Promotion of cooperation mechanism on the stability of delay-induced host-generalist parasitoid model

Ye, Yong; Zhao, Yi; Zhou, Jiaying

doi:10.1016/j.chaos.2022.112882

Cited by 6 publications

(4 citation statements)

References 42 publications

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“…The results show that although rumors spread faster on scale-free networks than on ER networks, the propagation scale is diametrically opposite [18]. Additionally, delay as an influencing factor in dynamical models widely exists in social environments and is often introduced [7,[22][23][24][25]. For example, in the stage of rumor propagation, it takes a certain amount of time for susceptible individuals to become rumor spreaders, as well as for rumor-infected individuals to dispel their doubts about the rumor [26][27][28].…”

Section: Introductionmentioning

confidence: 97%

Pattern formation in reaction-diffusion information propagation model on multiplex simplicial complexes

Zhou

Zhao

2023

Preprint

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Turing pattern for explaining the spatial distribution in nature mostly focused on continuous media and existing networks but there are few attempts at studying them on the systems with high-order interactions. Considering that high-order interactions have a particularly significant impact on rumor propagation, this article establishes a generalized reaction-diffusion rumor propagation model based on a multiplex network, where simplicial complexes are employed to describe the high-order structures. It aims to provide the spatial distribution patterns of the population participating in rumor propagation and identify structural factors that affect such patterns. We theoretically give the necessary conditions for Turing instability in the single-layer and multiplex networks with consideration of high-order interactions. In the numerical simulation, we demonstrate that Turing pattern is controlled by adjusting the diffusion coefficient, high-order structure intensity, and average degree of the network. The results conclude that: (i) in a single-layer network, Turing pattern only exists when high-order interactions appear, and the difference in diffusion rate plays a decisive role, (ii) in a multiplex network, Turing pattern can still be observed under the same diffusion rates, which are affected by the difference of higher-order intensity between two layers, and (iii) in the existing networks, the average degree of the network takes an important impact on Turing pattern. All these findings contribute to comprehending the impact of network structure on pattern formation, especially the high-order interactions on Turing pattern.

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Section: Introductionmentioning

confidence: 97%

Pattern formation in reaction-diffusion information propagation model on multiplex simplicial complexes

Zhou

Zhao

2023

Preprint

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“…Cooperation, where individuals willingly bear costs to benefit others, is a widespread phenomenon across various biological and social spheres, as highlighted in numerous studies [1][2][3][4][5][6][7]. The subject is so significant that it has been listed as one of the top 25 scientific puzzles by the renowned journal Science in 2005 [8].…”

Section: Introductionmentioning

confidence: 99%

Social norms and cooperation in higher-order networks

Ma,

Jiang,

Fang

et al. 2024

Proc. R. Soc. A.

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Recent research has been focused on understanding how cooperation is fostered through various mechanisms in cognitive settings, particularly through pairwise interactions. However, real-world interactions often extend beyond simple dyads, including multiple cliques with both pairwise and higher-order interactions. These complex interactions influence how individuals perceive and adapt their strategies based on social norms. We here introduce a model that explores the evolution of collective strategies and social norms within a heterogeneous environment, encompassing both dyadic and three-body interactions. We find that social norms play a crucial role in promoting cooperation in comparison with simply imitating the most successful neighbour. We also show that the rise of prosocial norms leads to increased cooperation across various social dilemmas, often resulting in shifts from defective to cooperative behaviour. Additionally, we observe that a moderate level of information privacy helps sustaining prosocial norms and curtails antisocial tendencies, even in situations where mutual defection might seem advantageous. Our research thus offers insights into the evolution of cooperation through the lens of social norm diffusion in higher-order networks.

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“…As early as around 1925, Lotka and Volterra proposed a mathematical model to describe the population evolution of snow rabbits (prey) and lynx (predators), known as the Lotka-Volterra model [1]. Since then, researchers have conducted in-depth research and improvement on the model based on real-life backgrounds [2][3][4][5][6][7][8][9][10][11][12]. Turing demonstrated in 1952 that variations in the diffusion constants of activator and inhibitor species might cause the uniform state to destabilize and result in the spontaneous formation of periodic spatial patterns [13].…”

Section: Introductionmentioning

confidence: 99%

Pattern Formation in a Predator–Prey Model with Allee Effect and Hyperbolic Mortality on Multiplex Networks

Shi,

Zhou,

2023

Mathematics

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With the rapid development of network science, Turing patterns on complex networks have attracted extensive attention from researchers. In this paper, we focus on spatial patterns in multiplex ER (Erdös-Rényi) random networks, taking the predator–prey model with Allee effect and hyperbolic mortality as an example. In theory, the threshold condition for generating Turing patterns is given using the Turing instability theory of multiplex networks. Numerically, we design relevant experiments to explore the impact of network topology on Turing patterns. The factors considered include model parameters, diffusion rate, average degree of the network, and differences in the average degree of different layers. The results indicate that the importance of diffusion rate and network average degree for Turing patterns is affirmed on the single-layer network. For multiplex networks, the differentiation of average degrees in different layers controls the generation of Turing patterns, which are not affected by the diffusion rates of the two populations. More interestingly, we observe the switching of Turing patterns and spatiotemporal patterns. We believe that these findings contribute to a better understanding of self-organization on complex networks.

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Promotion of cooperation mechanism on the stability of delay-induced host-generalist parasitoid model

Cited by 6 publications

References 42 publications

Pattern formation in reaction-diffusion information propagation model on multiplex simplicial complexes

Pattern formation in reaction-diffusion information propagation model on multiplex simplicial complexes

Social norms and cooperation in higher-order networks

Pattern Formation in a Predator–Prey Model with Allee Effect and Hyperbolic Mortality on Multiplex Networks

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