Technical Note—On Dynamic Pricing with Covariates
研究提出在协变量无固定统计模式时也能实现近优动态定价的算法，并给出理论界和数值验证，对需要灵活定价的企业有用。
The study On Dynamic Pricing with Covariates introduces novel methods for setting prices when key market factors, or covariates, vary over time. Although most existing literature assumes these factors follow a fixed distribution, the authors show such assumptions are unnecessary for achieving near-optimal pricing strategies. They provide new pricing algorithms with upper regret bounds that match lower regret bounds (up to a logarithmic factor)—even when covariates exhibit no fixed statistical pattern. In particular, the analysis underscores how these algorithms can efficiently adapt to changing market conditions, enabling businesses to continuously optimize their pricing strategies. In addition, the paper extends these results to a constrained scenario with limited, nonreplenishable inventory, unveiling how a variation metric quantifies the effect of temporal shifts in covariates on pricing performance. Numerical experiments validate the practicality of these algorithms. By relaxing common statistical assumptions, this research offers a more flexible approach for data-driven dynamic pricing solutions.