Fair Allocation based on Diminishing Differences
Authors: Erel Segal-Halevi, Haris Aziz, Avinatan Hassidim
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulations based on a simple random model show that with high probability, a necessarily-proportional allocation does not exist but a necessarily-DDproportional allocation exists. Moreover, that allocation is proportional according to the underlying cardinal utilities. |
| Researcher Affiliation | Academia | Erel Segal-Halevi Bar-Ilan University erelsgl@gmail.com Haris Aziz Data61 CSIRO and UNSW haris.aziz@data61.csiro.au Avinatan Hassidim Bar-Ilan University avinatan@cs.biu.ac.il |
| Pseudocode | Yes | Repeat as long as there are items: For i = 1 . . . n: give agent i his best remaining item. For i = n . . . 1: give agent i his best remaining item. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper states: "To simulate such rankings, we determined for each item a market value drawn uniformly at random from [1, 2]. We determined the value of each item to each agent as the item s market value plus noise drawn uniformly at random from [ A, A], where A is a parameter.". This indicates custom-generated data for simulations, not a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper uses randomly generated utility profiles for simulations but does not specify any train/validation/test splits, fold cross-validation, or specific random seeds for data partitioning, as it's a simulation study. |
| Hardware Specification | No | The paper mentions running "Simulations" but does not specify any hardware used (e.g., CPU, GPU models, memory, etc.). |
| Software Dependencies | No | The paper mentions running "Simulations" but does not specify any software versions (e.g., programming languages, libraries, or specific solvers with versions). |
| Experiment Setup | No | The paper describes the random generation of utility profiles for simulations: "We determined for each item a market value drawn uniformly at random from [1, 2]. We determined the value of each item to each agent as the item s market value plus noise drawn uniformly at random from [ A, A], where A is a parameter." It also mentions repeating the experiment "1000 times for different values of A {0.1, . . . , 1} and for different numbers of items 2m items for m {2, . . . , 8}". This provides some setup parameters, but lacks common hyperparameters or system-level training settings typically found in experimental setups for machine learning models. |