Fair Division Under Cardinality Constraints
Authors: Arpita Biswas, Siddharth Barman
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that the existence and algorithmic guarantees established for these solution concepts in the unconstrained setting can essentially be achieved under cardinality constraints. Furthermore, focusing on the case wherein all the agents have the same additive valuation, we establish that EF1 allocations exist even under matroid constraints. |
| Researcher Affiliation | Academia | Arpita Biswas and Siddharth Barman Indian Institute of Science arpitab@iisc.ac.in, barman@iisc.ac.in |
| Pseudocode | Yes | Algorithm 1 ALG 1 and Algorithm 2 Greedy-Round-Robin (ALG 2) are present in the paper. |
| Open Source Code | No | The paper does not provide concrete access to its own source code. It mentions Spliddit (www.spliddit.org) as an example of fair division algorithms, but this is a third-party tool. |
| Open Datasets | No | This paper is theoretical and focuses on algorithm design and proofs of existence/guarantees; it does not involve empirical studies with data, training, or datasets. |
| Dataset Splits | No | This paper is theoretical and does not involve dataset splits for training, validation, or testing. |
| Hardware Specification | No | This paper is theoretical and does not describe empirical experiments that would require specific hardware specifications. |
| Software Dependencies | No | This paper is theoretical and does not mention specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | This paper is theoretical and does not include details about an experimental setup, such as hyperparameters or training configurations, as it does not conduct empirical experiments. |