Closing the Loop: Bringing Humans into Empirical Computational Social Choice and Preference Reasoning

Nicholas Mattei

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

Early Career. Pages 5169-5173.

https://doi.org/10.24963/ijcai.2020/729

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Abstract

Research in both computational social choice and preference reasoning uses tools and techniques from computer science, generally algorithms and complexity analysis, to examine topics in group decision making. This has brought tremendous progress in the last decades, creating new avenues for research and results in areas including voting and resource allocation. I argue that of equal importance to the theoretical results are impacts in research and development from the empirical part of the computer scientists toolkit: data, system building, and human interaction. I highlight work by myself and others to establish data driven, application driven research in the computational social choice and preference reasoning areas. Along the way, I highlight interesting application domains and important results from the community in driving this area to make concrete, real-world impact.

Keywords

  • Agent-based and Multi-agent Systems: Computational Social Choice
  • Agent-based and Multi-agent Systems: Voting
  • Agent-based and Multi-agent Systems: Algorithmic Game Theory
  • AI Ethics: Moral Decision Making
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