Parameter-Adaptive Dynamic Pricing
This work addresses a practical limitation in dynamic pricing for sectors like e-commerce and transportation, offering a more flexible solution, though it appears incremental as it builds on existing bandit methods.
The paper tackles the problem of dynamic pricing without prior knowledge of demand function parameters, such as Hölder smoothness and Lipschitz constants, by introducing an adaptive algorithm that partitions the domain and uses a linear bandit structure, resulting in improved regret bounds validated through numerical experiments.
Dynamic pricing is crucial in sectors like e-commerce and transportation, balancing exploration of demand patterns and exploitation of pricing strategies. Existing methods often require precise knowledge of the demand function, e.g., the H{ö}lder smoothness level and Lipschitz constant, limiting practical utility. This paper introduces an adaptive approach to address these challenges without prior parameter knowledge. By partitioning the demand function's domain and employing a linear bandit structure, we develop an algorithm that manages regret efficiently, enhancing flexibility and practicality. Our Parameter-Adaptive Dynamic Pricing (PADP) algorithm outperforms existing methods, offering improved regret bounds and extensions for contextual information. Numerical experiments validate our approach, demonstrating its superiority in handling unknown demand parameters.