david_reinstein

• david_reinstein 13 Apr 2021 19:21 UTC

  1 point

  in reply to: david_reinstein’s comment on: Pos­si­ble mis­con­cep­tions about (strong) longtermism

  In response, you stated:

  However, it is worth noting that it is possible that longtermists may end up reducing suffering today as a by-product of trying to improve the far future.

  It might be worth re-stating this. Thinking about objective functions and constraints, either

  R1. SLT implies that resources should be devoted in a way that does less to reduce current suffering (i.e., implies more current suffering than absent SLT) or

  R2. SLT does not change our objective function, or it coincidentally implies an allocation that does has no differential effect on current suffering (a ‘measure zero’, i.e., coincidental result)

  R3. SLT implies that resources should be devoted in a way that leads to less current suffering

  R3 seems unlikely to be the case, particularly if we imagine bounds on altruistic capacity. And, if there were an approach that could use the same resources to reduce current suffering even more, it already should have been chosen in the absence of SLT.

  If R2 is the case then SLT is not important for our resource decision so we can ignore it.

  If R1 holds (which seems most likely to me), then following SLT does imply an increase in current suffering, and we are back to the main objection

• david_reinstein 13 Apr 2021 19:11 UTC

  1 point

  on: Pos­si­ble mis­con­cep­tions about (strong) longtermism

  Possible misconception: “Greaves and MacAskill say we can ignore short-term effects. That means longtermists will never reduce current suffering. This seems repugnant.”

  ‘This seems repugnant’ doesn’t seem like a justifiable objection to me, so not something an advocate of SLT should be obliged to take on directly.

  If I said “this doctor’s theory of liver deterioration suggests that I should reduce my alcohol intake, which seems repugnant to me”, you would not feel compelled to respond that “actually, some of the things the doctor is advocating could allow you to drink more alcohol”.

  (I suspect that beyond the “this seems repugnant” there is a more coherent critique—and that is the critique we should focus on.)

• david_reinstein 2 Apr 2021 2:46 UTC

  3 points

  on: Should EA Buy Distri­bu­tion Rights for Foun­da­tional Books?

  Good arguments. I’d personally love if we found a way to move to a different economic model for all information goods. But particularly here, free distribution seems important.

  Possibly worth considering: motivating future authors with prize-based incentives; prize based on number of downloads/​reads/​upvotes of their books. Of course the authors may be credit-constrained, but perhaps others could finance them by buying shares in the future potential prizes?

• david_reinstein 25 Mar 2021 23:15 UTC

  1 point

  in reply to: Gregory_Lewis’s comment on: EA Sur­vey 2018 Series: Dona­tion Data

  Thanks Greg, I appreciate the feedback.

  Some of this depends on what our goal is here. Is it to maximize ‘prediction’ and if so, why? Or is it something else? … Maybe to identify particularly relevant associations in the population of interest.

  For prediction, I agree it’s good to start with the largest amount of features (variables) you can find (as long as they are truly ex-ante) and then do a fancy dance of cross-validation and regularisation, before you do your final ‘validation’ of the model on set-aside data.

  But that doesn’t easily give you the ability to make strong inferential statements (causal or not), about things like ‘age is likely to be strongly associated with satisfaction measures in the true population’. Why not? If I understand correctly:

  The model you end up with, which does a great job at predicting your outcome

  1. … may have dropped age entirely or “regularized it” in a way that does not yield an unbiased or consistent estimator of the actual impact of age on your outcome. Remember, the goal here was prediction, not making inferences about the relationship between of any particular variable or sets of variables …

  2. … may include too many variables that are highly correlated with the age variable, thus making the age coefficient very imprecise

  3. … may include variables that are actually ‘part of the age effect you cared about, because they are things that go naturally with age, such as mental agility’

  4. Finally, the standard ‘statistical inference’ (how you can quantify your uncertainty) does not work for these learning models (although there are new techniques being developed)

• david_reinstein 6 Feb 2021 4:59 UTC

  1 point

  on: A list of EA-re­lated podcasts

  Thanks for this. I’ve integrated this list as well as @pablo’s and a couple I added (‘Not Overthinking’ and ‘Great.com Talks With’) into an Airtable

  View only

  or you can on collaborate this base HERE

• david_reinstein 5 Feb 2021 18:27 UTC

  4 points

  on: LSE EA’s Fel­low­ship Ap­pli­ca­tion Scores Moder­ately Pre­dicted En­gage­ment and Dis­cus­sion Quality

  I think we tend to confuse ‘lack of strong statistical significance’ with ‘no predictive power’.

  A small amount of evidence can substantially improve our decision-making...

  … even if we cannot conclude that ’data with a correlation this large or larger would be very unlikely to be generated (p<0.05) if there were no correlation in the true population.

  1. We, very reasonably, substantially update our beliefs and guide our decisions based on small amounts of data. See, e.g., the ‘Bayes rule’ chapter of Algorithms to Live By

  2. I believe that for optimization problems and decision-making problems we should use a different approach both to design and to assessing results… relative to when we are trying to measure and test for scientific purposes.

  This relates to ‘reinforcement learning’ and to ‘exploration sampling’.

  We need to make a decision in one direction or another, and we need to consider costs and benefits of collecting and using these measures I believe we should be taking a Bayesian approach, updating our belief distribution,

  … and considering the value of the information generated (in industry, the ‘lift’, ‘profit curve’ etc) in terms of how it improves our decision-making.

  Note: I am exploring these ideas and hoping to learn, share and communicate more. Maybe others in this forum have more expertise in ‘reinforcement learning’ etc.

• david_reinstein 2 Feb 2021 23:06 UTC

  1 point

  in reply to: John_Maxwell’s comment on: EA Sur­vey 2019 Series: How many peo­ple are there in the EA com­mu­nity?

  Whether or not it’s good or bad, it’s a cool idea!

• david_reinstein 30 Jan 2021 3:01 UTC

  1 point

  in reply to: ThomasWoodside’s comment on: Yale EA’s Fel­low­ship Ap­pli­ca­tion Scores were not Pre­dic­tive of Even­tual En­gage­ment

  Thanks for sharing the confidence intervals. I guess it might be reasonable to conclude from your experience that the interview scores have not been informative enough to justify their cost.

  What I am saying is that it doesn’t seem (to me) that the data and evidence presented allows you to say that. (But maybe other analysis or inference from your experience might in fact drive that conclusion, the ‘other people in San Francisco’ in your example.)

  But if I glance at just the evidence/​confidence intervals it suggests to me that there may be a substantial probability that in fact there is a strongly positive relationship and the results are a fluke.

  On the other hand I might be wrong. I hope to get a chance to follow up on this:

  • We could simulate a case where the measure has ‘the minimum correlation to the outcome to make it worth using for selecting on’, and see how likely it would be, in such a case, to observe the correlations as low as you observed

  • Or we could start with a minimally informative ‘prior’ over our beliefs about the measure, and do a Bayesian updating exercise in light of your observations; we could then consider the posterior probability distribution and consider whether it might justify discontinuing the use of these scores

• david_reinstein 29 Jan 2021 15:49 UTC

  1 point

  on: Yale EA’s Fel­low­ship Ap­pli­ca­tion Scores were not Pre­dic­tive of Even­tual En­gage­ment

  Interesting, but based on the small sample and limited range of scores (and I also agree with the points made by Moss and Rhys-Bernard) …

  I’m not sure whether you have enough data/​statistical power to say anything substantially informative/​conclusive. Even saying ‘we have evidence that there is not a strong relation’ may be too strong.

  To help us understand this, can you report (frequentist) confidence intervals around your estimates? (Or even better, a Bayesian approach involving a flat but informative prior and a posterior distribution in light of the data?)

  I’ll try to say more on this later. A good reference is: Harms and Lakens (2018), “Making ‘null effects’ informative: statistical techniques and inferential frameworks”

  Also, even ‘insignificant’ results may actually be rather informative for practical decision-making… if they cause us to reasonably substantially update our beliefs. We rationally make inferences and adjust our choices based on small amount of data all the time, even if we can’t say something like ‘it is less than 1% likely that what I just saw would have observed by chance’. Maybe 12% (p>0.05 !) of the time the dark cloud I see in the sky will fade away, but seeing this cloud still makes me decide to carry an umbrella… as now the expected benefits outweigh the costs..

• david_reinstein 25 Jan 2021 13:22 UTC

  3 points

  on: EAG sur­vey data analysis

  Can you share the data and or code?

• david_reinstein 25 Jan 2021 13:21 UTC

  3 points

  on: EAG sur­vey data analysis

  Our demographic analysis of community-related variables yielded no significant differences based on participants’ age, ethnicity, or gender.

  Lack of power or a tightly bounded null effect? Note that even without “strong statistical significance” in standard tests we can meaningfully update our beliefs.

  Of course we may need to adjust for multiple testing.

  Also, statistical inference after machine learning presents some challenges. Relevant here?

• david_reinstein 25 Jan 2021 13:19 UTC

  3 points

  on: EAG sur­vey data analysis

  As our models “only” examine the correlation between different variables and LTR, we cannot make any conclusive statements about a causal relationship linking “higher scores for community-related activities” to “higher LTR”. Technically, it is possible that the connections we found are spurious and do not represent the true causal mechanism which leads people to recommend an event more highly.

  Perhaps this could be presented earlier on— to me it seems likely that there is reverse causality, other channels, etc.

• david_reinstein 21 Jan 2021 14:21 UTC

  1 point

  on: EA Sur­vey Series 2019: EA cities and the cost of living

  this seems to confirm that most cities with large EA populations are quite expensive, which may serve as a barrier to EAs wishing to live near a large number of other EAs.

  Does this suggest a coordination problem, or are there important countervailing reasons why EAs live in the expensive places?

• david_reinstein 21 Jan 2021 14:21 UTC

  1 point

  on: EA Sur­vey Series 2019: EA cities and the cost of living

  Can someone share the data tables? Thanks.

• david_reinstein 13 Jan 2021 22:23 UTC

  1 point

  in reply to: Gregory_Lewis’s comment on: EA Sur­vey 2019 Series: Dona­tion Data

  I’m involved with doing this analysis this year, andI hope we can go in this direction. Perhaps not in the first iteration, but as we refine it.

• david_reinstein 13 Jan 2021 17:50 UTC

  3 points

  on: Are there EA-al­igned or­ga­ni­za­tions work­ing on im­prov­ing the effec­tive­ness of cor­po­rate so­cial re­spon­si­bil­ity/​cor­po­rate giv­ing strate­gies?

  Would love to touch base on this, let’s chat.

  I had a project doing some scoping related to this called innovationsinfundraising.org -- see especially the links HERE

• david_reinstein 21 Aug 2020 15:35 UTC

  2 points

  on: The case of the miss­ing cause pri­ori­ti­sa­tion research

  Great post! I laid down a variety of comments and suggestions within your post using hypothes.is. If you want to check it out (you need to install the browser ad-in and get a free account to see these.

  I prefer to comment within the text rather than here at the bottom, cutting and pasting quotes. Anyone else here tried hypothes.is?

  (By the way, I’m an academic economist. I don’t have any stake in hypothes.is. I just like it.)

• david_reinstein 19 Aug 2020 13:56 UTC

  5 points

  on: Growth and the case against ran­domista development

  I am an academic economist. I agree that economic development is important and is likely responsible for the majority of welfare gains in poor countries (although the spread of medical treatments, eliminating polio etc., are also huge). Yes, we have some good evidence that certain policies substantially inhibit development. And we should advocate against these policies.

  However, some parts of the argument seem a bit overstated or unfair to me. Some points

  1. “Randomista”: that is not the term the advocates would prefer, is it?

  2. Even if the best policies are pursued, the benefits will be slow and uneven. In the meanwhile, donations to prevent malaria, fund micronutrients, and even provide fistula and eye surgery can have a huge impact/​$.

     • I don’t think many donors will decide between giving $ to bednets and giving it to fund advocacy for pro-trade policies. However, presenting the benefits of the former as ‘a drop in a vast ocean’ will discourage giving overall

  3. The benefits of these health interventions are not primarily their impact on boosting economic growth/​income. They yield direct welfare benefits. The comparisons you highlight above make it seem as if the main intention of these is to boost growth/​income.

  4. The main issue: You state

  friendly economic policies can often be orders of magnitude more cost-effective than direct funding of evidence-based interventions.

  Perhaps, but that is not the issue from a donor point of view. The issue is the cost-effectiveness of money donated to support these policies. I see very little reason to believe that “funding a bunch more economists” (again, I say this as an economist myself) would have a substantial beneficial impact, much less on a per-dollar basis.

  Maybe it would, but I think there are orders of magnitude of uncertainty over this impact. The assumptions for this in the spreadsheet seem simply like guesses to me.

  My reason to be a bit skeptical… we have many many economists out there. I don’t see how more economists–or even more think tanks–will do much to clearly advance the argument against the known-to-be-bad growth policies.

• david_reinstein 19 Aug 2020 13:33 UTC

  3 points

  in reply to: Aaron Gertler’s comment on: Growth and the case against ran­domista development

  Agreed. Also you should call people by what they refer to themselves as. I think ‘Randomista’ comes across of a pejorative.

• david_reinstein 23 Dec 2019 9:52 UTC

  2 points

  in reply to: david_reinstein’s comment on: Im­por­tant EA-re­lated ques­tions EA would like to know from gen­eral public

  I just added some links to the shared google doc also