Playing the network backward: A Game Theoretic Attribution Framework

The paper proposes a game-theoretic framework that recasts backward attribution methods—such as gradients, LRP, and transformer-specific rules—as a two-player game on an extended network graph. Within this framework, gradients and the full alpha‑beta‑LRP family emerge as integrals over game trajectories under specific equilibria, turning attribution maps into projections of trajectory distributions. By expressing desired explanation properties (e.g., localisation focus, robustness to input noise, stable attention routing) as game-theoretic concepts, the authors derive novel adaptations of backward rules, one of which outperforms existing transformer-specific methods on ViT‑B/16 across all localisation metrics.