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Matematicheskaya Teoriya Igr i Ee Prilozheniya, 2024, Volume 16, Issue 2, Pages 92–112 (Mi mgta349)  

Bayesian learning in fish wars: dynamic estimation of unknown states and private information

Jiangjing Zhou, Ovanes Petrosian

Saint Petersburg State University
References:
Abstract: This paper investigates a unique variant of the «fish war» game, where participants are required to estimate unknown environmental parameters and the private information of adversaries based on received signals. We develop a dynamic game model where the evolution of the fish population is influenced by an unknown parameter, ϵ, and each player's payoff function incorporates their private information, δ. Utilizing Bayesian learning methods, we demonstrate how participants can update their estimates of these unknown parameters over time. We prove that these estimates converge to true values as time progresses. The paper further presents a Nash Equilibrium with Bayesian learning, providing a solution to this specialized game. Numerical simulations are included to illustrate the convergence of beliefs among players and to compare their control strategies under various scenarios.
Keywords: dynamic Bayesian learning, «fish war» game, private information, unknown parameters.
Funding agency Grant number
Saint Petersburg State University 94-06-2114
Received: 26.01.2024
Revised: 18.03.2024
Accepted: 03.06.2024
Document Type: Article
UDC: 519.83, 004.42
BBC: 22.18
Language: Russian
Citation: Jiangjing Zhou, Ovanes Petrosian, “Bayesian learning in fish wars: dynamic estimation of unknown states and private information”, Mat. Teor. Igr Pril., 16:2 (2024), 92–112
Citation in format AMSBIB
\Bibitem{ZhoPet24}
\by Jiangjing~Zhou, Ovanes~Petrosian
\paper Bayesian learning in fish wars: dynamic estimation of unknown states and private information
\jour Mat. Teor. Igr Pril.
\yr 2024
\vol 16
\issue 2
\pages 92--112
\mathnet{http://mi.mathnet.ru/mgta349}
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  • https://www.mathnet.ru/eng/mgta/v16/i2/p92
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