Learning Real-Life Approval Elections
🤖 AI Summary
This study investigates how to model voters’ independent approval behavior toward candidates using real-world election data. It proposes the Independent Approval Model (IAM) as a unified framework that encompasses several classical voting models and, for the first time, systematically introduces mixture IAMs to capture heterogeneity in voter preferences. Model parameters are learned from real elections in the Pabulib dataset via maximum likelihood estimation and Bayesian inference. Experimental results demonstrate that single-component IAMs struggle to capture the complexity of real elections, whereas mixture IAMs substantially improve model fit, thereby confirming their necessity and effectiveness in representing empirical voting behavior.
Application Category
📝 Abstract
We study the independent approval model (IAM) for approval elections, where each candidate has its own approval probability and is approved independently of the other ones. This model generalizes, e.g., the impartial culture, the Hamming noise model, and the resampling model. We propose algorithms for learning IAMs and their mixtures from data, using either maximum likelihood estimation or Bayesian learning. We then apply these algorithms to a large set of elections from the Pabulib database. In particular, we find that single-component models are rarely sufficient to capture the complexity of real-life data, whereas their mixtures perform~well.
Problem
Research questions and friction points this paper is trying to address.
approval elections
independent approval model
model learning
mixture models
real-life data
Innovation
Methods, ideas, or system contributions that make the work stand out.
Independent Approval Model
Mixture Models
Approval Elections
Bayesian Learning
Maximum Likelihood Estimation
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