Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Putting a Compass on the Map of Elections
Authors: Niclas Boehmer, Robert Bredereck, Piotr Faliszewski, Rolf Niedermeier, Stanisław Szufa
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We use them to analyze both a dataset provided by Szufa et al. and a number of real-life elections. |
| Researcher Affiliation | Academia | Niclas Boehmer1 , Robert Bredereck2 , Piotr Faliszewski3 , Rolf Niedermeier1 and Stanisław Szufa4 1Algorithmics and Computational Complexity, TU Berlin, Germany 2Humboldt-Universit at zu Berlin, Germany 3AGH University, Poland 4Jagiellonian University, Poland |
| Pseudocode | No | The paper describes algorithms (e.g., for EMD and recovering elections from matrices), but it does not present them in a structured pseudocode or algorithm block format. |
| Open Source Code | No | The paper states 'We provide details missing from this paper in its full version, available as a technical report [Boehmer et al., 2021].' but it does not provide a direct link or explicit statement about the open-sourcing of the code for the methodology described in this paper. |
| Open Datasets | Yes | We use them to analyze both a dataset provided by Szufa et al. and a number of real-life elections. ... datasets that we use (mostly from Pref Lib, due to Mattei and Walsh [2013]). |
| Dataset Splits | No | The paper describes analyzing datasets and real-life elections to understand their properties and positions on a map, but it does not mention or specify any training, validation, or test dataset splits for its experiments. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud instances) used to conduct its experiments or computations. |
| Software Dependencies | No | The paper mentions using a 'force-directed algorithm of Fruchterman and Reingold [1991]' and 'ILPs,' but it does not specify any software libraries, packages, or solvers with version numbers that would be necessary to replicate the work. |
| Experiment Setup | Yes | We consider their dataset with 10 candidates and 100 voters (see Figure 1 for its map). ... To generate an urn election, we choose α according to the Gamma distribution with shape parameter k = 0.8 and scale parameter θ = 1... In Figures 3c and 3d we visualize Mallows elections generated with φ [0, 1] and relφ [0, 0.5] chosen uniformly at random, respectively (we use rel-φ 0.5 because for larger values one obtains analogous elections, but reversed; e.g., both rel-φ = 0 and rel-φ = 1 lead to identity elections). |