About

This course would primarily focus on decision-making in the presence of multiple agents with preferences. This would cover topics on optimized democracy (voting rules, axioms, liquid democracy), embedded ethics (biases in algorithms, societal effects of algorithms, algorithmic fairness),  similar paradigms in ML (federated learning), and related topics from economics and computation (fair division, general equilibrium theory, and stable matching). 

The course is highly-interdisciplinary, and the main takeaway is to familiarize students with a class of problems that are of interest to operations research, computer science, economics, and law, and how the combined literature can help us design efficient solutions.