Fair and Efficient Resource Allocation with Partial Information
Authors: Daniel Halpern, Nisarg Shah
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the fundamental problem of allocating indivisible goods to agents with additive preferences. We consider eliciting from each agent only a ranking of her k most preferred goods instead of her full cardinal valuations. We characterize the value of k needed to achieve envy-freeness up to one good and approximate maximin share guarantee, two widely studied fairness notions. We also analyze the multiplicative loss in social welfare incurred due to the lack of full information with and without the fairness requirements. Our results answer these questions for all values of k, but for simplicity, we summarize the results for when complete rankings are given (k = m) in Figure 1. |
| Researcher Affiliation | Academia | Daniel Halpern1 , Nisarg Shah2 1Harvard University 2University of Toronto |
| Pseudocode | No | The paper describes algorithmic ideas in prose and refers to 'steps' but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any information or links regarding open-source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper and does not involve empirical experiments with datasets. |
| Dataset Splits | No | This is a theoretical paper and does not involve empirical experiments with datasets or their splits. |
| Hardware Specification | No | This is a theoretical paper and does not involve empirical experiments requiring hardware specifications. |
| Software Dependencies | No | This is a theoretical paper and does not involve empirical experiments requiring software dependencies. |
| Experiment Setup | No | This is a theoretical paper and does not involve empirical experiments with an experimental setup. |