The paradigm of tax-reward and tax-punishment strategies in the advancement of public resource management dynamics.

common resource evolutionary game theory tax-punishment tax-reward

Journal

Proceedings. Biological sciences
ISSN: 1471-2954
Titre abrégé: Proc Biol Sci
Pays: England
ID NLM: 101245157

Informations de publication

Date de publication:
Jun 2024
Historique:
medline: 12 6 2024
pubmed: 12 6 2024
entrez: 12 6 2024
Statut: ppublish

Résumé

In contemporary society, the effective utilization of public resources remains a subject of significant concern. A common issue arises from defectors seeking to obtain an excessive share of these resources for personal gain, potentially leading to resource depletion. To mitigate this tragedy and ensure sustainable development of resources, implementing mechanisms to either reward those who adhere to distribution rules or penalize those who do not, appears advantageous. We introduce two models: a tax-reward model and a tax-punishment model, to address this issue. Our analysis reveals that in the tax-reward model, the evolutionary trajectory of the system is influenced not only by the tax revenue collected but also by the natural growth rate of the resources. Conversely, the tax-punishment model exhibits distinct characteristics when compared with the tax-reward model, notably the potential for bistability. In such scenarios, the selection of initial conditions is critical, as it can determine the system's path. Furthermore, our study identifies instances where the system lacks stable points, exemplified by a limit cycle phenomenon, underscoring the complexity and dynamism inherent in managing public resources using these models.

Identifiants

pubmed: 38864335
doi: 10.1098/rspb.2024.0182
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

20240182

Auteurs

Lichen Wang (L)

College of Science, Northwest A&F University, Yangling 712100, People's Republic of China.

Yuyuan Liu (Y)

College of Science, Northwest A&F University, Yangling 712100, People's Republic of China.

Ruqiang Guo (R)

College of Science, Northwest A&F University, Yangling 712100, People's Republic of China.

Liang Zhang (L)

College of Science, Northwest A&F University, Yangling 712100, People's Republic of China.

Linjie Liu (L)

College of Science, Northwest A&F University, Yangling 712100, People's Republic of China.
College of Economics and Management, Northwest A&F University, Yangling 712100, People's Republic of China.

Shijia Hua (S)

College of Science, Northwest A&F University, Yangling 712100, People's Republic of China.

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Classifications MeSH