Imagine for the sake of argument that we’re happy to just absorb information, with no topic prioritization; we just want our map to get closer to the territory.
There’s still the question of exactly what our loss function is: how much do we like being a specific distance from the truth? Our loss function could be more like Bayes loss, which punishes overconfidence highly (assigning probability close to zero for an event which actually is true gets us an arbitrarily bad score); or, it could be more like Brier score (which caps the amount you can lose for any particular wrong idea).
I think deep readers are more like Bayes-score maximizers: skimming the abstract and getting wrong information feels like a big risk. No quantity of improved beliefs can necessarily compensate for one mistake, because mistakes can be arbitrarily bad. A bayes-score maximizer skimming something is conscious of exactly how many grains of salt go with each thing learned, because getting that wrong can be very costly—so they would feel a need to remember “I was speed-reading this, so I should doubt all my conclusions about it a little bit more”.
Broad readers are more like Brier-score maximizers: skimming the abstract and getting a wrong conclusion is only a bounded risk, so it’s easily balanced by the benefit of lots of knowledge. They don’t feel an overwhelming need to count grains of salt, because 95% wrong is not that different from 100% wrong; so they happily accept a bunch of noisy information in, without worrying too much about careful tabulation of the noise.
I don’t have too many intuitions about when Bayes score or Brier score will be closer to our true utility-of-knowledge functions. But I suspect deep reading is more useful for the kind of research where you’re trying to generate really new things, like totally new hypotheses or new areas of mathematics. Whereas broad reading may be more useful for “applied” type research, where you’re taking existing knowledge and using it in new areas.
If we assume that the accuracy improvements to researching a given question are logarithmic, then it would make sense to read broadly on unimportant questions and read deeply on crucial questions.
Signaling also seems relevant here. It might be advantageous to be widely informed, or to be seen as the kind of person who only speaks on their domain of expertise.
There could also be times when you just need to be conversant in the subject enough to know who to delegate the deeper research to.
So in general, I would expect the value of broad vs. deep research to be highly contextual.
But I wonder if the same habits that may lead people to anchor on an inappropriate reading speed also lead them to anchor on a sometimes inappropriate reading depth. It’s plausible to me that people who tend to read broadly by habit could reap significant gains by practicing deep reading on even an arbitrary subject, and vice versa.
It would be interesting if there was an equivalent to the DSM, but for reading habits. Could we imagine a test or a set of diagnostic criterion that could classify people both according to their level of reading proficiency, and also according to their habitual level of depth/breadth? So for example, a low-skill but deep reader might be a religious fundamentalist who has their text of choice practically memorized, yet who has very little familiarity with the nuances of interpretation. By contrast, we can imagine low-skill broad readers, who read all kinds of novels and newspapers but remember very little of it. And high-skill broad or deep readers, of course.
I think this is related to one of my perennial topics of interest, which is the path toward a specialization. A science student in undergrad or earlier reads broadly about science. At some point, if they continue on a scientific path, they eventually focus on a much narrower area, and their whole reading program focuses on acquiring knowledge that they perceive as directly useful to a specific research project.
As I’ve graduated into this phase, I’ve found that the deep, related, specialized, purposeful reading is vastly more satisfying than the broad, shallow, disconnected reading that came before. It makes me suspect that one reason people get turned off of science early is that they never get the experience of “cocooning” in a specialty in which all the articles you read are riffing off each other, interrelated, and building toward a goal. It’s the closest thing that I’ve found to programming, which also entails building an interrelated construct to make predictions and do useful work.
I’m also interested in whether and how “broad reading” can be done with an equivalent sense of purpose. There’s an article on Applied Divinity Studies, Beware the Casual Polymath, which I think can be characterized as a criticism of superficially high-skilled, but in fact low-skilled broad readers. It’s pointing out that just because you’re reading all kinds of Smart People Stuff doesn’t mean that you’re actually learning effectively.
I would imagine that a high-skilled broad reader would be somebody who has a role that involves lots of delegation and decision-making. The fictional example that comes to mind is a member of the Bartlett senior staff in the West Wing, who have to understand a huge number of issues of national significance, but only just enough to know who to delegate to or what positions are at least not-insane. For them, making reasonable, if not necessarily perfectly optimized, choices, but making a decision, is much more important than getting the exact right answer. So I would describe them as a depiction of a high-skilled, broad reader.
Imagine for the sake of argument that we’re happy to just absorb information, with no topic prioritization; we just want our map to get closer to the territory.
There’s still the question of exactly what our loss function is: how much do we like being a specific distance from the truth? Our loss function could be more like Bayes loss, which punishes overconfidence highly (assigning probability close to zero for an event which actually is true gets us an arbitrarily bad score); or, it could be more like Brier score (which caps the amount you can lose for any particular wrong idea).
I think deep readers are more like Bayes-score maximizers: skimming the abstract and getting wrong information feels like a big risk. No quantity of improved beliefs can necessarily compensate for one mistake, because mistakes can be arbitrarily bad. A bayes-score maximizer skimming something is conscious of exactly how many grains of salt go with each thing learned, because getting that wrong can be very costly—so they would feel a need to remember “I was speed-reading this, so I should doubt all my conclusions about it a little bit more”.
Broad readers are more like Brier-score maximizers: skimming the abstract and getting a wrong conclusion is only a bounded risk, so it’s easily balanced by the benefit of lots of knowledge. They don’t feel an overwhelming need to count grains of salt, because 95% wrong is not that different from 100% wrong; so they happily accept a bunch of noisy information in, without worrying too much about careful tabulation of the noise.
I don’t have too many intuitions about when Bayes score or Brier score will be closer to our true utility-of-knowledge functions. But I suspect deep reading is more useful for the kind of research where you’re trying to generate really new things, like totally new hypotheses or new areas of mathematics. Whereas broad reading may be more useful for “applied” type research, where you’re taking existing knowledge and using it in new areas.
If we assume that the accuracy improvements to researching a given question are logarithmic, then it would make sense to read broadly on unimportant questions and read deeply on crucial questions.
Signaling also seems relevant here. It might be advantageous to be widely informed, or to be seen as the kind of person who only speaks on their domain of expertise.
There could also be times when you just need to be conversant in the subject enough to know who to delegate the deeper research to.
So in general, I would expect the value of broad vs. deep research to be highly contextual.
But I wonder if the same habits that may lead people to anchor on an inappropriate reading speed also lead them to anchor on a sometimes inappropriate reading depth. It’s plausible to me that people who tend to read broadly by habit could reap significant gains by practicing deep reading on even an arbitrary subject, and vice versa.
It would be interesting if there was an equivalent to the DSM, but for reading habits. Could we imagine a test or a set of diagnostic criterion that could classify people both according to their level of reading proficiency, and also according to their habitual level of depth/breadth? So for example, a low-skill but deep reader might be a religious fundamentalist who has their text of choice practically memorized, yet who has very little familiarity with the nuances of interpretation. By contrast, we can imagine low-skill broad readers, who read all kinds of novels and newspapers but remember very little of it. And high-skill broad or deep readers, of course.
I think this is related to one of my perennial topics of interest, which is the path toward a specialization. A science student in undergrad or earlier reads broadly about science. At some point, if they continue on a scientific path, they eventually focus on a much narrower area, and their whole reading program focuses on acquiring knowledge that they perceive as directly useful to a specific research project.
As I’ve graduated into this phase, I’ve found that the deep, related, specialized, purposeful reading is vastly more satisfying than the broad, shallow, disconnected reading that came before. It makes me suspect that one reason people get turned off of science early is that they never get the experience of “cocooning” in a specialty in which all the articles you read are riffing off each other, interrelated, and building toward a goal. It’s the closest thing that I’ve found to programming, which also entails building an interrelated construct to make predictions and do useful work.
I’m also interested in whether and how “broad reading” can be done with an equivalent sense of purpose. There’s an article on Applied Divinity Studies, Beware the Casual Polymath, which I think can be characterized as a criticism of superficially high-skilled, but in fact low-skilled broad readers. It’s pointing out that just because you’re reading all kinds of Smart People Stuff doesn’t mean that you’re actually learning effectively.
I would imagine that a high-skilled broad reader would be somebody who has a role that involves lots of delegation and decision-making. The fictional example that comes to mind is a member of the Bartlett senior staff in the West Wing, who have to understand a huge number of issues of national significance, but only just enough to know who to delegate to or what positions are at least not-insane. For them, making reasonable, if not necessarily perfectly optimized, choices, but making a decision, is much more important than getting the exact right answer. So I would describe them as a depiction of a high-skilled, broad reader.