- Tytuł:
- Computing a mechanism for a Bayesian and partially observable Markov approach
- Autorzy:
-
Clempner, Julio B.
Poznyak, Alexander S. - Powiązania:
- https://bibliotekanauki.pl/articles/24200692.pdf
- Data publikacji:
- 2023
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
dynamic mechanism design
partially observable Markov chains
games with private information
Bayesian equilibrium
mechanizm dynamiczny
łańcuchy Markova
równowaga Bayesa - Opis:
- The design of incentive-compatible mechanisms for a certain class of finite Bayesian partially observable Markov games is proposed using a dynamic framework. We set forth a formal method that maintains the incomplete knowledge of both the Bayesian model and the Markov system’s states. We suggest a methodology that uses Tikhonov’s regularization technique to compute a Bayesian Nash equilibrium and the accompanying game mechanism. Our framework centers on a penalty function approach, which guarantees strong convexity of the regularized reward function and the existence of a singular solution involving equality and inequality constraints in the game. We demonstrate that the approach leads to a resolution with the smallest weighted norm. The resulting individually rational and ex post periodic incentive compatible system satisfies this requirement. We arrive at the analytical equations needed to compute the game’s mechanism and equilibrium. Finally, using a supply chain network for a profit maximization problem, we demonstrate the viability of the proposed mechanism design.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2023, 33, 3; 463--478
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł