Amortizing Structural Interventions in Online Opinion Dynamics

Minimizing opinion polarization and disagreement on social platforms is constrained by incomplete information, as individual innate opinions are private and unobservable. Recent approaches formulate this task as a low rank matrix bandit, employing an initial stage to estimate the latent opinion subspace. We show that the Friedkin-Johnsen objective admits an exact closed form representation in known, opinion free features, eliminating the need for subspace estimation. This structure allows us to delineate the amortization boundary, where offline pretraining replaces online search at a measured price. Under social homophily, a pretrained policy achieves regret governed by the rank of the shared opinion subspace plus its imitation error, with no polynomial dependence on network size and only a logarithmic penalty for enlarging the candidate intervention pool. Guided by this amortizable regime, we introduce the Learned Dynamics Tracker (LDT), a compact filter trained offline to denoise parameter estimates and deployed without updates across drifting streams. In streaming deployments, LDT achieves the lowest dynamic regret across all six horizons with statistically significant and widening margins, reducing regret by 21 percent over the strongest baseline at the longest horizon. Across twenty transfer settings, seven of them on real network topology, LDT leads the prior state of the art in every one.