Strategic Campaign Management in Apportionment Elections
Authors: Robert Bredereck, Piotr Faliszewski, Michal Furdyna, Andrzej Kaczmarczyk, Martin Lackner
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also run extensive experiments on real-world election data and measure the effectiveness of our method. We complement our study by performing a series of experiments on election data from Austria and Poland. |
| Researcher Affiliation | Academia | Technische Universit at Berlin, Chair of Algorithmics and Computational Complexity, Berlin, Germany AGH University, Krak ow, Poland TU Wien, Institute of Logic and Computation, Vienna, Austria |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. Algorithms are described in prose within the text. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper mentions using "vote counts from Polish parliamentary elections from years 2011, 2015, and 2019" and "Austrian parliamentary elections from years 1994–2019" but does not provide specific access information (URL, DOI, or formal citation with author/year) for these datasets. |
| Dataset Splits | No | The paper does not provide specific dataset split information (like train/validation/test splits), as the experiments are conducted on historical election data rather than using typical machine learning data splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | For a given vote distribution p, party Pi, and desired number of seats ℓ, we express the effectiveness of a bribing strategy S as follows. Let x be the smallest number such that after adding x votes supporting Pi, party Pi gets (at least) additional ℓseats. Then, let y be the number of votes that S moves to Pi to obtain ℓadditional seats. We define the effectiveness of S as x/y... We consider the following bribing strategies for party Pi: 1. optimal bribery... 2. weakest/strongest rival... 3. balanced bribery... To study the effect of the number of districts, we start with the original partition of the country into 41 electoral districts, and then we decrease their number by merging districts. We do this sequentially, always merging two districts chosen uniformly at random, until only one large district remains. The result for a given number of districts is computed as follows: (1) we take the average of the number of bribed voters over five trials, each for a different districting (computed as described above); (2) we compute the average number of vote transfers per gained seat, and (3) we divide it by the overall number of voters. |