Dynamic Pricing

Dynamic pricing, the practice of adjusting prices based on real-time factors, aims to optimize revenue by adapting to changing market conditions and customer behavior. Current research emphasizes developing algorithms, such as contextual bandits, Thompson sampling, and reinforcement learning, to handle various complexities including multiple objectives (revenue, fairness, service quality), sequential customer interactions, and strategic buyer behavior, often within the framework of generalized linear models or Markov Decision Processes. These advancements have significant implications for diverse sectors like e-commerce, ride-sharing, and the energy grid, enabling more efficient resource allocation and improved profitability while addressing fairness concerns.