Noyce Conference Room
  US Mountain Time
Moon Duchin

Our campus is closed to the public for this event.

Abstract: These days large language models are transforming many fields with their ability to generate rich, human-like text.  In this talk I’ll introduce some generative models for rankings, seeking to create an engine for simulating ranked elections.  In other words, faced with a large number of candidates that break into several types, how are people likely to rank their preferences?  This is a key step in a research program to better understand how different voting rules lead to qualitatively different representation, according to various possible norms of representative democracy.  I’ll particularly pay attention to the question of whether STV, a popular multi-winner ranked choice election method, tends to produce proportional representation.


Moon DuchinMoon DuchinProfessor, Mathematics at Tufts University
SFI Host: 
Cris Moore

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