Individually Stable Dynamics in Coalition Formation over Graphs

Coalition formation over graphs is a well studied class of games whose players are vertices and feasible coalitions must be connected subgraphs. In this setting, the existence and computation of equilibria, under various notions of stability, has attracted a lot of attention. However, the natural process by which players, starting from any feasible state, strive to reach an equilibrium after a series of unilateral improving deviations, has been less studied. We investigate the convergence of dynamics towards individually stable outcomes under the following perspective: what are the most general classes of preferences and graph topologies guaranteeing convergence? To this aim, on the one hand, we cover a hierarchy of preferences, ranging from the most general to a subcase of additively separable preferences, including individually rational and monotone cases. On the other hand, given that convergence may fail in graphs admitting a cycle even in our most restrictive preference class, we analyze acyclic graph topologies such as trees, paths, and stars.

Authors

Institutions

Publication Details

Journal
Proceedings of the AAAI Conference on Artificial Intelligence
Published
2025-04-11
DOI
https://doi.org/10.1609/aaai.v39i13.33512
Primary Topic
Opinion Dynamics and Social Influence
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Individually Stable Dynamics in Coalition Formation over Graphs

Angelo Fanelli, Ayumi Igarashi, Laurent Gourvès, Luca Moscardelli
Proceedings of the AAAI Conference on Artificial Intelligence
Opinion Dynamics and Social Influence
article

Individually Stable Dynamics in Coalition Formation over Graphs

Angelo Fanelli, Ayumi Igarashi, Laurent Gourvès, Luca Moscardelli
article en

Abstract

Coalition formation over graphs is a well studied class of games whose players are vertices and feasible coalitions must be connected subgraphs. In this setting, the existence and computation of equilibria, under various notions of stability, has attracted a lot of attention. However, the natural process by which players, starting from any feasible state, strive to reach an equilibrium after a series of unilateral improving deviations, has been less studied. We investigate the convergence of dynamics towards individually stable outcomes under the following perspective: what are the most general classes of preferences and graph topologies guaranteeing convergence? To this aim, on the one hand, we cover a hierarchy of preferences, ranging from the most general to a subcase of additively separable preferences, including individually rational and monotone cases. On the other hand, given that convergence may fail in graphs admitting a cycle even in our most restrictive preference class, we analyze acyclic graph topologies such as trees, paths, and stars.

Proceedings of the AAAI Conference on Artificial IntelligenceVol. 39(13)
Centre National de la Recherche Scientifique (FR), Université Paris Sciences et Lettres (FR), University of Chieti-Pescara (IT), Lamsade (FR), Université Paris Dauphine-PSL (FR), The University of Tokyo (JP)
Istituto Nazionale di Alta Matematica "Francesco Severi", Agence Nationale de la Recherche, Ministero dell’Istruzione, dell’Università e della Ricerca, Gruppo Nazionale per il Calcolo Scientifico
Openalex Percentile: Top 76%
Opinion Dynamics and Social Influence
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Individually Stable Dynamics in Coalition Formation over Graphs — Angelo Fanelli, Ayumi Igarashi, et al. · Proceedings of the AAAI Conference on Artificial Intelligence (2025) | TGRS Research Map | TGRS