Primarily about Primaries
Authors: Allan Borodin, Omer Lev, Nisarg Shah, Tyrone Strangway1804-1811
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using Simulations to Go Beyond Worst Case: ...we compare the distortion of a voting rule under the direct and primary systems, in the average case over simulated instances. ...We generate 1000 instances satisfying the following restrictions...Finally, we run five voting rules plurality, Borda, STV, Copeland and maximin on both instances under the direct and primary systems, and measure the distortion. ...See Figure 1 for selected simulation results. ...We average the distortion numbers across the 1000 instances. |
| Researcher Affiliation | Academia | Allan Borodin University of Toronto bor@cs.toronto.edu; Omer Lev Ben-Gurion University omerlev@bgu.ac.il; Nisarg Shah University of Toronto nisarg@cs.toronto.edu; Tyrone Strangway University of Toronto tyrone@cs.toronto.edu |
| Pseudocode | No | The paper includes mathematical formulations, theorems, and proofs, but no structured pseudocode or algorithm blocks are present. |
| Open Source Code | No | The paper does not provide any statement or link regarding the public availability of source code for the methodology described. |
| Open Datasets | No | The paper describes generating its own simulation data: 'We generate 1000 instances satisfying the following restrictions. First, we place a set V of n = 1000 voters at uniformly random locations in [0, 1]k.' However, there is no concrete access information (link, DOI, citation) provided for this generated dataset to make it publicly available. |
| Dataset Splits | No | The paper describes generating and using 1000 instances for simulation, but it does not specify any train/validation/test dataset splits, nor does it reference predefined splits or provide specific splitting methodology. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, that would be needed to replicate the experiment. |
| Experiment Setup | Yes | We fix the metric space to be [0, 1]k for k {1, 3, 5, 7, 9} with the Euclidean distance. We generate 1000 instances satisfying the following restrictions. First, we place a set V of n = 1000 voters at uniformly random locations in [0, 1]k. ... Next, we place m/2 = 10 candidates (call this set A 1) uniformly at random on one side of the hyperplane, and m/2 = 10 candidates (call this set A1) uniformly at random on the other side. |