Learning Real-Life Approval Elections

Prelegent(ci)

Piotr Faliszewski

Afiliacja

AGH University of Science and Technology

Język referatu

angielski

Termin

16 stycznia 2025 12:00

Informacje na temat wydarzenia

online

Seminarium

Seminarium „Gry, mechanizmy i sieci społeczne”

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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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