Online Fair Division: Analysing a Food Bank Problem
Authors: Martin Damyanov Aleksandrov, Haris Aziz, Serge Gaspers, Toby Walsh
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 9 Experiments To determine the impact on social welfare of these mechanisms we ran a number of experiments. We used a wide range of problem instances: random 0/1 utilities, random Borda utilities, correlated 0/1 and Borda utilities generated with the P olya-Eggenberger urn model, as well as 0/1 and Borda utilities from Pref Lib.org [Mattei and Walsh, 2013]. For reasons of space, we report here just results with random 0/1 utilities. We observed similar trends with the other classes (see [Aleksandrov et al., 2015]). We varied the number of agents from 2 to 5, and the number of items from 2 to 10. We sampled 100 instances at each data point, computing the optimal (offline) allocation, and all simple pure Nash equilibria by brute force. In Figure 1, we plot (1) the competitive ratios ( like and balanced ), (2) the price of anarchy ( balanced) and (3) the ratio between the egalitarian welfare of the best simple pure Nash equilibrium and the optimal allocation ( balanced+ ). As these are ratios, we plot geometric means. Arithmetic means are similar. |
| Researcher Affiliation | Academia | Martin Aleksandrov and Haris Aziz and Serge Gaspers and Toby Walsh NICTA and UNSW, Sydney, Australia {martin.aleksandrov, haris.aziz, serge.gaspers, toby.walsh}@nicta.com.au |
| Pseudocode | No | The paper describes the LIKE and BALANCED LIKE mechanisms in prose and provides mathematical analysis, but it does not include any pseudocode blocks or clearly labeled algorithm sections. |
| Open Source Code | No | The paper does not provide any statement about releasing source code for the described mechanisms or mention any repository links. |
| Open Datasets | Yes | We used a wide range of problem instances: random 0/1 utilities, random Borda utilities, correlated 0/1 and Borda utilities generated with the P olya-Eggenberger urn model, as well as 0/1 and Borda utilities from Pref Lib.org [Mattei and Walsh, 2013]. |
| Dataset Splits | No | The paper mentions sampling instances and running experiments, but it does not specify any train/validation/test dataset splits (e.g., percentages or counts) or refer to standard predefined splits for its experimental setup. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run its experiments. |
| Software Dependencies | No | The paper describes algorithms (LIKE and BALANCED LIKE mechanisms) but does not list any specific software dependencies, libraries, or their version numbers used in the implementation or for the analysis presented. |
| Experiment Setup | No | The paper specifies experimental parameters such as "varied the number of agents from 2 to 5, and the number of items from 2 to 10" and "sampled 100 instances at each data point", and mentions "computing the optimal (offline) allocation, and all simple pure Nash equilibria by brute force." These describe the scope of the experiments but do not constitute specific hyperparameters (like learning rates, batch sizes) or system-level training settings typically found in experimental setups. |