“Reverse Gerrymandering”: Manipulation in Multi-Group Decision Making
Authors: Omer Lev, Yoad Lewenberg2069-2076
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | While the results on convergence are tight, we are interested to see the effects of the decentralized iterative dynamic on the overall welfare of the system... For each of these 320 potential settings, we ran 1,000 scenarios... The average proportion of agents that prefer the final position over the opening position for non-strategic agents with deterministic tie-breaking, lexicographic utility... |
| Researcher Affiliation | Academia | Omer Lev Ben-Gurion University of the Negev Beersheba, Israel omerlev@bgu.ac.il Yoad Lewenberg The Hebrew University of Jerusalem, Israel yoadlew@cs.huji.ac.il |
| Pseudocode | No | The paper describes mathematical models and iterative processes with formal definitions, but does not include any structured pseudocode or algorithm blocks labeled as such. |
| Open Source Code | No | The paper does not provide any statements or links regarding the release or availability of open-source code for the described methodology. |
| Open Datasets | No | We ran each simulation both with randomized preferences as well as with single-peaked preferences... In order to simplify data analysis and comparison, we ran a few experiments with a large variety of voter numbers, but here we will present the extensive simulations we have done of the iterative dynamic with 53 voters. The paper describes generating simulated data based on preference types and voter numbers, but does not refer to a publicly available dataset with a formal citation or access information. |
| Dataset Splits | No | The paper describes a simulation-based study with various parameters and scenarios, but does not mention dataset splits (e.g., training, validation, test) in a manner typical for machine learning or data-driven experiments. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, cloud instances) used for running its simulations or experiments. |
| Software Dependencies | No | The paper does not provide any specific software names with version numbers or list reproducible ancillary software dependencies. |
| Experiment Setup | Yes | Naturally, in addition to that we have as parameters the environment s variables: number of voters (n), candidates (m), districts (k), and bounds on district sizes (b , b+), the initial district allocation, etc. We examined the effects of the number of districts (we ran simulations with 2,3,5 and 10), and the size of the gap between maximal and minimal group size b+ b (we ran simulations with 1,2,3,4,5). A simulation setting included a choice of number of the district, a choice of the number of candidates, a choice of the size of gap, whether voter preferences were uniformly randomly generated or single-peaked, whether districts were fractional or deterministic, whether agents utilities were global or lexicographic, and whether agents were vote-strategic or not. For each of these 320 potential settings, we ran 1,000 scenarios, each beginning in the truthful state (for vote-strategic agents), and advancing from there. |