Bachelor of Arts
Machine learning, Mechanism design, Social choice, Voting rules, Social welfare, Neural networks, Artificial intelligence
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.
Firebanks-Quevedo, Daniel, "Machine Learning? In MY Election? It's More Likely Than You Think: Voting Rules via Neural Networks" (2020). Honors Papers. 688.