Democratic Fair Allocation of Indivisible Goods
Authors: Erel Segal-Halevi, Warut Suksompong
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the problem of fairly allocating indivisible goods to groups of agents. ... We present protocols for democratic fair allocation among two or more arbitrarily large groups of agents with monotonic, additive, or binary valuations. Our protocols approximate both envy-freeness and maximin-share fairness. |
| Researcher Affiliation | Academia | 1 Department of Computer Science, Ariel University 2 Department of Computer Science, Stanford University |
| Pseudocode | Yes | RWAV proceeds as follows. (a) Initially, each member pays to his group account some amount of fiat money to be calculated later. (b) Whenever it is the group s turn to pick, each member is assigned a positive weight to be calculated later. (c) For each good, the total weight is calculated as the sum of the weights of the members who desire this good. The group picks a good with a maximal total weight. (d) Every member whose desired good was picked by the group pays his weight to the group account. (e) When it is the other group s turn to pick, each member whose desired good was picked by the other group receives his weight from the group account. This rule has one exception: it is not executed for the second group in the first turn of the first group (so that each execution of step (e) is preceded by an execution of step (d)). |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that the code is publicly available. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on a specific dataset. Therefore, there is no mention of dataset availability. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments on a specific dataset. Therefore, there is no mention of training, validation, or test splits. |
| Hardware Specification | No | The paper is theoretical and does not conduct experiments requiring specific hardware. There is no mention of GPU, CPU, or other hardware specifications. |
| Software Dependencies | No | The paper is theoretical and describes algorithms and proofs; it does not mention specific software dependencies with version numbers for implementation or experimentation. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments. Thus, there are no details on experimental setup, hyperparameters, or training configurations. |