Efficient Resource Allocation with Secretive Agents
Authors: Soroush Ebadian, Rupert Freeman, Nisarg Shah
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct simulations on synthetic data and real data from Spliddit.org to evaluate the empirical performance of our algorithms with respect to the utilitarian social welfare. |
| Researcher Affiliation | Academia | Soroush Ebadian1 , Rupert Freeman2 and Nisarg Shah1 1University of Toronto 2University of Virginia |
| Pseudocode | No | The paper describes allocation rules and mathematical expressions for them but does not present a formal pseudocode block or algorithm. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for its methodology or a link to a code repository. |
| Open Datasets | No | The paper mentions using 'real-world goods division instances from Spliddit.org' but does not provide a specific link, DOI, repository, or formal citation for accessing this dataset, nor does it state that the generated synthetic data is publicly available. |
| Dataset Splits | No | The paper describes using synthetic data generated 'over 1000 randomly generated instances' and Spliddit data by 'randomly sampled k (secretive) agents' for '1000 such simulations'. However, it does not specify traditional training, validation, or test dataset splits (e.g., percentages, counts, or predefined partitions) for reproducing data partitioning. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as CPU or GPU models, or cloud computing specifications. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers that would be needed to replicate the experiments. |
| Experiment Setup | Yes | For synthetic data, the paper states utilities are 'sampled i.i.d. from a Dirichlet distribution with m concentration parameters all set at 1, i.e. Dir(1, . . . , 1)' and that 'Each reported datum is the average of welfare ratios over 1000 randomly generated instances'. For Spliddit data, it states 'randomly sampled k (secretive) agents' and reports 'the average of 1000 such simulations'. |