Fairness in Multi-Agent Sequential Decision-Making
Authors: Chongjie Zhang, Julie A Shah
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on resource allocation problems show that this fairness criterion provides a more favorable solution than the utilitarian criterion, and that our game-theoretic approach is significantly faster than linear programming. |
| Researcher Affiliation | Academia | Chongjie Zhang and Julie A. Shah Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, MA 02139 {chongjie,julie a shah}@csail.mit.edu |
| Pseudocode | Yes | Algorithm 1: An iterative approach to computing the regularized maximin fairness policy |
| Open Source Code | No | The paper does not provide any links or explicit statements about the availability of its source code. |
| Open Datasets | No | The paper describes a 'resource allocation problem' in a 'pulse-line manufacturing plant' for its experiments, but it does not specify a publicly available or open dataset with concrete access information (e.g., link, DOI, or formal citation to an established benchmark). |
| Dataset Splits | No | The paper does not explicitly provide details about training, validation, and test splits for the data used in its experiments. |
| Hardware Specification | Yes | We used Java for our implementation and Gurobi 2.6 [5] for solving linear programming and ran experiments on a 2.4GHz Intel Core i5 with 8Gb RAM. |
| Software Dependencies | Yes | We used Java for our implementation and Gurobi 2.6 [5] for solving linear programming |
| Experiment Setup | Yes | We use a discount factor λ = 0.95. |