JamesN

Karma: 91

I am currently the Director of Impactful Government Careers—an organisation focused on helping individuals find, secure, and excel in high impact civil service careers. My main interests are in improving institutional decision making as I believe even small changes could have substantial benefits for humanity.

I’ve spent the last 5 years working in the heart of the UK Government, with 4 of those at HM Treasury. My roles have included:

Head of Development Policy, HM Treasury
Head of Strategy, Centre for Data Ethics and Innovation
Senior Policy Advisor, Strategy and Spending for Official Development Assistance, HM Treasury

These roles have involved: advising UK Ministers on policy, spending, and strategy issues relating to international development; assessing the value for money of proposed high-value development projects; developing the 2021 CDEI Strategy and leading the organisational change related to this.

I have recently completed an MSc in Cognitive and Decision Sciences at UCL, where I have focused my research on probabilistic reasoning and improving individual and group decision-making processes. My final research project involved an experimental study into whether a short course (2 hours) on Bayesian reasoning could improve individual’s single-shot accuracy when forecasting geopolitical events. On the side, a colleague and me run a small project helping to improve predictive reasoning: https://www.daymark-di.com/

JamesN 11 Jan 2024 13:46 UTC
6 points
1 ∶ 0
on: JamesN’s Quick takes
As my toddler continues to grow, my wife and I are reaching the point that all parents do (if not already done) where we have to decide what to do about his education. Obviously education and life aren’t fixed so there is always a necessary amount of flexibility to any decision.
My issue (UK focus): Broadly there are three stages in the UK − 1) nursery, 2) primary school, 3) secondary/high school. With the world becoming increasingly uncertain and complex, I have very little confidence that any current institution (in stage 2 and 3) is set up and run well enough to prepare a child for the future—beyond the basic reading and writing skills. As a small example for instance, there is a shocking lack of investment in both resources and capability on key topics such as computer science, maths, engineering, rationality, and political science. However, given this is a quick post I won’t go into the depth of my reasoning of why I feel there is a structural problem with the education system that would be almost impractical (or at least, too politically costly) to resolve. To add some potential credence, this isn’t just wandering thoughts but they have been generated through the experiences of multiple family (including my wife) and friends who work/worked in primary or secondary schools.
My question: What are others’ thoughts on this (both parents and non-parents)? Do you have similar concerns? Do you not? If you do, what are your intended actions to mitigate?

JamesN 13 Dec 2023 8:54 UTC
2 points
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on: New Meta-Analysis: Exercise is a Highly Effective (But Neglected) Treatment for Depression
Nice post, and I like the short snappy sections for quick reading—thanks!
My main reflection is whether this is actually a neglected? Exercise is a huge industry, and I feel the psychological benefits as well as the physical are quite often utilised in marketing and advertising (though I may be biased given I go to the gym quite regularly). Perhaps the specific link to depression is leveraged less, but I feel its there by proxy in a lot of cases.
I guess a fairer question would be: what would need to exist, that doesn’t currently, for this to be assessed as not a neglected issue?

JamesN 13 Dec 2023 8:42 UTC
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in reply to: Karthik Tadepalli’s comment on: Karthik Tadepalli’s Quick takes
This is an important reflection, and one I’ve found myself querying when seeing various programs claim they are hyper effective. Incredibly well performing interventions are rare, but we might expect to see a higher number of them to be showcased on this forum given there is already a selection bias from the membership/readership here.
However, I do feel the community naturally creates an incentive to inflate (conciously or not) the CEA of interventions—afterall, if you aren’t working on something which can compete with AMF, then why take money away from that? The fix to this being you live in the ambiguity of your intervention and argue that under certain assumptions, your program could be better.
As you effectively note, the problem is could (a priori) judgments are riddled with reasoning risks and errors, which is why I feel the community could do more to better support and also challenge reasoning methods (cognitive and computational). For example, lots of posts mention key uncertainties people have on their interventions, but they often don’t state the second order probabilities of them (not even GiveWell does this consistently) along with how much that uncertainty fundamentally underpins the intervention. A relatively simple fix, which could be a community norm.

JamesN 29 Nov 2023 19:11 UTC
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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
8 points
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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?

JamesN19 Nov 2023 14:33 UTC

2 points

0 comments1 min readEA link

JamesN 16 Nov 2023 15:11 UTC
6 points
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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
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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
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in reply to: Yarrow B.’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
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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

JamesN9 Nov 2023 9:37 UTC

2 points

10 comments1 min readEA link

JamesN 5 Nov 2023 16:51 UTC
1 point
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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
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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
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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
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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

JamesN24 Oct 2023 12:09 UTC

13 points

2 comments9 min readEA link

JamesN 13 Oct 2023 18:13 UTC
2 points
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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
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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
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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
10 points
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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