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Learning Agents for Iterative Voting

Stéphane Airiau, Assistant Professor
Computer Science, University Paris - Dauphine

Abstract
Many voting rules are available for making a collective decision. The axiomatic approach in social choice is about investigating the properties of the rules. Depending on the context and what properties are important for a particular decision, one can then recommend a (small) set of particular voting rules. Another approach to collective decision could use multiagent learning and let agents repeatedly interact to decide on the decision, and this is what we start to investigate. By conducting extensive simulations, we show that our agents are capable of learning how to make decisions at the level of well-known voting procedures, and that these decisions maintain good choice-theoretic properties when increasing the number of agents or candidates.

Bio
Stéphane Airiau is assistant professor of Computer Science at the university Paris Dauphine. His research lies in artificial intelligence and more precisely in the field of multiagent systems. He is interested in engineering good and fair collective decision mechanisms.

Thursday, November 21, 2019 at 3:00pm to 4:00pm

Graf Hall, Conerence Room (310)
1680 SW Monroe Avenue, Corvallis, OR 97331

Event Type

Lecture or Presentation

Event Topic

Research

Organization
Electrical Engineering and Computer Science, Mechanical, Industrial, and Manufacturing Engineering
Contact Name

Dylan Jones

Contact Email

jonesdy@oregonstate.edu

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