If one were to make a distribution of the amount of knowledge different people have about, say, macroeconomics, I would suspect the distribution to be somewhat lognormal; they would have tails to both ends, but be very skewed to the right. Most people have almost no knowledge of macroeconomics, some have a bit, then there is a long tail of fewer and fewer people who make up the experts.
The above graph doesn’t exactly resemble what I’d expect for macroeconomics but acts as a rough heuristic. The large numbers represent halvings of the remaining percentiles (3/4th, 7/8th, 15/16th, etc).[1]
I’m going to posit the following claims:[2]
Claim 1: It’s easy to judge where on the curve people are who are lower than you.
Claim 2: It’s difficult to judge where on the curve people are who are higher than you, absent of sophisticated systems to support this.
Given these, let’s imagine a few situations:
Say you’re the local economist for a state government. You have some actions you really would like the government to take, even though your colleagues wouldn’t typically approve. You’re around a +4 on the macroeconomic scale, and your most knowledgeable colleagues are around a +2. Could you get away with pretending that macroeconomics has a very confident stance that happens to align with what you want to see happen? How would you do so?
You’re a local radio intellectual who’s a +3 on the macroeconomic scale. Almost all of your listeners are below a +1.5. You’ve been essentially lying to them for some time about macroeconomic theory because it helps your political message. A professor who’s a +5 starts writing a few articles that call you out on your lies. How do you respond?
You’re a college student who’s a +3 on the macroeconomic scale. Your professors are all a good deal higher and will be the primary ones evaluating your studies. You want to legitimately learn macroeconomics. How do you treat your professors?
Overconfident talking down, humble or hostile talking up
I think the answers I’d expect from these questions can be summarized in the phrase “Overconfident talking down, humble or hostile talking up.”
When you’re communicating with people who know less than you, and you have little accountability from people who know more, then you generally have the option of claiming to be more knowledgeable than you are, and lying in ways that are useful to you.
When you’re communicating with people who know more than you, you have two options. You can accept their greater state of knowledge, causing you to speak more honestly about the pertinent topics. Or, you could reject their credibility, claiming that they really don’t know more than you. Many people who know less than you both may believe you over them.
There are many examples of this. One particularly good one may be the history of schisms in religious organizations. Religious authorities generally know a lot more about their respective religions than the majority of citizens. Each authority has a choice; they could either accept the knowledge of the higher authorities, or they could reject the higher authorities. If they reject above authority, they would be incentivized to discredit that authority and express overconfidence in their own new beliefs. If they succeed, some followers would believe them, giving them both the assumption of expertise and also the flexibility of not having to be accountable to other knowledgeable groups. If they both defect on their previous authorities and fail, then they may wind up in a very poor position, so after defecting it’s very important to ensure that their existing audience gives them full support.
The Economics of Knowledge Signaling
In slightly more economic terms, one could say that there are strong signals going up the chain of knowledge (from the nonexperts to the experts), and weak signals going down it. The market for knowledgeable expertise is one with relatively low transparency and typical incentives to lie and deceive, similar to the markets for lemons.
I’m not claiming with this that all of the overconfidence and discrediting is knowingly dishonest.[3] I’m also not claiming that this is original; much is quite obvious and parts are definitely studied. That said, I do get the impression that the science of signaling is still pretty overlooked (much of this is from Robin Hanson), and this is one area I think may not be well understood as a holistic economic system.
Finally, I’m reminded of the old joke:
“Once I saw this guy on a bridge about to jump. I said, “Don’t do it!” He said, “Nobody loves me.” I said, “God loves you. Do you believe in God?”
He said, “Yes.” I said, “Are you a Christian or a Jew?” He said, “A Christian.” I said, “Me, too! What franchise?” He said, “Protestant.” I said, “Me, too! Northern Baptist or Southern Baptist?” He said, “Northern Baptist.” I said, “Me, too! Northern Conservative Baptist or Northern Liberal Baptist?” He said, “Northern Conservative Baptist.” I said, “Me, too! Northern Conservative Baptist Great Lakes Region, or Northern Conservative Baptist Eastern Region?” He said, “Northern Conservative Baptist Great Lakes Region.”
I said, “Me, too!” “Northern Conservative Baptist Great Lakes Regions Council of 1879 or Northern Conservative Baptist Great Lakes Region Council of 1912?” He said “Northern Conservative Baptist Great Lakes Council of 1912.” I said, “Die, heretic!” And I pushed him over.
One may wonder what incentives seem to lead to such heartfelt but predictably frequent divisions.
This is similar to the log-odds scale. Standard deviation could also be used, but I find it a bit unintuitive, especially for non-normal distributions.
These mostly come from lots of anecdotal evidence, some general reasoning, and my memories of a few research studies. I’ve spent around 40 minutes attempted to locate useful studies for this post, but haven’t, though I’m quite sure I remember reading about related ones several years ago. If you have any recommended links, please post in the comments.
Overconfident talking down, humble or hostile talking up
A Distribution of Knowlege
If one were to make a distribution of the amount of knowledge different people have about, say, macroeconomics, I would suspect the distribution to be somewhat lognormal; they would have tails to both ends, but be very skewed to the right. Most people have almost no knowledge of macroeconomics, some have a bit, then there is a long tail of fewer and fewer people who make up the experts.
The above graph doesn’t exactly resemble what I’d expect for macroeconomics but acts as a rough heuristic. The large numbers represent halvings of the remaining percentiles (3/4th, 7/8th, 15/16th, etc).[1]
I’m going to posit the following claims:[2]
Claim 1: It’s easy to judge where on the curve people are who are lower than you.
Claim 2: It’s difficult to judge where on the curve people are who are higher than you, absent of sophisticated systems to support this.
Given these, let’s imagine a few situations:
Say you’re the local economist for a state government. You have some actions you really would like the government to take, even though your colleagues wouldn’t typically approve. You’re around a +4 on the macroeconomic scale, and your most knowledgeable colleagues are around a +2. Could you get away with pretending that macroeconomics has a very confident stance that happens to align with what you want to see happen? How would you do so?
You’re a local radio intellectual who’s a +3 on the macroeconomic scale. Almost all of your listeners are below a +1.5. You’ve been essentially lying to them for some time about macroeconomic theory because it helps your political message. A professor who’s a +5 starts writing a few articles that call you out on your lies. How do you respond?
You’re a college student who’s a +3 on the macroeconomic scale. Your professors are all a good deal higher and will be the primary ones evaluating your studies. You want to legitimately learn macroeconomics. How do you treat your professors?
Overconfident talking down, humble or hostile talking up
I think the answers I’d expect from these questions can be summarized in the phrase “Overconfident talking down, humble or hostile talking up.”
When you’re communicating with people who know less than you, and you have little accountability from people who know more, then you generally have the option of claiming to be more knowledgeable than you are, and lying in ways that are useful to you.
When you’re communicating with people who know more than you, you have two options. You can accept their greater state of knowledge, causing you to speak more honestly about the pertinent topics. Or, you could reject their credibility, claiming that they really don’t know more than you. Many people who know less than you both may believe you over them.
There are many examples of this. One particularly good one may be the history of schisms in religious organizations. Religious authorities generally know a lot more about their respective religions than the majority of citizens. Each authority has a choice; they could either accept the knowledge of the higher authorities, or they could reject the higher authorities. If they reject above authority, they would be incentivized to discredit that authority and express overconfidence in their own new beliefs. If they succeed, some followers would believe them, giving them both the assumption of expertise and also the flexibility of not having to be accountable to other knowledgeable groups. If they both defect on their previous authorities and fail, then they may wind up in a very poor position, so after defecting it’s very important to ensure that their existing audience gives them full support.
The Economics of Knowledge Signaling
In slightly more economic terms, one could say that there are strong signals going up the chain of knowledge (from the nonexperts to the experts), and weak signals going down it. The market for knowledgeable expertise is one with relatively low transparency and typical incentives to lie and deceive, similar to the markets for lemons.
I’m not claiming with this that all of the overconfidence and discrediting is knowingly dishonest.[3] I’m also not claiming that this is original; much is quite obvious and parts are definitely studied. That said, I do get the impression that the science of signaling is still pretty overlooked (much of this is from Robin Hanson), and this is one area I think may not be well understood as a holistic economic system.
Finally, I’m reminded of the old joke:
One may wonder what incentives seem to lead to such heartfelt but predictably frequent divisions.
This is similar to the log-odds scale. Standard deviation could also be used, but I find it a bit unintuitive, especially for non-normal distributions.
These mostly come from lots of anecdotal evidence, some general reasoning, and my memories of a few research studies. I’ve spent around 40 minutes attempted to locate useful studies for this post, but haven’t, though I’m quite sure I remember reading about related ones several years ago. If you have any recommended links, please post in the comments.
The first few chapters of The Elephant in the Brain go into this.