We consider multi-agent coalitional games with uncertainty in the coalitional values. We provide a novel methodology to study the stability of the grand coalition in the case where each coalition constructs ambiguity sets for the (possibly) unknown probability distribution of the uncertainty. As a less conservative solution concept compared to worst-case approaches for coalitional stability, we consider a stochastic version of the so-called core set, i.e., the expected value core. Unfortunately, without exact knowledge of the probability distribution, the evaluation of the expected value core is an extremely challenging task. Hence, we propose the concept of distributionaly robust (DR) core. Leveraging tools from DR optimization under the Wasserstein distance, we provide finite-sample guarantees that any allocation which lies in the DR core is also stable with respect to the true probability distribution and show the asymptotic consistency of the DR core. We dedicate the last section to the computational tractability of finding an allocation in the DR core.

Distributionally robust stability of payoff allocations in stochastic coalitional games / Pantazis, George; Franci, Barbara; Grammatico, Sergio; Margellos, Kostas. - (2023), pp. 7617-7622. (Intervento presentato al convegno 62nd IEEE Conference on Decision and Control, CDC 2023 tenutosi a Singapore (Singapore) nel 13-15 December 2023) [10.1109/cdc49753.2023.10383218].

Distributionally robust stability of payoff allocations in stochastic coalitional games

Franci, Barbara;
2023

Abstract

We consider multi-agent coalitional games with uncertainty in the coalitional values. We provide a novel methodology to study the stability of the grand coalition in the case where each coalition constructs ambiguity sets for the (possibly) unknown probability distribution of the uncertainty. As a less conservative solution concept compared to worst-case approaches for coalitional stability, we consider a stochastic version of the so-called core set, i.e., the expected value core. Unfortunately, without exact knowledge of the probability distribution, the evaluation of the expected value core is an extremely challenging task. Hence, we propose the concept of distributionaly robust (DR) core. Leveraging tools from DR optimization under the Wasserstein distance, we provide finite-sample guarantees that any allocation which lies in the DR core is also stable with respect to the true probability distribution and show the asymptotic consistency of the DR core. We dedicate the last section to the computational tractability of finding an allocation in the DR core.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/3003652