JamesN

• JamesN 29 Nov 2023 19:11 UTC

  1 point

  1 ∶ 0

  in reply to: JordanStone’s comment on: Effec­tive Selfishism?

  I agree—though not so much on everything gets funded anyway point.

  I think there is also a wider meta question which is what is the best use of EA’s marginal time/​energy/​money. My (highly unjustified) judgement would be that people donating for such causes aren’t motivated by effectiveness, or at least are motivated much more by emotion. So the likelihood of changing their donation based on an argument around effectiveness may be quite hard to achieve.

  I’m also not sure on the scale of difference between the worst and best charities for such causes (i.e. is the best cancer charity 100x better than the worst)? It’d be great to know, but assuming not, this would also reduce the benefit of any success.

  A more effective solution achieve the same goal by proxy would seem to be just influencing the existing major funds or initiatives to focus more on the marginal impact of every £ they receive.

• JamesN 29 Nov 2023 12:54 UTC

  6 points

  0 ∶ 0

  on: The Odyssean Process

  Thanks for posting this!

  I want to highlight up front that I am a big supporter of any work that aims to improve institutional decision making. I believe it’s a highly impactful area with unparalleled potential given the decision power (in both terms of spending and benefit potential) of large institutions is immense. I personally feel there’s a big moral and EA argument in supporting solutions that could practically deliver benefits (even small returns given the scale).

  In terms of cleaner questions upfront which get to the heart of my uncertainties:

  1. How much will the elements combined improve the quality of decisions that are made? Tied to this—which elements could be cut if needed for time without undermining the benefits you’d expect?

  2. Are there examples of previous decisions that have been made that have been run through this process, to show what different outcomes would have been generated?

  3. Given the time investment needed to implement this process, why is it advantageous over existing solutions that have been shown to provide substantial improvements in decision making quality (under experimentation) but often face complaints over needing significant time and expertise investment (e.g. training on and aggregating Bayesian models)?

  Further reflections if interested/​useful
  Having read your paper, I have some concerns over how the solution can be implemented at a beneficial scale. I raise this particularly as a number of the problems you’ve mentioned in the White Paper (e.g. unstructured/​limited consultations with experts or limited analysis of the problem space) are driven more by time constraints rather than a clear framework of how to do it. This is an important consideration as planning for catastrophic risks is only half of the problem—we can’t consistently (or at all) predict black swan events and thus decision making at speed in crises is incredibly (if not more) important, as Covid showed us.

  Given decision science research, I query the heavy reliance on expert judgment as a key node to improve the predictive accuracy, as there’s a healthy body of evidence that suggests quality of reasoning as opposed to domain expertise is a better predictor for such accuracy. Your White Paper actually seems to account for this by proxy when it highlights specific reasoning methods to drive improved accuracy (e.g. IDEA framework).

  In addition, I’m less sure how beneficial the democratic/​deliberation process with citizens is for the risks you are targeting. The examples you note (such as abortion and LGBTQ+ issues) are primarily social issues which lend themselves well to citizen assemblies given they are moral in nature. On the other side, planning policy is quite heavily democratised in the UK and arguably has led to very bad outcomes given wider economic or societal benefits from construction are less tangible than personal concerns around changes to the local area. These externalities aren’t always accurately priced into people’s incentives and thus their judgements aren’t necessarily what’s best for society. Do you see a similar issue for catastrophic risks/​how will you mitigate if so?

[Question] How much do you spend on M&E?

• JamesN 16 Nov 2023 15:11 UTC

  6 points

  0 ∶ 0

  on: JamesN’s Quick takes

  With a number of charity evaluators recommendations coming out over the last few days/​weeks, has there been any further development on AI safety/​GCR evaluator(s)? A need that was raised in the post below (I don’t know if best EA forum practice is to ressurrect an old thread or not, so I apologies if it’s better I just comment in there).

  https://​​forum.effectivealtruism.org/​​posts/​​QahDdRzpPuat8ujx2/​​why-isn-t-there-a-charity-evaluator-for-longtermist-projects

• JamesN 15 Nov 2023 15:44 UTC

  4 points

  0 ∶ 0

  on: HearMeOut—Net­work­ing While Fund­ing Char­i­ties (Look­ing for a founder and beta users)

  A unique and interesting concept.

  I can see the logic but I’d suspect the payments for meetings would need to be quite low in most instances as I wonder whether you create weird incentives where time strapped high-in-demand people slap high fees on meetings and people who have limited funds have yet another barrier to meeting such people. Could also make some very moral conscious people maximise the charge they put on the meeting slot given the time:cost benefit is now not just their own time, but also the opportunity cost of what someone else was willing to pay.

  How do you see mitigating the risk that it’ll no longer just be your idea that gets you through the door, but the depth of your pockets?

  Separately, am I correct that there is no funds to create the MVP? It feels like something which a few thousand £/​$’s could build from a reputable programmer? Would that be useful and possible to get the concept off the ground and able to be tested?

• JamesN 13 Nov 2023 16:23 UTC

  2 points

  0 ∶ 0

  in reply to: Yarrow Bouchard’s comment on: JamesN’s Quick takes

  Very fair identification of some sloppy wording by me there with “increasing”. Apologies, my main focus was on the relatively high risk. Though as you’ve noted the WSJ survey had a median probability of 50%, and Forbes (link below) notes the NY Fed recession probability indicator is at 56% (albeit decreased from its previous 66%) - an unusually accurate indicator. Assessments which should be making central banks and economic ministries a little nervous.

  Even with some forecasters reducing their probabilities, the relative risk and high level of uncertainties underpinning them would suggest to me it’d be a good time to review plans to weather any downturn.

  https://​​www.forbes.com/​​advisor/​​investing/​​is-a-recession-coming/​​

• JamesN 9 Nov 2023 9:37 UTC

  10 points

  1 ∶ 0

  on: JamesN’s Quick takes

  Do EA funding organisations have contingency plans (I use the term loosely) to manage downside risks to cash-flow in the eventuality of a recession? With the possibility of a recession in the coming 6-18 months seemingly increasing, it would be prudent to model the scenarios and think through how to smooth the funding profile if needed. Given cash is already constrained post the collapse of “that will shall not be named”, it feels a recession (and thus implications to donations/​value of investments etc.) could compound existing funding issues. I don’t expect to get a financial plan from any of the big funders, but I thought worth raising the question in the low probability scenario it isn’t already part of their modelling.

JamesN’s Quick takes

• JamesN 5 Nov 2023 16:51 UTC

  1 point

  0 ∶ 0

  on: How Long Do Policy Changes Mat­ter? New Paper

  Interesting post, thank you!

  Your analysis on Congress/​National level legislation is particularly telling as it seems to show that if you can survive the next political cycle/​election, then the probability of the legislation remaining in place becomes almost static between 40-60% (apart from a drop at the 20-35 year mark).

  This fits anecdotally with my experience and aligns with the reality that actually changing legislation can be incredibly tricky, especially on policies that are controversial within a political party. See the U.K.s 0.7% international aid legislation as an example.

  Does your research look at any potential practical predictors (beyond neglectfulness) of a policy sticking for longer (such as complexity of language, cross-party support, integration in larger bill)? I’m out so haven’t read the full paper, but do just point me to that if it’s in there and I’ll look later!

• JamesN 4 Nov 2023 13:06 UTC

  4 points

  0 ∶ 0

  on: Clean Water—the in­cred­ible 30% mor­tal­ity re­ducer we can’t explain

  I was intrigued by this when you told me about it at EAG Nick, so it’s great to see you’ve written it in a post here as it absolutely merits further investigation.

  As you say, finding out why this (may) be true is highly important. Given the potential scale of impact here and the complexity of interactions which may be creating that effect, this feels a great candidate for some sort of causal/​Bayesian model to be created as part of a research project. If done correctly this then could be used to inform several RCTs that can try and find the true effect of potential predictors (of which we seem to only have very data poor priors for currently).

• JamesN 30 Oct 2023 15:42 UTC

  9 points

  1 ∶ 0

  on: Im­pact Eval­u­a­tion in EA

  Nice post!

  I do agree there is a potential gap for more impact evaluation in EA space and that it is common place for many non-EA NGOs/​organisations to be required to have a certain percentage of their programme set aside for monitoring & evaluation purposes… so it feels something similar for EA organisations could be easily achieved.

  A potential option—though would need far more exploration—is a central EA organisation that is funded by 5% of all OP/​GW/​EA Funds grants. So if a Open Phil gives a $1m grant, $50k is allocated to the central EA impact evaluation organisation who now need to add that recipient org to their list of orgs to work with and do an independent evaluation of at some agreed point (depending on grant objectives etc.).

  One thing I would stress in particular links to your point around the difficulty of doing M&E on several of the largest EA cause areas (esp. in the GCR space) that have very long (or potentially non-existent) feedback loops and unclear metrics to track. Rather than just accepting its too difficult to do impact evaluation, the focus should be on the process of decision making and reasoning in those organisations, which can act as a ‘best alternative’ proxy. This can then be evaluated, through independent assessment of items such as theory of changes and use of decision methods like Bayesian belief networks of the route to impact/​change.

• JamesN 24 Oct 2023 15:15 UTC

  1 point

  0 ∶ 0

  in reply to: Kirsten’s comment on: Im­pact­ful Govern­ment Ca­reers—Why, What, Where

  Yes we are, though I’m always ambitious on how we can expand! Unfortunately, the US picture was solely a choice to avoid any risk of copyright, and it looked slightly more impressive than the U.K. based images...

  However, I’ll edit if it creates too much confusion.

IGC—Why, What, Where

• JamesN 13 Oct 2023 18:13 UTC

  2 points

  1 ∶ 0

  in reply to: CuriousEA’s comment on: What do Marginal Grants at EAIF Look Like? Fund­ing Pri­ori­ties and Grant­mak­ing Thresh­olds at the EA In­fras­truc­ture Fund

  I agree.

  I appreciate the examples are adapted and so shouldn’t be taken literally, but if this post is being used to convince donors it’d be helpful to understand the marginal gains expected if possible.

  To me, EAIF is one of the strongest forces to build the community and catalyse the work done by other topic area specifc projects/​funds (incl. LTFF). I wonder how many people working in successful EA orgs. first got involved through and now propegate their research/​policies/​ideas through EAIF funded orgs.? It’ll be great to see how the EAIF evolves over the coming months to hopefully maximise the impact of it’s funding—an exciting time!

• JamesN 13 Oct 2023 8:49 UTC

  1 point

  0 ∶ 0

  on: How can I best use my ca­reer to pass im­pact­ful AI and Biose­cu­rity policy.

  I’m providing a comment as opposed an answer due to my high level of uncertainty on my “answer”.

  My experience is mainly in U.K. policy/​Government, though I have experience working with the US internationally so have seen how their internal general decision-making systems work. I also think this post (if not already seen) is a good initial filter on fit for policy vs politician: https://​​forum.effectivealtruism.org/​​posts/​​BjgE7EpCKodQ3nAyp/​​two-cheap-ways-to-test-your-fit-for-policy-work

  Answer: My prior is that role in a politicians team or powerful lobby groups is best in the US. Given practically all the very senior jobs in the US civil service are political appointments, there are big risks of a lower ceiling long-term if you aren’t within a political machine. Equally, with the system of government the US has (presidential and federalised), there is more opportunity in congressional and even lower political appointments (e.g. state governor) to influence policy. Due to this increased political surface area, lobbying also seems pretty influential.

  The counter to my argument would be if the person is less clear on their policy area of interest, or had limited understanding of how to get things done in Government (the machine is still very important even if you have a powerful politician behind you). Given their an undergrad then that could very well be the case, which may mean a civil service or even think tank role may be preferable to gain said knowledge.

  I think aiming to be a politician straight out of an undergrad is highly unlikely to be successful and potentially a negative move. They could limit their flexibility that early career gives them. Equally with limited experience of how it works, they may not fully appreciate the skills the traits needed to actually deliver change and turn policy ideas into legislation etc. I’m fully behind the ambition, but I think it’s far more valuable to spend a few years building the knowledge and contacts before diving into a political career. Esp. if they are quite heavily interested/​focused on a single issue (bio + AI).

  Note: I’ve assumed this is a US based ask. If it was U.K. based I’d have very different views (and much more certain ones) given different power dynamics between politicians, civil service, and lobby/​think tanks etc.

  I hope that somewhat helps!

• JamesN 10 Oct 2023 21:01 UTC

  9 points

  0 ∶ 0

  on: Thoughts on Ke­nan Malik’s re­cent EA Article

  Thanks for posting this detailed and thoughtful review. I think it’s very valuable to have such responses, even just on this forum.

  This may be evident from the starting parts of your post (and may illustrate my own naivety), but is there no EA press or comms unit that journalists contact before publishing such articles? I appreciate much of the criticism is focused on the works by MacAskill and Singer so maybe their office was contacted. I also accept it’s not desirable to have a big comms. function that speaks for EA and makes the community more formal than it is. However, I agree with your reflection that having no rebuttal or standard lines (from say CEA) means no way to counter the potential damaging effects from an inaccurate narrative.

• JamesN 8 Oct 2023 16:02 UTC

  3 points

  0 ∶ 0

  on: Quan­tified Col­lec­tive In­tel­li­gence: In­te­grat­ing Fore­cast­ing into De­ci­sion-Making

  Thanks for this post—I really enjoyed reading and contemplating it the last few days. I’m a big supporter of helping make forecasting more decision relevant and finding ways to help integrate it into the decision-making process.

  In summary I’m unsure how I feel about the potential benefits of this framework versus other methods, and particularly how likely it is to fit within the conditions that policy (especially in central Governments) is formed. I’ve laid out a few more detailed thoughts of mine below for some reflections, as I note you are still looking to refine this, but upfront I’ve put some questions which even if you didn’t want to respond to here, may be helpful when thinking of where to take this.

  1. What’s the specific error in policy-making that you see this process as overcoming most effectively (e.g. using prediction as a basis of better understanding the interactions of policy decisions; aggregating reasoning and judgements to improve decision accuracy; improving transparency and consistency of reasoning to reduce misinterpretations and improve prediction accuracy; etc.)?

  2. This framework is quite lengthy and has several steps that are interdependent. The challenge with other methods which have been found to have significant accuracy benefits (e.g. aggregating individual Bayesian networks/​models^[1]) is that they are complex and time-consuming. How do you see the case for this QCI over just recommending organisations invest in Bayesian modelling, given the time/​accuracy trade-off seems very close?

  3. A lot of policy making is negotiation and stakeholder management. How do you see this process fitting with that? Do you see it as trying to rise above it and being a tool which is “pure” and then hoping that people politicians have a strong enough will to push through the solution. Or would you suggest integrating such stakeholders views into the “collective expertise” process—potentially risking accuracy due to misaligned incentives.

  My more specific points

  To remove the risk of misinterpretation, I understand the QCI process to be:

  1) Identify the purpose of the decision (i.e. exploratory, achieving an objective, risk mitigation) --> 2) Identify the predictors in the causal chain --> 3) Forecast the future of these predictors --> 4) Predict how policy interventions change those forecasts.

  Assuming I’ve not missed a key component, my main reflection is that every stage involves highly complex and uncertain reasoning, but the system doesn’t seek to build that. To be fair, you may have omitted that due to space but I feel that’s a big gap given how people’s ability to reason under uncertainty is fundamental to this process working. Almost every policy decision is a prediction task: e.g. “will x action lead to y outcome”, and forecasting the future is already intrinsic to most policy-making processes. The issue typically is: (1) people don’t realise that’s what they are doing so don’t structure their decision making correctly; and (2) people don’t like uncertainty and fail to employ good reasoning to counter the numerous errors that their judgements involve. I accept good forecasters avoid some of these errors by nature of them being good forecasters, but I’m sceptical of how well that should be assumed to translate into the other non-forecast elements of this process. I acknowledge there are some easy gains to be had just from (1)/​process itself, namely around quantification of predictions as that can help reduce misinterpretation errors. However, even there I’d suggest there are significant benefits that can be provided to the process from simple actions such clearly standardising such quantification (i.e. verbal to numeric probability tables) and capturing second order probabilities (for the most important predictors are expressed etc.). The creation of the concept diagram is really good for this and could be an easy step towards helping land better reasoning practices.

  I’m also curious on why SME has been leveraged so heavily through-out, most notably in the forecasting process. I see the need for SMEs to help structure the forecasting questions but less clear on why their involvement in the forecasts themselves would be beneficial? Unless the SMEs have been trained/​are accurate forecasters already, the evidence I’m aware of leans to the argument that expertise and experience is no real predictor to forecasting accuracy, so aggregating SME’s forecasts with Supers’ would seem to risk decreasing accuracy. The rationale may be to sacrifice accuracy for buy-in, which I’d appreciate. Ultimately, it increases the importance of how methods such as Delphi is implemented, as there is evidence that an important element of Delphi’s effectiveness is how accurate the majority and/​or most vocal member of the group—as they have an outsized impact on how people integrate/​weigh information and arguments^[2] (better reasoning methods would help mitigate this).

  1. ^

     BARD: A structured technique for group elicitation of Bayesian networks to support analytic reasoning

  2. ^

     Network Structures of Collective Intelligence: The Contingent Benefits of Group Discussion

• JamesN 18 Sep 2023 13:35 UTC

  2 points

  1 ∶ 0

  on: Fore­cast­ing for Policy (FORPOL) - Main take­aways, prac­ti­cal learn­ings & report

  Thanks for writing this up! As someone who recently ran a forecasting event at a UK Government department for my MSc research project, I fully appreciate some of your challenges (e.g. around attrition and creating a variety of questions).

  In your experience, how well did the participants feel the link was between the forecasts they were making and any decisions that were being made on the area/​topic? Did they feel like the forecasts would influence/​be integrated effectively when a decision on the relevant area was being made? If so, did you notice any improvement in forecasting accuracy? My reason for asking is an issue that is typically raised around forecasting is that it lacks decision-relevance, and that even if forecasts are elicited they have limited influence on the final decision. It’d be interesting to know if you found that perception as well, and if not, if there were any incentive benefits (i.e. if they felt their forecast would inform decisions, then did they become more accurate/​try harder).

  Out of interest, was there any training provided to participants, before during or after the tournament?

• JamesN 17 Sep 2023 15:37 UTC

  14 points

  2 ∶ 0

  on: Who Is a Good Fit for a Ca­reer in Non­profit En­trepreneur­ship? (CCW2023)

  Interesting read, thanks for sharing!

  Out of interest, have CE looked into more specific testing of which of those traits best predict success? For instance, by reviewing which of the CE incubated charities have been most successful and if there were personality traits (I assume recorded at the time of co-founder recruitment) that were disproportiately present?

  I respect 27 charities is still too small of a sample size to be reliable as a data set, but it could be analysis which could be built upon as the numbers of charities that were incubated grew and matured.

• JamesN 17 Sep 2023 11:49 UTC

  7 points

  2 ∶ 2

  in reply to: MaxDalton’s comment on: An­nounc­ing the Meta Co­or­di­na­tion Fo­rum 2023

  I do very much agree with Nathan’s sentiment here.

  I appreciate the original post announcing this forum is aims to expectation manage and temper potential concerns people will have about this group producing a ‘grand strategy’ for EA or similarly agree solutions to all the big problems. However, there is also acknowledgment that the event is aiming to help plan the next two years and set the trajectory going forward.

  These are important topics and issues (as reflected by the significant senior time involved in the event), and pretty much all of them require a lot of individual and group reasoning under uncertainty. As such I do think there is a very beneficial role for robust methods to help facilitate discussion and decision-making.

  I don’t know what things you may already be planning to implement, so I’m mostly just putting a flag down to say if you haven’t already, it’d be worth investing in such methods. So I’m not entirely ‘talk and no suggestion’, some very basic things to introduce (if not already) at low/​cost and effort could be:

  • Clear framework for all attendees on how uncertainty and predictions should be communicated during the event to ensure consistency and transparency of reasoning between attendees, to help reduce misinterpretation errors which is always a risk in such forums/​events.

  • External/​third party (to the attendees but could still be EA) to provide a mediation and challenge function (similar to what Nathan suggested).

  • Collection of prior positions on key topics, with confidence %’s provided before the event. With updating rounds during and at the event of the event, with short notes on what contributed to any % change (one to show impact of the event, but also helps identify where and with whom the more intractable differences lie—which can help more focused action/​discussion later).