Geodesic
Dynamic decision-making under uncertainty: Bayesian learning in environmental game theory
Vestnik Sankt-Peterburgskogo universiteta. Prikladnaâ matematika, informatika, processy upravleniâ, Tome 20 (2024) no. 2, pp. 289-297 Cet article a éte moissonné depuis la source Math-Net.Ru

Voir la notice de l'article

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.
Keywords: dynamic Bayesian learning, pollution control games, ecological uncertainty, optimal control strategy. 
@article{VSPUI_2024_20_2_a12,
     author = {J. Zhou and O. L. Petrosyan and H. Gao},
     title = {Dynamic decision-making under uncertainty: {Bayesian} learning in environmental game theory},
     journal = {Vestnik Sankt-Peterburgskogo universiteta. Prikladna\^a matematika, informatika, processy upravleni\^a},
     pages = {289--297},
     year = {2024},
     volume = {20},
     number = {2},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/VSPUI_2024_20_2_a12/}
}
TY  - JOUR
AU  - J. Zhou
AU  - O. L. Petrosyan
AU  - H. Gao
TI  - Dynamic decision-making under uncertainty: Bayesian learning in environmental game theory
JO  - Vestnik Sankt-Peterburgskogo universiteta. Prikladnaâ matematika, informatika, processy upravleniâ
PY  - 2024
SP  - 289
EP  - 297
VL  - 20
IS  - 2
UR  - http://geodesic.mathdoc.fr/item/VSPUI_2024_20_2_a12/
LA  - en
ID  - VSPUI_2024_20_2_a12
ER  - 
%0 Journal Article
%A J. Zhou
%A O. L. Petrosyan
%A H. Gao
%T Dynamic decision-making under uncertainty: Bayesian learning in environmental game theory
%J Vestnik Sankt-Peterburgskogo universiteta. Prikladnaâ matematika, informatika, processy upravleniâ
%D 2024
%P 289-297
%V 20
%N 2
%U http://geodesic.mathdoc.fr/item/VSPUI_2024_20_2_a12/
%G en
%F VSPUI_2024_20_2_a12
J. Zhou; O. L. Petrosyan; H. Gao. Dynamic decision-making under uncertainty: Bayesian learning in environmental game theory. Vestnik Sankt-Peterburgskogo universiteta. Prikladnaâ matematika, informatika, processy upravleniâ, Tome 20 (2024) no. 2, pp. 289-297. http://geodesic.mathdoc.fr/item/VSPUI_2024_20_2_a12/

[1] Van der Ploeg F., De Zeeuw A., “A differential game of international pollution control”, Systems Control Letters, 17:6 (1991), 409–414 | DOI | MR | Zbl

[2] Long N. V., “Pollution control: A differential game approach”, Annals of Operations Research, 37:1 (1992), 283–296 | DOI | MR | Zbl

[3] Hoel M., “Emission taxes in a dynamic international game of CO$_2$ emissions”, Conflicts and Cooperation in Managing Environmental Resources, Springer, Berlin–Heidelberg, 1992, 39–70 | DOI

[4] Veijo K., Matti P., Tahvonen O., “Transboundary air pollution and soil acidification: A dynamic analysis of an acid rain game between Finland and the USSR”, Environmental and Resource Economics, 2 (1992), 161–181 | DOI

[5] Masoudi N., Santugini M., Zaccour G., “A dynamic game of emissions pollution with uncertainty and learning”, Environmental and Resource Economics, 64:3 (2016), 349–372 | DOI

[6] Ulph A., Maddison D., “Uncertainty, learning and international environmental policy coordination”, Environmental Resource Economics, 9:4 (1997), 451–466

[7] Arrow K. J., Fisher A. C., “Environmental preservation, uncertainty, and irreversibility”, Quarterly Journal of Economics, 88:2 (1974), 312–319 | DOI

[8] De Zeeuw A., Zemel A., “Regime shifts and uncertainty in pollution control”, Journal of Economic Dynamics and Control, 36:7 (2012), 939–950 | DOI | MR | Zbl

[9] Mirman L. J., Santugini M., “Learning and technological progress in dynamic games”, Dynamic Games and Applications, 4 (2014), 58–72 | DOI | MR | Zbl

[10] Liu Z., “Games with incomplete information when players are partially aware of others' signals”, Journal of Mathematical Economics, 65 (2016), 58–70 | DOI | MR | Zbl

[11] Martins A. C., “Continuous opinions and discrete actions in opinion dynamics problems”, International Journal of Modern Physics C, 19:4 (2008), 617–624 | DOI | Zbl

[12] Martins A. C., “Mobility and social network effects on extremist opinions”, Physical Review E, 78:3 (2008), 036104 | DOI

[13] Martins A. C., “Bayesian updating rules in continuous opinion dynamics models”, Journal of Statistical Mechanics: Theory and Experiment, 2009, no. 02, P02017

[14] Lorenz J., “Continuous opinion dynamics under bounded confidence: A survey”, International Journal of Modern Physics C, 18:12 (2007), 1819–1838 | DOI | Zbl

[15] Sirbu A., Loreto V., Servedio V. D. P., Tria F., “Opinion dynamics: Models, extensions and external effects”, Participatory Sensing, Opinions and Collective Awareness, 2017, 363–401 | DOI