Keep Your Distance: Land Division With Separation
Authors: Edith Elkind, Erel Segal-Halevi, Warut Suksompong
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove upper and lower bounds on achievable maximin share guarantees when the usable shapes are squares, fat rectangles, or arbitrary axes-aligned rectangles, and explore the algorithmic and query complexity of finding fair partitions in this setting.Our positive results are constructive, in the sense that, given each agent s 1-out-of-k maximin partition (i.e., a partition into k pieces where the value of each piece is at least the agent s maximin share), we can divide the land among the agents so that each agent gets her 1-out-of-k share, using a natural adaptation of the standard Robertson Webb model [Robertson and Webb, 1998]. |
| Researcher Affiliation | Academia | Edith Elkind1 , Erel Segal-Halevi2 and Warut Suksompong3 1Department of Computer Science, University of Oxford 2Department of Computer Science, Ariel University 3School of Computing, National University of Singapore elkind@cs.ox.ac.uk, erelsgl@gmail.com, warut@comp.nus.edu.sg |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. Methods are described in prose. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It is a theoretical paper that proposes algorithms but does not provide their implementation. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on a dataset, thus no dataset access information for training is provided. |
| Dataset Splits | No | This paper is theoretical and does not involve empirical experiments requiring dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not describe any specific hardware used for experiments as it focuses on theoretical contributions. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers as it focuses on theoretical work rather than implementation. |
| Experiment Setup | No | The paper does not contain specific experimental setup details, hyperparameters, or training configurations as it focuses on theoretical proofs and algorithms rather than empirical studies. |