Shapley Data Valuation under Commitment: Adaptive Manipulation and Interaction Laundering

Shapley-based data valuation can be manipulated when a provider adapts its submission to the coalition being evaluated. However, it remains unclear how restricting such adaptation affects manipulation and whether sufficiently strong commitment can eliminate overvaluation. We study this question using commitment partitions, which specify which coalition evaluations must reuse the same provider action. We characterize the loss in maximal manipulation gain caused by commitment and show that stronger commitment reduces, but does not eliminate, manipulation. In particular, even under one fixed submission, a multi-artifact provider can increase its Shapley payment while no coalition utility improves. We identify the underlying mechanism as interaction laundering, where interaction value shifts toward interactions in which the provider owns a larger share, and realize this behavior in both entity-matching and learned-representation settings. Finally, we show that adaptive and committed manipulation require different defenses. Reversible adaptive manipulation calls for removing exposed payment sensitivity, while one-sided committed degradation can be addressed by Best-Bundle, which applies free disposal before provider-level allocation.