Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Approximating Fair Division on D-Claw-Free Graphs
Authors: Zbigniew Lonc
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | For this class of graphs we prove that there is an allocation assigning each agent a connected bundle of value at least 1/d of her maximin share. Moreover, for the same class of graphs of goods, we show a theorem which speci๏ฌes what fraction of the proportional share can be guaranteed to each agent if the values of single goods for the agents are bounded by a given fraction of this share. Finding a polynomial time algorithm for constructing a 1/d-mms allocation for (d+1)-claw-free graphs of goods will be a subject of our future research. |
| Researcher Affiliation | Academia | Zbigniew Lonc Warsaw University of Technology, Poland EMAIL |
| Pseudocode | Yes | Algorithm 1 allocate(N , G X, c) 1 T := N ; 2 for j = 1, 2, . . . , s do 3 sort the agents i T non increasingly according to the key f(i, j): ij 1, ij 2, . . . , ij |T |; 4 kj := the largest p such that f(ij p, j) p; 5 Ij := {ij 1, ij 2, . . . , ij kj}; 6 agents in Ij distribute the vertices of V (Gj) among themselves according to the allocation A(Ij, Gj); 7 T := T Ij; 8 if T = then 9 return |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not use datasets, thus no information about public availability of a dataset is provided. |
| Dataset Splits | No | The paper is theoretical and does not use datasets, therefore no training/test/validation splits are discussed. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers required for replication. |
| Experiment Setup | No | The paper is theoretical and does not provide details about an experimental setup, hyperparameters, or system-level training settings. |