arXiv:2605.27794v1 Announce Type: cross Abstract: This paper studies adaptive targeting under network interference in a bandit setting, where treatments applied to one individual may affect others through spillover effects. We consider a linear model in a sparse regime, where each individual's outcome can be affected by at most a few others. We first establish a regret lower bound showing that ignoring the network structure and reducing the problem to a standard linear bandit inevitably leads to inefficient learning, particularly in large populations. To understand how structural information c
This research addresses a fundamental challenge in applying AI targeting methods in real-world scenarios where individual actions have spillover effects, indicating a maturation of AI research into complex, interdependent systems.
Understanding and modeling network interference is crucial for developing robust, ethical, and effective AI agents or targeting systems that operate in interconnected environments, impacting fields from advertising to public health.
The focus on 'network interference' and 'spillover effects' in bandit settings shifts AI optimization from isolated individual models to systems that account for societal and systemic interconnectivity.
- · AI researchers (network science)
- · Social media platforms
- · Public health organizations
- · Econometric modelers
- · AI models ignoring network effects
- · Traditional individual-centric targeting strategies
Improved effectiveness and ethical considerations for AI-driven targeting and intervention systems due to explicit accounting for network effects.
Development of new AI frameworks and tools specifically designed to learn and manage complex interdependencies in large-scale systems.
More sophisticated and potentially more subtle forms of societal influence via AI, requiring advanced regulatory and oversight mechanisms.
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