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What's with the concept 'radicalizing social media algorithms'? This is like that idea that 'Putin hacked the elections'... by putting ads on facebook or something? How can people simultaneously defend democracy and believe that the average person is the cognitive equivalent of a fast food public wifi network?
If democracy is so great, why do we need to ban doctors from posting their opinions online? Why do we need to prevent people from taking horse deworming medication but make sure they get to vote?
If AI is racist, if Silicon Valley companies, the most powerful, data-driven, progressive companies ever, still can't seem to make DEI quotas, then perhaps they have a point?
I've made a few effortposts on this topic, ussually around the question of 'shouldn't we just let only the smart people vote?'
The basic idea is that if you have a true and powerful signal and a large enough amount of data collection, you can be ok even in a system with humongous amounts of noise.
It's ok if the average voter is so dumb that their voting behavior is near-random. So long as it's not completely random and they're probabilisticly influenced by the true signal of 'good candidate' at least a little, then if we average over tens of millions of voters we can recover that signal with high likelihood.
(whereas choosing any nonrandom subsample of the population to do the choosing, like 'the smart/informed people', is more likely to produce an artifact since their homogeneity makes them more likely to be biased in the same direction by the same factors)
This sounds rather like the "wisdom of crowds" argument — or at least adjacent to it. But, AIUI, that only really applies when the "noise" is unbiased; like in the classic "guess how many items in the jar," the average of people's "noisy" estimates converges toward the true value because people are equally likely to overestimate as underestimate. Does that still apply in voting? I think a case can be made that for various reasons — ranging from human cognitive biases to media institutions — the "noise" is not unbiased; the average "dumb voter" will tend to deviate from the "true signal" in particular directions, ensuring the aggregate "wisdom of the crowd" will be similarly biased away from the "true signal" in those same directions.
That is probably the largest falsifiable assumption underlying the rationale, that the noise is not normally distributed around the true signal.
The truest answer is 'that is possible and is a major weakness of the theory,' but, 3 mitigating arguments:
We have a 2-party system, in which each side has arguments and narratives trying to push people towards their direction. Voting simulations tend to suggest that in this situation, or even situations with 3 or 4 parties, it is natural for those opposing parties to center around the true center of public preference, and pull in either direction away from it. Since both parties have roughly similar number of devotees (which is not coincidental, they will change their positions until that equilibrium is reached in the long-run, which is part of why it centers that way in real life), we can expect/hope that the noise produced by those things roughly cancels out. (of course this conflates 'central voter preference' and 'the best government', but that gets into deeper philosophical discussions of what a 'good government' even is, which we're eliding atm)
Even if the population has net biases where everyone/the large majority are off in the same direction, these should be for specific issues or domains. Government is hugely complex and multi-faceted; it's possible for the government to be bad on 5 axes, ok on 10 axes, and good on 20 axes, or w/e. Even if there's some big universal bias that drives people away from the true signal on on axis or another, hopefully any axis without a singular such factor will still have pretty randomly distributed noise, and we'll do well on a lot of other metrics anyway.
In particular, I was comparing universal voting to some type of restricted voting where (for example) only people with a certain IQ or passing a certain competence test or etc. are allowed to vote. While it may be true that the general population of all voters could be systematically biased in some way, it's still much more likely that a smaller group selected on a specific metric, which therefore has less cognitive and experiential diversity, would have a similar or stronger bias of some kind. If you are trying to avoid systematic biases, it's really hard to do better than huge random samples in a situation like this, even if huge random samples aren't guaranteed to be perfect either. (and ofc I personally don't think you're going to do better hoping to get lucky with a benevolent monarch or any other system humans have tried, but maybe better non-voting systems are theoretically possible)
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Isn't the point of democracy that all candidates are 'good' candidates?
The alarming headlines about 'Russian bots' and 'radicalizing algorithms' are especially jarring. If 10 years of government work can be undone by a few hours of exposure to a Russian bot or an algorithm, perhaps your 'true signal' is not that true.
'Our glorious democratic education' vs their 'abhorrent authoritarian brainwashing'.
I suppose these media products are not to be taken at face value. Just an nth reinforcement 'you are a good person for not falling for the Eastasia propaganda'.
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Replying to myself to copy/paste one of those long posts, in case anyone is interested. Huge wall of text warning, with a lot of stuff that's probably too basic for the audience here:
This was in response to someone basically asking 'Wouldn't elections work better if only people who are educated on the issues and know enough to make good decisions were allowed to vote? Isn't it crazy that we let stupid and ignorant people make these decisions when they can't possibly know enough to decide well?'
My response:
So this is a really basic research methods question that scientists often have to deal with, mainly concerned with how to find a weak signal in a noisy data set.
Lets say that there's some 'correct' result for everyone election, the result that will lead to the best outcomes for the most number of people based on each of their individual preferences and needs, or whatever. The question is, what is the best way to arrive at that outcome as often as possible, or to arrive as closes as possible to it every time?
There are two main reasons this is a difficult problem. The first is that no one can see the future and it's impossible for anyone to truly know what the long-term consequences of any particular electoral outcome will be. The second is that it's impossible for anyone to truly know and understand the needs and preferences of all 350 million citizens and determine what the best outcome for all of them would be, even if they could predict it.
This creates a ton of uncertainty and disagreement about what the best electoral outcome is (who people should vote for), as we can see clearly at every election cycle.
In terms of statistical analysis, we would call this disagreement and uncertainty 'noise' - lots of disparate, high-variance, semi-random data about how people think everyone should vote. And we would call the 'correct' electoral outcome the 'signal' - the true result that we're trying to discover.
In this framing, an election is just a measurement, designed to try to capture the signal, and filter out the noise. Our elections tend to have a low signal-to-noise ratio, because it's so hard for anyone to know what the actual best outcome would be, and there's so much disagreement about what we should do.
As it turns out, scientists have been dealing with this problem in all kinds of domains since the invention of statistics, and they have a good handle on what works and what doesn't.
What you're suggesting is, basically, take a smaller number of data points from a restricted domain (people who pass the test), which you believe to have much less noise (less misinformation) and a much stronger signal (better understanding).
This is a good method in many domains - physicists, for instance, will go to great lengths to reduce noise in their experiments by shielding equipment or working far underground, even if this is expensive and limits the amount of data they can gather. If they can eliminate enough noise, they only need a very little data to confirm their hypotheses, because those hypotheses are very precise, and the systems they deal with are well-understood.
But the danger with restricting your domain and excluding subjects based on a specific criterion is that it can introduce bias into your measurements, leading you to very accurately measure the wrong signal. Physicists don't have to worry about this much, because physics works the same underground and behind shielding as it doe anywhere else. But any science that has to deal with people has to worry about this a lot, because people are very easily biased, and different groups of people can vary from each other in all kinds of ways.
In your example, it may be that people who pass your test know more about the world overall, but have some specific set of strong, incorrect beliefs that is currently in fashion among the educated classes, or was introduced to the curriculum they tend to study by the agencies that make that curriculum, or that concerns areas of study or ways of life (like plumbing or farm work) that the educated tend to have little contact with. And even if they have no systematically mistaken beliefs, their priorities and needs may still be systematically divergent from the rest of the general population - they may not appreciate the true needs of the poor, they may prioritize art and science over industry and safety, they may fall preferentially along one political or religious alignment, etc. Basically, as long as they have any systematic biases that make them different from the rest of the population, you cannot get the 'correct' signal from any type of measurement of them, because their 'signal' is something different that aligns with their biases. Their 'signal' may still be pretty good, but it can't ever be 'correct'.
How do scientists who deal with these types of problems try to measure the real signal amidst tons of noise, then? The answer is random sampling of lots and lots and lots of data points, averaged out with each other to converge on the correct signal.
See, when you have enough data points, it doesn't really hurt you much to add a 'noisy' data point (ie someone who knows nothing and acts randomly). Because that noise will tend to be randomly distributed, and cancel out with someone else who was randomly noisy in the other direction when you average everything together. So letting people with 'zero knowledge' vote is not a problem. The only type of voter that's a problem is one with 'negative information' - beliefs and preferences that actively drive them away from the correct signal. And even those people will tend to cancel out with people who have negative knowledge going the other direction... if you sample from every walk of life and every group, instead of limiting yourself to a single specific group with a tendency towards one specific flavor of negative knowledge.
Because the thing about a true signal is, that we expect it to have some impact on most data points, even if those points themselves have huge variance. Like if you give all the kids in one school platform shoes with 2" heels and measure their heights, there will still be lots of variance in height and there will be tons of kids in that school who are shorter than tons of kids in another school even with their shoes on, but if you take the average height it will still come out 2 inches taller, because the shoes still increased everyone's random noisy heights at once.
With elections, it's a bit more complicated, but it's the same idea. Maybe one person is an idiot about everything except farm policies, but they can tell a good farm policy from a bad one and that true information affects their vote. Maybe another person knows nothing about policy, but is a really good judge of character and will tend to vote for more honest and benevolent candidates. Maybe a third person has been through civil forfeiture and understands the reality of that situation much better than the average person, and lets that influence their vote when politicians make a proposal about it. etc.
Each of those people may have a lot of 'noise' in their heads about every topic other than the one they're good at, but that noise will be mostly random across individuals and will cancel out. As long as they have some knowledge or understanding that gives them good, 'correct' beliefs about the way to vote, and those beliefs influence their actual vote in some way, then that means they're being influenced by the 'signal' and will be adding true information about the signal to our data set when we measure them.
This is how psychologists, social scientists, and other scientists that deal with people and other complex and unpredictable phenomena, almost always design their studies: random sampling of as much data as possible, with statistical analysis to find the signal among the noise. It's simply the most practical and reliable way to go about things with situations this complex. And in the case of elections, that translates to allowing everyone to vote, and encouraging as many people to vote as possible.
It sounds counter-intuitive when you think about a single idiot voting. But when you think about that idiot as someone who only has one tiny spark of good information, and then think about the electoral process as adding the tiny sparks of tens of millions of people together to illuminate the truth, it makes a lot more sense.
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By redefining the word "democracy" to mean something much more disconnected from the views of "the average person"?
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Attributing to stupidity that which is usually attributed to malice is helpful when you're not allowed to outright call your outgroup evil.
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