Mixed Fair Division: A Survey
Authors: Shengxin Liu, Xinhang Lu, Mashbat Suzuki, Toby Walsh
JAIR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this survey, besides describing the state of the art, we outline a number of interesting open questions and future directions in three mixed fair division settings: (i) indivisible goods and chores, (ii) divisible and indivisible goods (mixed goods), and (iii) indivisible goods with subsidy which can be viewed like a divisible good. |
| Researcher Affiliation | Academia | Shengxin Liu EMAIL School of Computer Science and Technology Harbin Institute of Technology, Shenzhen Building L, Harbin Institute of Technology Campus Xili Shenzhen University City, Shenzhen, China Xinhang Lu EMAIL Mashbat Suzuki EMAIL Toby Walsh EMAIL School of Computer Science and Engineering The University of New South Wales Building K17, UNSW Sydney, NSW 2052, Australia |
| Pseudocode | No | The paper describes algorithms (e.g., "double round-robin algorithm" and "top-trading envy-cycle elimination algorithm") in prose and references algorithms in other works (e.g., "Bhaskar et al., 2021, Algorithm 3"), but does not include any structured pseudocode or algorithm blocks within its own text. |
| Open Source Code | No | The paper is a survey and does not present new methods or experimental results. It mentions that "fair division methods have been deployed in practice (Budish et al., 2017) and made publicly available (Goldman & Procaccia, 2015; Igarashi & Yokoyama, 2023; Han & Suksompong, 2024; Shah, 2017)", referring to tools from *other* research, not code for the present survey itself. |
| Open Datasets | No | This paper is a survey of research in mixed fair division and does not present experiments that use specific datasets. Therefore, it does not provide access information for any datasets. |
| Dataset Splits | No | This paper is a survey and does not present experiments that would require dataset splits. Therefore, no dataset split information is provided. |
| Hardware Specification | No | This paper is a survey and does not present experimental results from its own research. Therefore, it does not specify any hardware used for running experiments. |
| Software Dependencies | No | This paper is a survey and does not present experimental results from its own research. Therefore, it does not specify any software dependencies with version numbers. |
| Experiment Setup | No | This paper is a survey and does not present experimental results from its own research. Therefore, it does not provide any experimental setup details such as hyperparameters or training configurations. |