Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Online Posted Pricing with Unknown Time-Discounted Valuations
Authors: Giulia Romano, Gianluca Tartaglia, Alberto Marchesi, Nicola Gatti5682-5689
AAAI 2021 | Venue PDF | 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... |