Posts

Shapley values: Better than counterfactuals 2019-10-10T10:26:24.220Z · score: 64 (26 votes)
Why do social movements fail: Two concrete examples. 2019-10-04T19:56:02.028Z · score: 82 (38 votes)
EA Mental Health Survey: Results and Analysis. 2019-06-13T19:55:37.127Z · score: 44 (21 votes)

Comments

Comment by nunosempere on Shapley values: Better than counterfactuals · 2019-10-15T07:37:56.339Z · score: 1 (1 votes) · EA · GW
  1. would surprise me; can you think of a source?
Comment by nunosempere on Shapley values: Better than counterfactuals · 2019-10-11T10:15:08.578Z · score: 2 (2 votes) · EA · GW

Fair point re: uncertainty. The situation seems pretty symmetric, though: if a politician builds roads just to get votes, and an NGO steps in and does something valuable with that, the politician's counterfactual impact is still the same as the NGO's, so both the Shapley value and counterfactuals have that problem (?). Maybe one can exclude agents acording to how close their goals are to yours, e.g., totally exclude a paperclip maximizer from both counterfactual and Shapley value calculations, and apply order indifference to allies only (?). This is something I haven't though about; thanks for pointing it out.

Fair point re: epistemic status. Changed my epistemic status.

Comment by nunosempere on Shapley values: Better than counterfactuals · 2019-10-11T10:08:26.209Z · score: 1 (1 votes) · EA · GW

I don't exactly claiming to have identified a problem with the counterfactual function, in itself. The counterfactual is perfectly well defined, and I like it, and it has done nothing wrong. I understand this. It is clear to me that it can't be added just like that. The function, per se, is fine.

What I'm claiming is that, because it can't be aggregated, it is not the right function to think about in terms of assigning impact to people in the context of groups. I am arguing about the area of applicability of the function, not about the function. I am claiming that, if you are optimizing for counterfactual impact in terms of groups, pitfalls may arise.

It's like, when you first see for the same time: -1 = sqrt(-1)*sqrt(-1) = sqrt((-1)*(-1)) = sqrt(1) = 1, therefore -1 = 1, and you can't see the mistake. It's not that the sqrt function is wrong, it's that you're using it outside it's limited fiefdom, so something breaks. I hope the example proved amusing.

I'm not only making statements about the counterfactual function, I'm also making statements about the concept which people have in your head which is called "impact", and how that concept doesn't map to counterfactual impact some of the time, and about how, if you had to map that concept to a mathematical function, the Shapley value is a better candidate.

Comment by nunosempere on Shapley values: Better than counterfactuals · 2019-10-11T09:48:17.198Z · score: 3 (2 votes) · EA · GW

1.

I have thought about this, and I'm actually biting the bullet. I think that a lot of people get impact for a lot of things, and that even smallish projects depend on a lot of other moving parts, in the direction of You didn't build that.

I don't agree with some of your examples when taken literally, but I agree with the nuanced thing you're pointing at with them, e.g., building good roads seems very valuable precisely because it helps other projects, if there is high nurse absenteeism then the nurses who show up take some of the impact...

I think that if you divide the thing's impact by, say 10x, the ordering of the things according to impact remains, so this shouldn't dissuade people from doing high impact things. The interesting thing is that some divisors will be greater than others, and thus the ordering will be changed. I claim that this says something interesting.

2.

Not really. If 10 people have already done it, your Shapley value will be positive if you take that bargain. If the thing hasn't been done yet, you can't convince 10 Shapley-optimizing altruists to do the thing for 0.5m each, but you might convince 10 counterfactual impact optimizers. As @casebach mentioned, this may have problems when dealing with uncertainty (for example: what if you're pretty sure that someone is going to do it?).

3.

You're right. The example, however, specified that the EAs were to be "otherwise idle", to simplify calculations.

Comment by nunosempere on Shapley values: Better than counterfactuals · 2019-10-11T08:42:48.260Z · score: 2 (2 votes) · EA · GW

In my mind, that gets a complexity penalty. Imagine that instead of ten people, there were 10^10 people. Then for that hack to work, and for everyone to be able to say that they convinced all the others, there has to be some overhead, which I think that the Shapley value doesn't require.

Comment by nunosempere on Shapley values: Better than counterfactuals · 2019-10-11T07:23:40.149Z · score: 2 (2 votes) · EA · GW

Good point!

Comment by nunosempere on Shapley values: Better than counterfactuals · 2019-10-10T17:10:31.941Z · score: 4 (3 votes) · EA · GW

What you say seems similar to a Stag hunt. Consider, though, that if the group is optimizing for their individual counterfactual impact, they'll want to coordinate to all do the 100 utility project. If they were optimizing their Shapley value, they'd instead want to coordinate to do 10 different projects, each worth 20 utility. 20*10 = 200 >100.

Comment by nunosempere on Why do social movements fail: Two concrete examples. · 2019-10-09T20:09:45.284Z · score: 2 (2 votes) · EA · GW

What concrete examples were you thinking of?

Comment by nunosempere on Why do social movements fail: Two concrete examples. · 2019-10-09T20:06:44.291Z · score: 7 (3 votes) · EA · GW

Regarding Spanish Enlightenment, I can't answer as decisively, because the sources I used were in Spanish, and they were combined with me just knowing things about Spanish literature and history, which made hypothesis generation much easier and much faster.

That being said, the English Wikipedia page Enlightenment in Spain might be a good starting point.

Comment by nunosempere on Why do social movements fail: Two concrete examples. · 2019-10-09T19:48:12.017Z · score: 18 (4 votes) · EA · GW

Here is, additionally, a list of behaviors/techniques recommended by General Semantics which stood out to me for some reason. The problem is, though, that I find it difficult to say whether they're representative; for that, see the first link in my other comment: An overview of general semantics . With that in mind:

Extensional devices:

  • Indexing : Muslim(1) is not Muslim(2); Feminist(1) is not Feminist(2);. Remember to look for the differences even among a group or category that presume similarities.
  • Dating : Steve(2008) is not Steve(1968); Steve’s-views-on-abortion(2008) are not Steve’s-views-on-abortion(1988). Remember that each person and each ‘thing’ we experience changes over time, even though the changes may not be apparent to us.
  • Quotes : ‘truth’, ‘reality’, ‘mind’, ‘elite’. Use quotes around terms as a caution to indicate you’re aware that there is an opportunity for misunderstanding if the term is particularly subject to interpretation, or if you’re being sarcastic, ironic, or facetious. o hyphen : mind-body, thinking-feeling. Use to join terms that we can separate in language, but can’t actually separate in the ‘real’ world. Remember that we can talk in terms that don’t accurately reflect the world ‘out there.’
  • etc.: Remember that our knowledge and awareness of anything is limited. We can’t sense or experience or talk about all of something, so we should maintain an awareness that “more could be said.”

Variations of English:

  • E-Prime: eliminate or reduce forms of the to be verbs (is, are, were, am, being, etc.). In particular, reduce those that we consider is of identity (ex. John is a liberal) and is of predication (ex. The rose is red.)
    • “What’s this? What’s that?” Don’t answer, “It’s a table,” but, “We call it a table.”
  • English Minus Absolutisms (EMA): eliminate or reduce inappropriate generalizations or expressions that imply allness or absolute attitudes. Examples include: all, none, every, totally, absolutely, perfect, without a doubt, certain, completely.

Holding a stone

Bruce Kodish led the sessions dealing with experiencing on the silent level. One exercise was seemingly quite simple. We were told to pick out a stone, bring it to class, then for a few minutes simply experience the stone on the silent level. In other words, to use our senses without verbalizing our reactions to our senses. My inability to accomplish this simple task was enlightening. It emphasized to me how language can get in the way of our moment-to-moment experiences with “what is going on.” It also demonstrated the extent to which I generate meanings for things. While I was unsuccessful in shutting off my verbalizing, I was quite proficient in coming up with all kinds of thoughts-and-feelings-and-meanings about an ordinary, arbitrary rock. If I can ‘make up’ so much meaning for a random inanimate object, perhaps it would be appropriate for me to be hesitant and inquiring in my future evaluations of relationships with more animate beings.

Ladders

General Semantics has several ladders, which illustrate different levels of abstraction. For example:

A)

  1. Something is going on
  2. I experience what’s going on
  3. I evaluate my experience of what’s going on
  4. From my evaluation of my experience of what’s going on, I respond to and give meaning to what is going.

Example: You misunderstood what I was trying to say / You didn't write clearly enough benefits from that.

B)

What Happens ≠ What I Sense ≠ How I Respond ≠ “What It Means”

C)

What we sense is not what happened - What we describe is not what we sense - What it means is not what we describe.

D)

E)

Here is an example of these ladders being used:

What this GS stuff meant to me, at that particular time, was that I didn’t have to be consumed with guilt over the fact that I had decided to end my marriage. Divorce didn’t have a predetermined meaning — our daughter wasn’t forever doomed to be neglected and miserable; I didn’t have to walk forever with my head bowed, ashamed of taking actions to further my own personal happiness; my wife didn’t have to forever grieve over what I had ‘done’ to her. It was certainly possible that each of these outcomes could occur, but they were not unavoidable consequences of the event called divorce. Source: Here is something about general semantics, by Steven Stockdale, who was once director of the Insititute of Semantics.

Note that CBT says something similar

Comment by nunosempere on Why do social movements fail: Two concrete examples. · 2019-10-09T19:45:48.112Z · score: 9 (4 votes) · EA · GW

Fortunately, I keep notes, so here is a list of links with respect to general semantics which kind of answer your questions.

An overview of general semantics

Reflections by a general semanticist
History of general semantics
A Brief History of General Semantics
A Brief History of General Semantics II
Drama because of having two organizations
A book length introduction
The Wikipedia page

Comment by nunosempere on Editing available for EA Forum drafts · 2019-07-24T19:25:32.838Z · score: 1 (3 votes) · EA · GW

As a datapoint, this would have been highly useful to me before writing this: EA Mental Health Survey: Results and Analysis. Can you have a look at it regardless?

Interestingly, regardless of when you make the offer publicly, there is someone who will have published something at t-1, so I don't feel too bad.

Comment by nunosempere on How Europe might matter for AI governance · 2019-07-14T16:23:30.885Z · score: 13 (6 votes) · EA · GW

You can post an image using standard markdown syntax:

![](link to the image)

For example, to insert the above image, I wrote:
![](https://nunosempere.github.io/ea/AI-Europe.png)
Comment by nunosempere on EA Mental Health Survey: Results and Analysis. · 2019-07-14T12:08:39.067Z · score: 4 (3 votes) · EA · GW

[this comment is also archived in my blog, here without indentation and thus easier to read]

Re: @Peter_Hurford. The 2019 SSC Survey does have an EA_ID question. Using that:

If you run some regressions, you get a significant correlation between EA affiliation and mental conditions; respondents who identified as EA differed from non-EAs by ~2-4% (see below). Note that the SSC Survey is subject to fewer biases than the EA Mental Health survey, and also note that it's still difficult to extract causal conclusions. Data available here

Plots:

Diagnosed + Intuited

                                                                            x   y         %
1                                                                      EA Yes 959 100.00000
2         Has been diagnosed with a mental condition, or thinks they have one 580  60.47967
3 Has not been diagnosed with a mental condition, and does not think they any 347  36.18352
4                                                          NA / Didn't answer 125  13.03441
                                                                            x    y          %
1                                                                    EA Sorta 2223 100.000000
2         Has been diagnosed with a mental condition, or thinks they have one 1354  60.908682
3 Has not been diagnosed with a mental condition, and does not think they any  795  35.762483
4                                                          NA / Didn't answer  167   7.512371
                                                                            x    y          %
1                                                                       EA No 4158 100.000000
2         Has been diagnosed with a mental condition, or thinks they have one 2416  58.104858
3 Has not been diagnosed with a mental condition, and does not think they any 1587  38.167388
4                                                          NA / Didn't answer  248   5.964406

Diagnosed

                                               x   y         %
1                                         EA Yes 959 100.00000
2     Has been diagnosed with a mental condition 314  32.74244
3 Has not been diagnosed with a mental condition 613  63.92075
4                             NA / Didn't answer 125  13.03441
                                               x    y          %
1                                       EA Sorta 2223 100.000000
2     Has been diagnosed with a mental condition  718  32.298695
3 Has not been diagnosed with a mental condition 1431  64.372470
4                             NA / Didn't answer  167   7.512371
                                               x    y          %
1                                          EA No 4158 100.000000
2     Has been diagnosed with a mental condition 1183  28.451178
3 Has not been diagnosed with a mental condition 2820  67.821068
4                             NA / Didn't answer  248   5.964406

Regressions

Linear

> # D$mentally_ill = Number of diagnosed mental ilnesses
> # D$mentally_ill2= Number of mental ilnesses, diagnosed + intuited
> summary(lm(D$mentally_ill ~ D$`EA ID`))

Call:
lm(formula = D$mentally_ill ~ D$`EA ID`)

Residuals:
    Min      1Q  Median      3Q     Max 
-0.5717 -0.5514 -0.4689  0.4486 10.4283 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)     0.46890    0.01424  32.935  < 2e-16 ***
D$`EA ID`Sorta  0.08252    0.02409   3.426 0.000617 ***
D$`EA ID`Yes    0.10284    0.03283   3.132 0.001742 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9008 on 7076 degrees of freedom
  (354 observations deleted due to missingness)
Multiple R-squared:  0.002421,	Adjusted R-squared:  0.002139 
F-statistic: 8.587 on 2 and 7076 DF,  p-value: 0.0001884
> summary(lm(D$mentally_ill2 ~ D$`EA ID`))

Call:
lm(formula = D$mentally_ill2 ~ D$`EA ID`)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.3711 -1.2638 -0.2638  0.7362  9.6289 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)     1.26380    0.02243  56.343   <2e-16 ***
D$`EA ID`Sorta  0.09637    0.03795   2.539   0.0111 *  
D$`EA ID`Yes    0.10729    0.05173   2.074   0.0381 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.419 on 7076 degrees of freedom
  (354 observations deleted due to missingness)
Multiple R-squared:  0.001216,	Adjusted R-squared:  0.0009338 
F-statistic: 4.308 on 2 and 7076 DF,  p-value: 0.0135
> summary(lm(D$mentally_ill>0 ~ D$`EA ID`))

Call:
lm(formula = D$mentally_ill > 0 ~ D$`EA ID`)

Residuals:
    Min      1Q  Median      3Q     Max 
-0.3387 -0.3341 -0.2955  0.6659  0.7045 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.295528   0.007323  40.354  < 2e-16 ***
D$`EA ID`Sorta 0.038581   0.012391   3.114  0.00186 ** 
D$`EA ID`Yes   0.043199   0.016889   2.558  0.01055 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.4633 on 7076 degrees of freedom
  (354 observations deleted due to missingness)
Multiple R-squared:  0.001835,	Adjusted R-squared:  0.001553 
F-statistic: 6.505 on 2 and 7076 DF,  p-value: 0.001505
> summary(lm(D$mentally_ill2>0 ~ D$`EA ID`))

Call:
lm(formula = D$mentally_ill2 > 0 ~ D$`EA ID`)

Residuals:
    Min      1Q  Median      3Q     Max 
-0.6301 -0.6036  0.3699  0.3965  0.3965 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)    0.603547   0.007692  78.466   <2e-16 ***
D$`EA ID`Sorta 0.026513   0.013014   2.037   0.0417 *  
D$`EA ID`Yes   0.022127   0.017738   1.247   0.2123    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.4867 on 7076 degrees of freedom
  (354 observations deleted due to missingness)
Multiple R-squared:  0.0006657,	Adjusted R-squared:  0.0003832 
F-statistic: 2.357 on 2 and 7076 DF,  p-value: 0.09481

Logistic

> summary(glm(D$mentally_ill>0 ~ D$`EA ID`, family=binomial(link='logit')))

Call:
glm(formula = D$mentally_ill > 0 ~ D$`EA ID`, family = binomial(link = "logit"))

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.9095  -0.9018  -0.8370   1.4807   1.5614  

Coefficients:
               Estimate Std. Error z value Pr(>|z|)    
(Intercept)    -0.86868    0.03464 -25.078  < 2e-16 ***
D$`EA ID`Sorta  0.17902    0.05737   3.120  0.00181 ** 
D$`EA ID`Yes    0.19971    0.07756   2.575  0.01003 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 8797.8  on 7078  degrees of freedom
Residual deviance: 8784.8  on 7076  degrees of freedom
  (354 observations deleted due to missingness)
AIC: 8790.8

Number of Fisher Scoring iterations: 4
> summary(glm(D$mentally_ill2>0 ~ D$`EA ID`, family=binomial(link='logit')))

Call:
glm(formula = D$mentally_ill2 > 0 ~ D$`EA ID`, family = binomial(link = "logit"))

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.4103  -1.3603   0.9612   1.0049   1.0049  

Coefficients:
               Estimate Std. Error z value Pr(>|z|)    
(Intercept)     0.42027    0.03231  13.007   <2e-16 ***
D$`EA ID`Sorta  0.11221    0.05514   2.035   0.0419 *  
D$`EA ID`Yes    0.09344    0.07517   1.243   0.2139    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 9439.1  on 7078  degrees of freedom
Residual deviance: 9434.4  on 7076  degrees of freedom
  (354 observations deleted due to missingness)
AIC: 9440.4

Number of Fisher Scoring iterations: 4
Comment by nunosempere on EA Mental Health Survey: Results and Analysis. · 2019-06-25T10:11:09.814Z · score: 1 (1 votes) · EA · GW

It stacks the answers to questions 5 and 6. It had an error beforehand, thanks for letting me know. It should be fixed now.

Comment by nunosempere on EA Mental Health Survey: Results and Analysis. · 2019-06-25T09:55:25.302Z · score: 3 (2 votes) · EA · GW

Using the 2017 SSC, I've looked at:

Has been diagnosed with a mental illness ~ Is EA

lm(formula = Has_mental_condition_diagnosis ~ Is_EA) 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 0.244434   0.006544  37.352   <2e-16 ***
Is_EA       0.027635   0.017688   1.562    0.118    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Weak relationship, is not significant.

Has been diagnosed with a mental illness or intuits having one ~ Is EA

> summary(lm(Has_mental_condition_diagnosis_or_intuited ~ Is_EA))

Call:
lm(formula = Has_mental_condition_diagnosis_or_intuited ~ Is_EA)

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.52513    0.00756  69.463   <2e-16 ***
Is_EA        0.03782    0.02043   1.851   0.0642 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Weak relationship, 0.05 < p < 0.10

Has taken SSRIs ~ Is EA.

lm(formula = Has_taken_SSRIs ~ Is_EA)

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.276337   0.006728  41.070   <2e-16 ***
Is_EA       -0.041894   0.018186  -2.304   0.0213 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Weak negative relationship (??).

Code used to produce that (in R)

## Download the file from https://slatestarcodex.com/2017/03/17/ssc-survey-2017-results/ , then move into the directory the file is in with:
## setwd("directory")

C <- read.csv(file="Survey_CSV.csv", header=TRUE, sep=",", stringsAsFactors=FALSE)

c(69:77) -> mental_conditions #  "Schizophrenia", Autism", "Depression", "Anxiety", "OCD", "Eatingdisorder" "Bipolar", "Alcoholism", "Drugaddiction".

Has_mental_condition_diagnosis=rep(0,dim(C)[1])
for(mental_condition in mental_conditions){
  ifelse(C[,mental_condition]=="I have a formal diagnosis of this condition", 1, Has_mental_condition_diagnosis) -> Has_mental_condition_diagnosis 
}

ifelse(C$EAID=="Yes", 1,0) -> Is_EA
summary(lm(Has_mental_condition_diagnosis ~ Is_EA))

Has_mental_condition_diagnosis_or_intuited = rep(0,dim(C)[1])
Diagnosis_or_intuited = c("I have a formal diagnosis of this condition","I think I might have this condition, although I have never been formally diagnosed" )
for(mental_condition in mental_conditions){
    ifelse(C[,mental_condition]%in%Diagnosis_or_intuited, 1, Has_mental_condition_diagnosis_or_intuited) -> Has_mental_condition_diagnosis_or_intuited 
  }

summary(lm(Has_mental_condition_diagnosis_or_intuited ~ Is_EA))

ifelse(C$SSRIs == "I have never taken these drugs", 0, 1) -> Has_taken_SSRIs
summary(lm(Has_taken_SSRIs ~ Is_EA))
Comment by nunosempere on EA Mental Health Survey: Results and Analysis. · 2019-06-13T18:06:03.317Z · score: 1 (1 votes) · EA · GW

I linked to a similar comparison: Efffective Altruists, not as mentally ill as you think, with the results you hypothesized:

This points out a limitation of my statistics above. All it shows is that effective altruists don’t differ from other rationalists in levels of mental illness. It’s possible and indeed likely that both effective altruists and rationalists differ from the general population in all kinds of ways. It’s even possible that self-hate and scrupulosity drive people into the rationality movement in general, although I can’t imagine why that would be. It’s just that they don’t seem to have any extra power to make people effective altruists once they’re there.

It would surprise me if the results don't hold any more, but it might be worth checking

Comment by nunosempere on EA Mental Health Survey: Results and Analysis. · 2019-06-13T17:58:46.606Z · score: 4 (3 votes) · EA · GW

Thanks!

As it happens, running a logistic instead of a linear regression for 'mentally ill or not' ~ involvement in EA doesn't change the results. I'll run the others at some point.

Comment by nunosempere on Candidate Scoring System, Fifth Release · 2019-06-05T10:45:40.931Z · score: 6 (5 votes) · EA · GW

being good at handling {[nuclear proliferation],[threatening states such as North Korea]}

Comment by nunosempere on EA grants available to individuals (crosspost from LessWrong) · 2019-05-21T14:26:10.385Z · score: 1 (1 votes) · EA · GW

I myself don't have any contacts inside that organization, I just happened to know that they exist.

Comment by nunosempere on EA grants available to individuals (crosspost from LessWrong) · 2019-02-07T17:07:24.863Z · score: 4 (3 votes) · EA · GW

Due to your proyects being on the area of voting, theory, you might want to contact: https://instituteforcompgov.org/about/

Comment by nunosempere on Introducing Sparrow: a user-friendly app to simplify effective giving · 2019-01-26T14:49:38.491Z · score: 2 (2 votes) · EA · GW

What probabilities do you have in mind for each event when you write the following?

With just 500 users we offset the impact costs to our EA investors in one year. With 100,000 users, we’d move $30 million per year to charity. Even more optimistically, if Sparrow were to acquire 3.3 million users...