415 research outputs found
Interdependent network reciprocity in evolutionary games
Besides the structure of interactions within networks, also the interactions between networks are of the outmost
importance. We therefore study the outcome of the public goods game on two interdependent networks that are
connected by means of a utility function, which determines how payoffs on both networks jointly influence the
success of players in each individual network. We show that an unbiased coupling allows the spontaneous
emergence of interdependent network reciprocity, which is capable to maintain healthy levels of public
cooperation even in extremely adverse conditions. The mechanism, however, requires simultaneous formation of
correlated cooperator clusters on both networks. If this does not emerge or if the coordination process is
disturbed, network reciprocity fails, resulting in the total collapse of cooperation. Network interdependence can
thus be exploited effectively to promote cooperation past the limits imposed by isolated networks, but only if the
coordination between the interdependent networks is not disturbe
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
Mesoscopic structure conditions the emergence of cooperation on social networks
We study the evolutionary Prisoner's Dilemma on two social networks obtained
from actual relational data. We find very different cooperation levels on each
of them that can not be easily understood in terms of global statistical
properties of both networks. We claim that the result can be understood at the
mesoscopic scale, by studying the community structure of the networks. We
explain the dependence of the cooperation level on the temptation parameter in
terms of the internal structure of the communities and their interconnections.
We then test our results on community-structured, specifically designed
artificial networks, finding perfect agreement with the observations in the
real networks. Our results support the conclusion that studies of evolutionary
games on model networks and their interpretation in terms of global properties
may not be sufficient to study specific, real social systems. In addition, the
community perspective may be helpful to interpret the origin and behavior of
existing networks as well as to design structures that show resilient
cooperative behavior.Comment: Largely improved version, includes an artificial network model that
fully confirms the explanation of the results in terms of inter- and
intra-community structur
If players are sparse social dilemmas are too: Importance of percolation for evolution of cooperation
Spatial reciprocity is a well known tour de force of cooperation promotion. A
thorough understanding of the effects of different population densities is
therefore crucial. Here we study the evolution of cooperation in social
dilemmas on different interaction graphs with a certain fraction of vacant
nodes. We find that sparsity may favor the resolution of social dilemmas,
especially if the population density is close to the percolation threshold of
the underlying graph. Regardless of the type of the governing social dilemma as
well as particularities of the interaction graph, we show that under pairwise
imitation the percolation threshold is a universal indicator of how dense the
occupancy ought to be for cooperation to be optimally promoted. We also
demonstrate that myopic updating, due to the lack of efficient spread of
information via imitation, renders the reported mechanism dysfunctional, which
in turn further strengthens its foundations.Comment: 6 two-column pages, 5 figures; accepted for publication in Scientific
Reports [related work available at http://arxiv.org/abs/1205.0541
Wisdom of groups promotes cooperation in evolutionary social dilemmas
Whether or not to change strategy depends not only on the personal success of
each individual, but also on the success of others. Using this as motivation,
we study the evolution of cooperation in games that describe social dilemmas,
where the propensity to adopt a different strategy depends both on individual
fitness as well as on the strategies of neighbors. Regardless of whether the
evolutionary process is governed by pairwise or group interactions, we show
that plugging into the "wisdom of groups" strongly promotes cooperative
behavior. The more the wider knowledge is taken into account the more the
evolution of defectors is impaired. We explain this by revealing a dynamically
decelerated invasion process, by means of which interfaces separating different
domains remain smooth and defectors therefore become unable to efficiently
invade cooperators. This in turn invigorates spatial reciprocity and
establishes decentralized decision making as very beneficial for resolving
social dilemmas.Comment: 8 two-column pages, 7 figures; accepted for publication in Scientific
Report
Human behavior in Prisoner's Dilemma experiments suppresses network reciprocity
During the last few years, much research has been devoted to strategic
interactions on complex networks. In this context, the Prisoner's Dilemma has
become a paradigmatic model, and it has been established that imitative
evolutionary dynamics lead to very different outcomes depending on the details
of the network. We here report that when one takes into account the real
behavior of people observed in the experiments, both at the mean-field level
and on utterly different networks the observed level of cooperation is the
same. We thus show that when human subjects interact in an heterogeneous mix
including cooperators, defectors and moody conditional cooperators, the
structure of the population does not promote or inhibit cooperation with
respect to a well mixed population.Comment: 5 Pages including 4 figures. Submitted for publicatio
Complex cooperative networks from evolutionary preferential attachment
In spite of its relevance to the origin of complex networks, the interplay
between form and function and its role during network formation remains largely
unexplored. While recent studies introduce dynamics by considering rewiring
processes of a pre-existent network, we study network growth and formation by
proposing an evolutionary preferential attachment model, its main feature being
that the capacity of a node to attract new links depends on a dynamical
variable governed in turn by the node interactions. As a specific example, we
focus on the problem of the emergence of cooperation by analyzing the formation
of a social network with interactions given by the Prisoner's Dilemma. The
resulting networks show many features of real systems, such as scale-free
degree distributions, cooperative behavior and hierarchical clustering.
Interestingly, results such as the cooperators being located mostly on nodes of
intermediate degree are very different from the observations of cooperative
behavior on static networks. The evolutionary preferential attachment mechanism
points to an evolutionary origin of scale-free networks and may help understand
similar feedback problems in the dynamics of complex networks by appropriately
choosing the game describing the interaction of nodes.Comment: 6 pages and 4 figures, APS format. Submitted for publicatio
Resolution of the stochastic strategy spatial prisoner's dilemma by means of particle swarm optimization
We study the evolution of cooperation among selfish individuals in the
stochastic strategy spatial prisoner's dilemma game. We equip players with the
particle swarm optimization technique, and find that it may lead to highly
cooperative states even if the temptations to defect are strong. The concept of
particle swarm optimization was originally introduced within a simple model of
social dynamics that can describe the formation of a swarm, i.e., analogous to
a swarm of bees searching for a food source. Essentially, particle swarm
optimization foresees changes in the velocity profile of each player, such that
the best locations are targeted and eventually occupied. In our case, each
player keeps track of the highest payoff attained within a local topological
neighborhood and its individual highest payoff. Thus, players make use of their
own memory that keeps score of the most profitable strategy in previous
actions, as well as use of the knowledge gained by the swarm as a whole, to
find the best available strategy for themselves and the society. Following
extensive simulations of this setup, we find a significant increase in the
level of cooperation for a wide range of parameters, and also a full resolution
of the prisoner's dilemma. We also demonstrate extreme efficiency of the
optimization algorithm when dealing with environments that strongly favor the
proliferation of defection, which in turn suggests that swarming could be an
important phenomenon by means of which cooperation can be sustained even under
highly unfavorable conditions. We thus present an alternative way of
understanding the evolution of cooperative behavior and its ubiquitous presence
in nature, and we hope that this study will be inspirational for future efforts
aimed in this direction.Comment: 12 pages, 4 figures; accepted for publication in PLoS ON
Different reactions to adverse neighborhoods in games of cooperation
In social dilemmas, cooperation among randomly interacting individuals is
often difficult to achieve. The situation changes if interactions take place in
a network where the network structure jointly evolves with the behavioral
strategies of the interacting individuals. In particular, cooperation can be
stabilized if individuals tend to cut interaction links when facing adverse
neighborhoods. Here we consider two different types of reaction to adverse
neighborhoods, and all possible mixtures between these reactions. When faced
with a gloomy outlook, players can either choose to cut and rewire some of
their links to other individuals, or they can migrate to another location and
establish new links in the new local neighborhood. We find that in general
local rewiring is more favorable for the evolution of cooperation than
emigration from adverse neighborhoods. Rewiring helps to maintain the diversity
in the degree distribution of players and favors the spontaneous emergence of
cooperative clusters. Both properties are known to favor the evolution of
cooperation on networks. Interestingly, a mixture of migration and rewiring is
even more favorable for the evolution of cooperation than rewiring on its own.
While most models only consider a single type of reaction to adverse
neighborhoods, the coexistence of several such reactions may actually be an
optimal setting for the evolution of cooperation.Comment: 12 pages, 5 figures; accepted for publication in PLoS ON
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