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].
Fair Allocation in Dynamic Mechanism Design
Authors: Alireza Fallah, Michael Jordan, Annie Ulichney
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Here, we present the results of a numerical experiment assessing the impact on utilities of varying the fairness constraints of each group. In particular, we implement the case where δ = 0.99, n = 1, vt 1 Uniform(0.5, 1.5), and vt 2 Uniform(0, 1) for t [2] for T = 2, 4. For each value of T, we consider combinations of fairness constraints on a discretized grid where α1 and α2 range over (0, 0.5) with increments of 0.1. For each pair α1, α2, we calculate the mean difference in utility between the optimal fair allocation at level α1, α2 and the unconstrained optimal allocation satisfying Assumption 2 (i.e., α1, α2 = 0), for the seller and buyers over 10,000 iterations of the mechanism. |
| Researcher Affiliation | Academia | Alireza Fallah University of California, Berkeley EMAIL Michael I. Jordan University of California, Berkeley and INRIA, Paris EMAIL Annie Ulichney University of California, Berkeley EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The paper provides the full details of the experimental procedure as well as the code to recreate the results of the experiment including all figures and tables. |
| Open Datasets | No | The paper describes generating synthetic data for its experiments but does not use or provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not specify validation splits or mention cross-validation. It describes simulating 10,000 iterations to calculate mean differences. |
| Hardware Specification | Yes | All experiments were performed using hardware with the following specifications: Intel Xeon CPU @ 2.20GHz, 1 CPU core. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with version numbers. |
| Experiment Setup | Yes | For each value of T, we consider combinations of fairness constraints on a discretized grid where α1 and α2 range over (0, 0.5) with increments of 0.1. For each pair α1, α2, we calculate the mean difference in utility between the optimal fair allocation at level α1, α2 and the unconstrained optimal allocation satisfying Assumption 2 (i.e., α1, α2 = 0), for the seller and buyers over 10,000 iterations of the mechanism. |