Optimized Democracy examines the mathematical and algorithmic foundations of democracy, running the gamut from theory to applications. The goal is to provide students with a rigorous perspective on, and a technical toolbox for, the design of better democratic systems. 

Topics include computational social choice (identifying optimal voting rules), political redistricting (avoiding gerrymandering), apportionment (fairly allocating seats on a representative body), sortition (randomly selecting citizens' assemblies), liquid democracy (transitively delegating votes), and weighted voting games (analyzing legislative power through cooperative game theory). For a detailed list of topics see the course schedule.

Recommended preparation: Students should have a basic understanding of probability theory and algorithms. Examples of concepts that are useful to know include Markov chains, concentration inequalities, NP-hardness and linear programming. Mathematical maturity (following proof sketches in real time) is expected. Although this is primarily a graduate course, undergraduate students who have previously taken Stat 110 and CS 1240 (or similar courses) are very welcome. 

Requirements: Grades are based on four homework assignments (10% × 4 = 40%), participation (15%), and research project (45%). The research project should raise novel technical questions and provide some nontrivial answers. 

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