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What Data to Test Election-Related Algorithms On?

Piotr Faliszewski (AGH University of Science and Technology)

 

There is a number of ways of generating synthetic preference data. For example, one might choose each possible vote uniformly at random, or use some urn model (with a given parameter of contagion) or (a mixture of) the Mallows model(s), with a given parameter of central coherence, or some Euclidean model, parametrized by the distribution of the ideal points of candidates and voters. However, if one is to evaluate an election-related algorithm, it is not clear which of these models to use, and for what parameters. In this talk I will present a principled approach to answering this question and I will show very colorful pictures showing that I am right.

 

 
Date
Speaker
Location
Language
25.02.2020
17:00
Piotr Faliszewski
TEL 512
English

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