this model is not useful for making decisions in the real world.
Seriously, why this idiosyncratic position on the diversification of charity donations? How is it different from diversification of investments?
It is common knowledge that diversification is a strategy used by risk-adverse agents to counter the negative effects of uncertainty. If there is no uncertainty, it’s obviously true that you should invest everything in the one thing that gives the highest utility (as long as the amount of money you invest is small enough that you don’t run into saturation effects, that is, as long as you can make the local linearity appoximation).
Why would charities behave any differently than profit-making assets? Do you think that charities have less uncertainties? That’s far from obvious. In fact, typical charities might well have more uncertainties, since they seem to be more difficult to evaluate.
The logic requires that your donations are purely altruistically motivated and you only care for good outcomes.
E. g. take donating to one of the organizations A, or B for cancer research. If your donations are purely altruistic and the consequences are the same you should have no preference on which of the organizations finds a new treatment. You have no reason to distinguish the case of you personally donating $ 1000 to both organizations and someone else doing the same from you donating $2000 to A and someone else donating $2000 to B. And once the donations are made you should have no preference between A or B finding the new treatment.
So the equivalent to your personal portfolio when making investments aren’t your personal donations, but the aggregate donations of everyone. And since you aren’t the only one making donations the donations are already diversified, so you are free to pick something underrepresented with high yield (which will almost certainly still be underrepresented afterwards). If you manage 0.1% of a $ 10,000,000 portfolio with 90% in government bonds it makes no sense to invest any of that 0.1% in government bonds in the name of diversification.
Why would charities behave any differently than profit-making assets? Do you think that charities have less uncertainties?
The confusion concerns whose risk is relevant. When you invest in stocks, you want to minimize the risk to your assets. So, you will diversify your holdings.
When you contribute to charities, if rational you should (with the caveats others have mentioned) minimize the risk that a failing charity will prove crucial, not the risk that your individual contribution will be wasted. If you take a broad, utilitarian overview, you incorporate the need for diversified charities in your utility judgment. If charity a and b are equally likely to pay off but charity a is a lot smaller and should receive more contributions to avoid risk to whatever cause, then you take that into account at the time of deciding on a and b, leading you to contribute everything to a for the sake of diversification. (It’s this dialectical twist that confuses people.)
If your contribution is large enough relative to the distinctions between charities, then diversification makes sense but only because your contribution is sufficient to tip the objective balance concerning the desirable total contributions to the charities.
Suppose we made a charity evaluator based on Statistical Prediction Rules, which perform pretty well. There is an issue though. The charities will try to fake the signals that SPR evaluates. SPR is too crude to resist deliberate cheating. Diversification then decreases payoff for such cheating; sufficient diversification can make it economically non viable for selfish parties to fake the signals. Same goes for any imperfect evaluation scheme, especially for elaborate processing of the information (statements, explanations, suggestions how to perform evaluation, et cetera) originating from the donation recipient.
You just can not abstract the imperfect evaluation as ‘uncertainty’ any more than you can abstract a backdoor in a server application as noise in the wire.
Diversification then decreases payoff for such cheating; sufficient diversification can make it economically non viable for selfish parties to fake the signals.
Diversification reduces the payoff for appearing better. Therefore it reduces the payoff of investing in fake signals of being better. But it also reduces the payoff of investments in actually being better! If a new project would increase humanitarian impact increases donations enough, then charities can afford to expand those efforts. If donations are insensitive to improvement, then the new project will be unaffordable.
Thus, e.g. GiveWell overwhelmingly channels funding to its top pick at a given time, partly to increase the expected direct benefit, and partly because they think that this creates incentives for improvement that dominate incentives for fake improvement. If the evaluation methods are worth using, they will include various signals that are costlier to fake than to honestly signal.
In the limit, if donors ignored quality indicators, spreading donations evenly among all charities, all this would do is incentivize the formation of lots of tiny charities that don’t do anything at all, just collect most of the diversification donations. If you can’t distinguish good from bad, you should focus on improving your ability to distinguish between them, not blindly diversify.
Diversification reduces the payoff for appearing better. Therefore it reduces the payoff of investing in fake signals of being better. But it also reduces the payoff of investments in actually being better!
Good charities are motivated by their objective. It’s rather bad charities for which actually performing better is simply one of the means to looking good for sake of some entirely different terminal goal. You are correct about the latter.
If you can’t distinguish good from bad, you should focus on improving your ability to distinguish between them, not blindly diversify.
I do concede that under unusually careful and secure (in the software security sense) evaluation it may be sufficiently resistant to cheating.
However, if you were parsing potentially turing-complete statements by the possible charity, verified the statement for approximate internal consistency, and then as a result of this clearly insecure process obtained enormously high number of, say, 8 lives per dollar, that’s an entirely different story. If your evaluation process got security hole, the largest number that falls through will be scam.
edit:
In the limit, if donors ignored quality indicators, spreading donations evenly among all charities, all this would do is incentivize the formation of lots of tiny charities that don’t do anything at all, just collect most of the diversification donations. If you can’t distinguish good from bad, you should focus on improving your ability to distinguish between them, not blindly diversify.
Wrong limit. The optimum amount of diversification is dependent to how secure is the evaluation process (how expensive it is for someone to generate a ‘donation basilisk’ output, which, upon reading, compels the reader to donate). Yes, ideally you should entirely eliminate the possibility of such ‘donation basilisk’ data, and then donate to the top charity. Practically, the degree of basilisk-proofness is a given that is very difficult to change, and you are making donation decision in the now.
Diversification then decreases payoff for such cheating; sufficient diversification can make it economically non viable for selfish parties to fake the signals.
I agree with the arguments against diversification (mainly due to its effect on lowering the incentive for becoming more efficient), but here’s a concrete instance of how diversification could make cheating nonviable.
Example: Cheating to fake the signals costs 5,000$ (in other words, 5,000$ to make it look like you’re the best charity). There are 10,000$ of efficient altruism funds that will be directed to the most efficient charity. By faking signals, you net 5,000$.
Now if diversification is used, let’s say at most 1⁄4 of the efficient altruism funds will be directed to a given charity (maybe evenly splitting the funds among the top 4 charities). Faking the signals now nets −2,500$. Thus, diversification would lower the incentive to cheat by reducing the expected payoff.
Suppose we made a charity evaluator based on Statistical Prediction Rules, which perform pretty well.
Is that just vanilla linear regression?
Diversification then decreases payoff for such cheating; sufficient diversification can make it economically non viable for selfish parties to fake the signals.
Even without cheating, evaluation is still problematic:
Suppose you have a formula that computes the expected marginal welfare (QUALYs, etc.) of a charity given a set of observable variables. You run it on a set of charities and it the two top charities get a very close score, one slightly greater than the other. But the input variables all affected by noise, and the formula contains several approximations, so you perform error propagation analysis and it turns out that the difference between these scores is within the margin of error.
Should you still donate everything to the top scoring charity even if you know that the decision is likely based on noise?
Should you still donate everything to the top scoring charity even if you know that the decision is likely based on noise?
If the charities are this close then you only expect to do very slightly better by giving only to the better scoring one. So it doesn’t matter much whether you give to one, the other, or both.
Ideally, you run your charity-evaluator function on huge selection of charities, and the one for which your charity-evaluator function gives the largest value, is in some sense the best, regardless of the noise.
More practically, imagine an imperfect evaluation function that due to a bug in it’s implementation multiplies by a Very Huge Number value of a charity whose description includes some string S which the evaluation function mis-processes in some dramatic way. Now, if the selection of charities is sufficiently big as to include at least one charity with such S in it’s description, you are essentially donating at random. Or worse than random, because the people that run in their head the computation resulting in production of such S tend to not be the ones you can trust.
Normally, I would expect people who know about human biases to not assume that evaluation would resemble the ideal and to understand that the output of some approximate evaluation will not have the exact properties of expected value.
Why would charities behave any differently than profit-making assets?
The difference (for some) isn’t in uncertainty, it’s in utility, which isn’t really made clear in the OP.
Risk aversion for personal investment stems from diminishing marginal utility: Going from $0 to $1,000,000 of personal assets is a significantly greater gain in personal welfare than going from $1,000,000 to $2,000,000. You use the first million to buy things like food, shelter, and such, while the second million goes to less urgent needs. So it makes sense to diversify into multiple investments, reducing the chance of severe falls in wealth even if this reduces expected value. E.g. for personal consumption one should take a sure million dollars rather than a 50% chance of $2,200,000.
If one assesses charitable donations in terms of something like “people helped by anyone” rather than something like “log of people helped by me” then there isn’t diminishing utility (by that metric): saving twice as many people is twice as good. And if your donations are small relative to the cause you are donating to, then there should be significantly diminishing returns to money in terms of lives saved: if you donate $1,000 and increase the annual budget for malaria prevention from $500,000,000 to $500,001,000 you shouldn’t expect that you are moving to a new regime with much lower marginal productivity.
But you might care about “log of lives saved by me” or “not looking stupid after the fact” or “affiliating with several good causes” or other things besides the number of people helped in your charitable donations. Or you might be donating many millions of dollars, so that diminishing impact of money matters.
It is common knowledge that diversification is a strategy used by risk-adverse agents to counter the negative effects of uncertainty.
When one is risk averse, one trades some expected gain to minimize potential loss. The relevant question is whether it makes any sense to be risk averse with respect to your charity donations.
I’d say no. Risk aversion for my own pile of money comes largely from decreasing marginal utility of each dollar in my pile when spent on me and mine. My first and last dollar to most charities are two drops in a bucket, with the same marginal “problem solving” power.
This doesn’t take into account the other benefits of charitable giving, such as signaling and good feelings. In both cases, I’d say that others and you respond more favorably to you the more charities you donate to. In that respect, at least, there is decreasing marginal utility for each dollar more spent on a particular charity. But I think that feel good aspect was not part of the assumed utility calculation. If your goal is to best solve problems, take aim at what you consider the best target, and shoot your wad.
There are many charities that provide goods or services that their donors can use, think of the Wikimedia Foundation or the Free Software Foundation or even the Singularity Institute (which operates Less Wrong). You can donate to these charities for non-altruistic motives other than mere signalling or good feelings, and these motives will likely have diminishing returns, naturally resulting in risk aversion. (Or you may reason that since your small donation isn’t going to make a difference, you can as well freeload, but that is the same argument against voting).
But let’s assume that we are considering only “save the starving children” type of charities, where the set of donors and the set of beneficiaries don’t overlap, and your donations can only buy welfare (measured in QUALYs or some other metric) for distant individuals you don’t personally know. Are you not risk averse?
Consider the following scenario: There are two possible charities. For every 100,000 euros of donations, charity A saves the lives of 50 children (that is, allows them to reach adulthood in a condition that enables them to provide for themselves). Charity B either saves 101 children per 100,000 euros or fails, completely wasting all the donated money, with a 50-50 chance. You have got 100 euros to donate. How do you split them?
This is not intended as a complete argument, rather it’s an elaboration of a point whose understanding might be helpful in understanding a more general argument. If this point, which is a simplified special case, is not understood, then understanding the more general argument would be even less likely. (Clarified this intent in the first paragraph.)
this model is not useful for making decisions in the real world.
Seriously, why this idiosyncratic position on the diversification of charity donations? How is it different from diversification of investments?
It is common knowledge that diversification is a strategy used by risk-adverse agents to counter the negative effects of uncertainty. If there is no uncertainty, it’s obviously true that you should invest everything in the one thing that gives the highest utility (as long as the amount of money you invest is small enough that you don’t run into saturation effects, that is, as long as you can make the local linearity appoximation).
Why would charities behave any differently than profit-making assets? Do you think that charities have less uncertainties? That’s far from obvious. In fact, typical charities might well have more uncertainties, since they seem to be more difficult to evaluate.
The logic requires that your donations are purely altruistically motivated and you only care for good outcomes.
E. g. take donating to one of the organizations A, or B for cancer research. If your donations are purely altruistic and the consequences are the same you should have no preference on which of the organizations finds a new treatment. You have no reason to distinguish the case of you personally donating $ 1000 to both organizations and someone else doing the same from you donating $2000 to A and someone else donating $2000 to B. And once the donations are made you should have no preference between A or B finding the new treatment.
So the equivalent to your personal portfolio when making investments aren’t your personal donations, but the aggregate donations of everyone. And since you aren’t the only one making donations the donations are already diversified, so you are free to pick something underrepresented with high yield (which will almost certainly still be underrepresented afterwards). If you manage 0.1% of a $ 10,000,000 portfolio with 90% in government bonds it makes no sense to invest any of that 0.1% in government bonds in the name of diversification.
Makes sense, but it seems to me that if there are many underrepresented high yield charities, you should still diversify among them.
The confusion concerns whose risk is relevant. When you invest in stocks, you want to minimize the risk to your assets. So, you will diversify your holdings.
When you contribute to charities, if rational you should (with the caveats others have mentioned) minimize the risk that a failing charity will prove crucial, not the risk that your individual contribution will be wasted. If you take a broad, utilitarian overview, you incorporate the need for diversified charities in your utility judgment. If charity a and b are equally likely to pay off but charity a is a lot smaller and should receive more contributions to avoid risk to whatever cause, then you take that into account at the time of deciding on a and b, leading you to contribute everything to a for the sake of diversification. (It’s this dialectical twist that confuses people.)
If your contribution is large enough relative to the distinctions between charities, then diversification makes sense but only because your contribution is sufficient to tip the objective balance concerning the desirable total contributions to the charities.
This is the most insightful thing I’ve read on LW today.
I don’t think philanthropists are risk-adverse in lives saved or quality-adjusted life years.
They might be risk-adverse in lives that could have been saved (but weren’t) or QALYs that could have existed (but didn’t).
There’s additional issue concerning imperfect evaluation.
Suppose we made a charity evaluator based on Statistical Prediction Rules, which perform pretty well. There is an issue though. The charities will try to fake the signals that SPR evaluates. SPR is too crude to resist deliberate cheating. Diversification then decreases payoff for such cheating; sufficient diversification can make it economically non viable for selfish parties to fake the signals. Same goes for any imperfect evaluation scheme, especially for elaborate processing of the information (statements, explanations, suggestions how to perform evaluation, et cetera) originating from the donation recipient.
You just can not abstract the imperfect evaluation as ‘uncertainty’ any more than you can abstract a backdoor in a server application as noise in the wire.
Diversification reduces the payoff for appearing better. Therefore it reduces the payoff of investing in fake signals of being better. But it also reduces the payoff of investments in actually being better! If a new project would increase humanitarian impact increases donations enough, then charities can afford to expand those efforts. If donations are insensitive to improvement, then the new project will be unaffordable.
Thus, e.g. GiveWell overwhelmingly channels funding to its top pick at a given time, partly to increase the expected direct benefit, and partly because they think that this creates incentives for improvement that dominate incentives for fake improvement. If the evaluation methods are worth using, they will include various signals that are costlier to fake than to honestly signal.
In the limit, if donors ignored quality indicators, spreading donations evenly among all charities, all this would do is incentivize the formation of lots of tiny charities that don’t do anything at all, just collect most of the diversification donations. If you can’t distinguish good from bad, you should focus on improving your ability to distinguish between them, not blindly diversify.
Good charities are motivated by their objective. It’s rather bad charities for which actually performing better is simply one of the means to looking good for sake of some entirely different terminal goal. You are correct about the latter.
I do concede that under unusually careful and secure (in the software security sense) evaluation it may be sufficiently resistant to cheating.
However, if you were parsing potentially turing-complete statements by the possible charity, verified the statement for approximate internal consistency, and then as a result of this clearly insecure process obtained enormously high number of, say, 8 lives per dollar, that’s an entirely different story. If your evaluation process got security hole, the largest number that falls through will be scam.
edit:
Wrong limit. The optimum amount of diversification is dependent to how secure is the evaluation process (how expensive it is for someone to generate a ‘donation basilisk’ output, which, upon reading, compels the reader to donate). Yes, ideally you should entirely eliminate the possibility of such ‘donation basilisk’ data, and then donate to the top charity. Practically, the degree of basilisk-proofness is a given that is very difficult to change, and you are making donation decision in the now.
I find this nonobvious; could you elaborate?
I agree with the arguments against diversification (mainly due to its effect on lowering the incentive for becoming more efficient), but here’s a concrete instance of how diversification could make cheating nonviable.
Example: Cheating to fake the signals costs 5,000$ (in other words, 5,000$ to make it look like you’re the best charity). There are 10,000$ of efficient altruism funds that will be directed to the most efficient charity. By faking signals, you net 5,000$.
Now if diversification is used, let’s say at most 1⁄4 of the efficient altruism funds will be directed to a given charity (maybe evenly splitting the funds among the top 4 charities). Faking the signals now nets −2,500$. Thus, diversification would lower the incentive to cheat by reducing the expected payoff.
Is that just vanilla linear regression?
Even without cheating, evaluation is still problematic:
Suppose you have a formula that computes the expected marginal welfare (QUALYs, etc.) of a charity given a set of observable variables. You run it on a set of charities and it the two top charities get a very close score, one slightly greater than the other. But the input variables all affected by noise, and the formula contains several approximations, so you perform error propagation analysis and it turns out that the difference between these scores is within the margin of error. Should you still donate everything to the top scoring charity even if you know that the decision is likely based on noise?
If the charities are this close then you only expect to do very slightly better by giving only to the better scoring one. So it doesn’t matter much whether you give to one, the other, or both.
Systematic errors are the problem.
Ideally, you run your charity-evaluator function on huge selection of charities, and the one for which your charity-evaluator function gives the largest value, is in some sense the best, regardless of the noise.
More practically, imagine an imperfect evaluation function that due to a bug in it’s implementation multiplies by a Very Huge Number value of a charity whose description includes some string S which the evaluation function mis-processes in some dramatic way. Now, if the selection of charities is sufficiently big as to include at least one charity with such S in it’s description, you are essentially donating at random. Or worse than random, because the people that run in their head the computation resulting in production of such S tend to not be the ones you can trust.
Normally, I would expect people who know about human biases to not assume that evaluation would resemble the ideal and to understand that the output of some approximate evaluation will not have the exact properties of expected value.
The difference (for some) isn’t in uncertainty, it’s in utility, which isn’t really made clear in the OP.
Risk aversion for personal investment stems from diminishing marginal utility: Going from $0 to $1,000,000 of personal assets is a significantly greater gain in personal welfare than going from $1,000,000 to $2,000,000. You use the first million to buy things like food, shelter, and such, while the second million goes to less urgent needs. So it makes sense to diversify into multiple investments, reducing the chance of severe falls in wealth even if this reduces expected value. E.g. for personal consumption one should take a sure million dollars rather than a 50% chance of $2,200,000.
If one assesses charitable donations in terms of something like “people helped by anyone” rather than something like “log of people helped by me” then there isn’t diminishing utility (by that metric): saving twice as many people is twice as good. And if your donations are small relative to the cause you are donating to, then there should be significantly diminishing returns to money in terms of lives saved: if you donate $1,000 and increase the annual budget for malaria prevention from $500,000,000 to $500,001,000 you shouldn’t expect that you are moving to a new regime with much lower marginal productivity.
But you might care about “log of lives saved by me” or “not looking stupid after the fact” or “affiliating with several good causes” or other things besides the number of people helped in your charitable donations. Or you might be donating many millions of dollars, so that diminishing impact of money matters.
Addressed in another comment
When one is risk averse, one trades some expected gain to minimize potential loss. The relevant question is whether it makes any sense to be risk averse with respect to your charity donations.
I’d say no. Risk aversion for my own pile of money comes largely from decreasing marginal utility of each dollar in my pile when spent on me and mine. My first and last dollar to most charities are two drops in a bucket, with the same marginal “problem solving” power.
This doesn’t take into account the other benefits of charitable giving, such as signaling and good feelings. In both cases, I’d say that others and you respond more favorably to you the more charities you donate to. In that respect, at least, there is decreasing marginal utility for each dollar more spent on a particular charity. But I think that feel good aspect was not part of the assumed utility calculation. If your goal is to best solve problems, take aim at what you consider the best target, and shoot your wad.
There are many charities that provide goods or services that their donors can use, think of the Wikimedia Foundation or the Free Software Foundation or even the Singularity Institute (which operates Less Wrong). You can donate to these charities for non-altruistic motives other than mere signalling or good feelings, and these motives will likely have diminishing returns, naturally resulting in risk aversion. (Or you may reason that since your small donation isn’t going to make a difference, you can as well freeload, but that is the same argument against voting).
But let’s assume that we are considering only “save the starving children” type of charities, where the set of donors and the set of beneficiaries don’t overlap, and your donations can only buy welfare (measured in QUALYs or some other metric) for distant individuals you don’t personally know. Are you not risk averse?
Consider the following scenario: There are two possible charities. For every 100,000 euros of donations, charity A saves the lives of 50 children (that is, allows them to reach adulthood in a condition that enables them to provide for themselves). Charity B either saves 101 children per 100,000 euros or fails, completely wasting all the donated money, with a 50-50 chance. You have got 100 euros to donate. How do you split them?
I would give only to B. I try to be risk-neutral in my giving.
This is not intended as a complete argument, rather it’s an elaboration of a point whose understanding might be helpful in understanding a more general argument. If this point, which is a simplified special case, is not understood, then understanding the more general argument would be even less likely. (Clarified this intent in the first paragraph.)
One difference: Utility in money is non-linear; utility in lives saved is linear.