Networked Fairness in Cake Cutting
Authors: Xiaohui Bei, Youming Qiao, Shengyu Zhang
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce a graphical framework for fair division in cake cutting, where comparisons between agents are limited by an underlying network structure. We generalize the classical fairness notions of envy-freeness and proportionality to this graphical setting. ... On the algorithmic frontier, we first propose a moving-knife algorithm that outputs an envy-free allocation on trees. ... Next, we give a discrete and bounded algorithm for computing a proportional allocation on descendant graphs. |
| Researcher Affiliation | Academia | 1School of Physical and Mathematical Sciences, Nanyang Technological University 2Centre for Quantum Software and Information, University of Technology Sydney 3Department of Computer Science and Engineering, The Chinese University of Hong Kong |
| Pseudocode | Yes | Algorithm 1 ALGTREE (T, r, (A1, . . . , An)) ... Algorithm 2 ALLOCATIONTREE (T, r) ... Algorithm 3 ALGDESCENDANTGRAPH (G) |
| Open Source Code | No | The paper does not provide any concrete access to source code, such as a repository link, explicit code release statement, or mention of code in supplementary materials. |
| Open Datasets | No | The paper describes theoretical algorithms for fair division in cake cutting and does not use or refer to publicly available datasets in the context of empirical training. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation using dataset splits (training, validation, test). |
| Hardware Specification | No | The paper is theoretical and does not describe specific hardware used for experiments. |
| Software Dependencies | No | The paper does not mention specific software names with version numbers or dependencies required for replication. |
| Experiment Setup | No | The paper describes theoretical algorithms and does not include details on experimental setup, hyperparameters, or training configurations. |