Replicator equation—a paradigm equation in evolutionary game dynamics—mathematizes the frequency dependent selection of competing strategies vying to enhance their fitness (quantified by the average payoffs) with respect to the average fitnesses of the evolving population under consideration. In this paper, we deal with two discrete versions of the replicator equation employed to study evolution in a population where any two players' interaction is modelled by a two-strategy symmetric normal-form game. There are twelve distinct classes of such games, each typified by a particular ordinal relationship among the elements of the corresponding payoff matrix. Here, we find the sufficient conditions for the existence of asymptotic solutions of the replicator equations such that the solutions—fixed points, periodic orbits, and chaotic trajectories—are all strictly physical, meaning that the frequency of any strategy lies inside the closed interval zero to one at all times. Thus, we elaborate on which of the twelve types of games are capable of showing meaningful physical solutions and for which of the two types of replicator equation. Subsequently, we introduce the concept of the weight of fitness deviation that is the scaling factor in a positive affine transformation connecting two payoff matrices such that the corresponding one-shot games have exactly same Nash equilibria and evolutionary stable states. The weight also quantifies how much the excess of fitness of a strategy over the average fitness of the population affects the per capita change in the frequency of the strategy. Intriguingly, the weight's variation is capable of making the Nash equilibria and the evolutionary stable states, useless by introducing strict physical chaos in the replicator dynamics based on the normal-form game.

1.
R.
Pastor-Satorras
and
A.
Vespignani
, “
Epidemic dynamics in finite size scale-free networks
,”
Phys. Rev. E
65
,
035108
(
2002
).
2.
A.
Traulsen
,
J. M.
Pacheco
, and
M. A.
Nowak
, “
Pairwise comparison and selection temperature in evolutionary game dynamics
,”
J. Theor. Biol.
246
,
522
529
(
2007
).
3.
M. A.
Nowak
and
K.
Sigmund
, “
Evolutionary dynamics of biological games
,”
Science
303
,
793
799
(
2004
).
4.
K. M.
Page
and
M. A.
Nowak
, “
Unifying evolutionary dynamics
,”
J. Theor. Biol
219
,
93
98
(
2002
).
5.
P. D.
Taylor
and
L. B.
Jonker
, “
Evolutionary stable strategies and game dynamics
,”
Math. Biosci.
40
,
145
156
(
1978
).
6.
R.
Cressman
and
Y.
Tao
, “
The replicator equation and other game dynamics
,”
Proc. Natl. Acad. Sci. U.S.A.
111
,
10810
10817
(
2014
).
7.
W.
Schnabl
,
P. F.
Stadler
,
C.
Forst
, and
P.
Schuster
, “
Full characterization of a strange attractor
,”
Physica D
48
,
65
90
(
1991
).
8.
B.
Skyrms
, “
Chaos and the explanatory significance of equilibrium: Strange attractors in evolutionary game dynamics
,”
J. Logic Lang. Inf.
1
,
111
(
1992
) .
9.
T.
Chawanya
, “
Infinitely many attractors in game dynamics system
,”
Progr. Theor. Exp. Phys.
95
,
679
(
1996
).
10.
Y.
Sato
,
E.
Akiyama
, and
J. D.
Farmer
, “
Chaos in learning a simple two-person game
,”
Proc. Natl. Acad. Sci. U.S.A.
99
,
4748
4751
(
2002
).
11.
Y.
Sato
and
J. P.
Crutchfield
, “
Coupled replicator equations for the dynamics of learning in multiagent systems
,”
Phys. Rev. E
67
,
015206
(
2003
).
12.
T.
You
,
M.
Kwon
,
H.-H.
Jo
,
W.-S.
Jung
, and
S. K.
Baek
, “
Chaos and unpredictability in evolution of cooperation in continuous time
,”
Phys. Rev. E
96
,
062310
(
2017
).
13.
B. M. R.
Stadler
and
P. F.
Stadler
, “
Molecular replicator dynamics
,”
Adv. Complex Syst.
06
,
47
77
(
2003
).
14.
T.
Brgers
and
R.
Sarin
, “
Learning through reinforcement and replicator dynamics
,”
J. Econ. Theory
77
,
1
14
(
1997
).
15.
G.
Silverberg
,
Evolutionary Modeling in Economics: Recent History and Immediate Prospects
, MERIT Research Memoranda (
MERIT, Maastricht Economic Research Institute on Innovation and Technology
,
1997
).
16.
J.
Wang
,
F.
Fu
,
T.
Wu
, and
L.
Wang
, “
Emergence of social cooperation in threshold public goods games with collective risk
,”
Phys. Rev. E
80
,
016101
(
2009
).
17.
D.
Helbing
and
A.
Johansson
, “
Evolutionary dynamics of populations with conflicting interactions: Classification and analytical treatment considering asymmetry and power
,”
Phys. Rev. E
81
,
016112
(
2010
).
18.
H.
Poincar
, “
Mmoire sur les courbes dfinies par une quation diffrentielle (i)
,”
J. Math. Pures Appl.
7
,
375
422
(
1881
); avaialable at http://eudml.org/doc/235914.
19.
I.
Bendixson
, “
Sur les courbes définies par des équations différentielles
,”
Acta Math.
24
,
1
88
(
1901
).
20.
R.
Cresssman
,
Evolutionary Dynamics and Extensive Form of Games
(
MIT Press
,
Cambridge, MA
,
2003
).
21.
D.
Vilone
,
A.
Robledo
, and
A.
Sánchez
, “
Chaos and unpredictability in evolutionary dynamics in discrete time
,”
Phys. Rev. Lett.
107
,
038101
(
2011
).
22.
M. J.
Osborne
,
An Introduction to Game Theory
(
Oxford University Press
,
Oxford
,
2003
).
23.
J. M.
Smith
,
Evolution and the Theory of Games
(
Cambridge University Press
,
Cambridge
,
1982
).
24.
P. J.
Hammond
, “
Utility invariance in non-cooperative games
,” in
Advances in Public Economics: Utility, Choice and Welfare: A Festschrift for Christian Seidl
, edited by
U.
Schmidt
and
S.
Traub
(
Springer US
,
Boston, MA
,
2005
), pp.
31
50
.
25.
H. A.
Orr
, “
Fitness and its role in evolutionary genetics
,”
Nat. Rev. Genet.
10
,
531
539
(
2009
).
26.
J.
Hofbauer
and
K.
Sigmund
,
Evolutionary Games and Population Dynamics
(
Cambridge University Press
,
Cambridge
,
1998
).
27.
F. J.
Weissing
, “
Evolutionary stability and dynamic stability in a class of evolutionary normal form games
,” in
Game Equilibrium Models I: Evolution and Game Dynamics
, edited by
R.
Selten
(
Springer
,
Berlin, Heidelberg
,
1991
), pp.
29
97
.
28.
E.
Dekel
and
S.
Scotchmer
, “
On the evolution of optimizing behavior
,”
J. Econ. Theory
57
,
392
406
(
1992
).
29.
A.
Cabrales
and
J.
Sobel
, “
On the limit points of discrete selection dynamics
,”
J. Econ. Theory
57
,
407
419
(
1992
).
30.
M.
Nowak
and
K.
Sigmund
, “
Chaos and the evolution of cooperation
,”
Proc. Natl. Acad. Sci. U.S.A.
90
,
5091
5094
(
1993
).
31.
C.
Hauert
, “
Effects of space in 2 × 2 games
,”
Int. J. Bifurcation Chaos
12
,
1531
1548
(
2002
).
32.
S.
Hummert
,
K.
Bohl
,
D.
Basanta
,
A.
Deutsch
,
S.
Werner
,
G.
Theißen
,
A.
Schroeter
, and
S.
Schuster
, “
Evolutionary game theory: Cells as players
,”
Mol. BioSyst.
10
,
3044
3065
(
2014
).
33.
T. D.
Rogers
and
D. C.
Whitley
, “
Chaos in the cubic mapping
,”
Math. Modell.
4
,
9
25
(
1983
).
34.
A.
Traulsen
,
M. A.
Nowak
, and
J. M.
Pacheo
, “
Stochastic dynamics of invasion and fixation
,”
Phys. Rev. E
74
,
011909
(
2006
).
35.
P. E.
Turner
and
L.
Chao
, “
Prisoner's dilemma in an RNA virus
,”
Nature
398
,
441
443
(
1999
).
36.
I.
Kareva
, “
Prisoner's dilemma in cancer metabolism
,”
PLoS One
6
(
12
),
e28576
(
2011
).
37.
J.
West
,
Z.
Hasnain
,
J.
Mason
, and
P. K.
Newton
, “
The prisoner's dilemma as a cancer model
,”
Convergent Sci. Phys. Oncol.
2
,
035002
(
2016
).
38.
J. M.
Binner
,
L. R.
Fletcher
,
V.
Kolokoltsov
, and
F.
Ciardiello
, “
External pressure on alliances: What does the prisoners' dilemma reveal?
,”
Games
4
,
754
775
(
2013
).
39.
A.
Alexandra
, “
Should Hobbes's state of nature be represented as a prisoner's dilemma?
,”
South. J. Philos.
30
,
1
16
(
1992
).
40.
C.
Fang
,
S. O.
Kimbrough
,
S.
Pace
,
A.
Valluri
, and
Z.
Zheng
, “
On adaptive emergence of trust behavior in the game of stag hunt
,”
Group Decis. Negotiation
11
,
449
467
(
2002
).
41.
B.
Skyrms
,
The Stag Hunt and the Evolution of Social Structure
(
Cambridge University Press
,
Cambridge
,
2003
).
42.
J. M.
Pacheco
,
F. C.
Santos
,
M. O.
Souza
, and
B.
Skyrms
, “
Evolutionary dynamics of collective action in n-person stag hunt dilemmas
,”
Proc. R. Soc. B
276
,
315
321
(
2009
).
43.
C.
Hilbe
, “
Local replicator dynamics: A simple link between deterministic and stochastic models of evolutionary game theory
,”
Bull. Math. Biol.
73
,
2068
2087
(
2011
).
44.
M. A.
Nowak
,
N. L.
Komarova
, and
P.
Niyogi
, “
Evolution of universal grammar
,”
Science
291
,
114
118
(
2001
).
45.
N. L.
Komarova
,
P.
Niyogi
, and
M. A.
Nowak
, “
The evolutionary dynamics of grammar acquisition
,”
J. Theor. Biol.
209
,
43
59
(
2001
).
46.
L. C.
Cole
, “
The population consequences of life history phenomena
,”
Q. Rev. Biol.
29
,
103
137
(
1954
).
47.
H. C. J.
Godfray
and
M. P.
Hassell
, “
Discrete and continuous insect populations in tropical environments
,”
J. Anim. Ecol.
58
,
153
174
(
1989
).
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