I've been following this with interest.

Re: Telecommunications performance, the red telephone might also be a a discontinuity in practical terms.

The 1962 Cuban Missile Crisis made the hotline a priority. During the standoff, official diplomatic messages typically took six hours to deliver; unofficial channels, such as via television network correspondents, had to be used too as they were quicker

That is, even though faster systems existed, they hadn't been implemented in the area of communications between the Soviet Union and the USA (pretty huge blindspot), but could be implemented more or less immediately, once both regimes actually bothered.

Also of interest to readers might be: some other discontinuities, one in passenger ship length and the other one on time needed to circumnavigate the Earth. AI impacts also has a couple of other discontinuities on their webpage, not mentioned/linked above:

• Ship size (also pretty recently added)
• From nuclear weapons
• Altitude
• ...

Running the results again on the SlateStarCodex Survey results of this year. EAs are less mentally ill than non-EAs, who are less mentally ill than respondents who identify as "sort of" EA. From this we should conclude that all three groups are all basically the same, because the difference is not significant at all.

The code used to find that out is, in R:

setwd("my/directory") ## directory in which the 2020ssc_public.xlsx file resides, downloadable from https://slatestarcodex.com/2020/01/20/ssc-survey-results-2020/
install.packages("openxlsx", dependencies = TRUE)
library(openxlsx)

D <- read.xlsx("2020ssc_public.xlsx")

mentally_ill_strict =  c("I have a formal diagnosis of this condition")
mentally_ill_loose =  c("I have a formal diagnosis of this condition", "I think I might have this condition, although I have never been formally diagnosed")

m <- mentally_ill_strict
D$MentallyIll_1 = D$Depression %in% m | D$Anxiety %in% m | D$OCD %in% m  | D$Eatingdisorder %in% m  | D$PTSD %in% m  | D$Alcoholism %in% m  | D$Drugaddiction %in% m  | D$Borderline %in% m  | D$Bipolar %in% m

m <- mentally_ill_loose
D$MentallyIll_2 = D$Depression %in% m | D$Anxiety %in% m | D$OCD %in% m  | D$Eatingdisorder %in% m  | D$PTSD %in% m  | D$Alcoholism %in% m  | D$Drugaddiction %in% m  | D$Borderline %in% m  | D$Bipolar %in% m

summary(lm(D$MentallyIll_1 ~ D$EAID))
summary(glm(D$MentallyIll_1 ~ D$EAID, family = "binomial")) ## This is a logistic regression

summary(lm(D$MentallyIll_1 ~ D$EAID))
summary(glm(D$MentallyIll_1 ~ D$EAID, family = "binomial")) ## This is a logistic regression

And the results are

Call:
lm(formula = D$MentallyIll_1 ~ D$EAID)

Residuals:
    Min      1Q  Median      3Q     Max 
-0.5256 -0.5151  0.4744  0.4849  0.5325 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.46753    0.04027  11.609   <2e-16 ***
D$EAIDNo     0.04757    0.04109   1.158    0.247    
D$EAIDSorta  0.05811    0.04154   1.399    0.162    
D$EAIDYes    0.03499    0.04328   0.808    0.419    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.4998 on 7335 degrees of freedom
Multiple R-squared:  0.0004201,	Adjusted R-squared:  1.124e-05 
F-statistic: 1.027 on 3 and 7335 DF,  p-value: 0.3792

> summary(glm(D$MentallyIll_1 ~ D$EAID, family = "binomial")) ## This is a logistic regression

Call:
glm(formula = D$MentallyIll_1 ~ D$EAID, family = "binomial")

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-1.221  -1.203   1.134   1.152   1.233  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)
(Intercept)  -0.1301     0.1615  -0.805    0.421
D$EAIDNo      0.1905     0.1648   1.156    0.248
D$EAIDSorta   0.2327     0.1666   1.397    0.162
D$EAIDYes     0.1401     0.1735   0.807    0.419

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 10167  on 7338  degrees of freedom
Residual deviance: 10164  on 7335  degrees of freedom
AIC: 10172

Number of Fisher Scoring iterations: 3

> summary(lm(D$MentallyIll_1 ~ D$EAID))

Call:
lm(formula = D$MentallyIll_1 ~ D$EAID)

Residuals:
    Min      1Q  Median      3Q     Max 
-0.5256 -0.5151  0.4744  0.4849  0.5325 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.46753    0.04027  11.609   <2e-16 ***
D$EAIDNo     0.04757    0.04109   1.158    0.247    
D$EAIDSorta  0.05811    0.04154   1.399    0.162    
D$EAIDYes    0.03499    0.04328   0.808    0.419    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.4998 on 7335 degrees of freedom
Multiple R-squared:  0.0004201,	Adjusted R-squared:  1.124e-05 
F-statistic: 1.027 on 3 and 7335 DF,  p-value: 0.3792

> summary(glm(D$MentallyIll_1 ~ D$EAID, family = "binomial")) ## This is a logistic regression

Call:
glm(formula = D$MentallyIll_1 ~ D$EAID, family = "binomial")

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-1.221  -1.203   1.134   1.152   1.233  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)
(Intercept)  -0.1301     0.1615  -0.805    0.421
D$EAIDNo      0.1905     0.1648   1.156    0.248
D$EAIDSorta   0.2327     0.1666   1.397    0.162
D$EAIDYes     0.1401     0.1735   0.807    0.419

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 10167  on 7338  degrees of freedom
Residual deviance: 10164  on 7335  degrees of freedom
AIC: 10172

Number of Fisher Scoring iterations: 3

Center for Assisting EA Study And Research: CAESAR.

Assisting ~ Aiding ~ Abetting

I started somewhat by accident...

Would you give a guess as to the probability you would have assigned to ending up doing something like this before you in fact did? How unlikely was this class of outcomes?

[The images don't load for me. What I usually do in these cases is to upload the image to github, and then add it to the EA forum with ![](imageURL)]

Curiously enough, 'twas me who fulfilled the bounty.

You could also consider providing your probabilities in the form of distributions, for example by answering a question like "What probability would you assign to tribbles [1] being sentient after 1000 more hours of research?" This would perhaps solve part of the problem of communicating the uncertainty which you want to communicate.

Some example answers might be:

In the first one your uncertainty is significant. You have a probability of ~45%, but you consider it likely that you will update a lot. You just don't know in which direction: You consider it equally likely that you will end up at 30% or at 70%.

In the second one, research has mostly been done and you've already mostly made up your mind. You have a probability of ~45%, and you believe that further research is most likely to move you to 42%, or to 47%, but not that much further. You'd be shocked if you ended up with a probability of more than 60%.

The third one is the distributional equivalent of you making a shrug. Your probability is 45%, but really, tribbles being so underexplored means that your distribution looks pretty much uniform.

Note that it's even possible that all three distributions have the same mean (~45%). This would mean that in all three cases you'd think that a bet of 45:55 would be a fair bet (that is, that it has an expected value of 0).

You could also predict the standard deviation of your distribution (how broad it is) after 1000h of research, 2000h, 5000h, etc., aggregate the distributions of all your researchers, and do other nice things.

[1] Fictional species.

Hey, regarding your question "What types of talent do you currently think [your organization // EA as a whole] will need more of over the next 5 years? (Pick up to 6)", I think you might want to word it somewhat differently, and perhaps disambiguate between:

• On a scale 0-10, how much [X] talent will [EA/your organization] need over the next 5 years?".
• On a scale 0-10, how much will the need for [X] talent in [EA/your organization] increase over the next 5 years?".

This mainly has the advantage of allowing for more granular comparison. For example, maybe management is always a solid 10, whereas government expertise is a solid 7, but both always fall in the top 6, and the difference might sometimes be important. I also think that the second wording is somewhat easier to read / requires less cognitive labor.

Two book recommendations are The Power of Survey Design: A User's Guide for Managing Surveys, Interpreting Results, and Influencing Respondents and Improving survey questions - Design and Evaluation. I should have a short review/summary and some checklists somewhere, if you're interested.

I researched this at some point; will try to write something up this week, though I might do so in a separate post. Thanks for asking the question!

I disagree; I see "supply chain accountability" as a very specific, almost technical term (the word international is a little bit redundant, though), which contains enough information for me to figure out what it means.