Research Cluster on Political Decision-Making

The Political Decision-Making Research Cluster combines the expertise of mathematicians, political scientists, and philosophers to investigate quantitative models of political decision-making. During the 2021-22 year we have three focus areas: the mathematics of redistricting, social choice theory, and mathematical modeling of polarization. Our immediate focus is applying Markov Chain Monte Carlo methods to provide relevant, timely analysis during the TX legislative redistricting cycle (MathForUnbiasedMapsTX, or MUM_TX). We will invite speakers pertaining to all three focus areas and talks are expected to be available to remote participants on Zoom.

 

Upcoming Events

Saturday, November 12, 2022

A Gerrychain Tutorial:
Tools for Detecting Gerrymandering

Presented by MUMTX

Sat., Nov 12, 2022 @ 8:30am-12pm

Visualization Lab, 1st floor of Ford Hall

Advances in computing power and Markov Chain Monte Carlo (MCMC) methods have enabled mathematics to become a powerful weapon in detecting gerrymandering. With the release of the 2020 US Census data, state and local redistricting is happening across the nation: there is no better time to learn how to use these tools.

This workshop will introduce GerryChain, the most widely recognized software package for generating large ensembles of valid district plans via MCMC. Participants will perform outlier analysis of a provided district plan and will learn to interpret graphical displays of the analyses.

Please use the link below to register. Space is limited to 30. RSVP by Wed, Nov 9.

https://forms.gle/fEo7iBRs4F7CzXFK8

Requirements:

Laptop with WIFI access
A Google account
Background in Python is helpful but not necessary

 

 

Previous Events

Thursday, March 31, 2022

Gerrymandering Symposium – Satellite Session @SMU

Scott Cook - Tarleton State University

William Hager - Texas Lutheran University

Betseygail Rand - Texas Lutheran University

Thurs, Mar 31 @ 7-9pm

Clements Hall 126 and on Zoom

Advances in computing power and Markov Chain Monte Carlo (MCMC) methods have enabled mathematics to become a powerful weapon in the fight against gerrymandering. With the release of the 2020 US Census data, state and local redistricting is happening across the nation: there is no better time to learn how to use these tools. This workshop will introduce GerryChain, the most widely recognized software package for generating large ensembles of valid district plans via MCMC to allow outlier analysis of a specific district plan. In addition, the 2022 workshop will also cover as much of the end-to-end pipeline as time permits:
  • acquisition of raw data (US Census, Texas Legislative Council, etc.)
  • data pre-processing & preparation for GerryChain
  • geospatial analysis & visualization
Background in Python will be helpful but not necessary. This is an SMU satellite session of the Gerrymandering Symposium at the 2022 Texas Section Annual Meeting of the Mathematical Association of America (https://maa.unt.edu/) hosted by University of North Texas in Denton.
If you would like to participate in this workshop, please RSVP to Brandy Stigler by email (see below). There are two ways in which you can participate. There is classroom space available at SMU for this event where TAs will be present to assist workshop participants; masks are encouraged but not required. You can also join remotely via Zoom (see link below). In either mode, please have a computer with internet connection.

Contact Brandilyn Stigler to RSVP at bstigler@smu.edu

Friday, March 25, 2022

Dynamical system models for voting and collective decisions

Vicky Chuqiao Yang, Affiliation here

Fri, Mar 25 @ 4-5pm

On Zoom

Join Vicky Chuqiao Yang as she presents an overview of two projects using quantitative behavioral models to study voting and collective-decision making, leveraging dynamical-system methods in mathematics. The first proposes a mechanism for the polarization of US parties in Congress and the second addresses whether a collective can arrive at the better of two options when some voters learn from others (social learning) instead of evaluating the options on their own (individual learning).

Contact: Brandilyn Stigler at bstigler@smu.edu

Friday, February 4, 2022

The Normative Basis of Support and Dissent of COVID Mandates

Rajat Deb, Professor of Economics

Fri, Feb 4 @ 4-5pm

On Zoom

The United States finds itself divided into two camps: some supporting and others opposing anti-COVID mandates. One group considers itself supporters of science and social welfare and the other sees itself as defenders of liberty and freedom. This event will use an interdisciplinary approach to examine the normative basis of these two positions.

Contact: Brandilyn Stigler at bstigler@smu.edu

https://smu.zoom.us/j/97060691794?pwd=WURRT2JQRTBFdjlYeUNtODQ1aktudz09

Friday, November 19, 2021

 

Working Group Meeting for the
DCII Research Cluster in Political Decision Making
Fri, Nov 19 @ 4-5pm
Clements Hall 126 with remote option
The Research Cluster in Political Decision Making invites you to our first working group meeting.  We will introduce the broad themes of focus for the year: the mathematics of redistricting, social choice theory, and mathematical modeling of polarization.  This will include some political and social context as well as areas where mathematics is/can be connected.   We will then report on our activities during the Fall semester, which have centered on applying ensemble analysis to proposed maps during the TX legislative redistricting cycle.  We hope that these remarks will spur discussions for future research efforts.  Refreshments will be provided – please bring your ideas!
Meeting ID: 923 6000 7533
Passcode: pdm2021
One tap mobile
+13462487799,,92360007533#,,,,*0502625# US (Houston)

 

MathForUnbiasedMapsTX (MUM_TX)

MathForUnbiasedMapsTX develops and implements Markov Chain Monte Carlo sampling methods to study the practice of redistricting; i.e. drawing single-member districts for the purpose of holding elections. We are applying these methods to the current TX redistricting cycle. By generating a large pool of legal plans, we can provide an unbiased baseline for districting plans. As candidate maps are released, we will compare them to their baseline on measures of partisan and racial gerrymandering.  

Summary of our Fair Redistricting Project