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

Department of System Design Engineering

Research output: Contribution to journal › Conference article › peer-review

1 Citation (Scopus)

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.

Original language	English
Pages (from-to)	31-36
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	58
Issue number	30
DOIs	https://doi.org/10.1016/j.ifacol.2025.01.152
Publication status	Published - 2024 Dec 1
Event	5th IFAC Workshop on Cyber-Physical Human Systems, CPHS 2024 - Antalya, Turkey Duration: 2024 Dec 12 → 2024 Dec 13

Keywords

Coalitional games
game theory
gradient dynamics
wind energy aggregation

ASJC Scopus subject areas

Control and Systems Engineering

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10.1016/j.ifacol.2025.01.152

Cite this

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title = "Learning-Allocation Dynamics in Coalitional Games with Transferable Utility",

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.",

keywords = "Coalitional games, game theory, gradient dynamics, wind energy aggregation",

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

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U2 - 10.1016/j.ifacol.2025.01.152

DO - 10.1016/j.ifacol.2025.01.152

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ER -

Learning-Allocation Dynamics in Coalitional Games with Transferable Utility

Abstract

Keywords

ASJC Scopus subject areas

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