Welfare Loss in Connected Resource Allocation
Authors: Xiaohui Bei, Alexander Lam, Xinhang Lu, Warut Suksompong
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide tight or asymptotically tight bounds on the price of connectivity for various large classes of graphs when there are two agents as well as for paths, stars and cycles in the general case. Many of our results are supplemented with algorithms which find connected allocations with a welfare guarantee corresponding to the price of connectivity. |
| Researcher Affiliation | Academia | Xiaohui Bei1 , Alexander Lam2 , Xinhang Lu3 and Warut Suksompong4 1Nanyang Technological University 2City University of Hong Kong 3UNSW Sydney 4National University of Singapore xhbei@ntu.edu.sg, alexlam@cityu.edu.hk, xinhang.lu@unsw.edu.au, warut@comp.nus.edu.sg |
| Pseudocode | Yes | Algorithm 1: Allocating a Graph with Connectivity 1 for Two Agents |
| Open Source Code | No | The paper does not contain an unambiguous statement of releasing source code for the methodology described, nor does it provide a direct link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any specific public or open datasets for empirical evaluation. It defines abstract instances for theoretical analysis. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software or library dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |