Learning Real-Life Approval Elections

Speaker(s)

Piotr Faliszewski

Affiliation

AGH University of Science and Technology

Language of the talk

English

Date

Jan. 16, 2025, noon

Information about the event

online

Seminar

Seminar Games, Mechanisms, and Social Networks

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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.

This is a joint work with Łukasz Janeczko, Andrzej Kaczmarczyk, Marcin Kurdziel, Grzegorz Pierczyński and Stanisław Szufa.

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