Most people base most of their judgements on intuition, rather than explicit calculations. Some people do base judgements on explicit calculations, and take such calculations at face value. But many others, especially on social questions, use calculations that include case-specific fudge factors which can be adjusted to ensure that calculations agree with case-specific intuitions. While this might estimate well when intuitions are far more informative than explicit calculations, this often seems to be done to achieve a hypocritical appearance of calculation-based decisions, while actually allowing intuitions to dominate.
I do believe that Bayesian adjustments are not included in most expected-value estimates of the kind I discuss. More at my comment on Less Wrong.
My understanding from our Google+ exchange is that we agree that the Bayesian adjustment described would have the property of requiring stronger evidence for more counterintuitive claims (all else equal), and that no other "anti-weird-claims" adjustment is needed or warranted.
I sympathize with your uneasiness regarding fudge factors. In my post, I state:
Of course there is a problem here: going with one’s gut can be an excuse for going with what one wants to believe, and a lot of what enters into my gut belief could be irrelevant to proper Bayesian analysis. There is an appeal to formulas, which is that they seem to be susceptible to outsiders’ checking them for fairness and consistency.But when the formulas are too rough, I think the loss of accuracy outweighs the gains to transparency. Rather than using a formula that is checkable but omits a huge amount of information, I’d prefer to state my intuition - without pretense that it is anything but an intuition - and hope that the ensuing discussion provides the needed check on my intuitions.
OK. I see you do like a lot of people.You tacitly decide whether cats or birds are more important. Then you attempt to quantitate the question.This is because normative or simply whimsical solutions are not supposed to be adequate.
Here is how I solved the problem. I fed the cat so the she didn't have to hunt birds,but now there are more rats. My back yard is like the welfare state.
How do you balance a dead cat vs a dead bird. It just isn’t a mathematical question.
Identify the common factor you most strongly care about, e.g. suffering or general intelligence, study cats and birds, see how they compare and then calculate which outcome minimizes suffering or increases intelligence. Or else decide how much more you care about cats than birds or vice versa and see if the numbers of cats or birds saved does outweigh the amount of value per cat or bird.
Everything is a math question, most are just too complicated to solve.
I doubt if people will really be rational about these things. Most people with lots of money to give are not mathematically worshipful. Giving comes from subjective calculations (fudge factors),Or which charity got to you first.
Say,I was considering giving $100000 to a charity that fed stray cats. Then I read a study put out by the Audubon Society showing that stray cats killed 10 thousand birds. Are you saying the "Bayesian prior" would help me make a rational decision? How do you balance a dead cat vs a dead bird. It just isn't a mathematical question.
Preferring concrete evidence does indeed seem to count against existential risk charites - much as Karnofsky claims. If it is hard for others to measure whether a cause is working, that increases the chances that it is not working, since it means it is likely to be challenging for the charity to measure their own progress themselves.
This brings to mind the famous quote from the great John Von Neumann -- "There is no sense in being precise when you have no idea what you are talking about"
I think an epsilon of paranoia is useful to regularise these sorts of analyses. Namely, one supposes that there is an adversary out there who is actively trying to lower your expected utility through disinformation (in order to goad you into making poor decisions), but is only able to affect all your available information by an epsilon. One should then adjust one’s computations of expected utility accordingly. In particular, the contribution of any event that you expect to occur with probability less than epsilon should probably be discarded completely.
What do you suggest people who do not yet possess the sufficient math background to formally analyze charities and use explicit calculations? Should such people avoid charitable giving until they learnt the math and concentrate all their resources on acquiring the necessary education, or solely rely on their intuitions?
Personally I am not able to follow most of the math in Karnofsky' article at the moment. What I got out of reading it, including the comments, is mainly that it seems to be incredible hard to evaluate charities and that there are many open problems.
Sigh. I am at work, working on the business case for a multi-million dollar investment project. My task this mornning is - precisely - to build into the spreadsheet some fudge factors which can be adjusted so that the business case will support the decision, made twelve months ago, to go ahead with the project.
"Most people base most of their judgements on intuition, rather than explicit calculations"
Translation: Those commenters that don't admire me.
"Some people do base judgements on explicit calculations"
Translation: Robin Hanson.
"But many others, especially on social questions, use calculations that include case-specific fudge factors which can be adjusted to ensure that calculations agree with case-specific intuitions. "
Translation: Whoever I intend to status assassinate for the purpose of getting their attention to witness my awesomeness.. In this case Holden Karnofsky.
You suggest that Holden's appeal to his intuition serves as rationalization choosing conventional over utilitairan-optimal charities without substantiating your claim. Can you point to a single example of a charity that you believe to have higher utilitarian expected value than GiveWell's top ranked charities and give a solid argument in favor of your position? If so, I'd be interested in hearing knowing more. If not, your suggestion is specious.
Hi Robin,
I do believe that Bayesian adjustments are not included in most expected-value estimates of the kind I discuss. More at my comment on Less Wrong.
My understanding from our Google+ exchange is that we agree that the Bayesian adjustment described would have the property of requiring stronger evidence for more counterintuitive claims (all else equal), and that no other "anti-weird-claims" adjustment is needed or warranted.
I sympathize with your uneasiness regarding fudge factors. In my post, I state:
Of course there is a problem here: going with one’s gut can be an excuse for going with what one wants to believe, and a lot of what enters into my gut belief could be irrelevant to proper Bayesian analysis. There is an appeal to formulas, which is that they seem to be susceptible to outsiders’ checking them for fairness and consistency.But when the formulas are too rough, I think the loss of accuracy outweighs the gains to transparency. Rather than using a formula that is checkable but omits a huge amount of information, I’d prefer to state my intuition - without pretense that it is anything but an intuition - and hope that the ensuing discussion provides the needed check on my intuitions.
OK. I see you do like a lot of people.You tacitly decide whether cats or birds are more important. Then you attempt to quantitate the question.This is because normative or simply whimsical solutions are not supposed to be adequate.
Here is how I solved the problem. I fed the cat so the she didn't have to hunt birds,but now there are more rats. My back yard is like the welfare state.
How do you balance a dead cat vs a dead bird. It just isn’t a mathematical question.
Identify the common factor you most strongly care about, e.g. suffering or general intelligence, study cats and birds, see how they compare and then calculate which outcome minimizes suffering or increases intelligence. Or else decide how much more you care about cats than birds or vice versa and see if the numbers of cats or birds saved does outweigh the amount of value per cat or bird.
Everything is a math question, most are just too complicated to solve.
I doubt if people will really be rational about these things. Most people with lots of money to give are not mathematically worshipful. Giving comes from subjective calculations (fudge factors),Or which charity got to you first.
Say,I was considering giving $100000 to a charity that fed stray cats. Then I read a study put out by the Audubon Society showing that stray cats killed 10 thousand birds. Are you saying the "Bayesian prior" would help me make a rational decision? How do you balance a dead cat vs a dead bird. It just isn't a mathematical question.
Preferring concrete evidence does indeed seem to count against existential risk charites - much as Karnofsky claims. If it is hard for others to measure whether a cause is working, that increases the chances that it is not working, since it means it is likely to be challenging for the charity to measure their own progress themselves.
This brings to mind the famous quote from the great John Von Neumann -- "There is no sense in being precise when you have no idea what you are talking about"
Terry Tao on a kind of fudge factor:
I think an epsilon of paranoia is useful to regularise these sorts of analyses. Namely, one supposes that there is an adversary out there who is actively trying to lower your expected utility through disinformation (in order to goad you into making poor decisions), but is only able to affect all your available information by an epsilon. One should then adjust one’s computations of expected utility accordingly. In particular, the contribution of any event that you expect to occur with probability less than epsilon should probably be discarded completely.
@Robin Hanson
What do you suggest people who do not yet possess the sufficient math background to formally analyze charities and use explicit calculations? Should such people avoid charitable giving until they learnt the math and concentrate all their resources on acquiring the necessary education, or solely rely on their intuitions?
Personally I am not able to follow most of the math in Karnofsky' article at the moment. What I got out of reading it, including the comments, is mainly that it seems to be incredible hard to evaluate charities and that there are many open problems.
Sigh. I am at work, working on the business case for a multi-million dollar investment project. My task this mornning is - precisely - to build into the spreadsheet some fudge factors which can be adjusted so that the business case will support the decision, made twelve months ago, to go ahead with the project.
this isn't exactly a new or uncommon phenomenon..
Aron, your last eight comments have been simply rude, without compensating insight. I will delete further such comments by you.
"Most people base most of their judgements on intuition, rather than explicit calculations"
Translation: Those commenters that don't admire me.
"Some people do base judgements on explicit calculations"
Translation: Robin Hanson.
"But many others, especially on social questions, use calculations that include case-specific fudge factors which can be adjusted to ensure that calculations agree with case-specific intuitions. "
Translation: Whoever I intend to status assassinate for the purpose of getting their attention to witness my awesomeness.. In this case Holden Karnofsky.
"As I shall explain below, Holden Karnofsky..."
O rly?
Robin,
You suggest that Holden's appeal to his intuition serves as rationalization choosing conventional over utilitairan-optimal charities without substantiating your claim. Can you point to a single example of a charity that you believe to have higher utilitarian expected value than GiveWell's top ranked charities and give a solid argument in favor of your position? If so, I'd be interested in hearing knowing more. If not, your suggestion is specious.