Evolution and maintenance of cooperation via inheritance of neighborhood relationship

Tan, Shaolin; Lü, Jinhu; Yu, Xinghuo; Hill, David J.

doi:10.1007/s11434-013-5984-y

Cited by 21 publications

(9 citation statements)

References 61 publications

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“…As we know now, consensus is a specific kind of collective behavior, which describes a phenomenon of agreement among all agents. Over the past decades, consensus of MASs has received an increasing attention from various communities, such as social sciences, physics, biology, and engineering sciences [1]- [4].…”

Section: Introductionmentioning

confidence: 99%

Bridging the Gap Between Transmission Noise and Sampled Data for Robust Consensus of Multi-Agent Systems

Liu

Zhu

Lü

2015

IEEE Trans. Circuits Syst. I

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It is well known that multi-agent systems (MASs) are ubiquitous in natural and artificial systems. This paper aims at bridging the gap between transmission noise and sampled data for robust consensus of MASs. In detail, we have developed a theoretical framework for analyzing the robust consensus of MASs with sampled-data controllers and transmission noises. Using the delay-input and discretization approaches, we obtain two sufficient conditions on the existence of sampling periods and controller parameters for robust consensus of MASs, respectively. In particular, we deduce the estimates of the convergence speeds of consensus errors for the above two methods. Finally, numerical simulations are also given to validate our theoretical results.

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Section: Introductionmentioning

confidence: 99%

Bridging the Gap Between Transmission Noise and Sampled Data for Robust Consensus of Multi-Agent Systems

Liu

Zhu

Lü

2015

IEEE Trans. Circuits Syst. I

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“…It has been widely applied to explore the emergence of cooperation and strategy selection in real-world systems345678. An evolutionary dynamic model of structured population generally consists of three basic elements: a behavior set, a behavior updating rule, and an underlying population structure.…”

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confidence: 99%

Characterizing the effect of population heterogeneity on evolutionary dynamics on complex networks

Tan

Lü

2014

Sci Rep

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Recently, the impact of network structure on evolutionary dynamics has been at the center of attention when studying the evolutionary process of structured populations. This paper aims at finding out the key structural feature of network to capture its impact on evolutionary dynamics. To this end, a novel concept called heat heterogeneity is introduced to characterize the structural heterogeneity of network, and the correlation between heat heterogeneity of structure and outcome of evolutionary dynamics is further investigated on various networks. It is found that the heat heterogeneity mainly determines the impact of network structure on evolutionary dynamics on complex networks. In detail, the heat heterogeneity readjusts the selection effect on evolutionary dynamics. Networks with high heat heterogeneity amplify the selection effect on the birth-death process and suppress the selection effect on the death-birth process. Based on the above results, an effective algorithm is proposed to generate selection adjusters with desired size and average degree.

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“…The most intuitive approach of modeling such complex systems is to treat them as networks, where nodes represent component units and edges represent connectivity. Importantly, empirical findings have unraveled the presence of universal features in most socio-technical networks, e.g., small-world [10], scale-free (SF) [11], which inspires extensive studies towards a better understanding about the impact of population infrastructures (network connectivity) on dynamical processes [12][13][14][15], including robustness [16,17], synchronization [18][19][20], consensus [21][22][23][24], control [25][26][27][28], evolutionary game [29][30][31][32][33][34][35][36], traffic routing [37][38][39], selforganized criticality [40][41][42][43], etc.…”

Section: Network Epidemiologymentioning

confidence: 99%

Spatial epidemiology of networked metapopulation: An overview

Wang

2014

Preprint

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An emerging disease is one infectious epidemic caused by a newly transmissible pathogen, which has either appeared for the first time or already existed in human populations, having the capacity to increase rapidly in incidence as well as geographic range. Adapting to human immune system, emerging diseases may trigger large-scale pandemic spreading, such as the transnational spreading of SARS, the global outbreak of A(H1N1), and the recent potential invasion of avian influenza A(H7N9). To study the dynamics mediating the transmission of emerging diseases, spatial epidemiology of networked metapopulation provides a valuable modeling framework, which takes spatially distributed factors into consideration. This review elaborates the latest progresses on the spatial metapopulation dynamics, discusses empirical and theoretical findings that verify the validity of networked metapopulations, and the application in evaluating the effectiveness of disease intervention strategies as well.

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Evolution and maintenance of cooperation via inheritance of neighborhood relationship

Cited by 21 publications

References 61 publications

Bridging the Gap Between Transmission Noise and Sampled Data for Robust Consensus of Multi-Agent Systems

Bridging the Gap Between Transmission Noise and Sampled Data for Robust Consensus of Multi-Agent Systems

Characterizing the effect of population heterogeneity on evolutionary dynamics on complex networks

Spatial epidemiology of networked metapopulation: An overview

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