Reproduce “Pricing Today, Shaping Tomorrow: Contextual Dynamic Pricing in Purchase-Driven Evolving Systems” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Jinjiang Zou, Yang Li, Zemin Zheng
SHAPE learns prices for systems where purchases change later customers’ states. It adjusts quotes for a sale’s future value and reuses one pricing map as value estimates change. In a four-workload-band simulation over 48,000 periods, mean regret was 4,480.74 versus 7,627.40 for state-aware myopic pricing, a 41.25% reduction. This is a modeled result, not a field finding.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.