All of elifland's Comments + Replies

5 forecasters from Samotsvety Forecasting discussed the forecasts in this post.

First, I estimate that the chance of direct Great Power conflict this century is around 45%. 

Our aggregated forecast was 23.5%. Considerations discussed were the changed incentives in the nuclear era, possible causes (climate change, AI, etc.) and the likelihood of specific wars (e.g. US-China fighting over Taiwan).

Second, I think the chance of a huge war as bad or worse than WWII is on the order of 10%.

Our aggregated forecast was 25%, though we were unsure if t... (read more)

Related: Scalaby using labour tag and the concept of Task Y

Hey, thanks for sharing these other options. I agree that one of these choices makes more sense than forecasting in many cases, and likely (90%) the majority. But I still think forecasting is a solid contender and plausibly (25%) the best in the plurality of cases. Some reasons:

1. Which activity is best likely depends a lot on which is easiest to actually start doing, because I think the primary barrier to doing most of these usefully is "just" actually getting started and completing something. Forecasting may (40%)^[1] be the most fun and least intimidat

It also strikes against recent work on patient philanthropy, which is supported by Will MacAskill's argument that we are not living in the most influential time in human history.

Note that patient philanthropy includes investing in resources besides money that will allow us to do more good later; e.g. the linked article lists "global priorities research" and "Building a long-lasting and steadily growing movement" as promising opportunities from a patient longtermist view.

Looking at the Future Fund's Areas of Interest, at least 5 of the 10 strike me as... (read more)

At first I thought the scenarios were separate so they would be combined with an OR to get an overall probability, which then made me confused when you looked at only scenario 1 for determining your probability for technological feasibility.

I was also confused about why you assigned 30% to polygenic scores reaching 80% predictive power in Scenario 2 while assigning 80% to reaching saturation at 40% predictive power in the Scenario 1, because when I read 80% to reach saturation at 40% predictive power I read this as "capping out at around 40%" which would o... (read more)

Thanks for sharing Ryan, and that makes sense in terms of another unintended consequence of our judging criteria; good to know for future contests.

Great point. Perhaps we should have ideally reported the mean of this type of distribution, rather than our best guess percentages. I'm curious if you think I'm underconfident here?

Edit: Yeah I think I was underconfident, would now be at ~10% and ~0.5% for being 1 and 2 orders of magnitude too low respectively, based primarily on considerations Misha describes in another comment placing soft bounds on how much one should update from the base rate. So my estimate should still increase but not by as much (probably by about 2x, taking into account possibility... (read more)

The estimate being too low by 1-2 orders of magnitude seems plausible to me independently (e.g. see the wide distribution in my Squiggle model ^[1]), but my confidence in the estimate is increased by it being the aggregated of several excellent forecasters, who were reasoning independently to some extent. Given that, my all-things-considered view is that 1 order of magnitude off^[2] feels plausible but not likely (~25%?), and 2 orders of magnitude seems very unlikely (~5%?).

1. ^{^}

   EDIT: actually looking closer at my Squiggle model I think it should be mo

I agree the risk should be substantially higher than for an average month and I think most Samotsvety forecasters agree. I think a large part of the disagreement may be on how risky the average month is.

From the post:

(a) may be due to having a lower level of baseline risk before adjusting up based on the current situation. For example, while Luisa Rodríguez’s analysis puts the chance of a US/Russia nuclear exchange at .38%/year. We think this seems too high for the post-Cold War era after new de-escalation methods have been implemented and lessons have bee

Adversarial collaborations on important topics

Epistemic Institutions

There are many important topics, such as the level of risk from advanced artificial intelligence and how to reduce it, among which there are  reasonable people with very different views. We are interested in experimenting with various types of adversarial collaborations, which we define as people with opposing views working to clarify their disagreement and either resolve the disagreement or identify an experiment/observation that would resolve it. We are especially excited about comb... (read more)

I found this thought-provoking. I'd be curious to hear more about your recommendations for readers. I'm wondering:

1. Would you recommend ~all readers try decreasing their sleep to ~6 hours a night and observe the effects? Or should they slowly decrease until the effects are negative?
   1. If not, how should they decide whether it makes sense for them?
2. What percentage of readers do you estimate would be overall more productive with ~6 hours of sleep than their unrestricted amount?
   1. How much individual variability do you think there is here?

Some background is that... (read more)

Thanks to Juan Cambeiro, the questions are now also viewable as a Metaculus series.

They generally only accept applications from registered charities, but speculation grants (a) might be a good fit for smaller projects (40%).

My read is that speculation grants are a way for projects applying to SFF to get funding more quickly, rather than a way for projects that aren't eligible for SFF to get funding (I believe SFP serves this purpose).

There results are pretty interesting! I'm surprised at how much optimism there is about 25 unique people/groups compared to 100 total entries; my intuition for expecting an average of about 4 entries per person/group was that most would only submit 1-2, but it only takes a few to submit on many questions to drive the average up substantially.

My answer to your question depends on how you define "good for the long-term future". When I think about evaluating the chance an action is good including of long-run effects, specifying a few more dimensions matters to me. It feels like several combinations of these could be reasonable and would often lead to fairly different probabilities.

Expected value vs. realized value

Does "good for the long-term future" mean: good in expectation, or actually having good observed effects?

What is the ground truth evaluation?

Is the ground truth evaluation one that would... (read more)

Some meta forecasting questions:

And the same on Manifold markets: here and here

Seconding Nuño's assessment that this comment is awesome. While waiting for his response I'll butt in with some quick off-the-cuff takes of my own.

On why no countries use prediction markets / forecasting to make crucial decisions:

My first reaction is "idk, but your comment already provides a really great breakdown of options that I would be excited to be turned into a top-level post."

If I had to guess I think it's some combination of universal human biases and fundamental issues with the value of prediction markets at present. On human biases, it see... (read more)

Really appreciate hearing your perspective!

On causal evidence of RCTs vs. observational data: I'm intuitively skeptical of this but the sources you linked seem interesting and worthwhile to think about more before setting an org up for this. (Edited to add:) Hearing your view  already substantially updates mine, but I'd be really curious to hear more perspectives from others with lots of experience working on this type of stuff, to see if they'd agree, then I'd update more. If you have impressions of how much consensus there is on this question that w... (read more)

This all makes sense to me overall. I'm still excited about this idea (slightly less so than before) but I think/agree there should be careful considerations on which interventions make the most sense to test.

I think it's really telling that Google and Amazon don't have internal testing teams to study productivity/management techniques in isolation. In practice, I just don't think you learn that much, for the cost of it.

What these companies do do, is to allow different managers to try things out, survey them, and promote the seemingly best practices throug

A variant I'd also be excited about (could imagine even moreso, could go either way after more reflection) that could be contained within the same org or separate: the same thing but for companies (particularly, startups) edit to clarify: test policies/strategies across companies, not on people within companies

I think the obvious answer is that doing controlled trials in these areas is a whole lot of work/expense for the benefit.

Some things like health effects can take a long time to play out; maybe 10-50 years. And I wouldn't expect the difference to be particularly amazing. (I'd be surprised if the average person could increase their productivity by more than ~20% with any of those)

I think our main disagreement is around the likely effect sizes; e.g. I think blocking out focused work could easily have an effect size of >50% (but am pretty uncertain wh... (read more)

Votes/considerations on why this is a good or bad idea are also appreciated!

Reflecting a little on my shortform from a few years ago, I think I wasn't ambitious enough in trying to actually move this forward.

I want there to be an org that does "human challenge"-style RCTs across lots of important questions that are extremely hard to get at otherwise, e.g. (top 2 are repeated from previous shortform. edited to clarify: these are some quick examples off the top of my head, should be more consideration into which are the best for this org):

1. Health effects of veganism
2. Health effects of restricting sleep
3. Productivity of remote vs. in-pers

I really appreciate that you break down explanatory factors in the way you do.

I'm happy that this was useful for you!

I have a hard time making a mental model of their relative importance compared to each other. Do you think that such an exercise is feasible, and if so, do any of you have a conception of the relative explanatory strength of any factor when considered against the others?

Good question. We also had some trouble with this, as it's difficult to observe the reasons many corporate prediction markets have failed to catch on. That being said, ... (read more)

Appreciate the compliment. I am interested in making it a Forum post, but might want to do some more editing/cleanup or writing over next few weeks/months (it got more interest than I was expecting so seems more likely to be worth it now). Might also post as is, will think about it more soon.

Hi Lizka, thanks for your feedback and think it touched on some of the sections that I'm most unsure about / could most use some revision which is great!

1. [Bottlenecks] You suggest "Organizations and individuals (stakeholders) making important decisions are willing to use crowd forecasting to help inform decision making" as a crucial step in the "story" of crowd forecasting’s success (the "pathway to impact"?) --- this seems very true to me. But then you write "I doubt this is the main bottleneck right now but it may be in the future" (and don't really

I wrote a draft outline on bottlenecks to more impactful crowd forecasting that I decided to share in its current form rather than clean up into a post [edited to add: I ended up revising into a post here].

Link

Summary:

1. I have some intuition that crowd forecasting could be a useful tool for important decisions like cause prioritization but feel uncertain
2. I’m not aware of many example success stories of crowd forecasts impacting important decisions, so I define a simple framework for how crowd forecasts could be impactful:
   1. Organizations and individuals (stakeho

A third perspective roughly justifies the current position; we should discount the future at the rate current humans think is appropriate, but also separately place significant value on having a positive long term future.

I feel that EA shouldn't spend all or nearly all of its resources on the far future, but I'm uncomfortable with incorporating a moral discount rate for future humans as part of "regular longtermism" since it's very intuitive to me that future lives should matter the same amount as present ones.

I prefer objections from the epistemic c... (read more)

Overall I like this idea, appreciate the expansiveness of the considerations discussed in the post, and would excited to hear takes from people working at social media companies.

Thoughts on the post directly

Broadly, we envision i) automatically suggesting questions of likely interest to the user—e.g., questions related to the user’s current post or trending topics—and ii) rewarding users with higher than average forecasting accuracy with increased visibility

I think some version of some type of boosting visibility based on forecasting accuracy seems promisi... (read more)

The forecasting accuracy of Forecast’s users was also fairly good: “Forecast's midpoint brier score [...] across all closed Forecasts over the past few months is 0.204, compared to Good Judgement's published result of 0.227 for prediction markets.”

For what it's worth , as noted in Nuño's comment this comparison holds little weight when the questions aren't the same or on the same time scales; I'd take it as fairly weak evidence from my prior that real-money prediction markets are much more accurate.

My forecast is pretty heavily based on the GoodJudgment article How to Become a Superforecaster. According to it they identify Superforecasters each autumn and require forecasters to have made 100 forecasts (I assume 100 resolved), so now might actually be the worst time to start forecasting. It looks like if you started predicting now the 100th question wouldn't close until the end of 2020. Therefore it seems very unlikely you'd be able to become a Superforecaster in this autumn's batch.

[Note: alexrjl clarified over PM that I should treat t... (read more)

Here's my forecast. The past is the best predictor of the future, so I looked at past monthly data as the base rate.

I first tried to tease out whether there was a correlation in which months had more activity between 2020 and 2019. It seemed there was a weak negative correlation, so I figured my base rate should be just based on the past few months of data.

In addition to the past few months of data, I considered that part of the catalyst for record-setting July activity might be Aaron's "Why you should put on the EA Forum" EAGx talk. Du... (read more)

I've recently gotten into forecasting and have also been a strategy game addict enthusiast at several points in my life. I'm curious about your thoughts on the links between the two:

• How correlated is skill at forecasting and strategy games?
• Does playing strategy games make you better at forecasting?

Relevant Metaculus question about whether the impact of the Effective Altruism movement will still be picked up by Google Trends in 2030 (specifically, whether it will have at least .2 times the total interest from 2017) has a community prediction of 70%

The efforts by https://1daysooner.org/ to use human challenge trials to speed up vaccine development make me think about the potential of advocacy for "human challenge" type experiments in other domains where consequentialists might conclude there hasn't been enough "ethically questionable" randomized experimentation on humans. 2 examples come to mind:

My impression of the nutrition field is that it's very hard to get causal evidence because people won't change their diet at random for an experiment.

Why We Sleep has been ... (read more)

I think we have good reason to believe veg*ns will underestimate the cost of not-eating-meat for others due to selection effects. People who it's easier for are more likely to both go veg*n and stick with it. Veg*ns generally underestimating the cost and non-veg*ns generally overestimating the cost can both be true.

The cost has been low for me, but the cost varies significantly based on factors such as culture, age, and food preferences. I think that in the vast majority of cases the benefits will still outweigh the costs and most would agree with a n... (read more)

If I was donating 90% every year, I think my probability of giving up permanently would be even higher than 50% each year. If I had zero time and money left to enjoy myself, my future self would almost certainly get demotivated and give up on this whole thing. Maybe I’d come back and donate a bit less but, for simplicity, let’s just assume that if Agape gives up, she stays given up.

The assumption that if she gives up, she is most likely to give up on donating completely seems not obvious to me. I would think that it's more likely she s... (read more)