54 research outputs found
Cooperation with both synergistic and local interactions can be worse than each alone
Cooperation is ubiquitous ranging from multicellular organisms to human
societies. Population structures indicating individuals' limited interaction
ranges are crucial to understand this issue. But it is still at large to what
extend multiple interactions involving nonlinearity in payoff play a role on
cooperation in structured populations. Here we show a rule, which determines
the emergence and stabilization of cooperation, under multiple discounted,
linear, and synergistic interactions. The rule is validated by simulations in
homogenous and heterogenous structured populations. We find that the more
neighbors there are the harder for cooperation to evolve for multiple
interactions with linearity and discounting. For synergistic scenario, however,
distinct from its pairwise counterpart, moderate number of neighbors can be the
worst, indicating that synergistic interactions work with strangers but not
with neighbors. Our results suggest that the combination of different factors
which promotes cooperation alone can be worse than that with every single
factor.Comment: 32 pages, 4 figure
High-Accuracy Approximation of Evolutionary Pairwise Games on Complex Networks
Previous studies have shown that the topological properties of a complex
network, such as heterogeneity and average degree, affect the evolutionary game
dynamics on it. However, traditional numerical simulations are usually
time-consuming and demand a lot of computational resources. In this paper, we
propose the method of dynamical approximate master equations (DAMEs) to
accurately approximate the evolutionary outcomes on complex networks. We
demonstrate that the accuracy of DAMEs supersedes previous standard pairwise
approximation methods, and DAMEs require far fewer computational resources than
traditional numerical simulations. We use prisoner's dilemma and snowdrift game
on regular and scale-free networks to demonstrate the applicability of DAMEs.
Overall, our method facilitates the investigation of evolutionary dynamics on a
broad range of complex networks, and provides new insights into the puzzle of
cooperation.Comment: 21 pages, 4 figure
Temporal higher-order interactions facilitate the evolution of cooperation
Motivated by the vital progress of modeling higher-order interactions by
hypernetworks, where a link connects more than two individuals, we study the
evolution of cooperation on temporal hypernetworks. We find that temporal
hypernetworks may promote cooperation compared with their static counterparts.
Our results offer new insights into the impact of network temporality in
higher-order interactions on understanding the evolution of cooperation,
suggesting traditional networks based on pairwise or static interactions may
underestimate the potential of local interactions to foster cooperation.Comment: 6 pages, 4 figure
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