First EuroSPARC was in 2016. Targeting 16-19 year olds, my prior would be participants should still mostly study, and not work full-time on EA, or only exceptionally.
Long feedback loops are certainly a disadvantage.
Also in the meantime ESPR underwent various changes and actually is not optimising for something like “conversion rate to an EA attractor state”.
I. I did spent a considerable amount of time thinking about prioritisation (broadly understood)
My experience so far is
some of the foundations / low hanging sensible fruits were discovered
when moving beyond that, I often run into questions which are some sort of “crucial consideration” for prioritisation research, but the research/understanding is often just not there.
often work on these “gaps” seems more interesting and tractable than trying to do some sort of “lets try to ignore this gap and move on” move
few examples, where in some cases I got to writing something
Nonlinear perception of happiness—if you try to add utility across time-person-moments, it’s plausible you should log-transform it (or non-linearly transform it) . sums and exponentiation do not commute, so this is plausibly a crucial consideration for part of utilitarian calculations trying to be based on some sort of empirical observation like “pain in bad”
Multi-agent minds and predictive processing—while this is framed as about AI alignment, super-short version of why this is relevant for prioritisation is: theories of human values depend on what mathematical structures you use to represent these values. if your prioritization depnds on your values, this is possible important
Another example could be the style of thought explained in Eliezer’s “Inadequate Equillibria”. While you may not count it as “prioritisation research”, I’m happy to argue the content is crucially important for prioritisation work on institutional change or policy work. I spent some time thinking about “how to overcome inadequate equillibria”, which leads to topics from game theory, complex systems, etc.
II. My guess is there are more people who work in a similar mode, trying to basically ‘build as good world model as you can’, dive into problems you run into, and at the end prioritise informally based on such a model. Typically I would expect such model to be in parts implicit / be some sort of multi-model ensemble / …
While this may not create visible outcomes labeled as prioritisation, I think it’s important part of what’s happening now
I posted a short version of this, but I think people found it unhelpful, so I’m trying to post somewhat longer version.
I have seen some number of papers and talks broadly in the genre of “academic economy”
My intuition based on that is, often they seem to consist of projecting complex reality into a space of single-digit real number dimensions and a bunch of differential equations
The culture of the field often signals solving the equations is profound/important, and the how you do the projection “world → 10d” is less interesting
In my view for practical decision making and world-modelling it’s usually the opposite: the really hard and potentially profound part is the projection. Solving the maths is in often is some sense easy, at least in comparison to the best maths humans are doing
While I overall think the enterprise is worth to pursue, people should in my view have a relatively strong prior that for any conclusions which depends on the “world-> reals” projection there could be many alternatives leading to different conclusions; while I like the effort in this post to dig into how stable the conclusions are, in my view people who do not have cautious intuitions about the space of “academic economy models” could still easily over-update or trust too much the robustness
If people are not sure, an easy test could be something like “try to modify the projection in any way, so the conclusions do not hold”. At the same time this will usually not lead to an interesting or strong argument, it’s just trying some semi-random moves is the model space. But it can lead to a better intuition.
I tried to do few tests in a cheap and lazy way (eg what would this model tell me about running at night on a forested slope?) and my intuitions was:
I agree with the cautious the work in the paper represents very weak evidence for the conclusions that follow only from the detailed assumptions of the model in the present post. (At the same time it can be an excellent academic economy paper)
I’m more worried about other writing about the results, such as linked post on Phil’s blog , which in my reading signals more of “these results are robust” than it’s safe
Harder and more valuable work is to point to something like some of the most significant way in which the projection fails” (aspects of reality you ignored etc.). In this case this was done by Carl Shulman and it’s worth discussing further
In practice I do have some worries about some meme ‘ah, we don’t know, but given we don’t know, speeding up progress is likely good’ (as proved in this good paper) being created in the EA memetic ecosystem. (To be clear I don’t think the meme would reflect what Leopold or Ben believe)
In my view
a safe way how to read the paper is as academic economy—the paper says what happens if you solve a particular set of equations
while the variable names used in the equations appear to point toward reality, in fact it is almost completely unclear if the model is a reasonable map of at least some aspect of the territory
Overall I think a good check for EAs if they should update based on this result is
would you be able to make different set of at first glance reasonable assumptions of the same type, leading to opposite conclusions?
where if the answer is “no”, I would suggest people basically should not update.
I’m not sure you’ve read my posts on this topic? (1,2)
In the language used there, I don’t think the groups you propose would help people overcome the minimum recommended resources, but are at the risk of creating the appearance some criteria vaguely in that direction are met.
e.g., in my view, the founding group must have a deep understanding of effective altruism, and, essentially, the ability to go through the whole effective altruism prioritization framework, taking into account local specifics to reach conclusions valid at their region. This basically impossible to implement as membership requirement in a fb group
or strong link(s) to the core of the community … this is not fulfilled by someone from the core hanging in many fb groups with otherwise unconnected ppl
Overall, I think sometimes small obstacles—such as having to find EAs from your country in the global FB group or on EA hub and by other means—are a good thing!
FWIW the Why not to rush to translate effective altruism into other languages post was quite influential but is often wrong / misleading / advocating some very strong prior on inaction, in my opinion
I don’t think this is actually neglected
in my view, bringing effective altruism into new countries/cultures is in initial phases best understood as a strategy/prioritisation research, not as “community building”
importance of this increases with increasing distance (cultural / economic / geographical / …) from places like Oxford or Bay
(more on the topic here)
I doubt the people who are plausibly good founders would actually benefit from such groups, and even less from some vague coordination due to facebook groups
actually I think on the margin, if there are people who would move forward with the localization efforts if such fb groups exist and other similar people express interest, and would not do that otherwise, their impact could be easily negative
I don’t think it’s reasonable to think about FHI DPhil scholarships and even less so RSP as a mainly a funding program. (maybe ~15% of the impact comes from the funding)
If I understand the funding landscape correctly, both EA funds and LTFF are potentially able to fund single-digit number of PhDs. Actually has someone approached these funders with a request like “I want to work on safety with Marcus Hutter, and the only thing preventing me is funding”? Maybe I’m too optimistic, but I would expect such requests to have decent chance of success.
For example, CAIS and something like “classical superintelligence in a box picture” disagree a lot on the surface level. However, if you look deeper, you will find many similar problems. Simple to explain example: problem of manipulating the operator—which has (in my view) some “hard core” involving both math and philosophy, where you want the AI to somehow communicate with humans in a way which at the same time allows a) the human to learn from the AI if the AI knows something about the world b) the operator’s values are not “overwritten” by the AI c) you don’t want to prohibit moral progress. In CAIS language this is connected to so called manipulative services.
Or: one of the biggest hits of past year is the mesa-optimisation paper. However, if you are familiar with prior work, you will notice many of the proposed solutions with mesa-optimisers are similar/same solutions as previously proposed for so called ‘daemons’ or ‘misaligned subagents’. This is because the problems partially overlap (the mesa-optimisation framing is more clear and makes a stronger case for “this is what to expect by default”). Also while, for example, on the surface level there is a lot of disagreement between e.g. MIRI researchers, Paul Christiano and Eric Drexler, you will find a “distillation” proposal targeted at the above described problem in Eric’s work from 2015, many connected ideas in Paul’s work on distillation, and while find it harder to understand Eliezer I think his work also reflects understanding of the problem.
For example: You can ask whether the space of intelligent systems is fundamentally continuous, or not. (I call it “the continuity assumption”). This is connected to many agendas—if the space is fundamentally discontinuous this would cause serious problems to some forms of IDA, debate, interpretability & more.
(An example of discontinuity would be existence of problems which are impossible to meaningfully factorize; there are many more ways how the space could be discontinuous)
There are powerful intuitions going both ways on this.
I think the picture is somewhat correct, and we surprisingly should not be too concerned about the dynamic.
My model for this is:
1) there are some hard and somewhat nebulous problems “in the world”
2) people try to formalize them using various intuitions/framings/kinds of math; also using some “very deep priors”
3) the resulting agendas look at the surface level extremely different, and create the impression you have
4) if you understand multiple agendas deep enough, you get a sense
how they are sometimes “reflecting” the same underlying problem
if they are based on some “deep priors”, how deep it is, and how hard to argue it can be
how much they are based on “tastes” and “intuitions” ~ one model how to think about it is people having boxes comparable to policy net in AlphaZero: a mental black-box which spits useful predictions, but is not interpretable in language
Overall, given our current state of knowledge, I think running these multiple efforts in parallel is a better approach with higher chance of success that an idea that we should invest a lot in resolving disagreements/prioritizing, and everyone should work on the “best agenda”.
This seems to go against some core EA heuristic (“compare the options, take the best”) but actually is more in line with what rational allocation of resources in the face of uncertainty.
Re: future of the program & ecosystem influences.
What bad things will happen if the program is just closed
for the area overlapping with something “community building-is”, CBG will become the sole source of funding, as meta-fund does not fund that. I think at least historically CBG had some problematic influence on global development of effective altruism not because of the direct impact of funding, but because of putting money behind some specific set of advice/evaluation criteria. (To clarify what I mean: I would expect the space would be healthier if exactly the same funding decisions were made, but less specific advice what people should do was associated; the problem is also not necessarily on the program side, but can be thought about as goodharting on the side of grant applicants/grant recipients.)
for x-risk, LTFF can become too powerful source of funding for new/small projects. In practice while there are positive impacts of transparency, I would expect some problematic impacts of mainly Oli opinions and advice being associated with a lot of funding. (To clarify: I’m not worried about funding decisions, but about indirect effects of the type “we are paying you so you better listen to us”, and people intentionally or unintentionally goodharting on views expressed as grant justification)
for various things falling in between the gaps of fund scope, it may be less clear what to do
it increases the risks of trying to found something like “EA startups”
it can make the case for individual donors funding things stronger
All of that could be somewhat mitigated if rest of the funding ecosystem adapts; e.g. by creating more funds with intentional overlap, or creating others stream of funding going e.g. along geographical structures.
As a side-note: In case of the Bay area, I’d expect some funding-displacement effects. BERI grant-making is strongly correlated with geography and historically BERI funded some things which could be classified as community building. LTFF is also somewhat Bay-centric, and also there seem to be some LTFF grants which could be hypothetically funded by several orgs. Also some things were likely funded informally by local philantrophists.
To make the model more realistic one should note
there is some underlying distribution of “worthy things to fund”
some of the good projects could be likely funded from multiple sources; all other things being equal, I would expect the funding to come more likely from the nearest source
meta: I considered commenting, but instead I’m just flagging that I find it somewhat hard to have an open discussion about the EA hotel on the EA forum in the fundraising context. The feeling part is
there is a lot of emotional investment in EA hotel,
it seems if the hotel runs out of runway, for some people it could mean basically loosing their home.
Overall my impression is posting critical comments would be somewhat antisocial, posting just positives or endorsements is against good epistemics, so the personally safest thing to do for many is not to say anything.
At the same time it is blatantly obvious there must be some scepticism about both the project and the outputs: the situation when the hotel seems to be almost out of runway repeats. While eg EA funds collect donations basically in millions $ per year, EA hotel struggles to collect low tens of $.
I think this equilibrium where
people are mostly silent but also mostly not supporting the hotel, at least financially
the the financial situation of the project is somewhat dire
talks with EA Grants and the EA Long Term Future Fund are in progress but the funders are not funding the project yet
is not good for anyone, and has some bad effects for the broader community. I’d be interested in ideas how to move out of this state.
In practice, it’s almost never the inly option—e.g. CZEA was able to find some private funding even before CBG existed; several other groups were at least partially professional before CBG. In general it’s more like it’s better if national-level groups are funded from EA
The reason may be somewhat simple: most AI alignment researchers do not participate (post or comment) on LW/AF or participate only a little. For more understanding why, check this post of Wei Dai and the discussion under it.
(Also: if you follow just LW, your understanding of the field of AI safety is likely somewhat distorted)
With hypothesis 4.&5. I expect at least Oli to have strong bias of being more enthusiastic in funding people who like to interact with LW (all other research qualities being equal), so I’m pretty sure it’s not the case
2.&3. is somewhat true at least on average: if we operationalize “private people” as “people who do you meet participating in private research retreats or visiting places like MIRI or FHI”, and “online people” as “people posting and commenting on AI safety on LW” than the first group is on average better.
1. is likely true in the sense that best LW contributors are not applying for grants
In my experience teaching rationality is more tricky than the reference class education, and is an area which is kind of hard to communicate to non-specialists. One of the main reasons seems to be many people have somewhat illusory idea how much they understand the problem.
I’ve suggested something similar for happiness (https://www.lesswrong.com/posts/7Kv5cik4JWoayHYPD/nonlinear-perception-of-happiness ). If you don’t want to introduce the weird asymmetry where negative counts and positive not, what you get out of that could be somewhat surprising—it possibly recovers more “common folk” altruism where helping people who are already quite well off could be good, and if you allow more speculative views on the space on mind-states, you are at risk of recovering something closely resembling some sort of “buddhist utilitarian calculus”.
As humans, we are quite sensitive to signs of social approval and disapproval, and we have some ‘elephant in the brain’ motivation to seek social approval. This can sometimes mess up with epistemics.
The karma represents something like sentiment of people voting on a particular comment, weighted in a particular way. For me, this often did not seemed to be a signal adding any new information—when following the forum closely, usually I would have been able to predict what will get downvoted or upvoted.
What seemed problematic to me was 1. a number of times when I felt hesitation to write something because part of my S1 predicted it will get downvoted. Also I did not wanted to be primed by karma when reading other’s comments.
On a community level, overall I think the quality of the karma signal is roughly comparable to facebook likes. If people are making important decisions, evaluating projects, assigning prices… based on it, it seems plausible it’s actively harmful.
It’s not an instance of complain, but take it as a datapoint: I’ve switched off the karma display on all comments and my experience improved. The karma system tends to mess up with my S1 processing.
It seems plausible karma is causing harm in some hard to perceive ways. (One specific way is by people updating on karma pattern mistaking them for some voice of the community / ea movement / … )