Achieving Envy-Freeness with Limited Subsidies under Dichotomous Valuations
Authors: Siddharth Barman, Anand Krishna, Yadati Narahari, Soumyarup Sadhukhan
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove that, under dichotomous valuations, there exists an allocation that achieves envy-freeness with a per-agent subsidy of either 0 or 1. Furthermore, such an envy-free solution can be computed efficiently in the standard value-oracle model. Notably, our results hold for general dichotomous valuations and, in particular, do not require the (dichotomous) valuations to be additive, submodular, or even subadditive. Also, our subsidy bounds are tight and provide a linear (in the number of agents) factor improvement over the bounds known for general monotone valuations. |
| Researcher Affiliation | Academia | 1Indian Institute of Science, Bengaluru 2Indian Institute of Technology, Kanpur |
| Pseudocode | Yes | Algorithm 1 ALG, Algorithm 2 EXTEND, Algorithm 3 FINDSINK |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve the use of datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits (training, validation, test). |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or the hardware used. |
| Software Dependencies | No | The paper is theoretical and does not describe software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup with hyperparameters or training configurations. |