Design-Based Bandits Under Network Interference: Trade-Off Between Regret and Statistical Inference

• URL: http://arxiv.org/abs/2510.07646v2
• Date: Fri, 10 Oct 2025 19:23:11 GMT
• Title: Design-Based Bandits Under Network Interference: Trade-Off Between Regret and Statistical Inference
• Authors: Zichen Wang, Haoyang Hong, Chuanhao Li, Haoxuan Li, Zhiheng Zhang, Huazheng Wang,
• Abstract summary: In multi-armed bandits with network interference (MABNI), the action taken by one node can influence the rewards of others, creating complex interdependence.<n>We introduce an anytime-valid confidence sequence along with a corresponding algorithm to balance the trade-off between regret minimization and inference accuracy.
• Score: 41.49815326663467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In multi-armed bandits with network interference (MABNI), the action taken by one node can influence the rewards of others, creating complex interdependence. While existing research on MABNI largely concentrates on minimizing regret, it often overlooks the crucial concern that an excessive emphasis on the optimal arm can undermine the inference accuracy for sub-optimal arms. Although initial efforts have been made to address this trade-off in single-unit scenarios, these challenges have become more pronounced in the context of MABNI. In this paper, we establish, for the first time, a theoretical Pareto frontier characterizing the trade-off between regret minimization and inference accuracy in adversarial (design-based) MABNI. We further introduce an anytime-valid asymptotic confidence sequence along with a corresponding algorithm, $\texttt{EXP3-N-CS}$, specifically designed to balance the trade-off between regret minimization and inference accuracy in this setting.

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