Fairness Towards Groups of Agents in the Allocation of Indivisible Items
Authors: Nawal Benabbou, Mithun Chakraborty, Edith Elkind, Yair Zick
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show experimentally that the classic algorithm of Lipton et al. [2004] equipped with a simple heuristic can produce TEF1 allocations with significantly reduced waste... We experimentally compared procedures L and H using the percentage of items wasted as our performance metric. We simulated two sets of problem instances... |
| Researcher Affiliation | Academia | Sorbonne Universit e, CNRS, Laboratoire d Informatique de Paris 6, LIP6 F-75005 Paris, France; Department of Computer Science, National University of Singapore, Singapore; Department of Computer Science, University of Oxford, United Kingdom |
| Pseudocode | Yes | Algorithm 1: PMURR({Np}p [k], M, (u(i, j))i N,j M) |
| Open Source Code | No | No explicit statement or link providing concrete access to source code for the methodology described in this paper was found. |
| Open Datasets | No | The paper describes generating synthetic data for simulations: 'We simulated two sets of problem instances... For each agent, we sampled m numbers uniformly at random from [0, 1] and normalized them to generate utilities for all m items.' However, it does not provide concrete access information (link, DOI, specific repository, or formal citation) for a publicly available dataset. |
| Dataset Splits | No | The paper describes a simulation setup ('We simulated two sets of problem instances... We report results averaged over 100 runs each.') rather than explicit train/validation/test dataset splits. There is no mention of specific percentages, sample counts, or predefined splits for reproducibility. |
| Hardware Specification | No | No specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running experiments were provided. |
| Software Dependencies | No | No specific software dependencies or versions (e.g., library names with version numbers) were mentioned for replication. |
| Experiment Setup | No | The paper describes the parameters for simulating problem instances (e.g., 'n = 100 agents partitioned into k = 3 types', 'm {50, 100} items', 'sampled m numbers uniformly at random from [0, 1] and normalized them to generate utilities'). However, it does not provide specific hyperparameter values or detailed system-level training settings as typically found in experimental setups for models. |