11,536 research outputs found
Cooperation in the snowdrift game on directed small-world networks under self-questioning and noisy conditions
Cooperation in the evolutionary snowdrift game with a self-questioning
updating mechanism is studied on annealed and quenched small-world networks
with directed couplings. Around the payoff parameter value , we find a
size-invariant symmetrical cooperation effect. While generally suppressing
cooperation for payoffs, rewired networks facilitated cooperative
behavior for . Fair amounts of noise were found to break the observed
symmetry and further weaken cooperation at relatively large values of .
However, in the absence of noise, the self-questioning mechanism recovers
symmetrical behavior and elevates altruism even under large-reward conditions.
Our results suggest that an updating mechanism of this type is necessary to
stabilize cooperation in a spatially structured environment which is otherwise
detrimental to cooperative behavior, especially at high cost-to-benefit ratios.
Additionally, we employ component and local stability analyses to better
understand the nature of the manifested dynamics.Comment: 7 pages, 6 figures, 1 tabl
Coevolutionary games - a mini review
Prevalence of cooperation within groups of selfish individuals is puzzling in
that it contradicts with the basic premise of natural selection. Favoring
players with higher fitness, the latter is key for understanding the challenges
faced by cooperators when competing with defectors. Evolutionary game theory
provides a competent theoretical framework for addressing the subtleties of
cooperation in such situations, which are known as social dilemmas. Recent
advances point towards the fact that the evolution of strategies alone may be
insufficient to fully exploit the benefits offered by cooperative behavior.
Indeed, while spatial structure and heterogeneity, for example, have been
recognized as potent promoters of cooperation, coevolutionary rules can extend
the potentials of such entities further, and even more importantly, lead to the
understanding of their emergence. The introduction of coevolutionary rules to
evolutionary games implies, that besides the evolution of strategies, another
property may simultaneously be subject to evolution as well. Coevolutionary
rules may affect the interaction network, the reproduction capability of
players, their reputation, mobility or age. Here we review recent works on
evolutionary games incorporating coevolutionary rules, as well as give a
didactic description of potential pitfalls and misconceptions associated with
the subject. In addition, we briefly outline directions for future research
that we feel are promising, thereby particularly focusing on dynamical effects
of coevolutionary rules on the evolution of cooperation, which are still widely
open to research and thus hold promise of exciting new discoveries.Comment: 24 two-column pages, 10 figures; accepted for publication in
BioSystem
Learning and innovative elements of strategy adoption rules expand cooperative network topologies
Cooperation plays a key role in the evolution of complex systems. However,
the level of cooperation extensively varies with the topology of agent networks
in the widely used models of repeated games. Here we show that cooperation
remains rather stable by applying the reinforcement learning strategy adoption
rule, Q-learning on a variety of random, regular, small-word, scale-free and
modular network models in repeated, multi-agent Prisoners Dilemma and Hawk-Dove
games. Furthermore, we found that using the above model systems other long-term
learning strategy adoption rules also promote cooperation, while introducing a
low level of noise (as a model of innovation) to the strategy adoption rules
makes the level of cooperation less dependent on the actual network topology.
Our results demonstrate that long-term learning and random elements in the
strategy adoption rules, when acting together, extend the range of network
topologies enabling the development of cooperation at a wider range of costs
and temptations. These results suggest that a balanced duo of learning and
innovation may help to preserve cooperation during the re-organization of
real-world networks, and may play a prominent role in the evolution of
self-organizing, complex systems.Comment: 14 pages, 3 Figures + a Supplementary Material with 25 pages, 3
Tables, 12 Figures and 116 reference
Emergence of Cooperation in Non-scale-free Networks
Evolutionary game theory is one of the key paradigms behind many scientific
disciplines from science to engineering. Previous studies proposed a strategy
updating mechanism, which successfully demonstrated that the scale-free network
can provide a framework for the emergence of cooperation. Instead, individuals
in random graphs and small-world networks do not favor cooperation under this
updating rule. However, a recent empirical result shows the heterogeneous
networks do not promote cooperation when humans play a Prisoner's Dilemma. In
this paper, we propose a strategy updating rule with payoff memory. We observe
that the random graphs and small-world networks can provide even better
frameworks for cooperation than the scale-free networks in this scenario. Our
observations suggest that the degree heterogeneity may be neither a sufficient
condition nor a necessary condition for the widespread cooperation in complex
networks. Also, the topological structures are not sufficed to determine the
level of cooperation in complex networks.Comment: 6 pages, 5 figure
Beyond pairwise strategy updating in the prisoner's dilemma game
In spatial games players typically alter their strategy by imitating the most
successful or one randomly selected neighbor. Since a single neighbor is taken
as reference, the information stemming from other neighbors is neglected, which
begets the consideration of alternative, possibly more realistic approaches.
Here we show that strategy changes inspired not only by the performance of
individual neighbors but rather by entire neighborhoods introduce a
qualitatively different evolutionary dynamics that is able to support the
stable existence of very small cooperative clusters. This leads to phase
diagrams that differ significantly from those obtained by means of pairwise
strategy updating. In particular, the survivability of cooperators is possible
even by high temptations to defect and over a much wider uncertainty range. We
support the simulation results by means of pair approximations and analysis of
spatial patterns, which jointly highlight the importance of local information
for the resolution of social dilemmas.Comment: 9 two-column pages, 5 figures; accepted for publication in Scientific
Report
Asymmetric evolutionary games
Evolutionary game theory is a powerful framework for studying evolution in
populations of interacting individuals. A common assumption in evolutionary
game theory is that interactions are symmetric, which means that the players
are distinguished by only their strategies. In nature, however, the microscopic
interactions between players are nearly always asymmetric due to environmental
effects, differing baseline characteristics, and other possible sources of
heterogeneity. To model these phenomena, we introduce into evolutionary game
theory two broad classes of asymmetric interactions: ecological and genotypic.
Ecological asymmetry results from variation in the environments of the players,
while genotypic asymmetry is a consequence of the players having differing
baseline genotypes. We develop a theory of these forms of asymmetry for games
in structured populations and use the classical social dilemmas, the Prisoner's
Dilemma and the Snowdrift Game, for illustrations. Interestingly, asymmetric
games reveal essential differences between models of genetic evolution based on
reproduction and models of cultural evolution based on imitation that are not
apparent in symmetric games.Comment: accepted for publication in PLOS Comp. Bio
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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Approximating evolutionary dynamics on networks using a Neighbourhood Configuration model
Evolutionary dynamics have been traditionally studied on homogeneously mixed and infinitely large populations. However, real populations are usually finite and characterised by complex interactions among individuals. Recent studies have shown that the outcome of the evolutionary process might be significantly affected by the population structure. Although an analytic investigation of the process is possible when the contact structure of the population has a simple form, this is usually infeasible on complex structures and the use of various assumptions and approximations is necessary. In this paper, we adopt an approximation method which has been recently used for the modelling of infectious disease transmission, to model evolutionary game dynamics on complex networks. Comparisons of the predictions of the model constructed with the results of computer simulations reveal the effectiveness of the process and the improved accuracy that it provides when, for example, compared to well-known pair approximation methods. This modelling framework offers a flexible way to carry out a systematic analysis of evolutionary game dynamics on graphs and to establish the link between network topology and potential system behaviours. As an example, we investigate how the Hawk and Dove strategies in a Hawk-Dove game spread in a population represented by a random regular graph, a random graph and a scale-free network, and we examine the features of the graph which affect the evolution of the population in this particular game
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