Representative Committees of Peers
Authors: Reshef Meir, Fedor Sandomirskiy, Moshe Tennenholtz
JAIR 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result is a characterization of the approximation ratio of k-sortition. For a committee of size k = 3 the ratio is equal to 1.316 (Example 4) and behaves as 1+Θ(1/√k), when k increases (Theorems 2 and 3). As a corollary of this result, we infer that the optimal committee size k for a population of size n is of the order of n^(2/3) given fixed preference-elicitation costs (Example 5). The paper primarily focuses on mathematical proofs, theorems, lemmas, and propositions, indicating a theoretical approach. |
| Researcher Affiliation | Academia | Reshef Meir EMAIL The Faculty of Industrial Engineering and Management, Technion Israel Institute of Technology, Technion City, Haifa 3200003, Israel Fedor Sandomirskiy EMAIL The Faculty of Industrial Engineering and Management, Technion Israel Institute of Technology, Technion City, Haifa 3200003, Israel International Laboratory of Game Theory and Decision Making, HSE University, Kantemirovskaya 3, St. Petersburg 194100, Russia Moshe Tennenholtz EMAIL The Faculty of Industrial Engineering and Management, Technion Israel Institute of Technology, Technion City, Haifa 3200003, Israel |
| Pseudocode | No | The paper only describes methods and rules using mathematical notation and prose (e.g., 'Definition 5 (k-SORT: k-sortition). A committee C N of size |C| = k is selected uniformly at random. The preferences of the committee members are aggregated using the simple majority rule (see Example 2).'), without structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about releasing code, nor does it provide links to source code repositories for the methodology described. |
| Open Datasets | No | The paper is theoretical and models preferences and populations abstractly. It does not describe the use of any specific datasets for empirical evaluation, nor does it provide concrete access information for any datasets. |
| Dataset Splits | No | As this is a theoretical paper focusing on mathematical proofs and analyses, there are no empirical experiments conducted with datasets, and therefore no mention of training/test/validation splits. |
| Hardware Specification | No | The paper presents theoretical analysis and mathematical proofs, without conducting empirical experiments. Therefore, no hardware specifications are mentioned for running experiments. |
| Software Dependencies | No | This paper focuses on theoretical analysis and does not describe any computational experiments. Thus, no specific software dependencies or versions are mentioned for replication. |
| Experiment Setup | No | The paper presents a theoretical framework and mathematical results, rather than empirical experiments. Consequently, there is no discussion of experimental setup details such as hyperparameters or training configurations. |