Is There a Fair Way to Divide Us? (Update) | People I (Mostly) Admire

Episode Overview

• Mathematician Moon Duchin explains her journey from abstract geometry to applying computational methods to solve the problem of political gerrymandering.
• The discussion centers on the core challenge of identifying gerrymandered maps: the lack of a baseline for what a "normal" or "fair" map looks like.
• Duchin details how her team developed algorithms to generate a massive "ensemble" of legally valid district plans, allowing them to identify partisan maps as statistical outliers.
• The conversation explores the crucial and non-intuitive distinction between a "neutral" map-drawing process and a genuinely "fair" political outcome.
• The podcast concludes by considering that a more fundamental solution may lie in changing voting systems, such as adopting multi-member districts with ranked-choice voting.

Key Concepts

• Gerrymandering vs. Redistricting: Redistricting is the required decennial process of redrawing electoral maps, while gerrymandering is the manipulation of those maps for partisan advantage using strategies like "packing" and "cracking."
• The Baseline Problem: The fundamental difficulty in proving a map is gerrymandered is the lack of a neutral baseline for comparison. The number of possible valid maps is astronomically large (a "googol"), making it impossible to know what a "typical" map looks like without advanced methods.
• The Ensemble Method (Markov Chains): Mathematicians use algorithms, specifically Markov Chain Monte Carlo methods, to sample the "wilderness" of all possible maps. By generating a large, representative sample (an "ensemble"), they can create a distribution of typical outcomes.
• Outlier Analysis: A proposed or enacted districting map is compared against the baseline distribution created by the ensemble. If its partisan outcome is an extreme statistical outlier (e.g., more partisan than 99% of the neutral plans), it provides strong evidence of gerrymandering.
• Neutrality vs. Fairness: A key insight is that a "blind" or "neutral" map-drawing process does not guarantee a fair outcome. The natural geographic clustering of voters (e.g., Democrats in cities) can mean that even randomly drawn maps will consistently favor one party.
• Alternative Voting Systems: As a potential solution to the inherent problems of single-winner districts, the conversation touches on moving towards systems like multi-member districts and ranked-choice voting to achieve more proportional representation.

Quotes

• At 0:01:13 - "We don't have a baseline. We don't know what normal districting looks like." - Duchin explaining the core problem that makes it difficult for courts to rule on gerrymandering.
• At 0:21:30 - "I don't know, maybe you have a hundred, maybe you have 25, I guess there's probably thousands, he said. Actually, probably the right scale to think about is a googol." - Duchin highlighting the sheer combinatorial complexity of the problem, quoting an exchange involving Justice Alito.
• At 0:27:16 - "My research group came along and came up with something that's analogous to the riffle shuffle... a big way to change plans one step at a time." - Duchin explaining her team's key innovation in developing more efficient algorithms to sample the universe of possible district plans.
• At 29:17 - "That doesn't mean it's fair. Because of where people live in Pennsylvania... the geography was such that that blind draw of plans was really quite favorable to Republicans." - Duchin distinguishing between a statistically "neutral" process and a "fair" outcome, noting that natural population distribution can create inherent bias.
• At 30:14 - "I remember when I said in the courtroom that blind isn't always fair, laughs went up around the courtroom. They thought I was joking." - Duchin sharing an anecdote that illustrates how counterintuitive and misunderstood the concept of fairness vs. blindness is, even in legal settings.

Takeaways

• Modern mathematics provides a powerful, objective tool to combat gerrymandering by creating a baseline of "normal" maps to prove when a specific plan is a statistical outlier.
• A "neutral" or "blind" redistricting process is not the same as a "fair" one; the underlying geography of how voters live can create a natural advantage for one party even in computer-generated maps.
• Judging a district by its shape is a poor indicator of its fairness, as visually "reasonable" plans can still be profoundly unfair in their political outcomes.
• The ultimate solution to unfair representation may not be better maps, but a move towards alternative voting systems like multi-member districts and ranked-choice voting.