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Coalitions naturally exist in many real-world systems involving multipledecision makers such as ridesharing, security, and online ad auctions, but thecoalition structure among the agents is often unknown. We propose and study animportant yet previously overseen problem -- Coalition Structure Learning(CSL), where we aim to carefully design a series of games for the agents andinfer the underlying coalition structure by observing their interactions inthose games. We establish a lower bound on the sample complexity -- defined asthe number of games needed to learn the structure -- of any algorithms for CSLand propose the Iterative Grouping (IG) algorithm for designing normal-formgames to achieve the lower bound. We show that IG can be extended to othersuccinct games such as congestion games and graphical games. Moreover, we solveCSL in a more restrictive and practical setting: auctions. We show a variant ofIG to solve CSL in the auction setting even if we cannot design the biddervaluations. Finally, we conduct experiments to evaluate IG in the auctionsetting and the results align with our theoretical analysis.

Learning Coalition Structures with Games