Weighted Voting Via No-Regret Learning


  • Nika Haghtalab Carnegie Mellon University
  • Ritesh Noothigattu Carnegie Mellon University
  • Ariel Procaccia Carnegie Mellon University


Voting systems typically treat all voters equally. We argue that perhaps they should not: Voters who have supported good choices in the past should be given higher weight than voters who have supported bad ones. To develop a formal framework for desirable weighting schemes, we draw on no-regret learning. Specifically, given a voting rule, we wish to design a weighting scheme such that applying the voting rule, with voters weighted by the scheme, leads to choices that are almost as good as those endorsed by the best voter in hindsight. We derive possibility and impossibility results for the existence of such weighting schemes, depending on whether the voting rule and the weighting scheme are deterministic or randomized, as well as on the social choice axioms satisfied by the voting rule.




How to Cite

Haghtalab, N., Noothigattu, R., & Procaccia, A. (2018). Weighted Voting Via No-Regret Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11466



AAAI Technical Track: Game Theory and Economic Paradigms