The paradigm of tax-reward and tax-punishment strategies in the advancement of public resource management dynamics
Abstract
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 to 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.
README: The paradigm of tax-reward and tax-punishment strategies in the advancement of public resource management dynamics
https://doi.org/10.5061/dryad.pnvx0k6w6
The data in the dataset originated from 'solution_to_the_equation' in Matlab, the results of which are represented in Figure 1A. The 'Phase_diagram' code was employed to produce all the phase diagrams. The 'Monte_Carlo' code corresponds to Monte Carlo simulations, which are depicted in Figures 7 and 8."
Description of the data and file structure
The data in the dataset originated from 'solution_to_the_equation' in Matlab, the results of which are represented in Figure 1A. The 'Phase_diagram' code was employed to produce all the phase diagrams. The 'Monte_Carlo' code corresponds to Monte Carlo simulations, which are depicted in Figures 7 and 8.
Code/Software
MatLab was utilized to generate all the figures in the paper.
Methods
We use Matlab for numerical simulations.