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 [1].
VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback
Authors: Kirthevasan Kandasamy, Joseph E Gonzalez, Michael I Jordan, Ion Stoica
JMLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present some simulation results in a single-parameter single-item environment... We have shown the pseudo-regrets for the welfare, the seller, and some of the agents in Figure 1 for all possible choices of the ζ and λ hyperparameters. |
| Researcher Affiliation | Academia | Kirthevasan Kandasamy EMAIL University of Wisconsin Madison, WI 53706, USA. Joseph E Gonzalez EMAIL Michael I Jordan EMAIL Ion Stoica EMAIL University of California, Berkeley, CA 94723, USA |
| Pseudocode | Yes | We now describe our algorithm for this setting, called VCG-Learn, which is outlined in Algorithm 1. ... Algorithm 1 VCG-Learn |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | We present some simulation results in a single-parameter single-item environment. Here, ten agents are competing for a single item and all of them are participating by rewards. When an agent receives the item, her value is drawn stochastically from a N(µ, 0.5) distribution where µ is chosen uniformly on a grid in the interval (0.2, 0.9). |
| Dataset Splits | No | The paper uses a simulated environment where data is generated stochastically; therefore, traditional training/test/validation dataset splits are not applicable or provided. |
| 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, such as library names with version numbers. |
| Experiment Setup | Yes | We present some simulation results in a single-parameter single-item environment. Here, ten agents are competing for a single item and all of them are participating by rewards. When an agent receives the item, her value is drawn stochastically from a N(µ, 0.5) distribution where µ is chosen uniformly on a grid in the interval (0.2, 0.9). Agent 1 has a value of 0.9 for receiving the item (and will be the agent who receives the item if values are known) and agent 10 has a value of 0.2. If an agent does not receive the item, their value is non-stochastically zero. ... The game is repeated for 3000 rounds. ... The figures were obtained by averaging over 50 independent runs and the shaded regions represent two standard errors. |