# Payoff distribution in robust coalitional games on time-varying networks

arXiv: https://arxiv.org/abs/2009.03783 · HTML: https://mlsanity.com/paper/2009.03783/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2020.

## Built on by (most cited first, 3 of 3)

- [On the Convergence of No-Regret Learning Dynamics in Time-Varying Games](https://mlsanity.com/paper/2301.11241/) (2023)
- [Optimization and Learning in Open Multi-Agent Systems](https://mlsanity.com/paper/2501.16847/) (2025)
- [Learning the Expected Core of Strictly Convex Stochastic Cooperative Games](https://mlsanity.com/paper/2402.07067/) (2024)

## Abstract (arXiv)

In this paper, we consider a sequence of transferable utility (TU) coalitional games where the coalitional values are unknown but vary within certain bounds. As a solution to the resulting family of games, we formalise the notion of "robust CORE". Our main contribution is to design two distributed algorithms, namely, distributed payoff allocation and distributed bargaining, that converge to a consensual payoff distribution in the robust CORE. We adopt an operator-theoretic perspective to show convergence of both algorithms executed on time-varying communication networks. An energy storage optimization application motivates our framework for "robust coalitional games".