Gerrymandering

Project Summary

Sophie Guo, Math/PoliSci major, Bridget Dou, ECE/CompSci major, Sachet Bangia, Econ/CompSci major, and Christy Vaughn spent ten weeks studying different procedures for drawing congressional boundaries, and quantifying the effects of these procedures on the fairness of actual election results.

Themes and Categories
Year
2015
Contact
Jonathan Mattingly
mathematics
jonm@math.duke.edu

Project Results

There has already been research done with North Carolina districts, described in http://today.duke.edu/2014/10/mathofredistricting. There, Jonathan Mattingly and Christy Vaughn showed that randomly re-drawing district boundaries would have dramatically changed election results. This summer's team extended the analysis to many more states, and found that states with independent election commissions (like Iowa) had statistically fairer results than states with very partisan districting systems (like Maryland).

L-R: Christy Vaughn; Sachet Bangia; Sophie Guo; Bridget Dou. Hard at work in SSRI.

Download the executive summary (PDF).

See Quantifying Gerrymandering, a website developed by Sachet Bangia, for more details about the project.

Gerrymandering work now posted on Arvix: https://arxiv.org/abs/1704.03360

Disciplines Involved

  • Political Science
  • Mathematics

Project Team

Undergraduates: Sophie Guo, Bridget Dou, and Sachet Bangia

Faculty Lead: Jonathan Mattingly, Professor, Mathematics

Graduate student mentor: Christy Vaughn, Program in Applied and Computational Mathematics, Princeton

Additional information

Relatively Prime podcast on the project:

Related People

Related Projects

Social and environmental contexts are increasingly recognized as factors that impact health outcomes of patients. This team will have the opportunity to collaborate directly with clinicians and medical data in a real-world setting. They will examine the association between social determinants with risk prediction for hospital admissions, and to assess whether social determinants bias that risk in a systematic way. Applied methods will include machine learning, risk prediction, and assessment of bias. This Data+ project is sponsored by the Forge, Duke's center for actionable data science.

Project Leads: Shelly Rusincovitch, Ricardo Henao, Azalea Kim

Project Manager: Austin Talbot

Producing oil and gas in the North Sea, off the coast of the United Kingdom, requires a lease to extract resources from beneath the ocean floor and companies bid for those rights. This team will consult with professionals at ExxonMobil to understand why these leases are acquired and who benefits. This requires historical data on bid history to investigate what leads to an increase in the number of (a) leases acquired and (b) companies participating in auctions. The goal of this team is to create a well-structured dataset based on company bid history from the U.K. Oil and Gas Authority; data which will come from many different file structures and formats (tabular, pdf, etc.). The team will curate these data to create a single, tabular database of U.K. bid history and work programs.

Project Lead: Kyle Bradbury

Project Manager: Artem Streltsov

Producing oil and gas in the Gulf of Mexico requires rights to extract these resources from beneath the ocean floor and companies bid into the market for those rights. The top bids are sometimes significantly larger than the next highest bids, but it’s not always clear why this differential exists and some companies seemingly overbid by large margins. This team will consult with professionals at ExxonMobil to curate and analyze historical bid data from the Bureau of Ocean Energy Management that contains information on company bid history, infrastructure, wells, and seismic survey data as well as data from the companies themselves and geopolitical events. The stretch goal of the team will be to see if they can uncover the rationale behind historic bidding patterns. What do the highest bidders know that other bidders do not (if anything)? What characteristics might incentivize overbidding to minimize the risk of losing the right to produce (i.e. ambiguity aversion)?

Project Lead: Kyle Bradbury

Project Manager: Hyeongyul Roh