Degree Year
2020
Document Type
Thesis - Open Access
Degree Name
Bachelor of Arts
Department
Computer Science
Advisor(s)
Samuel Taggart
Committee Member(s)
Adam Eck
Keywords
Machine learning, Mechanism design, Social choice, Voting rules, Social welfare, Neural networks, Artificial intelligence
Abstract
Impossibility theorems in social choice have represented a barrier in the creation of universal, non-dictatorial, and non-manipulable voting rules, highlighting a key trade-off between social welfare and strategy-proofness. However, a social planner may be concerned with only a particular preference distribution and wonder whether it is possible to better optimize this trade-off. To address this problem, we propose an end-to-end, machine learning-based framework that creates voting rules according to a social planner's constraints, for any type of preference distribution. After experimenting with rank-based social choice rules, we find that automatically-designed rules are less susceptible to manipulation than most existing rules, while still attaining high social welfare.
Repository Citation
Firebanks-Quevedo, Daniel, "Machine Learning? In MY Election? It's More Likely Than You Think: Voting Rules via Neural Networks" (2020). Honors Papers. 688.
https://digitalcommons.oberlin.edu/honors/688
