MADISON, Wis. (AP) — Wisconsin Democratic Gov. Tony Evers on Tuesday vetoed a redistricting proposal that the Republican-controlled Legislature passed last week in a last-ditch effort to avert the …
There’s no such thing as an apolitical, objective, quantitatively “right” or fair map. We can only judge maps off of subjective, political standards.
I appreciate your detailed discussion, but political standards are not the only way we can judge maps.
And in fact, I’m struggling to find a way to judge them that is more fair then whether they provide the same proportion of representation to the amount of votes received.
Is it a “good” outcome if you have the right ratio of dems to republicans, but zero black representatives when black people are a quarter of the population? If one in seven statewide voters is hindu, surely you should have 1:7 hindu representatives – but if the hindus are spread out that is sure to not happen unless districts are DESIGNED to select for one of those reps, which is going to water down everyone else’s votes to a meaningful degree even without changing the proportional representation.
Or how about maps that are designed to get you just the right representative breakdowns by whatever standards you want, but are designed to create nothing but no-contest races where the incumbent party has no chance of losing? That’s a pretty bad outcome that is honestly SUPER common in redistricting.
There should definitely be a high correlation between seats and votes, of this there is no argument. But there’s a lot of tension in trying to be fair about maps. Too much to pretend there’s an algorithm that can nail it. The gold standard is to have a complex, transparent, and non/multipartisan process to make those compromises
It’s an interesting question for sure. And not to suggest that the question and answer were a trap, but your response does seem to give objective criteria to redistricting plans, does it not?
I mean, we’re all in agreement on “proportional” representation. So what does that mean? At the basic level, republican/democrat representation is the coarsest measure, so we know redistricting plans are poor when you have 40% of votes from one party getting 60% of seats.
At a level deeper, there’s demographic representation, religious representation, and so on. So that’s another scale to judge “proportionate representation.” If hindus represent 5% of a state’s population, and a plan ends up (based on last-election voting data) giving hindu populations majority voting control for 5% of seats, that seems like a win. (For argument, it does carry some racial profiling and prejudice to presume “hindus” would vote as a bloc and therefore need grouped representation, just so it’s said. It’s complicated, I get it.)
But it seems like the political part is defining what values are important for fair districting, and you’re doing a great job defining those. After that point, an optimizing algorithm - optimizing those criteria with tie-breaker priorities - actually would seem like the fairest way, and also allow transparency as to what we are prioritizing in redistricting. By making it scientific, that would also allow improvements instead of endlessly repeating a “from scratch” battle royale political approach each time new districts are drawn up. Isn’t that preferable?
I don’t particularly disagree with what you’re saying. My criticism is that building these elaborately-complex algorithms that are deciding how to build the districts… it’s having the political apparatus chose the voters instead of the voters choosing the political apparatus.
In simple terms: a data-science approach to redistricting is TOO powerful. It is too effective at getting to a result. It fundamentally undermines the democratic process.
In a well-functioning democracy – which I am not going to pretend the US is – elections need to be competitive. They need to allow for surprises. Political arbitrage. Some amount of unfairness is the price we have to pay for that. And a theoretical algorithm that sufficiently takes into consideration EVERYTHING we need to ensure flawlessly “fair” elections where outcomes have a perfect proportional match to voter opinions/identities (and how do we even know they truly do in the first place?) may as well be trusted to pick the winners while it’s at it. We’re just in Foundation at that point.
It seems the more we discuss it, the eventual conclusion that emerges is that any act of subdividing representation (whether by geographical boundaries, demographics, or probably any criterion) inherently makes that representation less democratic under scrutiny. This is probably because any simplified/heuristic criterion will almost never be a perfect match to the specific type of representation an individual wants to prioritize, leading to mismatches between individuals and the defined criteria for “proportionate” representation.
So maybe the answer is broader elections with the voters themselves self-identifying the representation they want, and matching that to proportionally apportioned representatives? But I think we’ve just described political parties, so…I probably am not helping.
I appreciate your detailed discussion, but political standards are not the only way we can judge maps.
And in fact, I’m struggling to find a way to judge them that is more fair then whether they provide the same proportion of representation to the amount of votes received.
Is it a “good” outcome if you have the right ratio of dems to republicans, but zero black representatives when black people are a quarter of the population? If one in seven statewide voters is hindu, surely you should have 1:7 hindu representatives – but if the hindus are spread out that is sure to not happen unless districts are DESIGNED to select for one of those reps, which is going to water down everyone else’s votes to a meaningful degree even without changing the proportional representation.
Or how about maps that are designed to get you just the right representative breakdowns by whatever standards you want, but are designed to create nothing but no-contest races where the incumbent party has no chance of losing? That’s a pretty bad outcome that is honestly SUPER common in redistricting.
There should definitely be a high correlation between seats and votes, of this there is no argument. But there’s a lot of tension in trying to be fair about maps. Too much to pretend there’s an algorithm that can nail it. The gold standard is to have a complex, transparent, and non/multipartisan process to make those compromises
What a well reasoned and well described set of posts. You are a credit to our community!
It’s an interesting question for sure. And not to suggest that the question and answer were a trap, but your response does seem to give objective criteria to redistricting plans, does it not?
I mean, we’re all in agreement on “proportional” representation. So what does that mean? At the basic level, republican/democrat representation is the coarsest measure, so we know redistricting plans are poor when you have 40% of votes from one party getting 60% of seats.
At a level deeper, there’s demographic representation, religious representation, and so on. So that’s another scale to judge “proportionate representation.” If hindus represent 5% of a state’s population, and a plan ends up (based on last-election voting data) giving hindu populations majority voting control for 5% of seats, that seems like a win. (For argument, it does carry some racial profiling and prejudice to presume “hindus” would vote as a bloc and therefore need grouped representation, just so it’s said. It’s complicated, I get it.)
But it seems like the political part is defining what values are important for fair districting, and you’re doing a great job defining those. After that point, an optimizing algorithm - optimizing those criteria with tie-breaker priorities - actually would seem like the fairest way, and also allow transparency as to what we are prioritizing in redistricting. By making it scientific, that would also allow improvements instead of endlessly repeating a “from scratch” battle royale political approach each time new districts are drawn up. Isn’t that preferable?
I don’t particularly disagree with what you’re saying. My criticism is that building these elaborately-complex algorithms that are deciding how to build the districts… it’s having the political apparatus chose the voters instead of the voters choosing the political apparatus.
In simple terms: a data-science approach to redistricting is TOO powerful. It is too effective at getting to a result. It fundamentally undermines the democratic process.
In a well-functioning democracy – which I am not going to pretend the US is – elections need to be competitive. They need to allow for surprises. Political arbitrage. Some amount of unfairness is the price we have to pay for that. And a theoretical algorithm that sufficiently takes into consideration EVERYTHING we need to ensure flawlessly “fair” elections where outcomes have a perfect proportional match to voter opinions/identities (and how do we even know they truly do in the first place?) may as well be trusted to pick the winners while it’s at it. We’re just in Foundation at that point.
It seems the more we discuss it, the eventual conclusion that emerges is that any act of subdividing representation (whether by geographical boundaries, demographics, or probably any criterion) inherently makes that representation less democratic under scrutiny. This is probably because any simplified/heuristic criterion will almost never be a perfect match to the specific type of representation an individual wants to prioritize, leading to mismatches between individuals and the defined criteria for “proportionate” representation.
So maybe the answer is broader elections with the voters themselves self-identifying the representation they want, and matching that to proportionally apportioned representatives? But I think we’ve just described political parties, so…I probably am not helping.