170 research outputs found
Introduction to nonlinearities, business cycles, and forecasting.
Forecasting; Business cycles;
Coevolution of teaching activity promotes cooperation
Evolutionary games are studied where the teaching activity of players can
evolve in time. Initially all players following either the cooperative or
defecting strategy are distributed on a square lattice. The rate of strategy
adoption is determined by the payoff difference and a teaching activity
characterizing the donor's capability to enforce its strategy on the opponent.
Each successful strategy adoption process is accompanied with an increase in
the donor's teaching activity. By applying an optimum value of the increment
this simple mechanism spontaneously creates relevant inhomogeneities in the
teaching activities that support the maintenance of cooperation for both the
prisoner's dilemma and the snowdrift game.Comment: 10 pages, 4 figures; accepted for publication in New Journal of
Physic
Prisoner's dilemma in structured scale-free networks
The conventional wisdom is that scale-free networks are prone to cooperation
spreading. In this paper we investigate the cooperative behaviors on the
structured scale-free network. On the contrary of the conventional wisdom that
scale-free networks are prone to cooperation spreading, the evolution of
cooperation is inhibited on the structured scale-free network while performing
the prisoner's dilemma (PD) game. Firstly, we demonstrate that neither the
scale-free property nor the high clustering coefficient is responsible for the
inhibition of cooperation spreading on the structured scale-free network. Then
we provide one heuristic method to argue that the lack of age correlations and
its associated `large-world' behavior in the structured scale-free network
inhibit the spread of cooperation. The findings may help enlighten further
studies on evolutionary dynamics of the PD game in scale-free networks.Comment: Definitive version accepted for publication in Journal of Physics
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
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
Does strong heterogeneity promote cooperation by group interactions?
Previous research has highlighted the importance of strong heterogeneity for
the successful evolution of cooperation in games governed by pairwise
interactions. Here we determine to what extent this is true for games governed
by group interactions. We therefore study the evolution of cooperation in the
public goods game on the square lattice, the triangular lattice and the random
regular graph, whereby the payoffs are distributed either uniformly or
exponentially amongst the players by assigning to them individual scaling
factors that determine the share of the public good they will receive. We find
that uniformly distributed public goods are more successful in maintaining high
levels of cooperation than exponentially distributed public goods. This is not
in agreement with previous results on games governed by pairwise interactions,
indicating that group interactions may be less susceptible to the promotion of
cooperation by means of strong heterogeneity as originally assumed, and that
the role of strongly heterogeneous states should be reexamined for other types
of games.Comment: 12 pages, 4 figures; accepted for publication in New Journal of
Physics [related work available at http://arxiv.org/abs/0708.1746 and
http://www.matjazperc.com/
Modeling Evolutionary Dynamics of Lurking in Social Networks
Lurking is a complex user-behavioral phenomenon that occurs in all
large-scale online communities and social networks. It generally refers to the
behavior characterizing users that benefit from the information produced by
others in the community without actively contributing back to the production of
social content. The amount and evolution of lurkers may strongly affect an
online social environment, therefore understanding the lurking dynamics and
identifying strategies to curb this trend are relevant problems. In this
regard, we introduce the Lurker Game, i.e., a model for analyzing the
transitions from a lurking to a non-lurking (i.e., active) user role, and vice
versa, in terms of evolutionary game theory. We evaluate the proposed Lurker
Game by arranging agents on complex networks and analyzing the system
evolution, seeking relations between the network topology and the final
equilibrium of the game. Results suggest that the Lurker Game is suitable to
model the lurking dynamics, showing how the adoption of rewarding mechanisms
combined with the modeling of hypothetical heterogeneity of users' interests
may lead users in an online community towards a cooperative behavior.Comment: 13 pages, 5 figures. Accepted at CompleNet 201
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
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