Learning-Allocation Dynamics in Coalitional Games with Transferable Utility

A. Khaleghi; D. Bauso; T. Namerikawa

doi:10.1016/j.ifacol.2025.01.152

Learning-Allocation Dynamics in Coalitional Games with Transferable Utility

A. Khaleghi
, D. Bauso
, T. Namerikawa

システムデザイン工学科

研究成果: Conference article › 査読

1 被引用数 (Scopus)

抄録

We consider a set of players who have to make a joint decision under uncertainty and learn via gradient dynamics how to reach the maximal profit for the coalition while the game is running and the revenue gets allocated to the players. The main result is the convergence of the intertwined learning-allocation dynamics to a stable solution which yields the maximal profit for the coalition and a stable allocation in the core of the asymptotic game.

本文言語	English
ページ（範囲）	31-36
ページ数	6
ジャーナル	IFAC-PapersOnLine
巻	58
号	30
DOI	https://doi.org/10.1016/j.ifacol.2025.01.152
出版ステータス	Published - 2024 12月 1
イベント	5th IFAC Workshop on Cyber-Physical Human Systems, CPHS 2024 - Antalya, Turkey 継続期間: 2024 12月 12 → 2024 12月 13

ASJC Scopus subject areas

制御およびシステム工学

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10.1016/j.ifacol.2025.01.152

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abstract = "We consider a set of players who have to make a joint decision under uncertainty and learn via gradient dynamics how to reach the maximal profit for the coalition while the game is running and the revenue gets allocated to the players. The main result is the convergence of the intertwined learning-allocation dynamics to a stable solution which yields the maximal profit for the coalition and a stable allocation in the core of the asymptotic game.",

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author = "A. Khaleghi and D. Bauso and T. Namerikawa",

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AU - Bauso, D.

AU - Namerikawa, T.

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Y1 - 2024/12/1

N2 - We consider a set of players who have to make a joint decision under uncertainty and learn via gradient dynamics how to reach the maximal profit for the coalition while the game is running and the revenue gets allocated to the players. The main result is the convergence of the intertwined learning-allocation dynamics to a stable solution which yields the maximal profit for the coalition and a stable allocation in the core of the asymptotic game.

AB - We consider a set of players who have to make a joint decision under uncertainty and learn via gradient dynamics how to reach the maximal profit for the coalition while the game is running and the revenue gets allocated to the players. The main result is the convergence of the intertwined learning-allocation dynamics to a stable solution which yields the maximal profit for the coalition and a stable allocation in the core of the asymptotic game.

KW - Coalitional games

KW - game theory

KW - gradient dynamics

KW - wind energy aggregation

UR - https://www.scopus.com/pages/publications/85218005870

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DO - 10.1016/j.ifacol.2025.01.152

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Learning-Allocation Dynamics in Coalitional Games with Transferable Utility

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