ON SPATIAL ASYMMETRIC GAMES
ABSTRACT The stability of some spatial asymmetric games is discussed. Both linear and nonlinear asymptotic stability of asymmetric hawk-dove and prisoner's dilemma are studied. Telegraph reaction diffusion equations for the asymmetric spatial games are presented. Asymmetric games of parental investment is studied in the presence of both ordinary and cross diffusions.
- SourceAvailable from: Michel Droz[show abstract] [hide abstract]
ABSTRACT: We consider a new cellular automata rule for a synchronous random walk on a two-dimensional square lattice, subject to an exclusion principle. It is found that the macroscopic behavior of our model obeys the telegraphists's equation, with an adjustable diffusion constant. By construction, the dynamics of our model is exactly described by a linear discrete Boltzmann equation which is solved analytically for some boundary conditions. Consequently, the connection between the microscopic and the macroscopic descriptions is obtained exactly and the continuous limit studied rigorously. The typical system size for which a true diffusive behavior is observed may be deduced as a function of the parameters entering into the rule. It is shown that a suitable choice of these parameters allows us to consider quite small systems. In particular, our cellular automata model can simulate the Laplace equation to a precision of the order (/L)6, whereL is the size of the system and the lattice spacing. Implementation of this algorithm on special-purpose machines leads to the fastest way to simulate diffusion on a lattice.Journal of Statistical Physics 07/1991; 64(3):859-892. · 1.40 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: Two models are considered for the study of game dynamics in a spatial domain. Both models are continuous in space and time and give rise to reaction-diffusion equations. The spatial domain is homogeneous but the mobility of the individuals is allowed to depend upon the strategy. The models are analysed for spatial patterns (via a Turing instability) and also for the direction of the travelling wave that replaces one strategy by another. It is shown that the qualitative behaviour of the two models is quite different. When considering the existence of spatial patterns and deciding whether increased mobility is helpful or not, it is shown that the answers depend crucially upon the model equations. Since both models (in the absence of spatial variation) are quite standard, it is clear that considerable care has to be exercised in the formulation of spatial models and in their interpretation.Journal of Theoretical Biology 03/1997; 184(4):359-69. · 2.35 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: This study reports on the effect of adding negative interaction terms to the hypercycle equation. It is shown that there is a simple parameter condition at which the behaviour of the hypercycle switches from dominant catalysis to dominant suppression. In the suppression!dominated hypercycles the main attractor turns out to be different for cycles consisting of an even or odd number of species. In "odd" cycles there is typically a limit cycle attractor, whereas in "even" cycles there are two alternative stable attractors each containing half of the species. In a spatial domain, odd cycles create spiral waves. Even cycles create a "voting pattern", i.e. initial fluctuations are quickly frozen into patches of the alternative attractors and subsequently, very slowly, small patches will disappear and only one of the two attractors remains. In large cycles (both even and odd) there are additional limit cycle attractors[ In a spatial domain these limit cycles fail to form stable spiral waves, but they can form stable rotating waves around an obstacle. However, these waves are outcompeted by the dominant spatial pattern of the system[ In competition between even and odd cycles, the patches of even cycles are generally stronger than the spiral waves of odd cycles. If the growth parameters of the species vary a little, a patch will no longer contain only half of the species but will instead attract "predator" species from the other patch type. In such a system one of the patch types will slowly disappear and the final dynamics resembles that of a predator-prey system with multiple trophic levels. The conclusion is that adding negative interactions to a hypercycle tends to cause the cycle to break and thereafter the system attains an ecosystem type of dynamics.01/1995;
arXiv:cond-mat/0209597v1 [cond-mat.stat-mech] 25 Sep 2002
On Spatial Asymmetric Games
E. Ahmed1,2, A. S. Hegazi1,2and A. S. Elgazzar3
1Mathematics Department, Faculty of Science
35516 Mansoura, Egypt
2Mathematics Department, Faculty of Science
Al-Ain PO Box 17551, UAE
3Mathematics Department, Faculty of Education
45111 El-Arish, Egypt
The stability of some spatial asymmetric games is discussed. Both
linear and nonlinear asymptotic stability of asymmetric hawk-dove
and prisoner’s dilemma are studied. Telegraph reaction diffusion equa-
tions for the asymmetric spatial games are presented. Asymmetric
game of parental investment is studied in the presence of both ordi-
nary and cross diffusions.
In asymmetric games [Hofbauer and Sigmund 1998], different players have
different strategies and different payoffs. In reality, most games are asym-
metric e.g. battle of the sexes and owners-intruders games. The differential
equations of the asymmetrical games are
dt= pi[(Aq)i− pAq]
dt= qi[(Bp)i− qBp], i = 1,2,...,n, (1)
where A(B) is the payoff matrix of the first (second) player, and pi(qi) is
the fraction of adopters of the strategy i in the first (second) population,
The problem of Turing instability (diffusion induced instability) [Okubo
1980] for symmetric games have been already studied [Cressman and Vickers
1997]. In this case, the standard replicator equation for the symmetric game
is given by
= pi[(Ap)i− pAp] + D∇2pi
It is known [Okubo 1980] that including spatial effects may significantly
change the stability of equilibrium points. Also spatial effects are crucial
in many biological phenomena. Some authors [Boerlijst and Hogeweg 1995]
have studied general spatial hypercycle systems. Therefore studying spatial
asymmetric games is an important problem. Due to the difficulty in defin-
ing evolutionarily stable strategy (ESS) in asymmetric games [Hofbauer and
Sigmund 1998], we will concentrate on asymptotically stable strategies. The
equations of spatial asymmetric games are [Hofbauer et al 1997]
∂t= pi[(Aq)i− pAq] + D1∇2pi,
∂t= qi[(Bp)i− qBp] + D2∇2qi.
In this paper, we will attempt to answer the following questions:
1. Given an asymptotically stable solution to the system (1), does Turing
instability exist for the corresponding spatial game (3)?
2. Given an asymptotically unstable solution to the system (1), can dif-
fusion stabilize it?
3. Given an asymptotically linearly stable solution to the system (1), is it
Our typical examples will be the asymmetric hawk-dove (AHD) and the
asymmetric prisoner’s dilemma (APD) games.
The paper is organized as follows: In section 2, the asymmetric hawk-
dove game is studied. Conditions for Turing stability and nonlinear finite
amplitude instability are derived. In section 3, The asymmetric prisoner’s
dilemma is presented. Telegraph reaction diffusion equation is applied for
stability analysis of the asymmetric prisoner’s dilemma game in section 4. In
section 5, an asymmetric game of parental investment will be studied. Some
conclusions are summarized in section 6.
2Asymmetric hawk-dove game
In this case the possible strategies are hawk (H) and dove (D), and the payoff
matrices A and B in Eq. (1) become
2(v1− c1) v1
, B =
2(v2− c2) v2
where ci> vi, i = 1,2. The corresponding partial differential equations for
the spatial AHD are
∂t= D1∇2p +1
∂t= D2∇2q +1
2p(1 − p)(v1− c1q),
2q(1 − q)(v2− c2p),
where p (q) is the fraction of hawks in the population of the first (second)
player. It is direct to see that the solution p = 1, q = 0 is linearly asymptot-
ically stable solution for the system (5), without diffusion (D1= D2= 0).
The first question is about Turing (diffusion induced) instability [Okubo
1980]. It occurs if the following system
has an equilibrium solution (pss,qss) which is stable if D1= D2= 0, and the
corresponding linearized system
p = pss+ ε(x,t), q = qss+ η(x,t),
∂x2+ a11ε + a12η,
∂x2+ a21ε + a22η,
satisfies the condition
H(k2) = D1D2k4− (D1a22+ D2a11)k2+ a11a22− a12a21< 0
In this case diffusion will destabilize the solution (pss,qss). This is Turing
Applying the above procedure to the spatial AHD one gets:
Proposition (1): The equilibrium solution p = 1, q = 0 of the AHD
game is Turing stable.
The second question to be discussed is: Can diffusion stabilize an unstable
solution of the AHD game? Consider the internal solution p = v2/c2, q =
v1/c1. It is asymptotically unstable solution to the diffusionless case (D1=
D2= 0). Including diffusion and linearizing around p = v2/c2, q = v1/c1,
and assuming the following boundary conditions:
c2+ ε(x,t), q =v1
ε(0,t) = ε(1,t) = 0, η(0,t) = η(1,t) = 0,
Proposition (2): The interior solution p = v2/c2, q = v1/c1, with the
boundary conditions (9) is linearly asymptotically stable if
c2) ≥ 0. (10)
Linear stability analysis is useful if the perturbations of equilibrium are
infinitesimally small. This is not always the case in biological systems. In
this case one has to study finite amplitude instability (FAI) [Stuart 1989] of
the equilibrium solution. In the following, we generalize the work of Stuart
to the two species case. Consider the following equation
∂x2+ f(θ), θ(0,t) = θ(1,t) = 0. (11)
Linearizing around the solution θ = 0, i.e. let
θ(x,t) = v(x,t),
linearize in v, then
σφ = φ′′+ f′(0)φ, φ(0) = φ(1) = 0.
∂x2+ vf′(0), Let v = φ(x) exp(σt) ⇒
Set φ =?
1,2,.... Studying the stability of the first bifurcation point l = 1 using
Matkowsky two-time nonlinear stability analysis, one defines λ = a − k,
set λ = π2+ λ0ε2, ε is a small parameter. Decompose the time into fast t′
and slow τ, then
lalsin(πlx), then bifurcation points are given by f′(0) = (πl)2, l =
Expand u in powers of ε2, then u ≃ εv1+ε3v3+..., (notice that f′′(0) = 0),
then substituting in Eq. (12), one gets
al(τ)sin(πlx)exp(1 − l2)π2t′.
Substituting into the cubic term and setting the constant term in t′equal to
zero, one finally gets
dτ= λ0a1+ b(a1)3,
b = 4π4f′(0)
Thus a nonlinear (finite amplitude) instability arises if
λ0< 0 and |a1(0)| >
In the beginning of the section the condition f′′(0) = 0 was imposed, here
we will assume f′′(0) ?= 0. Thus we consider
∂t= D∇2u + f(u), f(0) = 0, u(0,t) = u(1,t) = 0.
The solution u = 0 is a steady state solution, so expanding near it we set
∂t′′, f′(0) = λ0+ ελ1.
Substituting in Eq. (14) and equating terms O(ε), one gets
al(t′′)sin(πlx)exp[(1 − l2)π2].
Let λ0= π, and consider the equation O(ε2), we set the secular term (inde-
pendent of t′) equal to zero, then
Thus the conditions for FAI are
f′′(0) > 0, λ1< 0 and a1(0) > −
For systems of two partial differential equations
∂t= D1∇2u1+ f(u1,u2),
u1(0,t) = u1(1,t) = u2(0,t) = u2(1,t) = 0,
f(0,0) = g(0,0) = 0.
∂t= D2∇2u2+ g(u1,u2),
Expanding near the steady state solutions u1= u2= 0, we get
g1= µ11+ εµ12, g2= µ21+ εµ22.
∂u1(0,0)= λ11+ ελ12, f2= λ21+ ελ22,
After some tedious calculations, we got the following conditions for FAI in
the system (16):
(i) (π2D1− λ11)(π2D2− µ21) − λ21µ11= 0.
(ii) λ12+ κλ22=
κ+ µ22< 0, where κ =π2D1−λ11
(iv) a1(0) > −
where b′is defined in Eq. (15). Applying the condition (i) to the spatial
AHD, one gets
(π2D1+ v1)(π2D2+ v2) < 0,
which is not possible, thus we find:
Proposition (3): There is no finite amplitude instability for the solution
p = 1, q = 0 of the AHD game.
3 Asymmetric prisoner’s dilemma game
In the prisoner’s dilemma game, the possible strategies are cooperate (C)
and defect (D). The payoff matrices A, B in Eq. (1) are given as follows:
, B =
such that 2Ri > Ti+ Si, and Ti > Ri > Pi > Si, where i = 1, 2. The
dynamical equations for the spatial asymmetric prisoner’s dilemma game
(spatial APD) are:
u1(1 − u1)[−(P1− S1) + u2(P1− S1− T1+ R1)],
u2(1 − u2)[−(P2− S2) + u1(P2− S2− T2+ R2)],
where u1(u2) is the fraction of cooperators in the first (second) players pop-
ulation. The solution u1 = u2 = 0 is linearly asymptotically stable. It
represents the always defect strategy.
Two questions arise the first is: can diffusion stabilize the cooperation
solution u1 = u2 = 1? And does the always defect solution have (FAI)
nonlinear instability? Using the techniques of the previous section we get:
(i) If Diπ2> Ti− Ri, then the cooperation solution is linearly asymptoti-
(ii) The always defect solution does not have FAI.
4Telegraph reaction diffusion in spatial asym-
The standard spatial games depend on the familiar reaction-diffusion equa-
+ f(u). (19)
A basic weakness in this equation is that the flux j reacts simultaneously to
the gradient of u consequently an unbounded propagation speed is allowed.
This manifests itself in many solutions to Eq. (1) e.g. (if f = 0), then
4Dt, u(x,0) = δ(x) i.e u(x,t) > 0∀x, ∀t > 0.
This is unrealistic specially in biological and economical systems, where it is
known that propagation speeds are typically small. To rectify this weakness,
Fick’s law is replaced by
j + τ∂j
and the resulting telegraph diffusion equation becomes
The corresponding telegraph reaction diffusion (TRD) is
∂t2+ (1 − τdf
∂t= D∇2u + f(u) (20)
The time constant τ can be related to the memory effect of the flux j as a
function of the distribution u as follows: Assume that [Compte and Metzle
j(x,t) = −
0K(t − t′)∂u(x,t′)
j + τ∂j
∂t= −τK(0)u(x,t) −
τ∂K(t − t′)
+ K(t − t′)
This equation is equivalent to the telegraph equation if
This lends further support that TRD is more suitable for economic and bi-
ological systems than the ordinary diffusion equation since e.g. it is known
that we take our decisions according to our previous experiences, so memory
effects are quite relevant. Further evidence comes from the work of Chopard
and Droz [Chopard and Droz 1991], where they have shown that starting
from discrete time and space then the continuum limit does not give the
standard reaction diffusion but the telegraph one.
Since it is known that new technologies, habits etc... takes time to spread,
we believe that TRD equation is more relevant than the ordinary diffusion
equation in modelling economic and biological systems [Ahmed et al 2001].
Now we apply TRD to spatial APD game. The TRD for a system of
equations are [Hadeler 1998]
hence applying it to the APD (18), we get
u2(P1− S1− T1+ R1)] = D1∇2u1+
u1(1 − u1)[−(P1− S1) + u2(P1− S1− T1+ R1)],
∂t(1 − 2u2)[−(P2− S2)+
u1(P2− S2− T2+ R2)] = D2∇2u2+
u2(1 − u2)[−(P2− S2) + u1(P2− S2− T2+ R2)].
The following question arises: Can diffusion stabilize the cooperation so-
lution u1= u2= 1? Using the techniques of the second section, we get
∂t(1 − 2u1)[−(P1− S1)+
Proposition (5): If the following conditions are satisfied
Diπ2> Ti− Ri, 1 > τ(Ti− Ri),
4τ(Diπ2− Ti+ Ri) ≤ [−1 + τ(Ti− Ri)]2, i = 1,2,
then the cooperation solution is linearly asymptotically stable.
Proof. Assume that
u1= 1 − ε1exp(σ1t)sin(πx), u2= 1 − ε2exp(σ2t)sin(πx).
Substituting one gets
2τ[(−1 + τ(T1− R1))±
(−1 + τ(T1− R1))2− 4τ (R1− T1+ D1π2)],
(−1 + τ(T2− R2))2− 4τ (R2− T2+ D2π2)].
2τ[(−1 + τ(T2− R2))±
Stability requires that the real part of σi, i = 1,2 is negative. The first two
conditions in the proposition guarantee this requirement. Furthermore since
ui, i = 1,2 are real and nonnegative, then the term under the square root
should be nonnegative. The third condition of the proposition guarantees
Thus the conditions for cooperation stability for TRD are more stringent
than those for ordinary diffusion (c.f. proposition (4)).
5Asymmetric game of parental investment
Parents are faced with the decision whether to care for the offsprings or to
desert. A model has been given for this asymmetric game [Krebs and Davies
2000]. Let p0, p1, p2, be the probabilities of survival of offsprings which are
not cared for, cared for by a single parent and cared for by both parents,
respectively, then p0< p1< p2. A deserting male has a chance q of mating
again while a caring (deserting) female has w1(w2) offsprings. The payoff
matrices for male (female) corresponding to the strategies C (care) or D
(desert) are denoted by A(B), and given by
w1p1(1 + q) w2p0(1 + q)
, B =
The spatial asymmetric equations for the above game are:
∂t= u[w1p2v + w2p1(1 − v) − w1p2uv − w2p1u(1 − v)−
w1p1(1 + q)v(1 − u) − w2p0(1 + q)(1 − u)(1 − v)] + D1∂2u
∂t= v[w1p2u + w1p2(1 − u) − w1p2uv − w2p1u(1 − v)−
w1p1v(1 − u) − w2p0(1 − u)(1 − v)] + D2∂2v
In this system, we introduced both ordinary and cross diffusion. Cross dif-
fusion is the diffusion of one type of species due to the presence of another
[Okubo 1980]. This phenomena is abundant in nature e.g. predator-prey sys-
tems where the predator diffuses towards the regions where the prey is more
abundant. On the other hand the prey tries to avoid predators by diffusing
away from it. Another area of application is in epidemics where susceptible
individuals try to avoid infected ones.
Here we will see that ordinary diffusion is unable of destabilizing the
(i) ESS1 where both male and female desert i.e. (u = 0,v = 0). It requires
w2p0> w1p1and p0(1 + q) > p1.
(ii) ESS2 where male cares and female desert i.e. (u = 1,v = 0). It requires
w2p1> w1p2and p0(1 + q) < p1.
(iii) ESS3 where female cares and male desert i.e. (u = 0,v = 1). It requires
w1p1> w2p0and p1(1 + q) > p2.
(iv) ESS4 where both male and female care i.e. (u = 1,v = 1). It requires
w1p2> w2p1and p1(1 + q) < p2.
Following steps similar to the previous games, we get
Proposition (6): The solution ESSi, i = 1,2,3,4 is destabilized if the
following condition is satisfied:
D12D21π4> (D1π2− ai)(D2π2− bi) (26)
a1 = w2p1− w2p0(1 + q), b1= w1p1− w2p0,
a2 = −w2p1+ w2p0(1 + q), b1= w1p2− w2p1,
a3 = w1p2− w1p1(1 + q), b1= −w1p1+ w2p0,
a4 = −w1p2+ w1p1(1 + q), b1= w2p1− w1p2.
Notice that all ai, bi, i = 1,2,3,4 are negative hence ordinary diffusion
cannot destabilize the ESS.
Applying the above procedure to the battle of the sexes [Schuster and
Sigmund 1981] where the female has two strategies coy or fast while the
male can be either faithful or philanderer. The male (female) payoff matrix
hence the spatial battle of the sexes equations are
, B =
∂t= u(1 − u)(−10 + 12v) + D1∂2u
∂t= v(1 − v)(5 − 8u) + D2∂2v
Proposition (7): Diffusion stabilizes the internal equilibrium of the sys-
Proof. There is a unique internal homogeneous equilibrium solution E =
(5/8,5/6), which (for the diffusionless case) is stable but not asymptotically
stable. Substituting with
8+ εexp(σt)sinπx, v =5
6+ ς exp(σt)sinπx, 1 ≥ x ≥ 0,
in Eq. (27), and linearizing in ε, ζ, one gets
(σ + D1π2)ε =45
16ζ, (σ + D2π2)ζ = −10
[(σ + D1π2)(σ + D2π2) +25
8]ζ = 0,
(σ + D1π2)(σ + D2π2) +25
σ2+ σ(D1π2+ D2π2) + (25
8+ D1D2π4) = 0.
Therefore the real part of σ is negative, then the internal equilibrium (in the
presence of diffusion) is asymptotically stable. It is clear that if the diffusion
coefficients are set equal to zero (D1= D2= 0), then one regains the stability
but not asymptotic stability. This completes the proof.
Based on replicator equations, a mathematical approach for the analysis of
spatial asymmetric games is introduced. Some questions regarding spatial
stability for asymmetric hawk-dove and the asymmetric prisoner’s dilemma
(APD) games are answered. Telegraph reaction diffusion equation is applied
for stability analysis of the asymmetric prisoner’s dilemma game. Asymmet-
ric game of parental investment is studied in the presence of both ordinary
and cross diffusions. Ordinary diffusion cannot destabilize the ESS for this
game. Conditions for destabilizing the ESS are given in the case of cross
We thank the referees for their helpful comments.
Ahmed E., Abdusalam H. A. and Fahmy I. (2001), Telegraph reaction
diffusion equations. Int. J. Mod. Phys. C 12, 717-726.
Boerlijst M. C. and Hogeweg P. (1995), Attractors and spatial patterns
in hypercycles with negative interactions. J. Theor. Biol. 176, 199-210.
Chopard B. and Droz M. (1991), Cellular automata model for the dif-
fusion equation, J. Stat. Phys. 64, 859-892.
Compte A. and Metzle R. (1997), The generalized Cattaneo equation
for the description of anomalous transport processes. J. Phys. A 30,
Cressman R. and Vickers G. T. (1997), Spatial and density effects in
evolutionary game theory. J. Theor. Biol. 184, 359-369.
Hadeler K. P. (1998). in O. Diekman et al (eds), Mathematics inspired
by Biology. Springer, Berlin.
Hofbauer J., Hutson V. and Vickers G. T. (1997), Travelling waves for
games in economics and biology. Nonlinear Analysis, Theory. Methods
and Applications 30, 1235-1244.
Hofbauer J. and Sigmund K. (1998), Evolutionary games and popula-
tion dynamics. Cambridge University Press, Cambridge.
Krebs J. R. and Davies N. B. (2000), An introduction to behavioral
ecology. Blakwell scientific pub., Oxford.
Okubo A. (1980), Diffusion and ecological problems. Springer, Berlin.
Schuster P. and Sigmund K. (1981), Coyness philandering and stable
strategies. Anim. Behav. 29, 186-192.
Stuart A. (1989), Nonlinear instability in dissipative finite difference
scheme. Siam Rev. 31, 191-220.