17 ms·

Fuck Nuance [pdf]

129 points by networked 11y ago

yummyfajitas 11y ago
This article is a truly fantastic critique of how modern social sciences (besides economics) are carried out and publicized in modern society. Any critique one dislikes - typically "your theory predicts X which is false" - can be dismissed by "nuance", "things are more complicated", "that's different in unspecified way", etc. In statistical terms, these dismissals are little more than a call for overfitting - if I can arbitrarily add more dimensions to a theory, I can make it fit any data set. This is a fantastic article that can be applied in so many places. In applied social sciences this critique is far easier to work around. "Yeah, my theory is pretty one-dimensional and does lack nuance. My code is in $REPO - feel free to run an experiment and see if your multidimensional theory generates any alpha over mine."
- p4wnc6 11y ago
  Why do you exclude economics? I see economics as practically leading the way with regard to dismissals like you suggest. One example I have first-hand experience with is the low-volatility anomaly. CAPM-like theories predict a roughly linear, positive-sloping relationship between risk and return. Yet decades worth of various empirical measurements of the riskiness of assets and portfolios unequivocally demonstrates that it is a negative-sloping relationship (e.g. for your willingness to bear an additional unit of risk, you actually receive less return). But everyone says basically the same thing. "Things are more complicated in real markets." It would be reasonable to say that CAPM is a mathematical result, and the evidence suggests the mathematical model required for CAPM simply fails to correspond to reality. But many finance folks, especially in academia, won't admit it. For them, CAPM must also be a descriptive theory. Two sources that illustrate what I'm talking about: [0] Baker, Bradley, and Wurgler, "Benchmarks as Limits to Arbitrage: Understanding the Low-Volatility Anomaly" FAJ Volume 61, 2011. < http://www.cfapubs.org/doi/pdf/10.2469/faj.v67.n1.4 http://www.cfapubs.org/doi/pdf/10.2469/faj.v67.n1.4 > [1] Baker, Malcolm P. and Bradley, Brendan and Taliaferro, Ryan, The Low Risk Anomaly: A Decomposition into Micro and Macro Effects (September 13, 2013). Financial Analysts Journal. < http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2210003 http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2210003 > *disclaimer: The authors of these two papers were my bosses in a previous role, and I wrote the code and generated the results for the second paper.
  - ryanmonroe 11y ago
    >t would be reasonable to say that CAPM is a mathematical result, and the evidence suggests the mathematical model required for CAPM simply fails to correspond to reality. But many finance folks, especially in academia, won't admit it. In other words, "Things are more complicated in real markets". I don't see any problem with wanting descriptive theories. Economists are interested in building models whose parameters can be tweaked in ways that correspond in a clear meaningful way to a possible actions (e.g. change government expenditure on X by Y). If you try to just maximize predictive power you're missing the point. Economics can be used to predict the future, but I would say the main goal of the field is to produce theory that will provide valid methods of altering the future.
    - p4wnc6 11y ago
      This doesn't make a lot of sense in this example with CAPM. The economic theory provides something that is abjectly wrong. It is literally claiming there is a positive relationship between two quantities when the evidence suggests the relationship is negative. The theory is grossly qualitatively wrong, not merely just on the edge of being quantitatively wrong in a few cases or something.
    - tormeh 11y ago
      So you're saying that we need wrong theories that can guide policymakers in the right direction? That sounds like a bad idea. It's basically "I want you to do this thing. Let me invent a theory that makes me sound more persuasive. Don't worry about the details!" Yes, we need theories that are easy enough to reason about that they provide useful insight, but I don't think this is even close to being too complicated.
  - yummyfajitas 11y ago
    What you are describing in economics - while perhaps a failure mode - is the exact opposite of what this article discusses. The critique of this article is that valid abstractions and theories are rejected due to a lack of "nuance", rather than a failure to explain and predict reality: Second is the ever more extensive expansion of some theoretical system in a way that effectively closes it o from rebuttal or disconrmation by anything in the world...It is an evasion of the demand that a theory be refutable. Your critique of some economic theories is a totally different one - that they make specific predictions which don't come true.
- jerf 11y ago
  At first "overfitting" was the concept that leaped to mind as I read it, but overfitting is sort of something different. You have a complicated data set, and when you overfit, you keep adding dimensions of freedom, and then setting those dimensions of freedom to produce the original values that were passed in, such that it fails to correctly predict any other instance drawn from some conceptual "same distribution". In this case, the theories do the "adding many dimensions of freedom" part, but they don't really fix the values; they probably haven't got data to fix in the first place, and it's not mathematical enough to be that precise about the fitting. I think it's more like these theories are often not so much hypotheses themselves, but the descriptions of hypotheses spaces, and the addition of "nuance" means that in a lot of cases, what sounds like a "theory" is really just the description of a space that happens to contain all or most of the possibility space in question. As much fun as it may be to do that, and as much as it may even be a necessary first step to a real theory, in practice that is not generally the difficult part of the process. (There are exceptions to that which may leap to mind but I submit they would be the exceptions, rather than the rule.) When one is working with fuzzy words (even if by necessity!) and not math, the fuzzy words make it very easy to make this mistake, I think. English does not have a strong distinction between a hypothesis space and a specific hypothesis in that space, as evidenced by the very fact I have to dip into jargon to even make that point! From that perspective, the solution that leaps to my mind is implement the observation that in many ways, the important thing about a theory is not what it describes, but what it excludes. It is too easy and cognitively tempting to try to create a theory that, when you fiddle with the parameters, describes everything you see, but the real questions are, what is excluded by your theory? Can I fiddle with the knobs in your theory to describe something that you say ought to exist, but does not seem to? Is your theory even well-enough specified that these are meaningful questions to ask about it? I don't know the answers to these questions for sociology. I have to admit I've tried to engage with it a couple of times, because what fan of Isaac Asimov doesn't want to learn about some real-life psychohistory, but I've always found it difficult to get traction with with my engineer mind. I can engage from a philosophical point of view, but that is not generally what I was looking for; I've got, well, philosophy itself for that. (Repeat previous sentence for history, psychology, and economics.) To p4wnc6's question, I'd submit that as dismal as the Dismal Science may sometimes be, it does often meet this bar. I fear economists themselves may often be quite sluggish at recognizing when their own theories are contradicted by the evidence because the theories are saying that what is currently happening (in the general sense, not the literal March 27, 2016 sense) is excluded by the theory, but that is a human problem, not an economic theory problem. Economic theories do exclude things; if supply rises and demand holds steady, the vast majority of possibilities where price goes up are excluded, and if price were to go up, the theories would be useful in investigating how such a thing actually happened by examining what such theories would still permit (e.g., perhaps supply isn't really rising because the new supply is actually a higher quantity of lower quality, and too much of the demand is for a higher quality than the new supply is supplying which means "real" supply is dropping, or something like that). They certainly aren't perfect and a given theory may even be wrong, but, well, the very fact that a theory can be wrong is evidence that it is falsifiable, and thus not "nuanced" into infinite psudo-explanatory power.
- Olscore 11y ago
  > In statistical terms, these dismissals are little more than a call for overfitting - if I can arbitrarily add more dimensions to a theory, I can make it fit any data set. Sure. However, this leads me to believe it is statistics that are the problem, and not nuance. You are in my opinion admitting that nuance invalidates most statistics, or at least allows for heavy manipulation of the statistics to fit any narrative. This is precisely why public policy should not be made using statistics. Society should not be likened to a turing machine. People, history and so many topics of discussion are full of nuance, detail and perspective; and disregarding nuance (or perspectives) is insulting to the truth.
  - yummyfajitas 11y ago
    No, on the contrary I'm saying that calls for nuance are often excuses to do bad statistics and bad science. Rather than throwing away our theories because they predict X and the world does Y, they instead say "actually the theory only applies under vague unspecified circumstances which you'd understand if you were as nuanced as I". Whether or not the theory fits the data, they cry "intersectionality" or "humans are so complex". In any case, your post begs the question. If we should ignore statistics, how should we make decisions? Emotionally pleasing verbal narratives? See also the related essay, "Pragmatism is Poison". It argues that the word "pragmatism" is used in software a lot to shut down discussion of bad choices, much the same way "nuance" is used to shut down discussion of good theories. https://einarwh.wordpress.com/2016/03/10/pragmatism-is-poison/ https://einarwh.wordpress.com/2016/03/10/pragmatism-is-poiso...
    - platz 11y ago
      No true scottsman will allow their theory to be critiqued. A true scottsman is more nuanced.
    - pron 11y ago
      > I'm saying that calls for nuance are often excuses to do bad statistics and bad science. Absolutely. But at least as often, calls for nuance are calls for better statistics and better science. How do you know which is which? Judgement. You can only say "fuck nuance" once you've demonstrated good judgment.
- pron 11y ago
  It is a fantastic article, but I don't think excessive, obsessive nuance is what plagues most people here. In any event, don't take it too far. In dynamical system, your theory can be simple and predict observation 100% yet be completely wrong -- not just a little wrong -- and fail to model any of the actual dynamics (e.g. x = 0 and x = t are both completely predictive, yet entirely false, models of x" = 0). It is those simplified theories that are no more than empirical observation, and suffer from another kind of overfitting. > In statistical terms, these dismissals are little more than a call for overfitting. can arbitrarily add more dimensions to a theory, I can make it fit any data set. In order to fit any social data set, you may need dozens if not hundreds of variables. If your data set contains many millions of data points, telling you that there may be four variables instead than two is certainly not "a call for overfitting"; it is no more excessive nuance than the entirety of special+general relativity. Or, don't use this as an excuse to stop questioning your theory once it predicts behavior in one setting. You may have completely missed the mark by misjudging the order of the equation. Let alone, when your theory doesn't even fit the data we have -- just the data you have -- it's nothing more than amateurism; don't confuse amateur smart-assedness with any sort of profundity; it is often wrong in the most boring way: uninformed wrong. Intuition that comes from years of study of human behavior may help a lot, but some believe they can come up with theories without spending the time to earn their intuition first. Whether overly nuanced or over-simplified, I think the chances that any of their theories are correct -- especially if they put little effort in forming them -- are low. And one thing is certain: the less you know, the far more likely it is that you'll err on the side of over-simplification than over-nuance. This paper is a warning to experts, who know enough to err with over-care. Amateurs (of the non-humble kind) are indeed completely protected from this particular kind of error by their ignorance, especially if they don't have the basic tools to even assess the system's level of complexity. This paper is a call against obsessive, exaggerated nuance. Not a call for oversimplification and sloppiness. And while one can err on both sides, historically, errors of over-simplification (when it comes to social theories) have been much more detrimental to the human race than errors of nuance. The latter kind may cause boring science; the former has led to atrocities. I think that many of the calls for more nuance are just warnings against careless errors of the first kind, simply because they are far more dangerous than errors of the second kind.
  - 11y ago
shas3 11y ago
Can we un-capitalize "Nuance"? For a second I thought it refers to the speech technologies company.
- nxzero 11y ago
  Agree, borders on click bait.
  - percentcer 11y ago
    It's a title.
    - vortico 11y ago
      It's a clickbaity title for the paper.
- riebschlager 11y ago
  This is possibly the most hacker-news-ish comment I've ever read.
  - PeCaN 11y ago
    Still can't top https://news.ycombinator.com/item?id=35079 https://news.ycombinator.com/item?id=35079
- ctoth 11y ago
  Same. I actually clicked hoping to read a rant against my least favorite speech company and their bad habit of buying up competition for the patents and then sitting on the technology. They own the rights to the speech synthesizer I use every day, Eloquence, which is no longer developed and will probably never improve, but is easily the most intelligible synthesizer at high speed.
  - rdtsc 11y ago
    Yap same here ;-) They squeezed us with their pricing model for years. I was nodding as I was reading the title, then found something else. Oh well.
twoodfin 11y ago
I realized as I was reading this that I know very little about sociology as a discipline. In particular, I'm unaware of any of its "Great Theories", a la General Relativity for physics, or Darwinian Evolution for biology. Any candidates? I'm not trying to be snide, I really would like to learn more about this. The Wikipedia page for sociology describes "theories" that sound more like schools of thought.
- hakanensari 11y ago
  Historical materialism
- p4wnc6 11y ago
  I would posit the social signalling theories of Robin Hanson as perhaps a theory of this magnitude. The problem is, though most of Hanson's theories have plausible explanations and "feel" right, there is very little experimental evidence and very few studies are directed in that area. In fact, you might even say it is a symptom of that type of signalling itself: as homo-hypocritus, we don't want signalling theories to be proven true so we make up reasons for deriding signalling explanations as not rigorous enough to even merit being properly tested or studied. Of course, this too is no better, because then do we just actually have no evidence to believe in signalling explanations, or are we using signalling excuses to suppress the meaning of that evidence? It's somewhat self-defeating. The next most important theory I would posit is Prospect Theory by Kahneman and Tversky. But there again, many aspects of it have not been successfully replicated, and too often limited experiments are generalized too much. One set of social theories that I would love to see given more quantitative foundations is the research from the book Moral Mazes. Robert Jackall carried out some lengthy longitudinal studies in which he collected anecdotal narrative and interview data from employees at all seniority levels through a couple different firms. He was able to see people rise up the corporate ladder, people get fired, executives fired and replaced, large layoffs, etc., and conduct informal interviews with employees all up and down the ladder. Based on his interviews, he put together a series of theories about how morality and ethical identity form within bureaucracy, and particularly how managers develop an understanding of ethical obligations to subordinates, and how certain factions of an organization (notably HR) come to function when there are competing concerns between ethical expectations and bureaucratic mandates. In every job I've had, it's been a frustrating experience of basically saying, "Yep, I know this chapter from Moral Mazes ..." because companies operate almost as if they've read the book as a field guide. Yet at the same time, it's still just a qualitative study that is scaffolded by academic sociology. If it were actually possible to collect that kind of data from corporations, I'd love to see some theories from the book explored more quantitatively.
c0da 11y ago
By calling for a theory to be more comprehensive, or for an explanation to include additional dimensions, or a concept to become more flexible and multifaceted, we paradoxically end up with less clarity. A further odd consequence is that the apparent scope of theories increases even as the range of their explanatory application narrows. This seems analogous to poor imperative programming to me: as your code becomes more complicated, the range of it's application narrows.
- ezrosent 11y ago
  I think this is better understood as a consequence of Bonini's paradox (https://en.wikipedia.org/wiki/Bonini%27s_paradox https://en.wikipedia.org/wiki/Bonini%27s_paradox).
  - solidsnack9000 11y ago
    I thought of the map-territory jawn, as well.
- deleted 11y ago
  [deleted]
im3w1l 11y ago
I assume simple theories are very good for advancing the science itself. But I don't think they are as actionable as complex ones, because reality is complex, and you need to account for that when creating policy.
- marcosdumay 11y ago
  Nuance is lethal for scientists, but lack of it is lethal to practitioners. And practical experience consist mostly of knowing what kinds of nuance are important for what kinds of problems.
deleted 11y ago
[deleted]
unabst 11y ago
I call this the "abstraction paradox". There is no way that an abstraction can fully equate what it abstracts. "Apple" could never equal a real apple. But abstraction allows us to think about real apples. In science, all theories and models are abstractions. Hence, they are always approximations, and can be invalidated or nullified with edge cases that can indeed be backed by evidence. But the opposite is also true. If a theory is based on evidence, then it will always be an approximation of the truth to some degree. Hence, there will always be hard evidence that validates the theory also -- ideally the evidence the theory is based on. The ultimate test is application. If a theory or model has purpose, as in, has a use, then the value of that theory can be measured by its consequences. Otherwise, frankly, academics arguing over what has no consequence will have no consequence outside the circles of those who argue over it. I sense "fuck nuance" as a message to them. We still use Newtonian mechanics where objects are big and slow enough. But we also use general relativity, quantum mechanics, quantum field theory, etc, where more accuracy is required. So we already know theories have limits and degrade with constraints, and we switch theories. This is true of EVERY THEORY. And that is the abstraction paradox. If you're arguing over someone just to prove them wrong, congratulations, you've just proved you're completely redundant. So fuck you. (in the spirit of the author)
- wyager 11y ago
  Abstractions need not be approximations. Schroedinger's equation is an abstraction, but as far as we know it's exact.
  - ludamad 11y ago
    Abstractions of social sciences do need to be approximations :-)
  - unabst 11y ago
    The moment the theory is the reality, it's exact. Like cartoons. Like definitions. Like math. But physics and science is about reality. It's about the physical apple, not the word apple. A real apple is physical. There is no amount of theory or symbolism that will produce a real apple in the minds and systems that simulate it. That's the paradox.
ktRolster 11y ago
The amount of nuance in this article is overwhelming. (The abstract/text ratio is ridiculous)
emmelaich 11y ago
I would distill my understanding of this paper as "Strong Opinions, Weakly Held" http://www.saffo.com/02008/07/26/strong-opinions-weakly-held/ http://www.saffo.com/02008/07/26/strong-opinions-weakly-held...
- B1FF_PSUVM 11y ago
  ... with Weird capitalization.
linhchi 11y ago
I think when debate comes down to differences in ideology and idiosyncracy, it's at the end of debating because these differences are impossible to compromise (they are all legitimate). And the world is at a point that everybody can believe in everything and all these beliefs are all grounded and justificable (using the available tools and knowledge we've accumulated). To deal with the somewhat disturbing thought that the world doesn't need to be saved, I resort to the mantra that: eventually, the reason we read, the reason we write, the choice we make, everyday, is just because we feel like.
omarforgotpwd 11y ago
I thought this was going to be a rant about voice recognition

129 points by networked 11y ago

This article is a truly fantastic critique of how modern social sciences (besides economics) are carried out and publicized in modern society. Any critique one dislikes - typically "your theory predicts X which is false" - can be dismissed by "nuance", "things are more complicated", "that's different in unspecified way", etc. In statistical terms, these dismissals are little more than a call for overfitting - if I can arbitrarily add more dimensions to a theory, I can make it fit any data set. This is a fantastic article that can be applied in so many places. In applied social sciences this critique is far easier to work around. "Yeah, my theory is pretty one-dimensional and does lack nuance. My code is in $REPO - feel free to run an experiment and see if your multidimensional theory generates any alpha over mine."

p4wnc6 11y ago
Why do you exclude economics? I see economics as practically leading the way with regard to dismissals like you suggest. One example I have first-hand experience with is the low-volatility anomaly. CAPM-like theories predict a roughly linear, positive-sloping relationship between risk and return. Yet decades worth of various empirical measurements of the riskiness of assets and portfolios unequivocally demonstrates that it is a negative-sloping relationship (e.g. for your willingness to bear an additional unit of risk, you actually receive less return). But everyone says basically the same thing. "Things are more complicated in real markets." It would be reasonable to say that CAPM is a mathematical result, and the evidence suggests the mathematical model required for CAPM simply fails to correspond to reality. But many finance folks, especially in academia, won't admit it. For them, CAPM must also be a descriptive theory. Two sources that illustrate what I'm talking about: [0] Baker, Bradley, and Wurgler, "Benchmarks as Limits to Arbitrage: Understanding the Low-Volatility Anomaly" FAJ Volume 61, 2011. < http://www.cfapubs.org/doi/pdf/10.2469/faj.v67.n1.4 http://www.cfapubs.org/doi/pdf/10.2469/faj.v67.n1.4 > [1] Baker, Malcolm P. and Bradley, Brendan and Taliaferro, Ryan, The Low Risk Anomaly: A Decomposition into Micro and Macro Effects (September 13, 2013). Financial Analysts Journal. < http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2210003 http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2210003 > *disclaimer: The authors of these two papers were my bosses in a previous role, and I wrote the code and generated the results for the second paper.
- ryanmonroe 11y ago
  >t would be reasonable to say that CAPM is a mathematical result, and the evidence suggests the mathematical model required for CAPM simply fails to correspond to reality. But many finance folks, especially in academia, won't admit it. In other words, "Things are more complicated in real markets". I don't see any problem with wanting descriptive theories. Economists are interested in building models whose parameters can be tweaked in ways that correspond in a clear meaningful way to a possible actions (e.g. change government expenditure on X by Y). If you try to just maximize predictive power you're missing the point. Economics can be used to predict the future, but I would say the main goal of the field is to produce theory that will provide valid methods of altering the future.
  - p4wnc6 11y ago
    This doesn't make a lot of sense in this example with CAPM. The economic theory provides something that is abjectly wrong. It is literally claiming there is a positive relationship between two quantities when the evidence suggests the relationship is negative. The theory is grossly qualitatively wrong, not merely just on the edge of being quantitatively wrong in a few cases or something.
  - tormeh 11y ago
    So you're saying that we need wrong theories that can guide policymakers in the right direction? That sounds like a bad idea. It's basically "I want you to do this thing. Let me invent a theory that makes me sound more persuasive. Don't worry about the details!" Yes, we need theories that are easy enough to reason about that they provide useful insight, but I don't think this is even close to being too complicated.
- yummyfajitas 11y ago
  What you are describing in economics - while perhaps a failure mode - is the exact opposite of what this article discusses. The critique of this article is that valid abstractions and theories are rejected due to a lack of "nuance", rather than a failure to explain and predict reality: Second is the ever more extensive expansion of some theoretical system in a way that effectively closes it o from rebuttal or disconrmation by anything in the world...It is an evasion of the demand that a theory be refutable. Your critique of some economic theories is a totally different one - that they make specific predictions which don't come true.
jerf 11y ago
At first "overfitting" was the concept that leaped to mind as I read it, but overfitting is sort of something different. You have a complicated data set, and when you overfit, you keep adding dimensions of freedom, and then setting those dimensions of freedom to produce the original values that were passed in, such that it fails to correctly predict any other instance drawn from some conceptual "same distribution". In this case, the theories do the "adding many dimensions of freedom" part, but they don't really fix the values; they probably haven't got data to fix in the first place, and it's not mathematical enough to be that precise about the fitting. I think it's more like these theories are often not so much hypotheses themselves, but the descriptions of hypotheses spaces, and the addition of "nuance" means that in a lot of cases, what sounds like a "theory" is really just the description of a space that happens to contain all or most of the possibility space in question. As much fun as it may be to do that, and as much as it may even be a necessary first step to a real theory, in practice that is not generally the difficult part of the process. (There are exceptions to that which may leap to mind but I submit they would be the exceptions, rather than the rule.) When one is working with fuzzy words (even if by necessity!) and not math, the fuzzy words make it very easy to make this mistake, I think. English does not have a strong distinction between a hypothesis space and a specific hypothesis in that space, as evidenced by the very fact I have to dip into jargon to even make that point! From that perspective, the solution that leaps to my mind is implement the observation that in many ways, the important thing about a theory is not what it describes, but what it excludes. It is too easy and cognitively tempting to try to create a theory that, when you fiddle with the parameters, describes everything you see, but the real questions are, what is excluded by your theory? Can I fiddle with the knobs in your theory to describe something that you say ought to exist, but does not seem to? Is your theory even well-enough specified that these are meaningful questions to ask about it? I don't know the answers to these questions for sociology. I have to admit I've tried to engage with it a couple of times, because what fan of Isaac Asimov doesn't want to learn about some real-life psychohistory, but I've always found it difficult to get traction with with my engineer mind. I can engage from a philosophical point of view, but that is not generally what I was looking for; I've got, well, philosophy itself for that. (Repeat previous sentence for history, psychology, and economics.) To p4wnc6's question, I'd submit that as dismal as the Dismal Science may sometimes be, it does often meet this bar. I fear economists themselves may often be quite sluggish at recognizing when their own theories are contradicted by the evidence because the theories are saying that what is currently happening (in the general sense, not the literal March 27, 2016 sense) is excluded by the theory, but that is a human problem, not an economic theory problem. Economic theories do exclude things; if supply rises and demand holds steady, the vast majority of possibilities where price goes up are excluded, and if price were to go up, the theories would be useful in investigating how such a thing actually happened by examining what such theories would still permit (e.g., perhaps supply isn't really rising because the new supply is actually a higher quantity of lower quality, and too much of the demand is for a higher quality than the new supply is supplying which means "real" supply is dropping, or something like that). They certainly aren't perfect and a given theory may even be wrong, but, well, the very fact that a theory can be wrong is evidence that it is falsifiable, and thus not "nuanced" into infinite psudo-explanatory power.
Olscore 11y ago
> In statistical terms, these dismissals are little more than a call for overfitting - if I can arbitrarily add more dimensions to a theory, I can make it fit any data set. Sure. However, this leads me to believe it is statistics that are the problem, and not nuance. You are in my opinion admitting that nuance invalidates most statistics, or at least allows for heavy manipulation of the statistics to fit any narrative. This is precisely why public policy should not be made using statistics. Society should not be likened to a turing machine. People, history and so many topics of discussion are full of nuance, detail and perspective; and disregarding nuance (or perspectives) is insulting to the truth.
- yummyfajitas 11y ago
  No, on the contrary I'm saying that calls for nuance are often excuses to do bad statistics and bad science. Rather than throwing away our theories because they predict X and the world does Y, they instead say "actually the theory only applies under vague unspecified circumstances which you'd understand if you were as nuanced as I". Whether or not the theory fits the data, they cry "intersectionality" or "humans are so complex". In any case, your post begs the question. If we should ignore statistics, how should we make decisions? Emotionally pleasing verbal narratives? See also the related essay, "Pragmatism is Poison". It argues that the word "pragmatism" is used in software a lot to shut down discussion of bad choices, much the same way "nuance" is used to shut down discussion of good theories. https://einarwh.wordpress.com/2016/03/10/pragmatism-is-poison/ https://einarwh.wordpress.com/2016/03/10/pragmatism-is-poiso...
  - platz 11y ago
    No true scottsman will allow their theory to be critiqued. A true scottsman is more nuanced.
  - pron 11y ago
    > I'm saying that calls for nuance are often excuses to do bad statistics and bad science. Absolutely. But at least as often, calls for nuance are calls for better statistics and better science. How do you know which is which? Judgement. You can only say "fuck nuance" once you've demonstrated good judgment.
pron 11y ago
It is a fantastic article, but I don't think excessive, obsessive nuance is what plagues most people here. In any event, don't take it too far. In dynamical system, your theory can be simple and predict observation 100% yet be completely wrong -- not just a little wrong -- and fail to model any of the actual dynamics (e.g. x = 0 and x = t are both completely predictive, yet entirely false, models of x" = 0). It is those simplified theories that are no more than empirical observation, and suffer from another kind of overfitting. > In statistical terms, these dismissals are little more than a call for overfitting. can arbitrarily add more dimensions to a theory, I can make it fit any data set. In order to fit any social data set, you may need dozens if not hundreds of variables. If your data set contains many millions of data points, telling you that there may be four variables instead than two is certainly not "a call for overfitting"; it is no more excessive nuance than the entirety of special+general relativity. Or, don't use this as an excuse to stop questioning your theory once it predicts behavior in one setting. You may have completely missed the mark by misjudging the order of the equation. Let alone, when your theory doesn't even fit the data we have -- just the data you have -- it's nothing more than amateurism; don't confuse amateur smart-assedness with any sort of profundity; it is often wrong in the most boring way: uninformed wrong. Intuition that comes from years of study of human behavior may help a lot, but some believe they can come up with theories without spending the time to earn their intuition first. Whether overly nuanced or over-simplified, I think the chances that any of their theories are correct -- especially if they put little effort in forming them -- are low. And one thing is certain: the less you know, the far more likely it is that you'll err on the side of over-simplification than over-nuance. This paper is a warning to experts, who know enough to err with over-care. Amateurs (of the non-humble kind) are indeed completely protected from this particular kind of error by their ignorance, especially if they don't have the basic tools to even assess the system's level of complexity. This paper is a call against obsessive, exaggerated nuance. Not a call for oversimplification and sloppiness. And while one can err on both sides, historically, errors of over-simplification (when it comes to social theories) have been much more detrimental to the human race than errors of nuance. The latter kind may cause boring science; the former has led to atrocities. I think that many of the calls for more nuance are just warnings against careless errors of the first kind, simply because they are far more dangerous than errors of the second kind.
- 11y ago

shas3 11y ago

Can we un-capitalize "Nuance"? For a second I thought it refers to the speech technologies company.

nxzero 11y ago
Agree, borders on click bait.
- percentcer 11y ago
  It's a title.
  - vortico 11y ago
    It's a clickbaity title for the paper.
riebschlager 11y ago
This is possibly the most hacker-news-ish comment I've ever read.
- PeCaN 11y ago
  Still can't top https://news.ycombinator.com/item?id=35079 https://news.ycombinator.com/item?id=35079
ctoth 11y ago
Same. I actually clicked hoping to read a rant against my least favorite speech company and their bad habit of buying up competition for the patents and then sitting on the technology. They own the rights to the speech synthesizer I use every day, Eloquence, which is no longer developed and will probably never improve, but is easily the most intelligible synthesizer at high speed.
- rdtsc 11y ago
  Yap same here ;-) They squeezed us with their pricing model for years. I was nodding as I was reading the title, then found something else. Oh well.

twoodfin 11y ago

I realized as I was reading this that I know very little about sociology as a discipline. In particular, I'm unaware of any of its "Great Theories", a la General Relativity for physics, or Darwinian Evolution for biology. Any candidates? I'm not trying to be snide, I really would like to learn more about this. The Wikipedia page for sociology describes "theories" that sound more like schools of thought.

hakanensari 11y ago
Historical materialism
p4wnc6 11y ago
I would posit the social signalling theories of Robin Hanson as perhaps a theory of this magnitude. The problem is, though most of Hanson's theories have plausible explanations and "feel" right, there is very little experimental evidence and very few studies are directed in that area. In fact, you might even say it is a symptom of that type of signalling itself: as homo-hypocritus, we don't want signalling theories to be proven true so we make up reasons for deriding signalling explanations as not rigorous enough to even merit being properly tested or studied. Of course, this too is no better, because then do we just actually have no evidence to believe in signalling explanations, or are we using signalling excuses to suppress the meaning of that evidence? It's somewhat self-defeating. The next most important theory I would posit is Prospect Theory by Kahneman and Tversky. But there again, many aspects of it have not been successfully replicated, and too often limited experiments are generalized too much. One set of social theories that I would love to see given more quantitative foundations is the research from the book Moral Mazes. Robert Jackall carried out some lengthy longitudinal studies in which he collected anecdotal narrative and interview data from employees at all seniority levels through a couple different firms. He was able to see people rise up the corporate ladder, people get fired, executives fired and replaced, large layoffs, etc., and conduct informal interviews with employees all up and down the ladder. Based on his interviews, he put together a series of theories about how morality and ethical identity form within bureaucracy, and particularly how managers develop an understanding of ethical obligations to subordinates, and how certain factions of an organization (notably HR) come to function when there are competing concerns between ethical expectations and bureaucratic mandates. In every job I've had, it's been a frustrating experience of basically saying, "Yep, I know this chapter from Moral Mazes ..." because companies operate almost as if they've read the book as a field guide. Yet at the same time, it's still just a qualitative study that is scaffolded by academic sociology. If it were actually possible to collect that kind of data from corporations, I'd love to see some theories from the book explored more quantitatively.

c0da 11y ago

By calling for a theory to be more comprehensive, or for an explanation to include additional dimensions, or a concept to become more flexible and multifaceted, we paradoxically end up with less clarity. A further odd consequence is that the apparent scope of theories increases even as the range of their explanatory application narrows. This seems analogous to poor imperative programming to me: as your code becomes more complicated, the range of it's application narrows.

ezrosent 11y ago
I think this is better understood as a consequence of Bonini's paradox (https://en.wikipedia.org/wiki/Bonini%27s_paradox https://en.wikipedia.org/wiki/Bonini%27s_paradox).
- solidsnack9000 11y ago
  I thought of the map-territory jawn, as well.
deleted 11y ago
[deleted]

im3w1l 11y ago

I assume simple theories are very good for advancing the science itself. But I don't think they are as actionable as complex ones, because reality is complex, and you need to account for that when creating policy.

marcosdumay 11y ago
Nuance is lethal for scientists, but lack of it is lethal to practitioners. And practical experience consist mostly of knowing what kinds of nuance are important for what kinds of problems.

deleted 11y ago

[deleted]

unabst 11y ago

I call this the "abstraction paradox". There is no way that an abstraction can fully equate what it abstracts. "Apple" could never equal a real apple. But abstraction allows us to think about real apples. In science, all theories and models are abstractions. Hence, they are always approximations, and can be invalidated or nullified with edge cases that can indeed be backed by evidence. But the opposite is also true. If a theory is based on evidence, then it will always be an approximation of the truth to some degree. Hence, there will always be hard evidence that validates the theory also -- ideally the evidence the theory is based on. The ultimate test is application. If a theory or model has purpose, as in, has a use, then the value of that theory can be measured by its consequences. Otherwise, frankly, academics arguing over what has no consequence will have no consequence outside the circles of those who argue over it. I sense "fuck nuance" as a message to them. We still use Newtonian mechanics where objects are big and slow enough. But we also use general relativity, quantum mechanics, quantum field theory, etc, where more accuracy is required. So we already know theories have limits and degrade with constraints, and we switch theories. This is true of EVERY THEORY. And that is the abstraction paradox. If you're arguing over someone just to prove them wrong, congratulations, you've just proved you're completely redundant. So fuck you. (in the spirit of the author)

wyager 11y ago
Abstractions need not be approximations. Schroedinger's equation is an abstraction, but as far as we know it's exact.
- ludamad 11y ago
  Abstractions of social sciences do need to be approximations :-)
- unabst 11y ago
  The moment the theory is the reality, it's exact. Like cartoons. Like definitions. Like math. But physics and science is about reality. It's about the physical apple, not the word apple. A real apple is physical. There is no amount of theory or symbolism that will produce a real apple in the minds and systems that simulate it. That's the paradox.

ktRolster 11y ago

The amount of nuance in this article is overwhelming. (The abstract/text ratio is ridiculous)

emmelaich 11y ago

I would distill my understanding of this paper as "Strong Opinions, Weakly Held" http://www.saffo.com/02008/07/26/strong-opinions-weakly-held/ http://www.saffo.com/02008/07/26/strong-opinions-weakly-held...

B1FF_PSUVM 11y ago
... with Weird capitalization.

linhchi 11y ago

I think when debate comes down to differences in ideology and idiosyncracy, it's at the end of debating because these differences are impossible to compromise (they are all legitimate). And the world is at a point that everybody can believe in everything and all these beliefs are all grounded and justificable (using the available tools and knowledge we've accumulated). To deal with the somewhat disturbing thought that the world doesn't need to be saved, I resort to the mantra that: eventually, the reason we read, the reason we write, the choice we make, everyday, is just because we feel like.

omarforgotpwd 11y ago

I thought this was going to be a rant about voice recognition