title: Dynamic decision-making under uncertainty: Bayesian learning in environmental game theory
metadata.name: dc.contributor.author
metadata.value: Zhou, Jiangjing
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Petrosian, Ovanes L.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Gao, Hongwei
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2024-08-19T21:23:54Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2024-08-19T21:23:54Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2024-06
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Zhou J., Petrosian O. L., Gao H. Dynamic decision-making under uncertainty: Bayesian learning in environmental game theory. Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes, 2024, vol. 20, iss. 2, pp. 289–297. https://doi.org/10.21638/spbu10.2024.213
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/spbu10.2024.213
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/47274
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: This paper investigates the issue of pollution control dynamic games defined over a finite time
  horizon, with a particular focus on parameter uncertainty within the ecosystem. We employ
  a dynamic Bayesian learning method to estimate uncertain parameters in the dynamic
  equation, differing from traditional single-instance Bayesian learning which does not involve
  continuous signal reception and belief updating. Our study validates the effectiveness of
  the dynamic Bayesian learning approach, demonstrating that, over time, the beliefs of the
  players progressively converge towards the true values of the unknown parameters. Through
  numerical simulations, we illustrate the convergence process of beliefs and compare optimal
  control strategies under different scenarios. The findings of this paper offer a new perspective
  for understanding and addressing the uncertainties in pollution control problems.
metadata.lang: en_GB
metadata.name: dc.description.sponsorship
metadata.value: This research was supported by Saint Petersburg State University (ID project: N 94062114).
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: en
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Applied Mathematics. Computer Science. Control Processes;Volume 20; Issue 2
metadata.lang: -
metadata.name: dc.subject
metadata.value: dynamic Bayesian learning
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: pollution control games
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: ecological uncertainty
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: optimal control strategy
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Dynamic decision-making under uncertainty: Bayesian learning in environmental game theory
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: vestnik_spbu10_2024_289.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/47274/1/vestnik_spbu10_2024_289.pdf
resources.format: Adobe PDF
resources.size: 437,37 kB