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If I understood the story correctly, Scott Aaronson was attacked mostly for paying too little attention to the feminist (well, not only theirs) concept of "privilege". I will try to paraphrase the concept of "privilege" (if I understand it correctly) using the terms of statistics in a way that, I imagine, might lead someone to accept the concept. This way, hopefully, I will be able to clearer express myself.
Suppose you can quantify suffering (let's use the word "suffering" even though in everyday language it is quite strong word, whereas I'll use it to describe even very small annoyances). And suppose you are trying to create a statistical model, that could predict total suffering of an individual without actually measuring his/her suffering without paying attention to a particular situation (just some kind of "total average suffering"), using explanatory variables that are easy to measure. Suppose you decide that you will use belonging to a specific social group of people as your explanatory variables. As you can see, nothing in these terms guarantees that this model will actually be good (i.e. if the error terms are symmetric, etc.), because, for example, it is not clear whether explanatory variables denoting whether a person belongs to a certain group are actually enough to make a model good, etc.
If you try, for example, linear regression, you will obtain something like this: S = a + b_1*x_1 + ... b_n*x_n + e. In addition to that, you can have additional variables of the form x_i*x_j or x_i*(1-x_j) to model interaction between different variables. Here S is total suffering, a is an intercept term, x_i is an expanatory Boolean variable denoting whether a person belongs to an i-th social group (some groups are mutually exclusive, some aren't, for example, let's say that we assign 1 to blue eyed people and 0 to others), and if b_1 is negative, then b_1 could be said to measure "privilege" of people who belong to i-th group. If I understand correctly, people who employ this concept use it this way. Let's denote Ŝ= a + b_1*x_1 + ... b_n*x_n and call it "predicted suffering".
As you can see, claims that privilege is very important and thus everyone must pay a lot of attention to it depend on many assumptions.
The model itself might be unsatisfactory if does not account for many important explanatory variables that are as important (or even more important) than those already in a model. Few people are interested in "testing" the model and justifying the variables, most people simply choose several variables and use them.
Modeling total average suffering without paying attention to a specific situation may be misleading if the values of b_i varies a lot depending on a situation.
Another thing is that it is not clear whether error terms e are actually small. If your model of total suffering fails to account for many sources of suffering, then error terms probably dwarfs predicted suffering. It is my impression that, when people see a linear model, their default thinking is that error terms as smaller (perhaps much smaller) than the conditional mean, unless explicitly stated otherwise. Therefore saying that a model has predictive power without saying that it has huge error terms might mislead a lot of people about what the model says.
Some people might claim that they, for some reason, are only interested in specific types of suffering, i.e. suffering from prejudice, biased institutions, politics, laws, conventions of public life or something like that. That doesn't mean that individual variation and error terms are small. If they aren't, then you cannot neglect their importance.
The values of coefficients b_i may be hard to determine.
But the problem I want to talk about the most is this. If you can observe the value of response variable S (total average suffering of an individual or total average suffering of an individual which is caused by a specific sets of reasons) then focusing on predicted value Ŝ is a mistake, since observation of response variable S screens off the the whole point of making a prediction Ŝ. For example, you can use university degrees to predict the qualifications of a job applicant, but if you can already observe their qualifications, you do not need to make predictions based on those degrees. It is my impression that most people, who talk about privilege, sometimes pay little attention to actual suffering S, but, due to mental habits obtained, perhaps, by reading the literature about the topic, pay a lot of attention to predicted suffering Ŝ. For example, Scott Aaronson describes his individual S in his comment here and gets a response here. The author says she empathizes with Scott Aaronson's story (S), then starts blaming Scott for not talking about Ŝ, and proceeds to talk about average (Ŝ) female and male nerds. Ŝ is not what any individual feels, but it seems to be the only thing some people are able to talk about. If the size of Ŝ does not dwarf model error terms e, then by talking about Ŝ and not talking about S they are throwing away the reality.
In addition to that, there is, If I understand correctly, another source of confusion, and it is ambiguity of the vague concepts "institutional" and "structural". If we are talking, e.g. about suffering from biased institutions, prejudices, structures in society etc. (if for some reason we are paying more attention to only this specific type of suffering), then S (and not Ŝ) is what actually measures it. However, it is my impression that some people use these words to refer to Ŝ only, without error terms e. In this case, they should remember, that S is what actually exists in the world and, if error terms are huge, then there might be very few situations where neglecting them talking about Ŝ instead actually illuminates anything. It is my impression that some people who are interested in things like "privilege" tend to overestimate the size of Ŝ and underestimate the size of e, perhaps due to availability heuristic.
Many people, who argue against feminists, tend to claim that the latter estimate Ŝ incorrectly. This may or may not be true, but I don't think that it is a good way to convince them to pay attention to problems that are different from what they are used to dealing with. Instead, I think that there might be a chance to convince them by emphasizing that S, and not Ŝ is what exists in the real world, emphasizing that error terms e may be huge, and not allowing them to change the topic from S to Ŝ. If you make them concede that a problem X, which their model does not use as a explanatory variable, exists and person_1, person_2, ... person_n suffer from problem X. Perhaps then they will not be hostile to the idea of noticing the pattern. To sum up, it seems to me that feminism tends to explain all things in top-down fashion and model their enemies as being top-down as well. My guess is that making them to think in "bottom-up" style terms may make their thinking somewhat less rigid.
Of course, all this is an attempt to guess how a specific part of a solution (stopping feminists from trying to complicate any kind of solution) might look like
One root pattern in the set of issues (race, gender, religion) is of between-group variance attracting more attention than within-group variance.
I suspect this pattern has deeper roots than a simple neglect of variance: At least some participants seem to fully accept that a model of suffering based only on group membership may involve too much noise to apply to individuals, but still feel very concerned about the predicted group differences, and don't feel a pressing need to develop better models of individual suffering.
(BTW, this is the heart of my crit... (read more)