Evolution of cooperation under threshold public goods game

When the number of cooperators does not reach the collective target, resulting in the collective risk social dilemma, the self-organizing behavior of the group leads to the loss of collective interest and the government intervention leads to the increase of collective interest. For these two situations, we study the evolution of cooperation under threshold public goods game in well-mixed population. The results show that the introduction of the threshold makes it possible to generate complex dynamics with two interior equilibria in the replication equation. For self-organizing behavior, increasing the threshold is a double-edged sword. Although raising the threshold may increase the level of cooperation, the risk of dominance of defection also increases. For government intervention behavior, increasing the threshold significantly promotes cooperation. The common result is that individuals tend to free-ride when the group size increases. In addition, the greater the impact of the two behaviors on collective interest, the more conducive to promoting cooperation. When there are fewer initial cooperators, the government should intervene in time to generate cooperation. When there are more initial cooperators, self-organizing behavior is more conducive to attracting cooperators than government intervention. We hope that the model and results proposed in this paper can contribute to addressing the challenges posed by global issues such as climate change and the degradation of the ecological environment.

Authors

Institutions

Publication Details

Journal
Chaos An Interdisciplinary Journal of Nonlinear Science
Published
2025-02-01
DOI
https://doi.org/10.1063/5.0256927
Citations
1
Primary Topic
Evolutionary Game Theory and Cooperation
Type
article
Field-Weighted Citation Impact
4.20

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Evolution of cooperation under threshold public goods game

Mingfeng He, Qiuhui Pan, Liyan Gao
1 citations
Chaos An Interdisciplinary Journal of Nonlinear Science
Evolutionary Game Theory and Cooperation
4.20
article

Evolution of cooperation under threshold public goods game

Mingfeng He, Qiuhui Pan, Liyan Gao
article en
1 citations

Abstract

When the number of cooperators does not reach the collective target, resulting in the collective risk social dilemma, the self-organizing behavior of the group leads to the loss of collective interest and the government intervention leads to the increase of collective interest. For these two situations, we study the evolution of cooperation under threshold public goods game in well-mixed population. The results show that the introduction of the threshold makes it possible to generate complex dynamics with two interior equilibria in the replication equation. For self-organizing behavior, increasing the threshold is a double-edged sword. Although raising the threshold may increase the level of cooperation, the risk of dominance of defection also increases. For government intervention behavior, increasing the threshold significantly promotes cooperation. The common result is that individuals tend to free-ride when the group size increases. In addition, the greater the impact of the two behaviors on collective interest, the more conducive to promoting cooperation. When there are fewer initial cooperators, the government should intervene in time to generate cooperation. When there are more initial cooperators, self-organizing behavior is more conducive to attracting cooperators than government intervention. We hope that the model and results proposed in this paper can contribute to addressing the challenges posed by global issues such as climate change and the degradation of the ecological environment.

Chaos An Interdisciplinary Journal of Nonlinear ScienceVol. 35(2)
Dalian University of Technology (CN)
National Natural Science Foundation of China
Climate action
Openalex Percentile: Top 10%
Evolutionary Game Theory and Cooperation
4.20
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.