Posts

2020: Forecasting in Review. 2021-01-10T16:06:32.082Z
Forecasting Newsletter: December 2020 2021-01-01T16:07:39.015Z
Real-Life Examples of Prediction Systems Interfering with the Real World (Predict-O-Matic Problems) 2020-12-03T22:00:26.889Z
Forecasting Newsletter: November 2020 2020-12-01T17:00:58.898Z
Announcing the Forecasting Innovation Prize 2020-11-15T21:12:39.009Z
Incentive Problems With Current Forecasting Competitions. 2020-11-09T16:20:06.394Z
Forecasting Newsletter: October 2020. 2020-11-01T13:09:50.542Z
Adjusting probabilities for the passage of time, using Squiggle 2020-10-23T18:55:30.860Z
A prior for technological discontinuities 2020-10-13T16:51:32.572Z
NunoSempere's Shortform 2020-10-13T16:40:05.972Z
AI race considerations in a report by the U.S. House Committee on Armed Services 2020-10-04T12:11:36.129Z
Forecasting Newsletter: September 2020. 2020-10-01T11:00:54.354Z
Forecasting Newsletter: August 2020. 2020-09-01T11:38:45.564Z
Forecasting Newsletter: July 2020. 2020-08-01T17:08:15.401Z
Forecasting Newsletter. June 2020. 2020-07-01T09:46:04.555Z
Forecasting Newsletter: May 2020. 2020-05-31T12:35:58.063Z
Forecasting Newsletter: April 2020 2020-04-30T16:41:35.849Z
What are the relative speeds of AI capabilities and AI safety? 2020-04-24T18:21:58.528Z
Some examples of technology timelines 2020-03-27T18:13:19.834Z
[Part 1] Amplifying generalist research via forecasting – Models of impact and challenges 2019-12-19T15:50:33.412Z
[Part 2] Amplifying generalist research via forecasting – results from a preliminary exploration 2019-12-19T15:49:45.901Z
What do you do when you find out you have inconsistent probabilities? 2018-12-31T18:13:51.455Z
The hunt of the Iuventa 2018-03-10T20:12:13.342Z

Comments

Comment by nunosempere on Real-Life Examples of Prediction Systems Interfering with the Real World (Predict-O-Matic Problems) · 2021-01-11T11:00:02.421Z · LW · GW

Two other examples:

  • Youtube's recommender system changes the habits of Youtube video producers (e.g., using keywords at the beginning of the titles, and at the beginning of the video now that Youtube can parse speech)
  • Andrew Yang apparently received death threats over a prediction market on the number of tweets. 
Comment by nunosempere on GraphQL tutorial for LessWrong and Effective Altruism Forum · 2021-01-06T11:14:47.555Z · LW · GW

I've come back to this occasionally, thanks. Here are two more snippets:

To get one post 

{
        post(
            input: {  
            selector: {
                _id: "Here goes the id"
            }      
            }) 
        {
            result {
            _id
            title
            slug
            pageUrl
            postedAt
            baseScore
            voteCount
            commentCount
            meta
            question
            url
            user {
                username
                slug
                karma
                maxPostCount
                commentCount
            }
            }
        }
}

or, as a JavaScript/node function:

let graphQLendpoint = 'https://forum.effectivealtruism.org/graphql' // or https://www.lesswrong.com/graphql. Note that this is not the same as the graph*i*ql visual interface talked about in the post. 

async function fetchPost(id){ 
  // note the async
  let response  = await fetch(graphQLendpoint, ({
    method: 'POST',
    headers: ({ 'Content-Type': 'application/json' }),
    body: JSON.stringify(({ query: `
       {
        post(
            input: {  
            selector: {
                _id: "${id}"
            }      
            }) 
        {
            result {
            _id
            title
            slug
            pageUrl
            postedAt
            baseScore
            voteCount
            commentCount
            meta
            question
            url
            user {
                username
                slug
                karma
                maxPostCount
                commentCount
            }
            }
        }
}`
})),
  }))
  .then(res => res.json())
  .then(res => res.data.post? res.data.post.result : undefined)  
  return response
}

 

To get a user

{
  user(input: {
    selector: {
      slug: "heregoestheslug"
    }
  }){
    result{
      username
      pageUrl
      karma
      maxPostCount
      commentCount
    }
  }
  
}

Or, as a JavaScript function

let graphQLendpoint = 'https://forum.effectivealtruism.org/graphql' // or https://www.lesswrong.com/graphql. Note that this is not the same as the graph*i*ql visual interface talked about in the post. 

async function fetchAuthor(slug){
  // note the async
  let response  = await fetch(graphQLendpoint, ({
    method: 'POST',
    headers: ({ 'Content-Type': 'application/json' }),
    body: JSON.stringify(({ query: `
       {
  user(input: {
    selector: {
      slug: "${slug}"
    }
  }){
    result{
      username
      pageUrl
      karma
      maxPostCount
      commentCount
    }
  }
  
}`
})),
  }))
  .then(res => res.json())
  .then(res => res.data.user? res.data.user.result : undefined)  
  return response
}
Comment by nunosempere on Anti-Aging: State of the Art · 2021-01-03T19:30:42.117Z · LW · GW

Thoughtful answer, thanks

Comment by nunosempere on Anti-Aging: State of the Art · 2021-01-03T13:27:52.385Z · LW · GW

The evidence is promising that in the next 5-10 years, we will start seeing robust evidence that aging can be therapeutically slowed or reversed in humans

 

Are you willing to bet on this? If so, how much?

Comment by nunosempere on Interactive exploration of LessWrong and other large collections of documents · 2020-12-31T09:05:04.895Z · LW · GW

Yes, I'd be interested, many thanks!

Comment by nunosempere on Range and Forecasting Accuracy · 2020-12-26T18:06:43.522Z · LW · GW

Cool. Once you rewrite that, and if you do so before the end of the year, I'd encourage you to resubmit it to this contest

In particular, the reason I'm excited about this kind of work is because it allows us to have at least some information about how accurate long-term predictions can be. Some previous work on this has been done, e.g., rating Kurzweil's predictions from the 90s but overall we have very little information about this kind of thing. And yet we are interested in seeing how good we can be at making predictions n years out, and potentially making decisions based on that. 

Comment by nunosempere on Interactive exploration of LessWrong and other large collections of documents · 2020-12-26T16:12:37.136Z · LW · GW

So here is something I'm interested in: I have a list of cause area candidates proposed in the EA Forum (available here) as a Google Sheet. Could I use a set-up similar to your own to find out similar posts?

Also, you should definitely post this to the EA forum as well. 

Comment by nunosempere on Probability theory implies Occam's razor · 2020-12-26T15:57:45.281Z · LW · GW

Waveman says:

I am not sure you actually justified your claim, that OR follows from the laws of probability with no empirical input. 

I wanted to say the same thing. 

The OP uses the example of age, but I like the example of shade of eye color better. If h is height and s is shade of eye color, then 

weight = alpha * a + beta * s

Then if beta is anything other than 0, your estimate will, on expectation, be worse. This feels correct, and it seems like this should be demonstrable, but I haven't really tried. 

Comment by nunosempere on Recommendation for a good international event betting site like predictit.org · 2020-12-26T15:47:36.356Z · LW · GW

I'd point you towards polymarket (polymarket.com). It trades in USDC (a  cryptocurrency pegged to the US dollar), which you can acquire at various exchanges, like Coinbase or the crypto.com app. 

Comment by nunosempere on Luna Lovegood and the Chamber of Secrets - Part 8 · 2020-12-22T22:27:30.792Z · LW · GW

How did Luna come to represent Ravenclaw at the dueling tournament? Did she sleepwalk even during Lockhart's class, and somehow win the spot by casting spells while sleepwalking?

 

She was sleepwalking, and thus was able to shrug off a Somnium from her opponent, and win. Possibly repeatedly.

Comment by nunosempere on What are the best precedents for industries failing to invest in valuable AI research? · 2020-12-15T21:28:16.848Z · LW · GW

I have some data on this on the top of my head from having read the history of 50 mostly random technologies (database.csv in the post):

  • People not believing that heavier than air flight was a thing, and Zeppelins eventually becoming obsolete
  • Various camera film producing firms, notably Kodak, failing to realize that digital was going to be a thing
  • (Nazi Germany not realizing that the nuclear bomb was going to be a thing)
  • London not investing in better sanitation until the Great Stink; this applies to mostly every major city.
  • People not investing in condoms for various reasons
  • People not coming up with the bicycle as an idea
  • Navies repeatedly not taking the idea of submarines seriously
  • Philip LeBon failing to raise interest in his "thermolamp"

So that's 8/50 of the top of my head (9/50 including Blockbuster, mentioned by another commenter)

I also have some examples of technology timelines here and some technology anecdotes from my sample of 50 technologies here, which might serve as inspiration. 

Comment by nunosempere on The Parable of Predict-O-Matic · 2020-12-13T17:34:51.004Z · LW · GW

The short story presents some intuitions which would be harder to get from a more theoretical standpoint. And these intuitions then catalyzed further discussion, like The Dualist Predict-O-Matic ($100 prize), or my own Real-Life Examples of Prediction Systems Interfering with the Real World (Predict-O-Matic Problems).

Personally, a downside of the post is that "Predict-O-Matic problems" isn't that great a category. I prefer "inner and outer alignment problems for predictive systems," which is neater. On the other hand, if I mention the Parable of the Predict-O-Matic people can quickly understand what I'm talking about. 

But the post provides a useful starting point. In particular, to me it suggests looking to prediction systems as a toy model for the alignment problem, which is something I've personally had fun looking into, and which strikes me as promising.  

Lastly, I feel that the title is missing a "the."

Comment by nunosempere on Real-Life Examples of Prediction Systems Interfering with the Real World (Predict-O-Matic Problems) · 2020-12-12T18:45:55.388Z · LW · GW

Thanks. I keep missing this one, because Good Judgment Open, the platform used to select forecasters, rewards both Brier score and relative Brier score.

Comment by nunosempere on Real-Life Examples of Prediction Systems Interfering with the Real World (Predict-O-Matic Problems) · 2020-12-12T18:44:47.170Z · LW · GW

You can see this effect for election predictions, such that there are plenty of smallish predictors which predicted the result of the current election closely (but such that it's easy to speculate that they're just a selection effect) 

Comment by nunosempere on Real-Life Examples of Prediction Systems Interfering with the Real World (Predict-O-Matic Problems) · 2020-12-12T18:43:18.398Z · LW · GW

Thanks to both; this is a great example; I might add it to the main text

Comment by nunosempere on Real-Life Examples of Prediction Systems Interfering with the Real World (Predict-O-Matic Problems) · 2020-12-12T18:42:42.578Z · LW · GW

Another example, from @albrgr

"This is kind of crazy: https://nber.org/digest-202012/corporate-reporting-era-artificial-intelligence Companies have learned to use (or exclude) certain words to make their corporate filings be interpreted more positively by financial ML algorithms."

Then quoting from the article:

The researchers find that companies expecting higher levels of machine readership prepare their
disclosures in ways that are more readable by this audience. "Machine readability" is measured in
terms of how easily the information can be processed and parsed, with a one standard deviation
increase in expected machine downloads corresponding to a 0.24 standard deviation increase in
machine readability. For example, a table in a disclosure document might receive a low readability
score because its formatting makes it difficult for a machine to recognize it as a table. A table in a
disclosure document would receive a high readability score if it made effective use of tagging so
that a machine could easily identify and analyze the content.
Companies also go beyond machine readability and manage the sentiment and tone of their
disclosures to induce algorithmic readers to draw favorable conclusions about the content. For
example, companies avoid words that are listed as negative in the directions given to algorithms.
The researchers show this by contrasting the occurrence of positive and negative words from the
Harvard Psychosocial Dictionary — which has long been used by human readers — with those
from an alternative, finance-specific dictionary that was published in 2011 and is now used
extensively to train machine readers. After 2011, companies expecting high machine readership
significantly reduced their use of words labelled as negatives in the finance-specific dictionary,
relative to words that might be close synonyms in the Harvard dictionary but were not included in
the finance publication. A one standard deviation increase in the share of machine downloads for a
company is associated with a 0.1 percentage point drop in negative-sentiment words based on the
finance-specific dictionary, as a percentage of total word count.

Comment by nunosempere on Parable of the Dammed · 2020-12-11T09:47:26.511Z · LW · GW

So for some realism which the original story didn't call for —it's a "parable trying to make a point", not a "detailed historical account of territorial feuds in 15th century Albania"—, we can look at how this works out in practice. To do this, we look to The Kanun of Lekë Dukagjini, which describes the sort of laws used to deal with this kind of thing in 15th century Albania. My details might be iffy here, but I did read the book and remember some parts.

In practice, there are several points of intervention, if I'm remembering correctly:

  • After the first murder, the extended family of the murdered goes after the murderer, to the extent that he can't safely go out of his home. If he is killed, the feud ends on the part of the murdered's family.
  • At any point, one of the families can ask a more powerful figure to mediate; in some regions this can be a cleric. The resolution might involve substantial amounts of money to be paid, which, crucially, is set beforehand by law, in excruciating detail depending on the conditions.
  • The lands wouldn't in fact be the most valuable resource here; it would be the working power of adult men, who can't get out because they would be killed in revenge. This cripples both families economically, so they do have an incentive to cooperate.

So, in practice

a clever couple from one of the families hatched an idea

I get the impression that this ends with the clever couple getting killed in the middle of the night by one of the more violent and impulsive cousins of the second family, and maybe the second family paying some reparations if they're caught. Probably less than, you know, if they'd killed a normal couple. That, or the dam gets destroyed. Or actually, the husband from the clever couple would have to ask the Patriarch of the family for permission, who would veto the idea because he wants to make the truce work, and is hesitant to lose more of his sons to a new feud. Also, with or without the discount factor rural people in Albania have, doing this kind of thing wouldn't be worth it. Or actually, the clever couple learnt in childhood that this kind of thing wasn't worth it, and got some lashes in the process. 

Violence escalates, and the feud breaks out anew - but peace is even harder to come by, now, since the river has been permanently destroyed as a Schelling point.

The Schelling point wasn't the river, the Schelling point was someone more powerful than you telling you not to start trouble. This is harder to game. Also, you don't have "the government", you have "the more powerful village cacique," or the priest, which works because you don't want to hell when you die. 

You do see a thing in rural Spain with territory boundaries being marked by stones, and those stones being moved, which kind of works if one side doesn't spend time in the land.

Comment by nunosempere on Forecasting Newsletter: November 2020 · 2020-12-10T15:31:33.634Z · LW · GW

Makes sense

Comment by nunosempere on Real-Life Examples of Prediction Systems Interfering with the Real World (Predict-O-Matic Problems) · 2020-12-08T16:47:34.762Z · LW · GW

Looks pretty fun!

Comment by nunosempere on Open & Welcome Thread - December 2020 · 2020-12-06T16:22:27.946Z · LW · GW

I'd like to point people to this contest, which offers some prizes for forecasting research. It's closing  on January the 1st, and hasn't gotten any submissions yet (though some people have committed to doing so.)

Comment by nunosempere on Real-Life Examples of Prediction Systems Interfering with the Real World (Predict-O-Matic Problems) · 2020-12-04T10:53:06.853Z · LW · GW

Which minimal conditions are necessary for a Predict-O-Matic scenario to appear?

One answer to that might be "either inner or outer alignment failures" in the forecasting system. See here for that division made explicit

Comment by nunosempere on Real-Life Examples of Prediction Systems Interfering with the Real World (Predict-O-Matic Problems) · 2020-12-04T08:12:54.633Z · LW · GW

Thanks, changed

Comment by nunosempere on Incentive Problems With Current Forecasting Competitions. · 2020-12-03T21:12:30.186Z · LW · GW

Thanks!

Comment by nunosempere on The LessWrong 2018 Book is Available for Pre-order · 2020-12-02T22:28:59.687Z · LW · GW

How ironic.

Comment by nunosempere on Luna Lovegood and the Chamber of Secrets - Part 3 · 2020-12-02T15:25:32.072Z · LW · GW

Maybe she reminds them of Harry.

Comment by nunosempere on Forecasting Newsletter: November 2020 · 2020-12-01T21:14:36.876Z · LW · GW

Thanks

Comment by nunosempere on Can We Place Trust in Post-AGI Forecasting Evaluations? · 2020-11-26T09:28:20.915Z · LW · GW

Yes, I can imagine cases where this setup wouldn't be enough.

Though note that you could still buy the shares the last year. Also, if the market corrects by 10% each year (i.e., a value of a share of yes increases from 10 to 20% to 30% to 40%, etc. each year), it might still be worth it (note that the market would resolve each year to the value of a share, not to 0 or 100).

Also note that the current way in which prediction markets are structured is, as you point out, dumb: you bet 5 depreciating dollars which then go into escrow, rather than $5 worth of, say, S&P 500 shares, which increase in value. But this could change.

Comment by nunosempere on Can We Place Trust in Post-AGI Forecasting Evaluations? · 2020-11-23T09:48:37.466Z · LW · GW

the failures of "quick resolution" (years)

Note that you can solve this by chaining markets together, i.e., having a market every year asking what the next market will predict, where the last market is 1y before AGI. This hasn't been tried much in reality, though.

Comment by nunosempere on AGI Predictions · 2020-11-21T10:27:52.806Z · LW · GW

That was fun. This time, I tried not to update too much on other people's predictions. In particular, I'm at 1% for "Will we experience an existential catastrophe before we build AGI?" and at 70% for "Will there be another AI Winter (a period commonly referred to as such) before we develop AGI?", but would probably defer to a better aggregate on the second one.

Comment by nunosempere on Range and Forecasting Accuracy · 2020-11-18T17:58:25.057Z · LW · GW

Another interesting this you can do is to calculate the accuracy score (Brier score - average of the Brier scores for the question), which adjusts for question difficulty. You gesture at this in your "Accuracy between questions" section.

If you do this, forecasts made further from the resolution time do worse, both in PredictionBook and in Metaculus (correlation is p<0.001, but very small). Code in R:

datapre <- read.csv("pb2.csv") ## or met2.csv
data <- datapre[datapre$range>0,]

data$brier = (data$result-data$probability)^2

accuracyscores = c() ## Lower is better, much like the Brier score.
ranges = c()
for(id in unique(data$id)){
  predictions4question = (data$id == id)
  
  briers4question = data$brier[predictions4question]
  accuracyscores4question = briers4question - mean(briers4question)
  ranges4question = data$range[predictions4question]
  
  accuracyscores=c(accuracyscores,accuracyscores4question)
  ranges=c(ranges, ranges4question)
}
summary(lm(accuracyscores ~ ranges))

Comment by nunosempere on Range and Forecasting Accuracy · 2020-11-18T17:55:11.898Z · LW · GW

Anyways, if I adjust for question difficulty, results are as you would expect; accuracy is worse the further removed the forecast is from the resolution.

Comment by nunosempere on Range and Forecasting Accuracy · 2020-11-18T17:52:25.592Z · LW · GW

So I was trying to adjust for longer term questions being easier by doing the follow:

  • For each question, calculate the average Brier score for available predictions
  • For each prediction, calculate the accuracy score as Brier score - average Brier scores of the question.

Correlate accuracy score with range. So I was trying to do that, and I thought, well, I might as well run the correlation between accuracy score and log range. But then some of the ranges are negative, which shouldn't be the case.

Comment by nunosempere on Range and Forecasting Accuracy · 2020-11-18T17:38:19.521Z · LW · GW

Why do some forecast have negative ranges?

Comment by nunosempere on Range and Forecasting Accuracy · 2020-11-18T17:21:09.951Z · LW · GW

Another interesting thing you can do with the data is to calculate the prior probability that a Metaculus or PB question will resolve positively:

data <- read.csv("met2.csv") ## or pb2.csv
data$brier = (data$result-data$probability)^2 
results = c()
for(id in unique(data$id)){
  predictions = ( data$id == id ) 
  result = data$result[predictions[1]]
  results = c(results, result)
}
mean(results) 

For Metaculus, this is 0.3160874, for PB this is 0.3770311

Comment by nunosempere on Range and Forecasting Accuracy · 2020-11-18T17:17:35.163Z · LW · GW

Nice post! I agree that the conclusion is counterintuitive.

For Metaculus, the results are pretty astonishing: the correlation is negative for all four options, meaning that the higher the range of the question, the lower the Brier score (and therefore, the higher the accuracy)! And the correlation is extremly low either: -0.2 is quite formidable.

I tried to replicate some of your analysis, but I got different results for Metaculus (I still got the negative correlation for PredictionBook, though). I think this might be to an extent an artifact of the way you group your forecasts:

In bash, add headers, so that I can open the files and see how they look

$ echo "id,questionrange,result,probability,range" > met2.csv
$ cat met.csv >> met2.csv
$ echo "id,questionrange,result,probability,range" > pb.csv
$ cat pb.csv >> pb2.csv

In R:


library(ggplot2)

## Metaculus
data <- read.csv("met2.csv")
data$brier = (data$result-data$probability)^2

summary(lm(data$brier ~ data$range)) ## Positive correlation.
ggplot(data=data, aes(x=range, y=brier))+
  geom_point(size=0.1)

### Normalize the range and the brier to get better units
data$briernorm = (data$brier - mean(data$brier))/sd(data$brier)
data$rangenorm = (data$range - mean(data$range))/sd(data$range)
summary(lm(data$briernorm ~ data$rangenorm))
   ### I get a correlation of ~0.02, on a standard deviation of 1, i.e., a correlation of 2%.

## Same thing for PredictionBook
data <- read.csv("pb2.csv")
data$brier = (data$result-data$probability)^2

summary(lm(data$brier ~ data$range)) ## Negative correlation.
ggplot(data=data, aes(x=range, y=brier))+
  geom_point(size=0.2)

### Normalize the range and the brier to get better units
data$briernorm = (data$brier - mean(data$brier))/sd(data$brier)
data$rangenorm = (data$range - mean(data$range))/sd(data$range)

summary(lm(data$briernorm ~ data$rangenorm))
### I get a correlation of ~-0.02, on a standard deviation of 1, i.e., a correlation of -2%.

Essentially, when you say

To compare the accuracy between forecasts, one can't deal with individual forecasts, only with sets of forecasts and outcomes. Here, I organise the predictions into buckets according to range.

This doesn't necessarily follow, i.e., you can still calculate a regression between Brier score and range (time until resolution).

Comment by nunosempere on Range and Forecasting Accuracy · 2020-11-18T17:13:30.343Z · LW · GW

Nitpicks: 

  • Some typos: ones => one's; closed questions (questions that haven't yet been resolved, but that can still be predicted on) => closed questions (questions that haven't yet been resolved, but that can't be predicted on); PredictionPook => PredictionBook
  • You don't clearly say when you start using Klong. Klong also sounds like it might be really fun to learn, but it's maybe a little suboptimal for replication purposes, because it isn't as well-known.
Comment by nunosempere on Scoring 2020 U.S. Presidential Election Predictions · 2020-11-15T23:10:30.293Z · LW · GW

Here is a similar post.

Comment by nunosempere on Open & Welcome Thread – November 2020 · 2020-11-10T23:57:51.362Z · LW · GW

In practice, you can't (monetarily) reward forecasters with unbounded scoring rules. You may also want scoring rules to be somewhat forgiving. 

Comment by nunosempere on Incentive Problems With Current Forecasting Competitions. · 2020-11-09T22:33:44.665Z · LW · GW

So specifically, in forecasting tournaments, if A knows that "X", and B knows that "X=>Y", and both leave a comment, then the aggregate can come to "Y", particularly if A and B are incentivized with respect and upvotes from other forecasters. In prediction markets, this is trickier (there may not even be a comments section).

Comment by nunosempere on Incentive Problems With Current Forecasting Competitions. · 2020-11-09T18:20:36.749Z · LW · GW

Yeah, it solves some, but not all. For example, not the "incentives not to share information and to produce corrupt information" e.g., PredictIt traders may have created fake polls in the past. 

Comment by nunosempere on Share your personal stories of prediction markets · 2020-11-08T21:45:35.562Z · LW · GW

I bet 50€ for myself and 25€ for a friend, which I roughly doubled. I did this by setting a betting budget I was willing to loose and Kelly betting on it. I used betfair.es

Comment by nunosempere on Babble Challenge: 50 thoughts on stable, cooperative institutions · 2020-11-05T11:49:34.523Z · LW · GW

This first line gets show in the index, even though it is in spoilers. This should probably be considered a bug.

  1. The world was less complex in the past, which made optimization easier (but institutions only have a set amount of optimization power).
  2. Values were less complex in the past, which made it easier to optimize for them.
  3. America is no longer an expanding empire. Samo Burja has a theory of functioning institutions in which having new worlds to explore and new realms to conquer makes institutions healthier because leaders they can provide some of the expanding pie to their subordinates, rather than fighting for pieces of the existing pie.
  4. Lack of a true rival/enemy/antagonist/threat to drive America to greatness. The Soviet Union might have been this. An alien invasion might have provided this. But right now Americans fight other Americans.
  5. Relatedly, America's culture of ever striving for more might have worked while they were an expanding empire, but backfires once they are not.
  6. The fall of the Soviet union enables the American underclass to gain class consciousness and rebel against bourgeoisie
  7. Americans have become dumber.
  8. Uninspiring topmost American political offices. Ulysses S. Grant, Roosvelt and Eisenhower give way to Ronald Reagan, Biden, and Trump.
  9. Lack of virtue in topmost American political offices. Marital scandals, corruption, underhanded tactics siphon off the spirit of a nation.
  10. American public officials have gained class consciousness as a separate political entity with its own interests separate from those of the masses they serve.
  11. American teachers being less respected means less teachers means worse role models means worse and less curious and capable students means civics can't be taught.
  12. America started soul-searching after WW2/Vietnam war/ 9/11 attacks / a variety of other events, and it hasn't stopped
  13. Stigmatization of cigarettes means that Americans are much more pent-up.
  14. American morals become more complex, which means that its easier to infringe on them, which means that more people gain small status points by pointing them out, which leads to societal conflict.
  15. Conquest's second law of politics "Any organization not explicitly right-wing sooner or later becomes left-wing" means that American conservatives leave to form their own organizations, and there are fewer institutions to uphold shared values.
  16. America has become more connected and homogeneous, which means that different states don't try different things and see what works
  17. In becoming more complex, American morals stifle trying out new things and seeing what works.
  18. Same but with laws rather than morals.
  19. Americans are less willing to casually disregard parts of their population, which makes everything more difficult.
  20. Lack of one clear common American pecking order leads to more infighting.
  21. A substantial population of Americans cease to identify as "Americans" in any meaningful sense.
  22. Americans have become more arrogant, which means that they cease to import habits and cultural technologies when they work better.
  23. American universities becoming more mediocre and producing worse leaders.
  24. America becoming less religious has ripple effects
  25. The decline of the impulse to beat up troublemakers in America means that there are more of them.
  26. American nerds collectively gaining more power while having poor social skills.
  27. Some ripple effect of the decline of the Italian-American mafia. For example, maybe knowing that if you pissed someone enough they could hire someone to kill you had a salutatory social effect.
  28. Rising income inequality in the United States due to the decline of the labor unions leads to social division.
  29. Poor American architecture leads to people using public social spaces less.
  30. Elite overproduction. In particular, you might be able to control a number of people with O(sqrt(n)) people or with O(log(n)) people, but this means that as n grows a smaller proportion of people can be leaders.
  31. American baby boomers or whatever generation is currently in power having had some nutrient deficiency in childhood which makes them less cooperative
  32. American public spaces smell worse / are more noisy / more polluted ... so people use them less.
  33. Cultural division being economically more profitable
  34. Old institutions having more prestige and thus having more to loose by shaking things up and trying new things.
  35. In combination with a lack of a world war to renew American institutions.
  36. Southern Americans not having been convincingly defeated in the first American civil war, or thinking that they could win a second round.
  37. Lack of a culture of creating small local institutions, spreading the know-how of how to do it.
  38. Succession problem having never been solved in America, which means that it still depends on great leaders to correct institutions.
  39. The decline of Freemasons or some other secret cabal as a stabilizing force behind key institutions. Crucially, old-school Freemasons apparently banned discussion of religion and politics.
  40. American institutions successfully being sabotaged by rival powers
  41. Americans giving less of a fuck about their institutions.
  42. Indisputable Canadian superiority in all things leads to either denial or a desire to imitate them, which spits the population.
  43. Same but with Scandinavian countries.
  44. Americans having more interesting things to distract themselves with. That is, creating institutions is as satisfactory as it ever was, but other things are more satisfactory in comparison.
  45. Rise of (individual) hedonism
  46. Some demographic change having ripple effects.
  47. Americans read less of the classics and thus notice fewer skulls and make mistakes again and again
  48. America having unprocessed trauma and subconsciously wanting to fail.
  49. There is now no large majority of Americans who have a common coherent extrapolated volition, so institutions can't implement it.
  50. There is nothing to explain; institutions are as healthy as they ever were, and, e.g. availability bias makes us only notice the failures.
Comment by nunosempere on How do you read the news critically? · 2020-11-01T16:26:59.175Z · LW · GW

My own answer is to make predictions (commonly on Good Judgment Open), and then notice a couple of months afterwards that the events which the news predicted (Mark Esper or Paul Guedes quitting or getting fired, Lukashenko being overthrown, etc.) tend not to happen.

Comment by nunosempere on Launching Forecast, a community for crowdsourced predictions from Facebook · 2020-10-21T11:47:36.995Z · LW · GW

Forecast's midpoint brier score (measured at the midpoint between a question’s launch and resolution dates) across all closed Forecasts over the past few months is 0.204, a bit better than Good Judgement's published result of 0.227 for prediction markets

The relative difficulty of the questions is probably important here, and the comparison "a bit better than Good Judgment" is probably misleading. In particular, I'd expect Good Judgement to have questions with longer time horizons (which are harder to forecast), if only because your platform is so young.

Our first priority is to build something that’s really fun for people who want to engage in rational debate about the future

How are you defining "really fun" as distinct from "addictive"?

Since June, the Forecast community has made more than 50,000 forecasts on a few hundred questions--and they're actually reasonably accurate.

50,000 forecasts isn't that much, maybe 30x the number of forecasts I've made, but if you scale this up to Facebook scale, I'd imagine you might be able to train a halfway decent ML system. I'd be keen to see a firm and binding ethical commitment which handles this eventuality before you accumulate the data, but I don't know how that would look in the context of Facebook's corporate structure and ethics track record.

Comment by nunosempere on NunoSempere's Shortform · 2020-10-20T21:41:08.617Z · LW · GW

This is a test to see if latex works

Comment by nunosempere on NunoSempere's Shortform · 2020-10-19T19:24:40.866Z · LW · GW

Fixed, thanks

Comment by nunosempere on What are some beautiful, rationalist artworks? · 2020-10-19T11:17:54.178Z · LW · GW

You could also have a calendar which doesn't require that adjustment.  

Comment by nunosempere on A prior for technological discontinuities · 2020-10-19T09:31:50.533Z · LW · GW

I'm refering to the Jalali calendar.

Comment by nunosempere on A prior for technological discontinuities · 2020-10-18T23:14:57.424Z · LW · GW

What? I feel like this comment doesn't answer to the post above at all. 

tl;dr of the post: If I look at 50 technologies which to a first approximation I expect to be roughly randomly chosen, I can broadly divide them into:

  • Probably with "big" discontinuities: Aviation, nuclear weapons, petroleum, printing, spaceflight, rockets, aluminium production, radar, radio, automobile, transistors, and PCR.
  • Probably with "medium" discontinuities: cryptography, glass, rail transport, water supply and sanitation, diesel car, automation, television, steam engine, timekeeping devices.
  • Probably with "small" discontinuities: cycling, furniture, robotics, candle making, sound recording, submarines, batteries, multitrack recording, paper, telescopes, wind power.
  • Probably not discontinuous: ceramics, film, oscilloscopes, photography, artificial life, calendars, chromatography, bladed weapons, condoms, hearing aids, telephones, internal combustion engine, manufactured fuel gases, perpetual motion machines, motorcylces, nanotech, portable gas stoves, roller coasters.

Using that, I can sort of get a prior for the likelihood of "big" discontinuities; which falls between 8% (4/50) and 24% (12/50). I can also get a rough probability of a discontinuity per year (~1% if the technology ever shows one). All of this has caveats, outlined in the post. 

***

Your first point, that if I paper-push hard enough I can make anything look continuous doesn't apply, because I'm not in fact doing that. For example, throughout WW2 there of were several iterations of the radar, each progressively less shitty, but progress was fast enough (due to the massive, parallel, state funding) that I'd still categorize it as a discontinuity (and note that it did get into the OODA loops of the Germans, the effectiveness of whose submarines greatly declined after the deployment of radar). Similarly, the Wright brothers also experimented with different designs, but overall their progress on heavier than air flight was rapid and anomalous enough that I categorized it as a discontinuity. Similarly, for transistors, there were of course many different transistors, but MOSFET transistors were substantially better on miniaturization than BJTs, even if MOSFETs were worse during their very first years. Similarly, transistors themselves were better than vacuum triodes, though I'm sure that if you squint you can also find something continuous somewhere.

Even if I were paper pushing, detecting 12/50 would still give you a lower bound for the probability of a "big" discontinuity (24% + however many I paper-pushed to the medium or small categories). Even if there wasn't a clear line between "continuous" and "discontinuous", I'd expect more continuous technologies to fall in the "medium", "small" and "probably not" buckets, and more discontinuous technologies in the "big" and "medium" buckets.

Some of your gripes could conceivably apply to some parts of AI Impacts' investigation (e..g, they don't categorize printing as a "large and robust" discontinuity), but I am not them. 

Big fan of your work, though. 

Comment by nunosempere on Bet On Biden · 2020-10-18T08:19:19.619Z · LW · GW

I concur with this. In my case, I set aside some amount of money I was comfortable losing, calculated the Kelly bet based on the expected win, and bet some amount on Biden. I used Betfair, which is available to Europeans.

Some commenters mention the EMH. As counterevidence, I present that Betfair is offering even odds that Biden will win at least one state Trump won last time. (The 538 model gives 96% to this) (This was wrong)