishment of moral and double moral standards through spatial interactions. PLoS Comput Biol 6(4):e1000758
Hofbauer J (2018) Minmax via replicator dynamics. Dyn Games Appl 8(3):637–640
Hofbauer J, Sigmund K (1998) Evolutionary games and population dynamics. Cambridge University Press, Cambridge
Imhof LA, Fudenberg D, Nowak MA (2005) Evolutionary cycles of cooperation and defection. Proc Natl Acad Sci USA 102(31):10797–10800
Kabir KMA, Tanimoto J (2019) Dynamical behaviors for vaccination can suppress infectious disease—a game theoretical approach. Chaos Soliton Fract 123:229–239
Kampen NGV (1983) Stochastic processes in physics and chemistry. Phys Today 36(2):78–80
Khalil HK (1996) Nonlinear systems. Prentice Hall, Upper Saddle River
Liu L, Chen X, Szolnoki A (2017) Competitions between prosocial exclusions and punishments in finite populations. Sci Rep 7:46634
Liu L, Wang S, Chen X, Perc M (2018) Evolutionary dynamics in the public goods games with switching between punishment and exclusion. Chaos 28(10):103105
Liu L, Chen X, Szolnoki A (2019) Evolutionary dynamics of cooperation in a population with probabilistic corrupt enforcers and violators. Math Models Methods Appl Sci 29:2127–2149
Nowak MA (2006) Five rules for the evolution of cooperation. Science 314(5805):1560–1563
Oliver P (1980) Rewards and punishments as selective incentives for collective action: theoretical investigations. Am J Sociol 85:1356–1375
Pacheco JM, Santos FC, Souza MO, Skyrms B (2009) Evolutionary dynamics of collective action in N-person stag hunt dilemmas. Proc R Soc B 276(1655):315–321
Perc M (2016) Phase transitions in models of human cooperation. Phys Lett A 380(36):2803–2808
Perc M, Gómez Gardeñes J, Szolnoki A, Floría LM, Moreno Y (2013) Evolutionary dynamics of group interactions on structured population: a review. J R Soc Interface 10(80):20120997
Perc M, Jordan JJ, Rand DG, Wang Z, Boccaletti S, Szolnoki A (2017) Statistical physics of human cooperation. Phys Rep 687:1–51
Sasaki T, Uchida S (2013) The evolution of cooperation by social exclusion. Proc R Soc B 280(1752):20122498
Sasaki T, Uchida S (2014) Rewards and the evolution of cooperation in public good games. Biol Lett 10(1):20130903
Sasaki T, Unemi T (2011) Replicator dynamics in public goods games with reward funds. J Theor Biol 287:109–114
Sasaki T, Brännström Å, Dieckmann U, Sigmund K (2012) The take-it-or-leave-it option allows small penalties to overcome social dilemmas. Proc Natl Acad Sci USA 109(4):1165–1169
Shi F, Zhang B (2019) Cournot competition, imitation, and information networks. Econ Lett 176:83–85
Sigmund K (2010) The calculus of selfishness. Princeton University Press, Princeton
Singh KI, Haslam MC (2017) An analysis of the replicator dynamics for an asymmetric Hawk–Dove game. Int J Differ Equ 2017:1–7
Szolnoki A, Perc M (2011) Group-size effects on the evolution of cooperation in the spatial public goods game. Phys Rev E 84(4):047102
Tanimoto J (2015) Fundamentals of evolutionary game theory and its applications. Springer, Berlin
Tanimoto J, Sagara H (2007) Relationship between dilemma occurrence and the existence of a weakly dominant strategy in a two-player symmetric game. Biosystems 90:105–114
Van Lange PAM, Balliet D, Parks CD, Van Vugt M (2013) Social dilemmas: the psychology of human cooperation. Oxford University Press, New York
Vasconcelos VV, Santos FC, Pacheco JM (2013) A bottom-up institutional approach to cooperative governance of risky commons. Nat Clim Change 3(9):797–801
Wang Z, Wang L, Szolnoki A, Perc M (2015) Evolutionary games on multilayer networks: a colloqiium. Eur Phys J B 88(5):124
Wang Q, He N, Chen X (2018) Replicator dynamics for public goods game with resource allocation in large populations. Appl Math Comput 328:162–170
Weitz JS, Eksin C, Paarporn K, Brown SP, Ratcliff WC (2016) An oscillating tragedy of the commons in replicator dynamics with game-environment feedback. Proc Natl Acad Sci USA 113(47):7518–7525
Xia C, Miao Q, Wang J, Ding S (2014) Evolution of cooperation in the traveler’s dilemma game on two coupled lattices. Appl Math Comput 246:389–398
Xia C, Ding S, Wang C, Wang J, Chen Z (2017) Risk analysis and enhancement of cooperation yielded by the individual reputation in the spatial public goods game. IEEE Syst J 11(3):1516–1525
Xia C, Li X, Wang Z, Perc M (2018) Doubly effects of information sharing on interdependent network reciprocity. New J Phys 20:075005
Zhang B (2016) Quantal response methods for equilibrium selection in normal form games. J Math Econ 64:113–123
Zhang B, Li C, Tao Y (2016) Evolutionary stability and the evolution of cooperation on heterogeneous graphs. Dyn Games Appl 6:567–579
Zhang Y, Chen X, Liu A, Sun C (2018) The effect of the stake size on the evolution of fairness. Appl Math Comput 321:641–653
Zhao K, Liu L, Chen X et al (2019) Behavioral heterogeneity in quorum sensing can stabilize social cooperation in microbial populations. BMC Biol 17:20
Acknowledgements
This research was supported by the National Natural Science Foundation of China (Grant Nos. 61976048 and 61503062) and by the Fundamental Research Funds of the Central Universities of China.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendices
Appendix A Evolutionary Dynamics in Infinite Populations Without Second-Order Punishment
To study the replicator dynamics in infinite populations without second-order punishment, we first compute the average payoffs of the three strategies by substituting the payoff equations (1) of three strategies into Eq. (3), which are, respectively, given as
Accordingly, by substituting \(x+y+z=1\) and \(\bar{P}=xP_{C}+yP_{D}+zP_{P}\) into Eq. (4), we have the following equation system
where \(P_{C}-P_{P}=(1-pq)\beta (N-1)y\) and \(P_{D}-P_{P}=c-\frac{rc}{N}+(1-pq)\beta (N-1)y-q\gamma -(1-pq)\alpha (N-1)(1-x-y)\).
From the equation (16), we can know that (0, 1), (1, 0), (0, 0), \((x_{0},0)\), and \((0,\frac{q\gamma -\sigma +(1-pq)\alpha (N-1)}{(1-pq)(\alpha +\beta )(N-1)})\) are boundary equilibrium points of the system for \(\sigma \ne q\gamma \) and \(pq\ne 1\), where \(\sigma =c-\frac{rc}{N}\) and \(0<x_{0}<1\). In addition, there are no interior equilibrium points for the equation system (16). Furthermore, we study the stability of these equilibria by judging the sign of the eigenvalues of Jacobian matrix [34], and the Jacobian matrix of equation system (16) can be written by calculating the first-order partial derivatives as
where \(\dot{x}=H(x,y)\) and \(\dot{y}=K(x,y)\). Correspondingly, we have
and
Thus the specific forms of the Jacobian matrix at the above four boundary equilibrium points are, respectively,
and
where \(a=\frac{\beta q\gamma +(1-pq)\beta \alpha (N-1)-\sigma \beta }{\beta +\alpha }\), \(b=\frac{q\gamma +(1-pq)\alpha (N-1)-\sigma }{(1-pq)(\beta +\alpha )(N-1)}(\sigma -q\gamma )\), and \(d=\frac{q\gamma +(1-pq)\alpha (N-1)-\sigma }{(1-pq)(\beta +\alpha )(N-1)}[(1-pq)\beta (N-1)-q\gamma +\sigma ]\).
We accordingly have the following results.
-
(1)
In the condition of \(-(1-pq)\alpha (N-1)<q\gamma -\sigma <0\), (1, 0), \((0,\frac{q\gamma -\sigma +(1-pq)\alpha (N-1)}{(1-pq)(\alpha +\beta )(N-1)})\), (0, 1), \((x_{0},0)\), and (0, 0) are equilibrium points. Since the largest eigenvalues of J(1, 0) and \(J(0,\frac{q\gamma -\sigma +(1-pq)\alpha (N-1)}{(1-pq)(\alpha +\beta )(N-1)})\) are both positive, the equilibrium points (1, 0) and \((0,\frac{q\gamma -\sigma +(1-pq)\alpha (N-1)}{(1-pq)(\alpha +\beta )(N-1)})\) are unstable, whereas the largest eigenvalue of J(0, 1) is negative, so the equilibrium point (0, 1) is stable. In addition, on the basis of Refs. [4, 26, 45], we can know that the PC edge consists of equilibrium points \((x_{0},0)\), and all those with \(\frac{\sigma -q\gamma }{(1-pq)\alpha (N-1)}<z\le 1\) are stable, but not asymptotically stable. Correspondingly, the equilibrium point (0, 0) is stable on the edge of PC for \(-(1-pq)\alpha (N-1)<q\gamma -\sigma <0\).
-
(2)
In the condition of \(0<q\gamma -\sigma <(1-pq)\beta (N-1)\), (1, 0), \((0,\frac{q\gamma -\sigma +(1-pq)\alpha (N-1)}{(1-pq)(\alpha +\beta )(N-1)})\), (0, 1), \((x_{0},0)\), and (0, 0) are equilibrium points. Since the largest eigenvalues of J(0, 1) and \(J(0,\frac{q\gamma -\sigma +(1-pq)\alpha (N-1)}{(1-pq)(\alpha +\beta )(N-1)})\) are both positive, the equilibrium points (0, 1) and \((0,\frac{q\gamma -\sigma +(1-pq)\alpha (N-1)}{(1-pq)(\alpha +\beta )(N-1)})\) are unstable. Similarly, on the basis of Refs. [4, 26, 45], we can know that the equilibrium points (0, 0) and \((x_{0},0)\) are stable on the edge of PC. Furthermore, since \(P_{D}-P_{C}=-q\gamma +\sigma -(1-pq)\alpha (N-1)z<0\) in the interior phase space, we have \(\dot{x}=x(1-x)(P_{C}-P_{D})>0\) and \(\dot{y}=y(1-y)(P_{D}-P_{C})<0\) according to Eq. (16), which reveal that x will always evolve into the state \(x=1\) for small perturbations. Correspondingly, the equilibrium point (1, 0) is stable for \(0<q\gamma -\sigma <(1-pq)\beta (N-1)\).
-
(3)
In the condition of \(q\gamma -\sigma \ge (1-pq)\beta (N-1)\), (1, 0), (0, 1), \((x_{0},0)\), and (0, 0) are equilibrium points. Since the largest eigenvalue of J(0, 1) is positive, the equilibrium point (0, 1) is unstable. In addition, similar to the case of \(0<q\gamma -\sigma <(1-pq)\beta (N-1)\), we can know that the equilibrium points (0, 0) and \((x_{0},0)\) are stable on the edge of PC, and the equilibrium point (1, 0) is also stable.
-
(4)
In the condition of \(q\gamma -\sigma <-(1-pq)\alpha (N-1)\), (1, 0), (0, 1), \((x_{0},0)\), and (0, 0) are equilibrium points. Since the largest eigenvalues of J(0, 0), \(J(x_{0},0)\), and J(1, 0) are all positive, the equilibrium points (0, 0), \((x_{0},0)\), and (1, 0) are unstable, whereas the largest eigenvalue of J(0, 1) is negative, so the equilibrium point (0, 1) is stable for \(q\gamma -\sigma <-(1-pq)\alpha (N-1)\).
-
(5)
In the condition of \(q\gamma -\sigma =-(1-pq)\alpha (N-1)\), (1, 0), (0, 1), \((x_{0},0)\), and (0, 0) are equilibrium points. Since the largest eigenvalues of J(1, 0) and \(J(x_{0},0)\) are positive, the equilibrium points (1, 0) and \((x_{0},0)\) are unstable, whereas the largest eigenvalue of J(0, 1) is negative, so the equilibrium point (0, 1) is stable. In addition, since \(P_{P}-P_{C}=-(1-pq)\beta (N-1)y<0\) and \(P_{D}-P_{C}=(1-pq)\alpha (N-1)(1-z)>0\) in the interior phase space, the equilibrium point (0, 0) is unstable for \(q\gamma -\sigma =-(1-pq)\alpha (N-1)\).
In addition to the above cases, we consider the special case of \(\sigma =q\gamma \). From the equation (16), we can know that (0, 1), (1, 0), (0, 0), \((x_{0},0)\), and \((0,\frac{(1-pq)\alpha (N-1)}{(1-pq)(\alpha +\beta )(N-1)})\) are boundary equilibrium points of the system for \(pq\ne 1\). Since the largest eigenvalue of \(J(0,\frac{(1-pq)\alpha (N-1)}{(1-pq)(\alpha +\beta )(N-1)})\) is positive, the equilibrium point \((0,\frac{(1-pq)\alpha (N-1)}{(1-pq)(\alpha +\beta )(N-1)})\) is unstable. In addition, we can know that there exist a continuum of equilibria on the edges of PC and CD. Similarly, on the basis of Refs. [4, 26, 45], the equilibria of PC edge are stable, but not asymptotically stable. Correspondingly, the equilibrium point (0, 0) is stable on the edges of PC. Furthermore, since \(P_{D}<P_{C}\) in the interior phase space, then \(\frac{d(y/x)}{dt}=(y/x)(P_{D}-P_{C})<0\). Accordingly, the frequency ratio of the defectors to cooperators decreases over time. Therefore, if rare punishers are introduced, the system into a stable population of the two types of defectors and cooperators. In other words, the equilibria of CD edge are stable, but not asymptotically stable. Besides, since \(P_{P}<P_{C}\) in the interior phase space, the equilibrium point (1, 0) can be the eventually evolutionary outcome.
Appendix B Evolutionary Dynamics in Infinite Populations with Second-Order Punishment
To study the replicator dynamics in infinite populations with second-order punishment, we first compute the average payoffs of the three strategies by substituting the payoff equations (2) of three strategies into Eq. (3), which are, respectively, given as
Correspondingly, by substituting \(x+y+z=1\) and \(\bar{P}=xP_{C}+yP_{D}+zP_{P}\) into Eq. (4), we have the following equation system
where \(P_{C}-P_{P}=(1-pq)\beta (N-1)y+\beta (N-1)x-\alpha (N-1)(1-x-y)\) and \(P_{D}-P_{P}=c-\frac{rc}{N}+(1-pq)\beta (N-1)y+\beta (N-1)x-q\gamma -(1-pq)\alpha (N-1)(1-x-y)\).
The equation system (23) has at most six equilibrium points which are (0, 0), (0, 1), (1, 0), \((0,\frac{q\gamma -\sigma +(1-pq)\alpha (N-1)}{(1-pq)(\alpha +\beta )(N-1)})\), \((\frac{\alpha }{\alpha +\beta },0)\), and \((x^{*},y^{*})\) for \(\sigma \ne q\gamma \), \(pq\ne 0\), and \(pq\ne 1\), respectively, where \(x^{*}=1-\frac{q\gamma -\sigma }{\alpha pq(N-1)}-\frac{1}{pq}+\frac{(q\gamma -\sigma )(\alpha +\beta )}{\alpha \beta (pq)^{2}(N-1)}\) and \(y^{*}=\frac{1}{pq}-\frac{(q\gamma -\sigma )(\alpha +\beta )}{\alpha \beta (pq)^{2}(N-1)}\). Here the first five are boundary equilibrium points, while the last one is an interior equilibrium point. In addition, the stability of six equilibria can be determined by the sign of eigenvalues of Jacobian matrix [34], and the Jacobian matrix of the equation system (23) can be obtained as
where \(\dot{x}=W(x,y)\) and \(\dot{y}=N(x,y)\). Correspondingly, we have
and
Thus the specific forms of the Jacobian matrix at the above six equilibrium points are, respectively,
and
where \(a=\frac{[(1-pq)\beta +\alpha ](q\gamma -\sigma )-pq(1-pq)\alpha \beta (N-1)}{(1-pq)(\alpha +\beta )}\),
\(b=\frac{[q\gamma -\sigma +(1-pq)\alpha (N-1)][(\alpha +\beta )(-q\gamma +\sigma )(2-pq)+(1-pq)(N-1)(\beta ^{2}+\alpha \beta )]}{[(1-pq)(\alpha +\beta )]^{2}(N-1)}\),
\(d=\frac{[q\gamma +(1-pq)\alpha (N-1)-\sigma ][(1-pq)\beta (N-1)-q\gamma +\sigma ]}{(1-pq)(\alpha +\beta )(N-1)}\), \(a_{1}=[q\gamma -\sigma -\frac{pq\alpha \beta (N-1)}{\alpha +\beta }+ \frac{\beta (N-1)[(1-pq)\beta +\alpha ]}{\alpha +\beta }]\frac{\alpha }{\alpha +\beta }\), \(b_{1}=\sigma -q\gamma +\frac{\alpha \beta (N-1)}{\alpha +\beta }-\frac{(1-pq)\alpha \beta (N-1)}{\alpha +\beta }\), \(a_{2}=x^{*}\{(1-x^{*})(N-1)(\alpha +\beta )-y^{*}(N-1)[\beta +(1-pq)\alpha ]\}\), \(b_{2}=x^{*}\{-y^{*}(1-pq)(N-1)(\alpha +\beta )+(1-x^{*})(N-1)[(1-pq)\beta +\alpha ]\}\), \(c_{2}=y^{*}\{(1-y^{*})(N-1)[\beta +(1-pq)\alpha ]-x^{*}(N-1)(\beta +\alpha )\}\), and \(d_{2}=y^{*}\{(1-y^{*})(1-pq)(N-1)(\alpha +\beta )-x^{*}(N-1)[(1-pq)\beta +\alpha ]\}\).
We accordingly have the following results.
-
(1)
In the condition of \(\frac{pq\alpha \beta (N-1)(1-pq)}{\alpha +\beta -pq\beta }<q\gamma -\sigma <\min \{(1-pq)\beta (N-1),\frac{pq\alpha \beta (N-1)}{\alpha +\beta }\}\), the equation system (23) has six equilibrium points which are (0, 0), (0, 1), (1, 0), \((0,\frac{q\gamma +(1-pq)\alpha (N-1)-\sigma }{(1-pq)(\alpha +\beta )(N-1)})\), \((\frac{\alpha }{\alpha +\beta },0)\), and \((x^{*},y^{*})\), respectively. Since the largest eigenvalues of \(J(0,\frac{q\gamma -\sigma +(1-pq)\alpha (N-1)}{(1-pq)(\alpha +\beta )(N-1)})\), \((\frac{\alpha }{\alpha +\beta },0)\), and J(0, 1) are positive, the equilibrium points \((\frac{\alpha }{\alpha +\beta },0)\), (0, 1), and \((0,\frac{q\gamma -\sigma +(1-pq)\alpha (N-1)}{(1-pq)(\alpha +\beta )(N-1)})\) are unstable, whereas the largest eigenvalues of J(0, 0) and J(1, 0) are both negative, so the equilibrium points (0, 0) and (1, 0) are stable. In addition, for the equilibrium point \((x^{*},y^{*})\), we assume that \(\lambda _{1}\) and \(\lambda _{2}\) are two eigenvalues of \(J(x^{*},y^{*})\). We thus have \(\lambda _{1}+\lambda _{2}=\frac{\partial W(x,y)}{\partial x}|_{(x^{*},y^{*})}+\frac{\partial N(x,y)}{\partial y}|_{(x^{*},y^{*})}=(\alpha +\beta )(N-1)(1-x^{*}-y^{*})(x^{*}+y^{*}-y^{*}pq)>0\), and correspondingly the equilibrium point \((x^{*},y^{*})\) is unstable.
-
(2)
In the condition of \((1-pq)\beta (N-1)\le q\gamma -\sigma <\frac{pq\alpha \beta (N-1)}{\alpha +\beta }\), the equation system (23) has five equilibrium points which are (0, 0), (0, 1), (1, 0), \((\frac{\alpha }{\alpha +\beta },0)\), and \((x^{*},y^{*})\), respectively. Similar to the case of \(\frac{pq\alpha \beta (N-1)(1-pq)}{\alpha +\beta -pq\beta }<q\gamma -\sigma <\min \{(1-pq)\beta (N-1),\frac{pq\alpha \beta (N-1)}{\alpha +\beta }\}\), we can know that the equilibrium points \((\frac{\alpha }{\alpha +\beta },0)\), (0, 1), and \((x^{*},y^{*})\) are unstable, while the equilibrium points (0, 0) and (1, 0) are stable for \((1-pq)\beta (N-1)\le q\gamma -\sigma <\frac{pq\alpha \beta (N-1)}{\alpha +\beta }\).
-
(3)
In the condition of \(-(1-pq)\alpha (N-1)<q\gamma -\sigma <0\), the equation system (23) has five equilibrium points which are (0, 0), (0, 1), (1, 0), \((0,\frac{q\gamma +(1-pq)\alpha (N-1)-\sigma }{(1-pq)(\alpha +\beta )(N-1)})\), and \((\frac{\alpha }{\alpha +\beta },0)\), respectively. Since the largest eigenvalues of J(1, 0), \(J(\frac{\alpha }{\alpha +\beta },0)\), and \(J(0,\frac{q\gamma -\sigma +(1-pq)\alpha (N-1)}{(1-pq)(\alpha +\beta )(N-1)})\) are positive, the equilibrium points \((\frac{\alpha }{\alpha +\beta },0)\), (1, 0), and \((0,\frac{q\gamma -\sigma +(1-pq)\alpha (N-1)}{(1-pq)(\alpha +\beta )(N-1)})\) are unstable, whereas the two eigenvalues of J(0, 1) and J(0, 0) are both negative; consequently, the equilibrium points (0, 1) and (0, 0) are stable.
-
(4)
In the conditions of \(0<q\gamma -\sigma \le \frac{pq\alpha \beta (N-1)(1-pq)}{\alpha +\beta -pq\beta }\) or \(\frac{pq\alpha \beta (N-1)}{\alpha +\beta }\le q\gamma -\sigma <(1-pq)\beta (N-1)\), the equation system (23) has five equilibrium points which are (0, 0), (0, 1), (1, 0), \((0,\frac{q\gamma +(1-pq)\alpha (N-1)-\sigma }{(1-pq)(\alpha +\beta )(N-1)})\), and \((\frac{\alpha }{\alpha +\beta },0)\), respectively. Since \(\sigma <q\gamma \), the largest eigenvalues of J(0, 1), \(J(\frac{\alpha }{\alpha +\beta },0)\), and \(J(0,\frac{q\gamma -\sigma +(1-pq)\alpha (N-1)}{(1-pq)(\alpha +\beta )(N-1)})\) are positive, the equilibrium points \((\frac{\alpha }{\alpha +\beta },0)\), (0, 1), and \((0,\frac{q\gamma -\sigma +(1-pq)\alpha (N-1)}{(1-pq)(\alpha +\beta )(N-1)})\) are unstable, whereas the two eigenvalues of J(1, 0) and J(0, 0) are both negative; consequently, the equilibrium points (1, 0) and (0, 0) are stable.
-
(5)
In the condition of \(q\gamma -\sigma \le -(1-pq)\alpha (N-1)\), the equation system (23) has four equilibrium points which are (0, 0), (0, 1), (1, 0), and \((\frac{\alpha }{\alpha +\beta },0)\), respectively. For \(q\gamma -\sigma +(1-pq)\alpha (N-1)<0\), the largest eigenvalues of J(1, 0), J(0, 0), and \(J(\frac{\alpha }{\alpha +\beta },0)\) are positive, so the equilibrium points (1, 0), (0, 0), and \((\frac{\alpha }{\alpha +\beta },0)\) are unstable, whereas the largest eigenvalue of J(0, 1) is negative, so the equilibrium point (0, 1) is stable. For \(q\gamma -\sigma +(1-pq)\alpha (N-1)=0\), since the largest eigenvalues of J(1, 0) and \(J(\frac{\alpha }{\alpha +\beta },0)\) are positive, the equilibrium points (1, 0) and \((\frac{\alpha }{\alpha +\beta },0)\) are unstable. Whereas the largest eigenvalue of J(0, 1) is negative, so the equilibrium point (0, 1) is stable. In addition, we cannot determine the stability of the equilibrium point (0, 0) based only on the sign of the eigenvalues of J(0, 0). Instead, we study its stability by using the center manifold theorem [34]. To do that, the equation system (23) becomes
$$\begin{aligned} \dot{x}&=x\{(1-x)[(1-pq)\beta (N-1)y+\beta (N-1)x-\alpha (N-1)(1-x-y)]\nonumber \\&\quad -y[(1-pq)\alpha (N-1)(x+y)+(1-pq)\beta (N-1)y+\beta (N-1)x]\},\nonumber \\ \dot{y}&=y\{(1-y)[(1-pq)\alpha (N-1)(x+y)+(1-pq)\beta (N-1)y+\beta (N-1)x]\nonumber \\&\quad -x[(1-pq)\beta (N-1)y+\beta (N-1)x-\alpha (N-1)(1-x-y)]\}. \end{aligned}$$(30)Using the center manifold theorem [34], we have that \(x=h(y)\) is the center manifold for the system (30). We start to try \(h(y)=O(|y|^{2})\), which results in the reduced system as
$$\begin{aligned} \dot{y}=(y^{2}-y^{3})(1-pq)(N-1)(\alpha +\beta )+O(|y|^{4}). \end{aligned}$$(31)Since \((1-pq)(N-1)(\alpha +\beta )\ne 0\), the equilibrium point (0, 0) is unstable.
-
(6)
In the condition of \(q\gamma -\sigma \ge \max \{(1-pq)\beta (N-1),\frac{pq\alpha \beta (N-1)}{\alpha +\beta }\}\), the equation system (23) has four equilibrium points which are (0, 0), (0, 1), (1, 0), and \((\frac{\alpha }{\alpha +\beta },0)\), respectively. Since the largest eigenvalues of J(0, 1) and \(J(\frac{\alpha }{\alpha +\beta },0)\) are both positive, the equilibrium points (0, 1) and \((\frac{\alpha }{\alpha +\beta },0)\) are unstable, whereas the largest eigenvalues of J(0, 0) and J(1, 0) are both negative; consequently, the equilibrium points (0, 0) and (1, 0) are stable.
Finally, for the special case of \(\sigma =q\gamma \), from Eq. (23), we can know that (0, 0), (0, 1), (1, 0), \((0,\frac{(1-pq)\alpha (N-1)}{(1-pq)(\alpha +\beta )(N-1)})\), and \((\frac{\alpha }{\alpha +\beta },0)\) are boundary equilibrium points of the system for \(pq\ne 1\). Since the largest eigenvalues of \(J(\frac{\alpha }{\alpha +\beta },0)\) and \(J(0,\frac{(1-pq)\alpha (N-1)}{(1-pq)(\alpha +\beta )(N-1)})\) are both positive, the largest eigenvalue of J(0, 0) is negative. Consequently, the equilibrium points \((\frac{\alpha }{\alpha +\beta },0)\) and \((0,\frac{(1-pq)\alpha (N-1)}{(1-pq)(\alpha +\beta )(N-1)})\) are unstable, whereas the equilibrium point (0, 0) is stable. In addition, we can still know that there exists a continuum of equilibria on the edge of CD. Similar to the special case \(\sigma =q\gamma \) in “Appendix A”, these equilibria are stable, but not asymptotically stable. Correspondingly, the equilibrium point (1, 0) is stable on the edge of CD. Furthermore, we know that \(P_{D}>P_{C}\) in the interior phase space. Therefore, the equilibrium point (0, 1) can be the eventually evolutionary outcome.
Rights and permissions
About this article
Cite this article
Wang, Q., Liu, L. & Chen, X. Evolutionary Dynamics of Cooperation in the Public Goods Game with Individual Disguise and Peer Punishment. Dyn Games Appl 10, 764–782 (2020). https://doi.org/10.1007/s13235-019-00339-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13235-019-00339-7