2024/07/10 by Achraf Bahamou, Bahamou, Achraf, Omar Besbes +3 · 1 citation
Computer Science · Decision Sciences · Engineering · #Advanced Database Systems and Queries #Applications (stat.AP) #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences #FOS: Mathematics #Optimization and Control (math.OC) #Scheduling and Optimization Algorithms #Simulation Techniques and Applications
paper · pdf · doi:10.48550/arxiv.2407.07316
openalex publication_date 2024/07/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Problem definition: We study a data-driven pricing problem in which a seller sets a price for a single item based on demand observed at a limited number of historical prices. Our goal is to quantify the value of such information and to guide efficient price experimentation under practical constraints. Methodology/results: Our main methodological contribution is an exact reduction that characterizes the maximin revenue ratio, defined as the worst-case revenue achievable using only past data relative to the optimal revenue under full information. This reduction transforms an infinite-dimensional problem into a tractable one-dimensional optimization problem, allowing us to compute near-optimal pricing policies with explicit guarantees and to precisely quantify the value of historical data. Managerial implications: Motivated by practical constraints that limit price changes, we first evaluate the value of local information and show that the sign of the revenue gradient at a single price can provide significant guidance. We then use our framework to design efficient price experiments: we develop a method to select the next price to test so as to maximize future robust performance, and show how to substantially reduce the number of experiments needed to achieve target revenue guarantees in dynamic pricing. Finally, we show that our approach remains effective with noisy demand data, achieving near-optimal performance with as few as 25 to 100 samples per price.