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Visualizing Coalition Formation: From Hedonic Games to Image Segmentation

arXiv 2603.07890 ↗First posted 9 Mar 2026cs.AI cs.CVcited by 0

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Abstract (arXiv)

We propose image segmentation as a visual diagnostic testbed for coalition formation in hedonic games. Modeling pixels as agents on a graph, we study how a granularization parameter shapes equilibrium fragmentation and boundary structure. On the Weizmann single-object benchmark, we relate multi-coalition equilibria to binary protocols by measuring whether the converged coalitions overlap with a foreground ground-truth. We observe transitions from cohesive to fragmented yet recoverable equilibria, and finally to intrinsic failure under excessive fragmentation. Our core contribution links multi-agent systems with image segmentation by quantifying the impact of mechanism design parameters on equilibrium structures.

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  1. 2025From Leiden to Pleasure Island: The Constant Potts Model for Community Detection as a Hedonic Gamecited by 2

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