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), fair division with applications to political redistricting (avoiding gerrymandering) and apportionment (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 124 (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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