41,189 research outputs found
Evolutionary Dilemmas in a Social Network
We simulate the prisoner's dilemma and hawk-dove games on a real social
acquaintance network. Using a discrete analogue of replicator dynamics, we show
that surprisingly high levels of cooperation can be achieved, contrary to what
happens in unstructured mixing populations. Moreover, we empirically show that
cooperation in this network is stable with respect to invasion by defectors.Comment: 13 pages, 9 figures; to be published in Lecture Notes in Computer
Science 200
Metric clusters in evolutionary games on scale-free networks
The evolution of cooperation in social dilemmas in structured populations has
been studied extensively in recent years. Whereas many theoretical studies have
found that a heterogeneous network of contacts favors cooperation, the impact
of spatial effects in scale-free networks is still not well understood. In
addition to being heterogeneous, real contact networks exhibit a high mean
local clustering coefficient, which implies the existence of an underlying
metric space. Here, we show that evolutionary dynamics in scale-free networks
self-organize into spatial patterns in the underlying metric space. The
resulting metric clusters of cooperators are able to survive in social dilemmas
as their spatial organization shields them from surrounding defectors, similar
to spatial selection in Euclidean space. We show that under certain conditions
these metric clusters are more efficient than the most connected nodes at
sustaining cooperation and that heterogeneity does not always favor--but can
even hinder--cooperation in social dilemmas. Our findings provide a new
perspective to understand the emergence of cooperation in evolutionary games in
realistic structured populations
Different perceptions of social dilemmas: Evolutionary multigames in structured populations
Motivated by the fact that the same social dilemma can be perceived
differently by different players, we here study evolutionary multigames in
structured populations. While the core game is the weak prisoner's dilemma, a
fraction of the population adopts either a positive or a negative value of the
sucker's payoff, thus playing either the traditional prisoner's dilemma or the
snowdrift game. We show that the higher the fraction of the population adopting
a different payoff matrix, the more the evolution of cooperation is promoted.
The microscopic mechanism responsible for this outcome is unique to structured
populations, and it is due to the payoff heterogeneity, which spontaneously
introduces strong cooperative leaders that give rise to an asymmetric strategy
imitation flow in favor of cooperation. We demonstrate that the reported
evolutionary outcomes are robust against variations of the interaction network,
and they also remain valid if players are allowed to vary which game they play
over time. These results corroborate existing evidence in favor of
heterogeneity-enhanced network reciprocity, and they reveal how different
perceptions of social dilemmas may contribute to their resolution.Comment: 7 two-column pages, 5 figures; accepted for publication in Physical
Review
From degree-correlated to payoff-correlated activity for an optimal resolution of social dilemmas
An active participation of players in evolutionary games depends on several
factors, ranging from personal stakes to the properties of the interaction
network. Diverse activity patterns thus have to be taken into account when
studying the evolution of cooperation in social dilemmas. Here we study the
weak prisoner's dilemma game, where the activity of each player is determined
in a probabilistic manner either by its degree or by its payoff. While
degree-correlated activity introduces cascading failures of cooperation that
are particularly severe on scale-free networks with frequently inactive hubs,
payoff-correlated activity provides a more nuanced activity profile, which
ultimately hinders systemic breakdowns of cooperation. To determine optimal
conditions for the evolution of cooperation, we introduce an exponential decay
to payoff-correlated activity that determines how fast the activity of a player
returns to its default state. We show that there exists an intermediate decay
rate, at which the resolution of the social dilemma is optimal. This can be
explained by the emerging activity patterns of players, where the inactivity of
hubs is compensated effectively by the increased activity of average-degree
players, who through their collective influence in the network sustain a higher
level of cooperation. The sudden drops in the fraction of cooperators observed
with degree-correlated activity therefore vanish, and so does the need for the
lengthy spatiotemporal reorganization of compact cooperative clusters. The
absence of such asymmetric dynamic instabilities thus leads to an optimal
resolution of social dilemmas, especially when the conditions for the evolution
of cooperation are strongly adverse.Comment: 8 two-column pages, 6 figures; accepted for publication in Physical
Review
Evolutionary game selection creates cooperative environments
The emergence of collective cooperation in competitive environments is a
well-known phenomenon in biology, economics and social systems. While most
evolutionary game models focus on the evolution of strategies for a fixed game,
how strategic decisions co-evolve with the environment has so far mostly been
overlooked. Here, we consider a game selection model where not only the
strategies but also the game can change over time following evolutionary
principles. Our results show that co-evolutionary dynamics of games and
strategies can induce novel collective phenomena, fostering the emergence of
cooperative environments. When the model is taken on structured populations the
architecture of the interaction network can significantly amplify pro-social
behaviour, with a critical role played by network heterogeneity and the
presence of clustered groups, distinctive features observed in real-world
populations. By unveiling the link between the evolution of strategies and
games for different structured populations, our model sheds new light on the
origin of social dilemmas ubiquitously observed in real-world social systems.Comment: 9 pages, 7 figure
Self-organization towards optimally interdependent networks by means of coevolution
Coevolution between strategy and network structure is established as a means to arrive at the optimal conditions needed to resolve social dilemmas. Yet recent research has highlighted that the interdependence between networks may be just as important as the structure of an individual network. We therefore introduce the coevolution of strategy and network interdependence to see whether this can give rise to elevated levels of cooperation in the prisonerʼs dilemma game. We show that the interdependence between networks self-organizes so as to yield optimal conditions for the evolution of cooperation. Even under extremely adverse conditions, cooperators can prevail where on isolated networks they would perish. This is due to the spontaneous emergence of a two-class society, with only the upper class being allowed to control and take advantage of the interdependence. Spatial patterns reveal that cooperators, once arriving at the upper class, are much more competent than defectors in sustaining compact clusters of followers. Indeed, the asymmetric exploitation of interdependence confers to them a strong evolutionary advantage that may resolve even the toughest of social dilemmas
Optimal interdependence between networks for the evolution of cooperation
Recent research has identified interactions between networks as crucial for the outcome of evolutionary
games taking place on them. While the consensus is that interdependence does promote cooperation by
means of organizational complexity and enhanced reciprocity that is out of reach on isolated networks, we
here address the question just how much interdependence there should be. Intuitively, one might assume
the more the better. However, we show that in fact only an intermediate density of sufficiently strong
interactions between networks warrants an optimal resolution of social dilemmas. This is due to an intricate
interplay between the heterogeneity that causes an asymmetric strategy flow because of the additional links
between the networks, and the independent formation of cooperative patterns on each individual network.
Presented results are robust to variations of the strategy updating rule, the topology of interdependent
networks, and the governing social dilemma, thus suggesting a high degree of universality
Conformity enhances network reciprocity in evolutionary social dilemmas
The pursuit of highest payoffs in evolutionary social dilemmas is risky and
sometimes inferior to conformity. Choosing the most common strategy within the
interaction range is safer because it ensures that the payoff of an individual
will not be much lower than average. Herding instincts and crowd behavior in
humans and social animals also compel to conformity on their own right.
Motivated by these facts, we here study the impact of conformity on the
evolution of cooperation in social dilemmas. We show that an appropriate
fraction of conformists within the population introduces an effective surface
tension around cooperative clusters and ensures smooth interfaces between
different strategy domains. Payoff-driven players brake the symmetry in favor
of cooperation and enable an expansion of clusters past the boundaries imposed
by traditional network reciprocity. This mechanism works even under the most
testing conditions, and it is robust against variations of the interaction
network as long as degree-normalized payoffs are applied. Conformity may thus
be beneficial for the resolution of social dilemmas.Comment: 8 two-column pages, 5 figures; accepted for publication in Journal of
the Royal Society Interfac
Evolution of emotions on networks leads to the evolution of cooperation in social dilemmas
We show that the resolution of social dilemmas in random graphs and scale-free networks is facilitated by
imitating not the strategy of better-performing players but, rather, their emotions. We assume sympathy and
envy to be the two emotions that determine the strategy of each player in any given interaction, and we define
them as the probabilities of cooperating with players having a lower and a higher payoff, respectively. Starting
with a population where all possible combinations of the two emotions are available, the evolutionary process
leads to a spontaneous fixation to a single emotional profile that is eventually adopted by all players. However,
this emotional profile depends not only on the payoffs but also on the heterogeneity of the interaction network.
Homogeneous networks, such as lattices and regular random graphs, lead to fixations that are characterized by
high sympathy and high envy, while heterogeneous networks lead to low or modest sympathy but also low envy.
Our results thus suggest that public emotions and the propensity to cooperate at large depend, and are in fact
determined by, the properties of the interaction network
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