Scholar
Ben Armstrong
Google Scholar ID: Ti_mW44AAAAJ
Tulane University
Citations & Impact
Citations
51
H-index
4
i10-index
1
Publications
16
Co-authors
8
list available
Publications
Resume (English only)
Academic Achievements
- Published 'Optimizing Viscous Democracy' which demonstrates that viscous democracy often significantly improves the quality of group decision-making over liquid democracy; preprint 'Designing Rules to Pick a Rule: Aggregation by Consistency' introduces a novel framework for rule picking rules (RPRs).
Research Experience
- Running the Second Workshop on Social Choice and Learning Algorithms in August 2025; defended PhD thesis in January 2025 and started postdoc at Tulane University; attending IJCAI in South Korea in August 2024 to present a main conference paper and give an invited talk at the Workshop on Democracy and AI; participating in AAMAS in New Zealand in May 2024 with an extended abstract, 2 workshop papers, and running the SCaLA workshop.
Education
- PhD from the David R. Cheriton School of Computer Science at the University of Waterloo, supervised by Kate Larson; currently a Postdoctoral Fellow in the Department of Computer Science at Tulane University, working with Nick Mattei.
Background
- Research interests: Experimental connections between social choice and machine learning. Currently exploring how (and whether!) machine learning can be useful for learning about voting rules, particularly what ML models can teach us about existing rules and how they can be used to create new ones. Also spent time investigating potential uses for liquid democracy and viscous democracy.
