解决线性上下文猜拳中单位间干扰问题，提升决策准确性
Linear Contextual Bandits with Interference
- 引入显式干扰建模的算法框架，量化个体行为对他人收益的影响
- 理论证明具有次线性后悔上界、有限样本上界和渐近性质
- 适用于存在群体互动的在线决策场景，如推荐系统或资源分配
干扰是因果推断中的关键概念，它通过考虑一个单元的行为对其他单元收益的影响，扩展了奖励建模过程。在上下文猜拳（CB）设置中，多个单元同时参与决策时，潜在的干扰会显著影响不同动作期望收益的估计，从而影响决策过程。尽管已有研究探讨了干扰感知下的多智能体与对抗性猜拳，但干扰在标准上下文猜拳中的影响及其理论基础仍严重未被充分探索。本文提出一个系统性框架，用于处理线性上下文猜拳（LinCB）中的干扰问题，弥合因果推断与在线决策之间的鸿沟。我们设计了一系列算法，显式量化奖励建模中的干扰效应，并提供全面的理论保证，包括次线性后悔上界、有限样本上界以及渐近性质。所提方法的有效性通过模拟实验及基于MovieLens数据生成的合成数据得到验证。
原文摘要 · Abstract (English)
Interference, a key concept in causal inference, extends the reward modeling process by accounting for the impact of one unit's actions on the rewards of others. In contextual bandit (CB) settings, where multiple units are present in the same round, potential interference can significantly affect the estimation of expected rewards for different arms, thereby influencing the decision-making process. Although some prior work has explored multi-agent and adversarial bandits in interference-aware settings, the effect of interference in CB, as well as the underlying theory, remains significantly underexplored. In this paper, we introduce a systematic framework to address interference in Linear CB (LinCB), bridging the gap between causal inference and online decision-making. We propose a series of algorithms that explicitly quantify the interference effect in the reward modeling process and provide comprehensive theoretical guarantees, including sublinear regret bounds, finite sample upper bounds, and asymptotic properties. The effectiveness of our approach is demonstrated through simulations and a synthetic data generated based on MovieLens data.
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