Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Fair Division of a Graph
Authors: Sylvain Bouveret, Katarína Cechlárová, Edith Elkind, Ayumi Igarashi, Dominik Peters
IJCAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 16,825 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,923 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we introduce and study a formal model for such scenarios. Specifically, we consider the problem of fair allocation of indivisible items in settings where there is a graph capturing the dependency relation between items, and each agent s share has to be connected in this graph. We prove a strong positive result for our setting: an MMS allocation always exists if the underlying graph is a tree, and can be computed efficiently. Our algorithm is an adaptation of the classic last-diminisher procedure for the divisible case. In contrast, we provide an example where the underlying graph is a cycle of length 8 and there is no MMS allocation. |
| Researcher Affiliation | Academia | Sylvain Bouveret LIG Grenoble INP, France EMAIL Katarína Cechlárová P.J. Šafárik University, Slovakia EMAIL Edith Elkind University of Oxford, UK EMAIL Ayumi Igarashi University of Oxford, UK EMAIL Dominik Peters University of Oxford, UK EMAIL |
| Pseudocode | Yes | Algorithm 1: A(I , (qi)i N ) input :I = (G , N , U ) and (qi)i N where G is a subtree of G, N is a subset of N, and u i = ui|V for all i N output :A valid allocation π such that ui(π(i)) qi for all i N |
| Open Source Code | No | The paper does not provide any statements about releasing source code for the described methodology or links to a code repository. |
| Open Datasets | No | The paper describes theoretical work and does not use datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for training, validation, or testing. |
| 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 specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |