Replicating Electoral Success
Authors: Kiran Tomlinson, Tanvi Namjoshi, Johan Ugander, Jon Kleinberg
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Beyond our theoretical analysis, we illustrate our results in extensive simulations; for five or more candidates, we find a tendency towards the emergence of two clusters, a mechanism suggestive of Duverger s Law, the empirical finding that plurality leads to two-party systems. Our simulations also explore several variations of the model, where we find the same general pattern: convergence to the center with four or fewer candidates, but not with five or more. |
| Researcher Affiliation | Collaboration | Kiran Tomlinson1,2, Tanvi Namjoshi1,3, Johan Ugander4, Jon Kleinberg1 1Cornell University 2Microsoft Research 3Princeton University 4Stanford University EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes mathematical definitions and theoretical results but does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Code https://github.com/tomlinsonk/pluralityreplicator-dynamics |
| Open Datasets | No | The paper describes a theoretical model and uses simulations. It does not utilize external, publicly available datasets. It states: "To model a large voting population and for tractability, we assume voters are uniform over [0, 1], but we later relax this assumption in simulation." indicating synthesized voter distributions for its model. |
| Dataset Splits | No | The paper does not use external datasets and therefore does not provide information on dataset splits. The simulations are described in terms of number of elections per generation and number of runs. |
| Hardware Specification | No | The paper describes its simulation setup, including the number of elections and generations, but does not specify any hardware details like GPU or CPU models used for running these simulations. |
| Software Dependencies | No | The paper provides a link to a GitHub repository for the code but does not explicitly list software dependencies with version numbers within the text. |
| Experiment Setup | Yes | Figure 2: Replicator dynamics runs for k = 2, . . . , 7 and 200 generations. Each plot shows 50 runs stacked on top of each other; each run has 100,000 elections per generation. Darker regions indicate higher candidate density with a log-scaled colormap. As our theory establishes, the candidate distribution converges to the center for k = 2, 3, 4, but not for k 5. We initialize F0 to be uniform. With perturbation noise, we add Gaussian noise with mean 0 and variance σ2 to each copied position. Figure 4 shows that the candidate distribution with a small amount of perturbation noise (σ2 = 0.005) converges to the center for k = 2, 3, 4 but does not for k 5. In the Memory variant, candidates sample from winner positions in any of the last m generations. In Figure 4, we see that adding m = 2 generations of memory has little impact. |