ESE 3090-Modeling & Design in Social Choice Systems 

Description

Social choice systems are all around us, from how we decide to split the check to who becomes president. This course introduces many conceptual and computational problems in the study of systems of social choice and offers a variety of tools to understand them. We will consider both micro and macro social choice systems; for the latter drawing on modern statistical techniques to understand (and reframe) questions like "what is a fair map of congressional districts?" In order to address modeling and design challenges in social choice systems we'll explore mathematical and software tools such as game theory, linear optimization, Monte Carlo / MCMC methods, and geographical data representation in Python.

The course has (loosely) three sections:

• Weeks 1-8: Social choice theory (what are social choice systems and how do we analyse or evaluate them?)

• Weeks 9-11: Games and apportionment (how do we fairly resolve conflict or share a communal resource?)

• Weeks 12-15: Redistricting problems (how do we design fair electoral district maps?)

Timings

• Lecture: TuTh 2:30-3:50pm in Weil 010.

• Office hours: M 3:30-5pm, Tu 4-5pm, W 11:30-12:30pm in Green 2155.

Resources

The course will draw from several resources including:

• Mathematics & Politics, Alan D. Taylor and Allison M. Pacelli (freely available through Springer with WashU login)

• Handbook of Computational Social Choice, ed. Felix Brandt, Vincent Conitzer, Ulle Endriss, Jérôme Lang and Ariel D. Procaccia (freely available through CUP)

• Political Geometry, ed. Moon Duchin and Olivia Walch (freely available here).

We will also reference the following articles:

• The Myth of the Condorcet Winner, Paul Edelman (freely available here)

• Monotonic Power Indices, Gina Richard (freely available here)

• Enlargement of the EU and Weighted Voting in its Council of Ministers, Dan S. Felsenthal and Moshé Machover (freely available here)

• Luxembourg in the Early Days of the EEC, Alexander Mayer (freely available here)

• An Impossibility Theorem for Gerrymandering, Boris Alexeev and Dustin G. Mixon (freely available here)

• The Adjusted Winner Procedure: Characterizations and Equilibria, H. Aziz, S. Brânzei, A. Filos-Ratsikas and S. Frederiksen (freely available here).

Safe + Brave

This is a largely discussion-based course where we will all commit to cultivating a safe and brave environment for all students to participate in. We use the five pillars of Arao and Clemens to frame what such a space consists of:

• “Controversy with civility” where varying opinions are accepted,

• “Owning intentions and impacts” in which participants acknowledge and discuss instances where a dialogue has affected the emotional well-being of another person,

• “Challenge by choice” where participants have an option to step in and out of challenging conversations,

• “Respect” where students show respect for one another’s basic personhood,

• “No attacks” where students agree not to intentionally inflict harm on one another.

You can also feel very free to call me out on words or actions that hinder these aims. Here is an anonymous google form where you can let me know about any concerns.

Key Connections

• (Computational) social choice theory

• Game theory

• Voting theory

• Linear programming / optimization

• Monte Carlo / MCMC methods

• Redistricting computations; Python (geo)pandas

Assessment

• Project - due 11:59pm, 12/10 (20%)

• Participation (30%)

• Homework (30%)

• Midterm - take-home due 11:59pm, 10/27 (20%)

Participation: This includes preparing for and actively engaging in discussion, and (roughly) biweekly self-reflections.

Completion: I will allow for one missed homework and one missed reflection without impacting your grade. After this point, each missed reflection counts as -1% of your grade up to -5%.

Virtual testing: I will offer a virtual option for exams and quizzes in circumstances such as quarantine due to sickness or potential exposure to covid-19.

Late submission: The final project and midterm are due at 11:59pm. They can still be submitted until 1am the next day but will receive a 10% penalty (applied to the grade they receive, so if you score 90% it will count as 81%).

Makeups: Because of the three features above I don't allow for makeups under any circumstances.

Praxis: Each homework contains a 'praxis prompt' where you will practice an additional academic skill: communication (via recording a video solution to one problem), collaboration (partnering with someone else to complete a problem and providing feedback), education (preparing a mock lesson plan to explain a concept).

Integrity

• Attempting to cheat in this course is unacceptable and will be strongly penalised. A first offense will be penalised with a zero grade on the relevant piece of assessment. A second offense will be penalised with an immediate fail grade.

• Collaboration is permitted (actually encouraged!) on homework assignments, however each student must write up solutions in their own words.  Please write the names of any other students you have collaborated with at the top of each assignment. Significant similarities between submissions from different students that fail to mention any collaboration counts as an act of cheating and will be penalised as such.

Syllabus