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Nonlinear model reference adaptive control approach for governance of the commons in a feedback-evolving game

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  • Yan, Fang
  • Hou, Xiaorong
  • Tian, Tingting
  • Chen, Xiaojie

Abstract

The governance of common-pool resources has vital importance for sustainability. However, in the realistic management systems of common-pool resources, the institutions do not necessarily execute the management policies completely, which will induce that the real implementation intensity is uncertain. In this paper, we consider a feedback-evolving game model with the inspection for investigating the management of renewable resource and assume that there exists the implementation uncertainty of inspection. Furthermore, we use the nonlinear model reference adaptive control approach to handle this uncertainty. We accordingly design a protocol, which is an update law of adjusting the institutional inspection intensity. We obtain a sufficient condition under which the update law can drive the actual system to reach the expected outcome. In addition, we provide several numerical examples, which can confirm our theoretical results. Our work presents a novel approach to address the implementation uncertainty in the feedback-evolving games and thus our results can be helpful for effectively managing the common-pool resources in the human social systems.

Suggested Citation

  • Yan, Fang & Hou, Xiaorong & Tian, Tingting & Chen, Xiaojie, 2023. "Nonlinear model reference adaptive control approach for governance of the commons in a feedback-evolving game," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    • Handle: RePEc:eee:chsofr:v:174:y:2023:i:c:s0960077923007622
    DOI: 10.1016/j.chaos.2023.113861
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    References listed on IDEAS

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    Cited by:

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    3. Gao, Yan & Li, Minlan & Hu, Yuanyuan & Wang, Rui-Wu & Wang, Chao, 2024. "Evolutionary dynamics in voluntary prisoner’s dilemma game with environmental feedbacks," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).

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