The Moderating Effect of Instant Runoff Voting
Authors: Kiran Tomlinson, Johan Ugander, Jon Kleinberg
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this work, we prove that IRV has a moderating effect relative to plurality voting in a precise sense, developed in a 1-dimensional Euclidean model of voter preferences. We develop a theory of exclusion zones, derived from properties of the voter distribution, which serve to show how moderate and extreme candidates interact during IRV vote tabulation. The theory allows us to prove that if voters are symmetrically distributed and not too concentrated at the extremes, IRV cannot elect an extreme candidate over a moderate. In contrast, we show plurality can and validate our results computationally. Our methods provide new frameworks for the analysis of voting systems, deriving exact winner distributions geometrically and establishing a connection between plurality voting and stick-breaking processes. The histograms are from 1 million simulation trials for k = 3, 4, 5 and 100,000 trials for k = 100, while the curves plotted for k = 3 (shown up to 1/2) are the exact density functions given in Propositions 1 and 2, with pieces separated by color. |
| Researcher Affiliation | Academia | Kiran Tomlinson1, Johan Ugander2, Jon Kleinberg1 1Cornell University 2Stanford University kt@cs.cornell.edu, jugander@stanford.edu, kleinberg@cornell.edu |
| Pseudocode | No | The paper focuses on mathematical proofs and theoretical analysis, and does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/tomlinsonk/irv-moderation. |
| Open Datasets | No | The paper models voters and candidates as being "drawn from a distribution F on the unit interval [0, 1]", which is a theoretical construct for simulations rather than a publicly accessible dataset. No specific dataset is mentioned or linked for public access. |
| Dataset Splits | No | The paper describes simulations based on theoretical distributions, but it does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running simulations or computations. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers used for its computations or simulations. |
| Experiment Setup | No | The paper describes the theoretical model and the distributions used for simulations but does not provide specific experimental setup details such as hyperparameters, optimizer settings, or other configuration information typical for empirical experiments. |