Let’s say you’re asking a thousand people to guess the date of the Battle of Bosworth Field. If I asked this right now in Less Wrong, I imagine it would receive some wildly different answers.
If you’re me, and you remember it because its anniversary is on your birthday, (or if you were paying attention in a specific history class) you’ll know the exact year (1485). These people are probably not very numerous, but their answers will all coincide and converge. This subgroup would also have a variance of zero.
All the people who were paying only a little bit of attention in that history class, or watched the first series of Blackadder, will not know the exact date, but they’ll probably guess to within a few decades. This subgroup has a wider variance, but it’s still pretty tight, and they’re answering a convergent question. There’s a correct answer, and the answer these people give is informed by it, even if it’s not correct. In the absence of systematic bias, we would expect roughly the same number of people to answer 1480 as 1490, and so the mean of this group should converge.
We now look at a wider variance subgroup, which includes all the people who only have a sketchy idea of when this battle was and what it was about. Some people will recall it’s got something to do with the Tudor dynasty, and Henry VIII was early 16th century. Some will recall that there was a King Richard involved, and dig up a late 14th century connection. They are all contributing some information to proceedings, (14th-16th Century), but in the absence of systematic bias, we’d expect people to be as wrong on one side as they are on the other. Even greater variance subgroups, who aren’t sure whether this battle was fought by Romans or Crusaders or Confederates, are still contributing some small quantity of information by giving answers in the range of human history. No-one’s going to say 3991 AD, or 6,000,000 BC.
As the variance gets wider, the population of any given subgroup gets larger, but the coherence of their answers gets smaller. If you take a hundred people who have absolutely no knowledge of human history and ask them when the Battle of Bosworth Field occurred, you’re basically asking them to pick a number. Their answers aren’t going to converge on anything, so they won’t systematically interfere with the overall distribution, while the answers that are more informed will converge on the correct answer.
But systematic bias does occur. American education on non-American history is notoriously sketchy. If our participants included a large number of Americans, they’re more likely to guess a date in American history through the availability heuristic. All of a sudden, the uninformed answers will start converging at some point in the late 19th Century, which will skew the overall distribution and pull the mean forward in time. The least wise parts of the crowd suddenly found a way to be a whole lot louder.
That’s what I meant by your noise converging on the same answer. In giving people an incorrect choice, you’re giving all the people who have no knowledge an opportunity to pick the same incorrect answer. If they didn’t have that answer to converge on, the mean of their answer wouldn’t be able to exert as much influence on the overall distribution.
Does that make sense?
(This also does point to an obvious source of systematic bias when dealing with dates: we have better records [and hence more available knowledge] of events closer to the present. History is lumpy, and forward-weighted, so any uninformed guess on the date of an event in the past is going to be distorted around points of greater historical interest, many of which occurred over the last century).
This seems like a round-about way to describe a bell curve...
But suppose in your example that we’re only asking those silly Americans, who, like myself, have only even heard of the Battle of Bosworth as a name and really know nothing about it except maybe some English people were involved or something. And so let’s assume that people are guessing as a bell curve around 1600 with a large variance of, say, 200 years or so. If the two options are 1600 and 1200, let’s say, then 15.8% of the people will be guessing 1200 (ie. think it’s earlier than 1400) and the rest are guessing 1600. This averages out to 1536 in the limit of large numbers.
So I guess I don’t understand your point still—it’s not converging to 1600 or anything like that. It is high, but their was a systematic bias towards being high so what else would you expect? In this example (which was chosen arbitrarily) the two options gave a more correct response than the free guess. Of course, we can come up with options that would make the free response better—choosing between, say, 2600 and 1200 gives an average of 1293 .
It doesn’t have to be a Gaussian distribution. We would expect it to look like one under reasonably assumed conditions, but systematic bias would skew it. A particularly large single source (say there was a Battle of Dosworth Field that happened 400 years later) could easily result in a bimodal distribution.
In order for Wisdom of Crowds to work (as it’s expected to work), people aren’t guessing along a Gaussian distribution. They’re applying knowledge they have, and some of that knowledge is useful information, while some of that knowledge is noise. All the useful information pulls the mean towards the true value, while all the noise pulls it away. The difference is that the useful information converges on a single value, (because it’s a convergent problem with a single correct answer), while all the noise pulls arbitrarily in all directions.
Provided there isn’t some reason for the noise itself to converge on a single value (and I think this is where my previous comments have not necessarily been clear, I’m talking about the noise converging, not the overall mean), the noise should cancel itself out.
It should be obvious that if you give people a right answer and a wrong answer, the noise will be weighted in the direction of the wrong answer (because there’s no corresponding error on the other side of the true value). Even if you have two wrong answers on either side of a true value, and ask people to pick the one closest to the true value, you will still have a skew problem, because unless the two values are equidistant to the true value (which defeats the point of the question), your noise is not going to be equally distributed around the true value.
Let’s say you’re asking a thousand people to guess the date of the Battle of Bosworth Field. If I asked this right now in Less Wrong, I imagine it would receive some wildly different answers.
If you’re me, and you remember it because its anniversary is on your birthday, (or if you were paying attention in a specific history class) you’ll know the exact year (1485). These people are probably not very numerous, but their answers will all coincide and converge. This subgroup would also have a variance of zero.
All the people who were paying only a little bit of attention in that history class, or watched the first series of Blackadder, will not know the exact date, but they’ll probably guess to within a few decades. This subgroup has a wider variance, but it’s still pretty tight, and they’re answering a convergent question. There’s a correct answer, and the answer these people give is informed by it, even if it’s not correct. In the absence of systematic bias, we would expect roughly the same number of people to answer 1480 as 1490, and so the mean of this group should converge.
We now look at a wider variance subgroup, which includes all the people who only have a sketchy idea of when this battle was and what it was about. Some people will recall it’s got something to do with the Tudor dynasty, and Henry VIII was early 16th century. Some will recall that there was a King Richard involved, and dig up a late 14th century connection. They are all contributing some information to proceedings, (14th-16th Century), but in the absence of systematic bias, we’d expect people to be as wrong on one side as they are on the other. Even greater variance subgroups, who aren’t sure whether this battle was fought by Romans or Crusaders or Confederates, are still contributing some small quantity of information by giving answers in the range of human history. No-one’s going to say 3991 AD, or 6,000,000 BC.
As the variance gets wider, the population of any given subgroup gets larger, but the coherence of their answers gets smaller. If you take a hundred people who have absolutely no knowledge of human history and ask them when the Battle of Bosworth Field occurred, you’re basically asking them to pick a number. Their answers aren’t going to converge on anything, so they won’t systematically interfere with the overall distribution, while the answers that are more informed will converge on the correct answer.
But systematic bias does occur. American education on non-American history is notoriously sketchy. If our participants included a large number of Americans, they’re more likely to guess a date in American history through the availability heuristic. All of a sudden, the uninformed answers will start converging at some point in the late 19th Century, which will skew the overall distribution and pull the mean forward in time. The least wise parts of the crowd suddenly found a way to be a whole lot louder.
That’s what I meant by your noise converging on the same answer. In giving people an incorrect choice, you’re giving all the people who have no knowledge an opportunity to pick the same incorrect answer. If they didn’t have that answer to converge on, the mean of their answer wouldn’t be able to exert as much influence on the overall distribution.
Does that make sense?
(This also does point to an obvious source of systematic bias when dealing with dates: we have better records [and hence more available knowledge] of events closer to the present. History is lumpy, and forward-weighted, so any uninformed guess on the date of an event in the past is going to be distorted around points of greater historical interest, many of which occurred over the last century).
This seems like a round-about way to describe a bell curve...
But suppose in your example that we’re only asking those silly Americans, who, like myself, have only even heard of the Battle of Bosworth as a name and really know nothing about it except maybe some English people were involved or something. And so let’s assume that people are guessing as a bell curve around 1600 with a large variance of, say, 200 years or so. If the two options are 1600 and 1200, let’s say, then 15.8% of the people will be guessing 1200 (ie. think it’s earlier than 1400) and the rest are guessing 1600. This averages out to 1536 in the limit of large numbers.
So I guess I don’t understand your point still—it’s not converging to 1600 or anything like that. It is high, but their was a systematic bias towards being high so what else would you expect? In this example (which was chosen arbitrarily) the two options gave a more correct response than the free guess. Of course, we can come up with options that would make the free response better—choosing between, say, 2600 and 1200 gives an average of 1293 .
It doesn’t have to be a Gaussian distribution. We would expect it to look like one under reasonably assumed conditions, but systematic bias would skew it. A particularly large single source (say there was a Battle of Dosworth Field that happened 400 years later) could easily result in a bimodal distribution.
In order for Wisdom of Crowds to work (as it’s expected to work), people aren’t guessing along a Gaussian distribution. They’re applying knowledge they have, and some of that knowledge is useful information, while some of that knowledge is noise. All the useful information pulls the mean towards the true value, while all the noise pulls it away. The difference is that the useful information converges on a single value, (because it’s a convergent problem with a single correct answer), while all the noise pulls arbitrarily in all directions.
Provided there isn’t some reason for the noise itself to converge on a single value (and I think this is where my previous comments have not necessarily been clear, I’m talking about the noise converging, not the overall mean), the noise should cancel itself out.
It should be obvious that if you give people a right answer and a wrong answer, the noise will be weighted in the direction of the wrong answer (because there’s no corresponding error on the other side of the true value). Even if you have two wrong answers on either side of a true value, and ask people to pick the one closest to the true value, you will still have a skew problem, because unless the two values are equidistant to the true value (which defeats the point of the question), your noise is not going to be equally distributed around the true value.