New algorithm tackles scalable policy learning under network interference

Researchers have developed a new Thompson sampling algorithm designed to optimize policy impact in dynamic networks where interference occurs. This algorithm addresses the scalability limitations of existing methods, which struggle with networks larger than fifteen units. The new approach enables policy optimization in large-scale networked systems by observing a new network each round and has demonstrated faster learning and superior performance compared to prior techniques in simulations. AI

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IMPACT Enables policy optimization in large-scale networked systems, potentially impacting areas like public health interventions and online marketplace strategies.

RANK_REASON Academic paper introducing a new algorithm for policy optimization under network interference. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Thompson sampling
• Aidan Gleich

• paper
• other