Republicans are requesting a liberal Wisconsin judge recuse herself from potentially considering reviewing the Badger State’s congressional maps.
Earlier this month, Democrats asked the Wisconsin Supreme Court to reconsider the state’s congressional maps, using the high court’s opinion in a separate elections maps lawsuit as reason to consider a redo over Wisconsin’s congressional maps.
But five members of Wisconsin’s GOP congressional delegation filed a motion Tuesday asking Justice Janet Protasiewicz to recuse herself from hearing the case, pointing to comments she made when she was a candidate running for a spot on the court last year as reason to not weigh in.
Among some of the comments Republicans pointed to included her calling the state’s maps “rigged” and saying she “would certainly welcome the opportunity to have a fresh look at our maps.” However, neither she, nor her Republican opponent, detailed how they would vote on a potential case while on the campaign trail.
The way optimization works is that you have a desired outcome, and you adjust inputs until you reach an optimal outcome. This is a specific area where algorithms are known to be effective. The desired outcome can be anything that is measurable. Whatever a person who looks at the result could possibly use to criticize it, for example.
You cannot simply dismiss an algorithmic solution. In fact, given enough time, it’s guaranteed that an algorithmic solution will be the best available solution. I don’t know how to refer to a person who denies this obvious fact, so I use the term technophobic. It could be simple ignorance or it could be some sort of irrational state of mind, but I think that’s all encompassed by the term technophobic.
There is still no objective measure of what is optimal for this problem. But I can tell you aren’t listening.
Yes that is why you have to run an optimization algorithm. To find the best known solution based on measurable results. I suggest you read up on numerical analysis.
And I am listening. I simply know a great deal more about this subject than you do, so your perception of this conversation is quite different from mine.
You run an optimization algorithm to figure out how to identify an optimal algorithm? That’s begging the question. It’s circular reasoning.
You said it yourself, you need to have a “desired outcome” in order to optimize. And the decision about the desired outcome is a political one, end of story. A political agent will optimize for the political result they want. Using an algorithm is of no advantage if you don’t change the external factors first.
That’s not at all what I said. Despite the fact that you seem to know very little about technology, up to this point you’ve at least been discussing the subject ethically. I would have thought straw man arguments were beneath you.
You’ve already listed several desired outcomes yourself, or at least suggested that you believe there are some. That voters are represented. Not only by population, but by modern and historic communities. Balancing many different factors to find what are called local extremum, or best known solutions, are problems that we’ve been using algorithms to solve for many decades. We can even use algorithms to prove that human solutions are suboptimal by looking at the algorithm’s results and comparing them to human results.
Maybe I am confused, did someone else say this?
Your entire belief here is predicated on the belief that there is an objective, optimal outcome. And there isn’t. If there were, you don’t need to run the elections in the first place.
You’re confusing two different things. You understand, correctly, that a well-designed bit of machine learning can get you efficiently and reliably to a certain result. What you don’t understand is that whether that result is good/optimal or not is a subjective question the computer cannot answer. The computer can only get better and better at tuning its various coefficients and neurons in order to get it closer to the result it has been TOLD is optimal.
The only decision that really matters is the decision about what optimal is. Which is a political question, in the case of redistricting.
Correct. There are lots of outcomes I think of as desirable. Some of those outcomes are in direct opposition to each other, too. Conditions where increasing the voice of some group reduces the voice of another. Which ones I care about most is a question of my personal politics.
Over and over again I repeat, the decisions about how to weigh these metrics against each other is a political one. One where political actors allowed to take on the problem in a political process will chose the inputs that get them to the results they want.
If you can flip a seat by saying that having proportional racial representation is twice as important as having proportional religious representation, a political actor that wants to flip that seat will tell you that proportional racial representation IS twice as important as proportional religious representation. Either way, you have either a race or religion NOT getting its full proportional representation reflected in the outcome.
And it’s not even just the proportional outcome that matters. The decision about how competitive to make the races is ALSO an input here with no objectively correct decision.
Do you understand that there is a difference between an optimal outcome and an optimal algorithm?
I also may be confusing the situation by using the word “optimal” slightly inaccurately. I tend to call “local extremum” “local optimum”. It’s not confusing to people who understand numerical analysis, but it is probably not appropriate when discussing things with a layman.
Whether the result is good is the entire point of the field of numerical analysis. And it’s something that you seem to know nothing about. This is where the giant gap is between us.
The truth of a statement is generally unrelated to how often the statement is repeated. If you find a solution where happiness is maximized or dissatisfaction is minimized, then what’s political about that? Just because the topic is political doesn’t mean that the solution is also political. Otherwise, we wouldn’t even be able to count votes.
You’re literally taking an example of a typical failure of your proposed solution and attributing it to a computer. Those numbers that you’re suggesting wouldn’t be given by a human. They would be learned or discovered by a computer. You don’t get to manipulate the inputs. You only get to say what is desired in the outputs.
What you’re describing is a human failure. And then you’re using that to say that a human solution is best.
And even if what you’re describing was possible, it still doesn’t answer the question of why, in your ideal scenario, is a panel of nonpartisan humans used, but when you imagine a computer scenario, you expect that all of these inputs will be determined by a single partisan human. Why wouldn’t you assume a panel of nonpartisan humans for both?
Your argument here is completely dishonest. You should assume the same level of partisan participation in both sides of the argument. Whether it’s completely drawing the map, or it’s adjusting inputs, or it’s deciding what outputs are best.
Please don’t project this onto me. The difference between these two things has been my core argument with you. The algorithm can be optimized to get a certain result. Your claim that the algorithm can ALSO define the desired result is preposterous circular reasoning. It’s clearly not even something you believe, yet you continue to argue with me about it.
Gibberish when you cannot objectively define what a good result is. Numerical analysis has many purposes. In this case, the purpose is to efficiently achieve a result based on input criteria. Whether or not that input criteria defines is “a fair election map” is a subjective question that reasonable people can and will disagree on. That disagreement is going to be political.
You can quit the condescension. It doesn’t impress me. I’ve been involved in data science research on similar optimization problems, so I know full well how it works and what the shortcomings are. The big one being that you need to know what is being optimized for. And “the most fair election map” is not a quantifiable outcome. You remind me of the PI on my last research project, handing me a giant dataset and saying “What does it mean?!?!”. Just couldn’t understand that I can’t give him an answer unless he has a meaningful question, and just couldn’t understand that p-hacking to come up with something publishable was pointless and dishonest.
You have your hammer and you’re SURE this problem is a nail. You just don’t even know where the jobsite is.
Yep, correct. So if you have a partisan process for deciding what is desired in the outputs, how are you going to get a good result? Which is why my entire point from the beginning was that chasing down magical algorithm solutions is pointless if you do not START with addressing the political problem.
In this case, Wisconson’s process for redistricting is partisan. That’s why this thread exists. That’s the problem being discussed. That’s the original sin. You’re not going to datascience a solution to that problem.
And moreover, these huge redistricting controversies all have that same thing in common – a partisan distracting process. Like magic, the issues seem to largely go away in places that don’t have a partisan redistricting process. GEE, WHO WOULDA THUNK IT.
It’s not an assumption. That is our starting point here. You need to listen to the people you are talking to and understand what is being discussed.
IDGAF if you want to use machine learning to design maps. That’s fine. Could be very helpful in making the maps, I’m sure.
But only AFTER you have limited the effects of politics in the decisions about the algorithm design. Otherwise, all that machine learning is still going to get you corrupt maps. They’ll just be really, really good and and hard-to-understand corrupt maps. Corrupt maps that achieve the goals and obfuscate the means they became corrupt through layer after layer of mostly-opaque and essentially impossible-to-comprehend models, graphs, and networks.
I’ll end this discussion by summarizing it for readers:
Me: if a partisan process is used to build maps, algorithms won’t get you fair maps. Start by addressing the partisan process.
You: algorithms can be optimized to get you perfect election maps and they can know what election map is perfect because they optimized to get a perfect map. This other guy is a dumb idiot who doesn’t know how algorithms work.
…and there’s the missing link that explains why you’re acting this way. I’ve worked with enough data scientists to see what’s going on. I don’t trust data scientists unless they have some other field of expertise in addition. People who’ve been involved with data scientists enough to see what they’re doing are probably going to often have similar outlooks to you.
I’m not trying to impress you. You may think you’re telling the truth, but you are in fact spreading misinformation. There is a 0% chance that you are correct. You don’t understand the subject and you’re just bloviating. It’s inherently offensive.
I wasn’t impressed by your summary, so I made my own:
Me: With a basic understanding of logic and algorithms, there will definitely be a hands-off computer approach to redistricting that will lead to better results than anything a human can make. This is guaranteed. There is no way to dispute this.
You: I will pretend like I never said anything like “Algorithmically-decided districts will also inherently ignore communities, both historic and demographic, again creating a high cracking likelihood and creating outsized representation to the dominant political groups.” or “These tech-bro-thinking solutions will never be the answer. The answer to redistricting* is to have a controlled political process with checks and balances.” I will now pretend like I had a different, less stupid opinion, than the one I started with and that I have been arguing this entire time. I am doing this because either I was up to this point, completely unable to articulate myself, or because I have secretly realized that I was mistaken, and now I am too stubborn to admit it.