Keeping Your Friends Close: Land Allocation with Friends
Authors: Edith Elkind, Neel Patel, Alan Tsang, Yair Zick
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We examine the problem of assigning plots of land to prospective buyers who prefer living next to their friends. In this setting, each agent s utility depends on the plot she receives and the identities of the agents who receive the adjacent plots. We are interested in mechanisms without money that guarantee truthful reporting of both land values and friendships, as well as Pareto optimality and computational efficiency. We explore several modifications of the Random Serial Dictatorship (RSD) mechanism, and identify one that performs well according to these criteria, We also study the expected social welfare of the assignments produced by our mechanisms when agents values for the land plots are binary; it turns out that we can achieve good approximations to the optimal social welfare, but only if the agents value the friendships highly. |
| Researcher Affiliation | Academia | Edith Elkind1 , Neel Patel2 , Alan Tsang2 and Yair Zick2 1University of Oxford 2National University of Singapore elkind@cs.ox.ac.uk, {neel,atsang,zick}@comp.nus.edu.sg |
| Pseudocode | No | The mechanisms are described in paragraph form (e.g., "ONLINE CHOOSE-TOGETHER RSD (ON-CT-RSD) is our first implementation of this idea. In this mechanism, at each step one of the unallocated agents is picked uniformly at random. The agent then picks a plot and may declare another unallocated agent as her friend; if she does, then her friend is the next to choose a plot (but cannot declare another friend).") and not in structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link for open-source code for the methodology. It only links to the arXiv version of the paper itself: "For omitted proofs, see the full version of the paper [Elkind et al., 2020]. Ar Xiv 2003.03558." |
| Open Datasets | No | The paper is theoretical and does not use or reference any publicly available datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve data splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not mention any specific hardware used for running experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies or their version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |