Multigoal Committee Selection
Authors: Maciej Kocot, Anna Kolonko, Edith Elkind, Piotr Faliszewski, Nimrod Talmon
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally test what levels of compromise exist for pairs of rules from the set {SNTV, Bloc, k-Borda, β-CC}. We consider elections generated according to the 2D Euclidean model (see the work of Enelow and Hinch [1984]): Each candidate and each voter is represented by a point in a 2D Euclidean space, and each voter forms his or her preference order by sorting the candidates points with respect to their distance from his or her point (from the closest to the farthest one). We generate the candidate points and the voter points by drawing them uniformly at random from a [ 3, 3] [ 3, 3] square. We consider elections with 100 candidates and 100 voters, and we consider committees of size 10. We chose these parameters as either they or similar ones are often used in the literature [Elkind et al., 2017a; Faliszewski et al., 2017; Celis et al., 2018]. |
| Researcher Affiliation | Academia | Maciej Kocot1 , Anna Kolonko1 , Edith Elkind2 , Piotr Faliszewski1 and Nimrod Talmon3 1AGH University, Krakow, Poland 2University of Oxford, Oxford, UK 3Ben-Gurion University, Be er Sheva, Israel |
| Pseudocode | No | The paper describes algorithmic steps within the text of Theorem 4 (e.g., 'Our algorithm works as follows: 1. We guess...'), but it does not present a formally labeled pseudocode block or algorithm figure. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | We consider elections generated according to the 2D Euclidean model (see the work of Enelow and Hinch [1984]): Each candidate and each voter is represented by a point in a 2D Euclidean space, and each voter forms his or her preference order by sorting the candidates points with respect to their distance from his or her point (from the closest to the farthest one). We generate the candidate points and the voter points by drawing them uniformly at random from a [ 3, 3] [ 3, 3] square. ... To verify the robustness of our results, we considered a few other variants of the Euclidean model, the impartial culture model, and Mallow’s model [Mallows, 1957]. |
| Dataset Splits | No | The paper describes experimental setup and data generation, but it does not specify explicit training, validation, or test dataset splits (e.g., percentages, sample counts, or k-fold cross-validation details). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'simple ILP formulations' but does not specify any software dependencies with version numbers (e.g., specific solvers or libraries). |
| Experiment Setup | Yes | We consider elections generated according to the 2D Euclidean model (see the work of Enelow and Hinch [1984]): Each candidate and each voter is represented by a point in a 2D Euclidean space, and each voter forms his or her preference order by sorting the candidates points with respect to their distance from his or her point (from the closest to the farthest one). We generate the candidate points and the voter points by drawing them uniformly at random from a [ 3, 3] [ 3, 3] square. We consider elections with 100 candidates and 100 voters, and we consider committees of size 10. ...Then, for each value p between 0 and 1 (with step 0.01) and for each election E that we generated, we compute the highest value q such that there is a committee S that is at least p-approximate with respect to R(1) and q-approximate with respect to R(2) (to this end, we use simple ILP formulations of our problems; this is necessary when β-CC is involved, for other cases Theorem 1 would suffice). |