Standard

Dynamic decision-making under uncertainty: Bayesian learning in environmental game theory. / Zhou, Jiangjing; Петросян, Ованес Леонович; Gao, Hongwei.

In: Вестник Санкт-Петербургского университета. Прикладная математика. Информатика. Процессы управления, Vol. 20, No. 2, 2024, p. 289–297.

Research output: Contribution to journal › Article › peer-review

Harvard

Zhou, J, Петросян, ОЛ & Gao, H 2024, 'Dynamic decision-making under uncertainty: Bayesian learning in environmental game theory', Вестник Санкт-Петербургского университета. Прикладная математика. Информатика. Процессы управления, vol. 20, no. 2, pp. 289–297. https://doi.org/10.21638/spbu10.2024.213

APA

Zhou, J., Петросян, О. Л., & Gao, H. (2024). Dynamic decision-making under uncertainty: Bayesian learning in environmental game theory. Вестник Санкт-Петербургского университета. Прикладная математика. Информатика. Процессы управления, 20(2), 289–297. https://doi.org/10.21638/spbu10.2024.213

Vancouver

Zhou J, Петросян ОЛ, Gao H. Dynamic decision-making under uncertainty: Bayesian learning in environmental game theory. Вестник Санкт-Петербургского университета. Прикладная математика. Информатика. Процессы управления. 2024;20(2):289–297. https://doi.org/10.21638/spbu10.2024.213

Author

Zhou, Jiangjing ; Петросян, Ованес Леонович ; Gao, Hongwei. / Dynamic decision-making under uncertainty: Bayesian learning in environmental game theory. In: Вестник Санкт-Петербургского университета. Прикладная математика. Информатика. Процессы управления. 2024 ; Vol. 20, No. 2. pp. 289–297.

BibTeX

@article{1d2fe73c3d454daeb24f0b0051c45faf,
title = "Dynamic decision-making under uncertainty: Bayesian learning in environmental game theory",
abstract = "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.",
author = "Jiangjing Zhou and Петросян, {Ованес Леонович} and Hongwei Gao",
year = "2024",
doi = "10.21638/spbu10.2024.213",
language = "English",
volume = "20",
pages = "289–297",
journal = " ВЕСТНИК САНКТ-ПЕТЕРБУРГСКОГО УНИВЕРСИТЕТА. ПРИКЛАДНАЯ МАТЕМАТИКА. ИНФОРМАТИКА. ПРОЦЕССЫ УПРАВЛЕНИЯ",
issn = "1811-9905",
publisher = "Издательство Санкт-Петербургского университета",
number = "2",

}

RIS

TY - JOUR

T1 - Dynamic decision-making under uncertainty: Bayesian learning in environmental game theory

AU - Zhou, Jiangjing

AU - Петросян, Ованес Леонович

AU - Gao, Hongwei

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

UR - https://www.mendeley.com/catalogue/076ff645-f14d-318b-8902-98445dbc63ff/

U2 - 10.21638/spbu10.2024.213

DO - 10.21638/spbu10.2024.213

M3 - Article

VL - 20

SP - 289

EP - 297

JO - ВЕСТНИК САНКТ-ПЕТЕРБУРГСКОГО УНИВЕРСИТЕТА. ПРИКЛАДНАЯ МАТЕМАТИКА. ИНФОРМАТИКА. ПРОЦЕССЫ УПРАВЛЕНИЯ

JF - ВЕСТНИК САНКТ-ПЕТЕРБУРГСКОГО УНИВЕРСИТЕТА. ПРИКЛАДНАЯ МАТЕМАТИКА. ИНФОРМАТИКА. ПРОЦЕССЫ УПРАВЛЕНИЯ

SN - 1811-9905

IS - 2

ER -

ID: 126141281