Object

Title: Computing a mechanism for a Bayesian and partially observable Markov approach

Contributor:

Foryś, Urszula - ed. ; Rejniak, Katarzyna - ed. ; Pękala, Barbara - ed. ; Bartłomiejczyk, Agnieszka - ed.

Subtitle:

.

Group publication title:

AMCS, volume 33 (2023)

Abstract:

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.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Resource Identifier:

oai:zbc.uz.zgora.pl:86682

DOI:

10.34768/amcs-2023-0034

Pages:

463-478

Source:

AMCS, volume 33, number 3 (2023) ; click here to follow the link

Language:

eng

License CC BY 4.0:

click here to follow the link

Rights:

Biblioteka Uniwersytetu Zielonogórskiego

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