Optimal Pricing Schemes for Identical Items with Time-Sensitive Buyers
Authors: Zhengyang Liu, Liang Shan, Zihe Wang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we consider this dilemma in the pricing regime, in which we try to find the optimal pricing scheme for identical items with heterogenous time-sensitive buyers. We characterize the revenue-optimal solution and propose an efficient algorithm to find it in a Bayesian setting. Our results also demonstrate the tight ratio between the value of wasted time and the seller s revenue, as well as that of two common-used pricing schemes, the k-step function and the fixed pricing. To explore the nature of the optimal scheme in the general setting, we present the closed forms over the product distribution and show by examples that positive correlation between the valuation of the item and the cost per unit time could help increase revenue. To the best of our knowledge, it is the first step towards understanding the impact of the time factor as a part of the buyer cost in pricing problems, in the computational view. |
| Researcher Affiliation | Academia | Zhengyang Liu1, Liang Shan2, Zihe Wang2* 1 Beijing Institute of Technology 2 Renmin University of China zhengyang@bit.edu.cn, shanliang@ruc.edu.cn, wang.zihe@ruc.edu.cn |
| Pseudocode | No | The paper describes algorithms (e.g., dynamic programming in Theorem 1's proof) and mathematical formulations, but it does not present them in a structured pseudocode block or algorithm environment. |
| Open Source Code | No | The paper does not contain any statement about releasing source code or a link to a code repository for its methodology. |
| Open Datasets | No | The paper discusses theoretical distributions (e.g., 'joint probability distribution F', 'discrete distribution of buyers types') and theoretical examples, but it does not use or refer to any specific publicly available datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware, and thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. The examples provided are analytical derivations, not empirical setups. |