Multiwinner Rules on Paths From k-Borda to ChamberlināCourant
Authors: Piotr Faliszewski, Piotr Skowron, Arkadii Slinko, Nimrod Talmon
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | for a visual justiļ¬cation of these intuitive claims, we point the reader to the work of Elkind et al. [2017a] and to the histograms in our experimental section and 4 Experimental Results In this section the goal is to illustrate our three paths between k-Borda and CC using the recent visual approach of Elkind et al. [2017a] |
| Researcher Affiliation | Academia | 1 AGH University, Krakow, Poland 2 Institut f ur Softwaretechnik und Theoretische Informatik, TU Berlin, Berlin, Germany 3 University of Auckland, Auckland, New Zealand 4 Weizmann Institute of Science, Rehovot, Israel |
| Pseudocode | No | The paper describes the simulated annealing heuristic used for computing winning committees in paragraph text: 'We begin by sampling a random committee S0. Then, in each iteration i, we take the committee Si 1 and form a temporary committee S i by randomly replacing one member in Si 1. If the score of S i is greater than that of Si 1, then we set Si to be S i; otherwise, we draw a random number between 0 and 1; if it is below pqi (where p and q are two parameters, we used p = 0.2 and q = 0.999), then we set Si = S i; otherwise, we set Si = Si 1. We execute 2000 iterations and output the highest-scoring committee encountered.' This is a textual description, not a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | No | All our elections are generated using the 2D Euclidean model, where both the candidates and voters ideal points are distributed uniformly on a [ 3, 3] [ 3, 3] square. (Explanation: The paper describes a data generation process following a model from a cited work, rather than providing concrete access information for a pre-existing public dataset.) |
| Dataset Splits | No | We have generated 5, 000 elections for each of our rules and computed their results using our heuristic (Explanation: The paper states it generated 5,000 elections and computed results using a heuristic, but it does not specify any training, validation, or test dataset splits or cross-validation methodology.) |
| Hardware Specification | No | The paper describes the experimental setup and the heuristic used for computation but does not provide any specific hardware details such as CPU or GPU models, or cloud computing specifications. |
| Software Dependencies | No | To compute earth-mover distances, we used its standard formulation as an integer linear program (ILP) and used an ILP solver. (Explanation: The paper mentions using an ILP solver but does not provide any specific software names with version numbers for reproducibility.) |
| Experiment Setup | Yes | We begin by sampling a random committee S0. Then, in each iteration i, we take the committee Si 1 and form a temporary committee S i by randomly replacing one member in Si 1. If the score of S i is greater than that of Si 1, then we set Si to be S i. Otherwise, we draw a random number between 0 and 1; if it is below pqi (where p and q are two parameters, we used p = 0.2 and q = 0.999), then we set Si = S i; otherwise, we set Si = Si 1. We execute 2000 iterations and output the highest-scoring committee encountered. |