Online Posted Pricing with Unknown Time-Discounted Valuations
Authors: Giulia Romano, Gianluca Tartaglia, Alberto Marchesi, Nicola Gatti5682-5689
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically evaluate the performances of our mechanisms in a number of experimental settings. |
| Researcher Affiliation | Academia | Giulia Romano, Gianluca Tartaglia, Alberto Marchesi, Nicola Gatti Politecnico di Milano, Piazza Leonardo da Vinci 32, I-20133, Milan, Italy |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper describes using a distribution F with finite support [vmin, vmax] for valuations in Monte Carlo simulations, but does not provide access information for a specific publicly available dataset. |
| Dataset Splits | No | The paper describes running Monte Carlo simulations with random valuations but does not specify training, validation, or test dataset splits in the traditional machine learning sense. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | We use the following parameters values for the experiments: λ {1, . . . , 20}, T {10, 20, 50, 100}, and h {2, . . . , 20}. ... For every combination of values of λ, T, h, we run 1000 Monte Carlo simulations... |