Evolution of global contribution in multi-level threshold public goods games with insurance compensation

Du, Jinming; Tang, Lixin

doi:10.1088/1742-5468/aa9bb6

Cited by 31 publications

(8 citation statements)

References 110 publications

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“…In addition, the probability distribution of j N can be defined as ϑ j (ω) . Thus, the expected value of the proportion of individuals choosing strategy A [70][71][72][73] in the population is called the average abundance function X A (ω):…”

Section: The Characteristics Of Average Abundance Function With Mutationmentioning

confidence: 99%

The characteristics of average abundance function with mutation of multi-player threshold public goods evolutionary game model under redistribution mechanism

Xia

2021

BMC Ecol Evo

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Background In recent years, the average abundance function has attracted much attention as it reflects the degree of cooperation in the population. Then it is significant to analyse how average abundance functions can be increased to promote the proliferation of cooperative behaviour. However, further theoretical analysis for average abundance function with mutation under redistribution mechanism is still lacking. Furthermore, the theoretical basis for the corresponding numerical simulation is not sufficiently understood. Results We have deduced the approximate expressions of average abundance function with mutation under redistribution mechanism on the basis of different levels of selection intensity $$\omega$$ ω (sufficiently small and large enough). In addition, we have analysed the influence of the size of group d, multiplication factor r, cost c, aspiration level $$\alpha$$ α on average abundance function from both quantitative and qualitative aspects. Conclusions (1) The approximate expression will become the linear equation related to selection intensity when $$\omega$$ ω is sufficiently small. (2) On one hand, approximation expression when $$\omega$$ ω is large enough is not available when r is small and m is large. On the other hand, this approximation expression will become more reliable when $$\omega$$ ω is larger. (3) On the basis of the expected payoff function $$\pi \left( \centerdot \right)$$ π ⋅ and function $$h(i,\omega )$$ h ( i , ω ) , the corresponding results for the effects of parameters (d,r,c,$$\alpha$$ α ) on average abundance function $$X_{A}(\omega )$$ X A ( ω ) have been explained.

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Section: The Characteristics Of Average Abundance Function With Mutationmentioning

confidence: 99%

The characteristics of average abundance function with mutation of multi-player threshold public goods evolutionary game model under redistribution mechanism

Xia

2021

BMC Ecol Evo

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“…During the evolutionary process, agents evaluate their fitness and then update their strategies. Different updating rules can be used to characterize the evolution of population, such as imitation process [34]- [36] and self-learning process [37]- [40]. In imitation process, the agent i compares its fitness with its neighbors'.…”

Section: Multi-agent System Modelmentioning

confidence: 99%

An Evolutionary Game Coordinated Control Approach to Division of Labor in Multi-Agent Systems

2019

IEEE Access

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In this paper, we propose an evolutionary game theoretic approach to coordinated control of multi-agent systems. In this mathematical framework, agents play games with their neighbors on the network, and update strategies through local interaction. In order to achieve a certain control objective of the system, we need to select the appropriate game type, design the calculation and evaluation methods of fitness, specify the interactive constraints and updating rules. During the evolutionary process of the system, agents have no predesigned dynamical equations. They adjust their behavior independently for the purpose of increasing their own benefits. The system achieves its final state in the process of individual interaction and autonomous decision-making. Taking division of labor problem as an example, we demonstrate the proposed control approach in detail. The performance of the theoretical method is verified by simulation on the regular graph, the general connected graphs, and heterogeneous scale-free networks, respectively. INDEX TERMS Evolutionary game theory, multi-agent systems, coordinated control, complex system dynamics, division and cooperation.

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“…Inserting Equations (17,18) into Equation (11) and Equations (19,20) into Equation 16, we can get the following equations when there are k groups full of G players:…”

Section: Local Income Redistributionmentioning

confidence: 99%

“…Especially when it comes to the situation that people are faced with the option of voluntary contribution to achieve a collective goal, where public goods cannot be provided in part, but only in whole after a certain cost (threshold) is covered. Threshold public goods game (TPGG) models nicely capture the main features of the above described social phenomena [11][12][13][14][15][16][17][18]. In the typical TPGG, the size of a proposed project and the associated total cost (threshold) are predetermined.…”

Section: Introductionmentioning

confidence: 99%

Evolution of global contribution in multi-level threshold public goods games with insurance compensation

Cited by 31 publications

References 110 publications

The characteristics of average abundance function with mutation of multi-player threshold public goods evolutionary game model under redistribution mechanism

The characteristics of average abundance function with mutation of multi-player threshold public goods evolutionary game model under redistribution mechanism

An Evolutionary Game Coordinated Control Approach to Division of Labor in Multi-Agent Systems

Evolution of Global Cooperation in Multi-Level Threshold Public Goods Games With Income Redistribution

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