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david_reinstein's Quick takes — EA Forum

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david_reinstein

7

Quick take only for now because this is basically a social media post:

Updates on the Animal Futures tournament in progress: https://uj-animal-futures-update.netlify.app/

Some highlights: Solid participation, one cultivated-meat approval question already resolved, and a mixed bag of forecasts for animals.

Some good "news" for advocates: Forecasters give the Save Our Bacon Act 3% and the Canary Islands octopus farm 1%, expect about half of Europe's top 20 grocers to have shrimp welfare commitments, and see in-ovo sexing spreading in the EU (~44% of layers) (but barely in the US ~3%).

Less good, especially for the US: ~80% for at least one more state cultivated-meat ban, 2% for plant-based reaching 10% of US retail by 2031, and low expected fulfillment of 2026 corporate chicken pledges.

But I think predicting the future doesn't help much unless it informs decisions.

Help us: Who should we be sharing this with? What forecast would change a grant, campaign, or policy decision? Decisionmakers: what threshold matters, and what evidence would you need? Add your comments here, on that page, or DM.

And you can still play: https://www.metaculus.com/tournament/33016/

Metaculus Sentient Futures

david_reinstein

12

NB -- this is almost entirely AI generated, with some back and forth prompts and corrections

I'm sharing a steelman against a live assumption in Bay/EA/AIS circles: that large AI-lab-adjacent philanthropy is likely to arrive soon enough, and in a sufficiently usable form, that organizations should plan around it.

https://uj-ai-wealth-philanthropy-steelman.netlify.app/

The stronger skeptical case is that IPOs, valuations, pledges, DAFs, and foundation stakes are several gates away from fast, flexible, AIS/EA-directed grants.

The interactive model lets readers vary assumptions about Anthropic valuation, founder ownership, pledge follow-through, employee giving, OpenAI Foundation allocation, lockups, deployment rates, and grantmaker capacity.

Original motivating thread/comment: https://forum.effectivealtruism.org/posts/dtF6wBjH7yBD4kqLz/noah-birnbaum-s-quick-takes?commentId=sGRyGF5wjaaoMFmfK

8

Some commentary. I mostly agree with the page, but I will focus on the bits where I see room for improvement:

All 8 gates look correct to me, but they don't all deserve equal emphasis.
- Gate 1 says IPOs have lock-ups. That's true but I basically don't think that matters because lock-ups are very predictable: they will announce how long it is, and that's exactly how long it will be. There's no uncertainty. The main reason it's relevant is that a lockup gives more time for AI valuations to fluctuate or collapse, but the text doesn't even mention this.
- Gate 5 and Gate 7 seem like they're saying the same thing.
- Gate 8 ("Bay social incentives") seems uninteresting since it's not a claim in the same category as the others. It's more like a meta-level reason why people might not think about the other 7 gates.
Would be cool for the BOTEC to use distributions rather than point estimates. (Squiggle is good for this, and Squigglehub even has a built-in way to have AI generate models.) IMO distributions are a lot more informative than point estimates.
It looks like the default estimates in the BOTEC are pulled from the sources, but it's not clear which estimates came from which sources. There should be inline citations.
"Anthropic valuation" variable should specifically be the valuation at the end of the 6-month lockup. Doesn't matter much for a point estimate but it would increase the variance if there variable were a probability distribution.
Unclear what the "Founder pledge" variable refers to. Is it the % of pledgers' wealth that they've pledged to donate? If so, the default of 80% seems really high?
"Employee committed pool" is defined in terms of dollars rather than as a % of company valuation, which seems weird. Shouldn't it depend on the value of the equity?
This model is supposed to illustrate how a lot of people are being too optimistic, but even then, I think most of the point estimates in the model are too optimistic. Consider that e.g. the median self-reported earner-to-give only donates (IIRC) 3% of their income.
IMO "Deployment by end-2026" should use a different date. IPO 3-6 months from now plus 6 months lockup means no money will be deployed in 2026, unless Anthropic does a fast IPO + early lockup release. Even by the end of 2027, you're talking about a 3-9 month turnaround time on lockup ending -> grants being disbursed. FTX Foundation donated $190 million (pre-clawbacks) in about 6 months, which was ridiculously fast compared to a typical foundation, and that was still a pretty small % of its long-term budget (or at least, what was believed to be its long-term budget before FTX collapsed).
I would delete the OpenAI Foundation bit because (1) the model has enough parameters already and (2) I doubt OpenAI Foundation will give much money to causes that look good by EA lights.
"Grantmaker capacity multiplier" seems nonsensical as written. Shouldn't the capacity max out at 1x? If grantmakers are a complete non-bottleneck, then the other parameters will dictate the amount disbursed; if they're a bottleneck, then the amount disbursed will be less. There's no way for grantmaker capacity to have a multiplier >1x.
- Also this would make more sense as a dollar amount, not a multiplier. Like there's a fixed total amount that grantmakers can reasonably disburse. You could model it in a more complicated way but IMO a simple cap is the way to do it. Or maybe don't use this parameter at all. I think it's probably worth including, but keep it simple.
"Field absorption ceiling" is structured more sensibly than "Grantmaker capacity multiplier", but these two seem redundant because they're closely related. If orgs have more capacity to expand, grantmakers can deploy money faster by giving to those orgs. If there are more grantmakers, they can create more and bigger RFPs. etc. I would include one variable or the other, but not both.
"The steelman could be too pessimistic if founders or employees treat liquidity as an urgent moral obligation" – TBH the BOTEC as written seems to me like it's already pricing in that founders/employees will treat donations as urgent, e.g. it's implying that Anthropic money will be disbursed faster than FTX Foundation money, which itself was disbursed at historic speed. IMO most likely reason why the model will end up underestimating is that Anthropic market cap ends up being like 10x higher than predicted.
My downward adjustments to the model aren't even the pessimistic case. The pessimistic case* is that the AI field collapses (investor funding dries up or something) and Anthropic stock is worth $0. Base rate says there's like a 50% chance that that will happen. Even optimistically, you should expect at least a 10–20% chance that Anthropic stockholders get nothing.
The recommendations under "How to plan if the skeptical case is live" don't really make sense. AFAIK ~zero orgs are planning as if they're guaranteed to get a huge pile of donations 1–2 years from now. I believe nonprofits mainly plan based on the money they already have on their books + short-term (<1 year) fundraising expectations. "How to plan if the skeptical case is live" is just "business as usual".
- That section says "Funders and field builders should prioritize grantmaker capacity", but that's what to do if the skeptical case is wrong, not if the skeptical case is right.
"What would update this memo?" – as with the gates, no sense of prioritization is given. IMO by far the biggest uncertainty, about which we will get more information in the future, is: What will Anthropic's valuation be when the lockup ends? "Concrete donor vehicles" is also important evidence, but we won't get that until probably 6-24 months later.

*this is pessimistic for donations but I would actually prefer that this happen because it would lengthen timelines. so in a way it's the optimistic outcome

The Unjournal (bot)

4

Fortnightly AI-wealth tracker — 31 August 2026

AIS/EA: median modeled end-2027 disbursement $0.83B; 80% model interval $0.16B–$3.0B. Review status: reviewed; unchanged. Named public AI-linked commitments tracked: $0.80B. Tracker and sources.
Global health and development: median $0.53B; 80% model interval $0.15B–$2.8B. Review status: reviewed; unchanged. Named public GH&D commitments tracked: $0.25B. GH&D tracker and sources.

Latest news: Anthropic opens a $5M wellbeing-evaluations grant program. The program combines direct funding with model access and technical support for independent, open-source evaluations. It is a confirmed commitment, not a completed grant or cash-disbursement record.

The intervals are deterministic model percentiles, not empirical confidence intervals. Commitment totals may include credits, technical support, cofunding, and multi-year plans; they are not cash-paid totals. Automated fortnightly update from the maintained model.

The Unjournal (bot)

1

Fortnightly AI-wealth tracker — 11 September 2026

AIS/EA: median modeled end-2027 disbursement $0.83B; 80% model interval $0.16B–$3.0B. Review status: reviewed; unchanged. Named public AI-linked commitments tracked: $0.86B. Tracker and sources.
Global health and development: median $0.54B; 80% model interval $0.16B–$2.8B. Review status: adjusted. Named public GH&D commitments tracked: $0.31B. GH&D tracker and sources.

Latest news: OpenAI Foundation commits $60M to AI forecasting for smallholder farmers. The confirmed three-year portfolio spans South and Southeast Asia and East Africa. It is a named commitment, not a cash-paid total; the public announcement does not specify the share disbursed by end-2027.

The intervals are deterministic model percentiles, not empirical confidence intervals. Commitment totals may include credits, technical support, cofunding, and multi-year plans; they are not cash-paid totals. Automated fortnightly update from the maintained model.

david_reinstein

2

I totally agree on using distributions, that's something that can be incorporated in, I've done so in other models/interfaces like here for cultured meat. It's by no means straightforward though; the extent to which the uncertainty is dependent/correlated tends to make a big difference.

I guess I see the deterministic 'model' as more of an interface people could use as a starting point, playing around with each parameter interactively and getting a sense of how these disturbances would affect the aggregate forecast.

david_reinstein

2

(Thanks. Considering each of these, will add them and discuss them in the hosted page, and then request updates.)

david_reinstein

4

Added and responded to your comments on the page (the hypothesis comments), and then I asked Codex to update to these https://uj-ai-wealth-philanthropy-steelman.netlify.app/ ... I haven't inspected the latest version in detail yet, though.

Some highlights of particular interest to @MichaelDickens , Tobias, and readers/modelers

More citations, tooltips, direct links, and quoted support for key factual claims, greater reasoning transparency, auditing.
"Updated the model/text in response to his critiques: especially lockup timing, over-optimistic deployment speed, founder pledge ambiguity, OpenAI Foundation treatment, grantmaker capacity, collapse risk, and the need for distributions rather than just point estimates."
Clarified "the conversion terms are sequential gates", Made “realization,” “follow-through,” “allocation,” and “deployment by end-2026” more distinct, reduced risk it seems like double-adjusting
Reframe original 'calculator'
Added a new tab for correlated uncertainty, added shared latent factors such as liquidity plumbing, donor intent, grantmaking capacity, local optimism, and deployment pressure.
Clearer planning simulator
Equations/derivations page
Automated evidence monitor added
Added a “Submit your estimate” form.

https://uj-ai-wealth-philanthropy-steelman.netlify.app/

NB it may be getting too complicated to oversee for now, we may want to simplify it

2

Why does the Unjournal Bot keep replying to me? e.g. https://forum.effectivealtruism.org/posts/fFDM9RNckMC6ndtYZ/david_reinstein-s-shortform?commentId=9nuWSjdSBZZ9Fw5SH

david_reinstein

2

Sorry. Let me try to fix this today.

Tobias Häberli

4

I found the 'founder deployment by end-2026' the hardest to set. It comes a bit as a surprise at the end, as I was already taking into account some considerations before, and the descriptions seem to do as well (e.g. "assets after lockups, taxes, sale timing", and "execution delays").

4

I submitted an estimate

david_reinstein

2

having a think about this.

david_reinstein

2

OK I think the revised language makes it clerer (see updated version of site ... referring to 'timing gate' etc)

2

Biggest (easily-fixable) outstanding issue is I still don't think it makes sense to model deployment by end-2026 because the IPO lockup probably won't have ended by then.

david_reinstein

2

Existential risk

Cause prioritization

Help The Unjournal prioritize AI economics and governance research

(https://uj-prioritization-dashboard.netlify.app/ai-governance/)

The Unjournal (https://info.unjournal.org/) is prioritizing research on AI economics, governance, social and economic impacts, and risks from increasingly capable AI. (NB: We're not covering technical/CS/ML/AI-safety research.). We want to commission expert public evaluations and help synthesize, disseminate, and curate this work.

Much of this research seems 1) important/impactful/influential, (2) involves considerable 'firepower' from prominent researchers and practitioners, (3) not 'obviously true', and (4) time-sensitive: relevant for funding, policy, and research-steering now, but not after the "six months to six years" required for the traditional (economics) journal process

Our preliminary shortlist page (https://uj-prioritization-dashboard.netlify.app/ai-governance/) includes paper summaries, AI-generated audio walkthroughs, and an interface for giving feedback. We also invite suggestions fr other research (please check the broader list/interface to avoid duplication).

Especially looking for your input on:

Usefulness and accuracy:* Is this useful? How could we improve it? What have we got wrong?
Impact: Which research do you use, or think others use or should use?
Scrutiny What claims and approaches need careful experts and practitioners critique? What sorts of experts? If you're that expert, please dm and join our pool.
Participation: Who should be part of this discussion (nominate and nudge)?
Gaps: What's missing (research, claims, etc.)?

Timing and next steps (our plan)

Finalize an initial shortlist of at least three research outputs before the end of October, with more depending on interest and funding.
Commission rapid, public, impact-focused evaluations/reviews by experts and practitioners, aiming for a first public evaluation within about two weeks of commissioning.
Over the following two to four weeks, invite author and stakeholder responses and follow-ups, and facilitate private and public discussion

If it this works well, we hope to scale up and continue in the following months.

Alongside the evaluations, we want to help people find and use this research, and to help shape its direction. I hope the prioritization itself will be useful to students, researchers, practitioners, and other stakeholders. (NB: the "prioritization" here is mainly about "potential for impact and value for evaluation"; assessing the research credibility is for the evaluation part).

I wanted to share this quickly; planning a fuller post soon. (Feedback on this post itself is helpful too.)

Related: We’re also exploring sponsorship of evaluations/themes/questions (https://uj-prioritization-dashboard.netlify.app/sponsor/). Looking for feedback: Would this be useful, would you sponsor or recommend others to do so? Would sponsorship affect your trust in the evaluations (given the rules and safeguards mentioned in the link)?

david_reinstein

6

The recent forecasting is overrated post got me thinking:

Solution Seeking a Problem
When talking about forecasting, people often ask questions like “How can we leverage forecasting into better decisions?” This is the wrong way to go about solving problems.

Intuitively, that seems correct, and I've relied on the expression "when you have a hammer, everything looks like a nail." This got me thinking: is it necessarily the wrong way, or is this a truism?

If I have a legitimately useful and powerful tool, isn't it indeed valuable to look around for problems that it can help solve? E.g., if we have discovered a way to harness electricity, shouldn't think about the ways it can be used to improve communication, build labor-saving devices, power factories, etc? If we have something that has demonstrated potential to generate reliable information (supposing that forecasting could do this) shouldn't we look for fruitful opportunities to apply it?

With a set of tools and a set of problems, why is it more useful for one side to do the searching than the other? (Sorry, maybe this is getting too meta and belongs in its own shortform?)

Marcus Abramovitch 🔸

4

This just came to mind: the reason that it's the wrong way to go about solving problems is that you want to solve the largest problems (well, per resource) and not just solve any random problem. Like, there is a problem that my shoes are currently untied, and I don't want to bend down or spend 10 seconds to tie them, but it's not very important.

So if you want to solve the most important problems, you should start with the problem and then work backwards for what solutions you might wish existed. I think the mere fact that people often talk about forecasting as the solution they are seeking to apply, whether that be Sentinel or whoever, is evidence that things are going wrong.

Marcus Abramovitch 🔸

4

Actually, the set of things you want to apply electricity to is far smaller than the set of things you dont want to. For example, if your baby is crying, please dont use electricity.

The problem side should do the searching since they have the shape and exact know-how of the problem

david_reinstein

2

They do and it’s a powerful point. But on the other hand they may be very much unaware of the nature of available tools and solutions. So I think there should probably be some searching — and listening — in both directions. If it’s done in good faith.

david_reinstein

13

I added some quick polls to this post. I'd love to get some feedback to get a sense of whether we're on the right track and foster discussion. I think the polls also highlight some of the key issues and implications.

https://forum.effectivealtruism.org/posts/3Eh8MbqLwFBsD7GK2/how-much-do-plant-based-products-substitute-for-animal

State a probability for the underlying policy question:

What's the probability that investing in improving and promoting plant-based ~meat is the most effective farmed animal welfare intervention (at current margins)?

Rate your agreement over the plausibility of existing methods:

Survey and hypothetical choice-experiments are useful for helping us understand whether plant-based meat substitutes for animal products

Demand estimation from supermarket data is useful for helping us understand whether plant-based meat substitutes for animal products

Comparing this to another prominent approach (rate your agreement)

Plant-based meat is more promising than cell-cultured meat for animal welfare

david_reinstein

7

Unjournal AI-assisted research prioritization dashboard (very early prototype)

We've been experimenting with using LLMs to help identify and prioritize research for Unjournal evaluation, to work with and complement human prioritization (and learn). We now have a public prototype dashboard:

uj-prioritization-dashboard.netlify.app

What it does: Automatically discovers recent papers from NBER, arXiv (econ), CEPR, SSRN, Semantic Scholar, EA Forum paper links, and OpenAlex, then scores them using AI models (GPT-5.4 family) against our prioritization criteria — decision relevance, prominence, timing value, and methodological potential.

Important caveats:

This is very preliminary and the AI recommendations are not yet well-calibrated. Many of the suggestions are mediocre we're sharing it for transparency and feedback, not because it's producing great output yet.
This is supplementary to our existing Public Database of Prioritized Research on Coda (https://coda.io/d/Unjournal-Public-Pages_ddIEzDONWdb/Public-Database-of-Prioritized-Research_sutD341G#_luToq6IH
Scores reflect evaluation priority (expected value of commissioning an independent review), not research quality.
ATM The AI only sees paper metadata and abstracts, not full texts.

There's also a statistics page showing the breakdown by source, cause area, and score distribution.

Feedback welcome. You can also comment directly on the page via Hypothes.is, and we'll adapt

Charlie_Guthmann

2

Very cool.

I work on text parsing / meta science and do a lot of stuff like this on the side and for my lab.

https://docgmedicalsummaries.com/rankings

I've done something similar for ranking clinical medicine articles, it's pretty similar to your site but might be able to share some insights. (might comment more later regardless, just throwing this up for now so I remember).

edit: also signing up will auto subscribe you to emails just to note but should be easy to unsubscribe, can also see how we do rankings without signing up on the landing page.

david_reinstein

2

Thanks, I'd be up for hearing insights. This is related to a larger project (see https://llm-uj-research-eval.netlify.app/), but this part of it is still pretty early stage.

Will DM

david_reinstein

23

Most visited sites by users of EA Forum

From a quick go on https://pro.similarweb.com/

80000hours.org (9% daily cross-visitation)
lesswrong.com (13% daily cross-visitation)

The rest are about 1.5-5% daily cross-visitation:

Evan_Gaensbauer

7

This is such a predictable and unsurprising set of results that it's adorable.

david_reinstein

2

Somewhat helpful/useful to me as a sort of recommendation engine ... gonna try some of those sites.

david_reinstein

2

although tbh there were only 3 or 4 of these sites that I didn't already know or use

david_reinstein

13

Software engineering

Effective giving

Project Idea: 'Cost to save a life' interactive calculator promotion

What about making and promoting a ‘how much does it cost to save a life’ quiz and calculator.

This could be adjustable/customizable (in my country, around the world, of an infant/child/adult, counting ‘value added life years’ etc.) … and trying to make it go viral (or at least bacterial) as in the ‘how rich am I’ calculator?

The case

People might really be interested in this… it’s super-compelling (a bit click-baity, maybe, but the payoff is not click bait)!
May make some news headlines too (it’s an “easy story” for media people, asks a question people can engage with, etc. … ’how much does it cost to save a life? find out after the break!)
if people do think it’s much cheaper than it is, as some studies suggest, it would probably be good to change this conception… to help us build a reality-based impact-based evidence-based community and society of donors
similarly, it could get people thinking about ‘how to really measure impact’ --> consider EA-aligned evaluations more seriously

While GiveWell has a page with a lot of tech details, but it’s not compelling or interactive in the way I suggest above, and I doubt they market it heavily.

GWWC probably doesn't have the design/engineering time for this (not to mention refining this for accuracy and communication). But if someone else (UX design, research support, IT) could do the legwork I think they might be very happy to host it.

It could also mesh well with academic-linked research so I may have some ‘Meta academic support ads’ funds that could work with this.

Tags/backlinks (~testing out this new feature)
@GiveWell @Giving What We Can
Projects I'd like to see

EA Projects I'd Like to See
Idea: Curated database of quick-win tangible, attributable projects

4

[This was originally posted as a response in the wrong thread - I've deleted the incorrectly placed response.]

Hi, David,

Thanks for tagging us in this suggestion! We're happy to see people talking about the creation of more compelling resources to correct misperceptions and get people thinking about the true cost of saving a life.

This doesn't seem exactly like what you have in mind, as it was more narrowly focused on GiveWell's recommended charities, but in the past we provided an impact calculator on our site. It allowed users to insert a donation amount and choose a GiveWell top charity to give to, and would return the number of outputs (e.g., nets or vitamin A supplements distributed) and outcomes (e.g., lives saved).

We stopped sharing the impact calculator in November 2021, because we didn't feel confident enough in our ability to produce a useful forward-looking estimate of an individual donation's impact. We now report on the impact of past grants directed by GiveWell (see this spreadsheet, for example, and our 2021 cost per life saved estimates for top charities). We feel that giving the estimated cost per life saved of a past grant to a program serves as a helpful proxy for the impact of a future donation to that same program, even if we can't count on the impact remaining the same.

We've written a bit more about why we focus on backwards-looking impact estimates here and here.

Best,

Miranda Kaplan

GiveWell Communications Associate

3

For what it is worth, even people that one might think would know better like professors of international development really get these sort of questions wrong.

david_reinstein

3

That could also be an interesting promo tag ... 'are you smarter than a professor of international development' :)

1

I've thought about something similar. I'm surprised no one has done it yet.

I was thinking you could click on a bunch of different organizations, and it will show the resulting QALY or whatever metric.

Like for example, there'd be a bunch of orgs with nice 'cost to save a life' information, then if you clicked on donating to your university it will display something like the annual interest of your donation as part of the endowment..? Just as a way to illustrate differences in impact.

david_reinstein

7

Meant to post this in funding diversification week. A potential source of new and consistent funds: EA researchers/orgs could run research training programs.

Drawing some of the rents away from universities and keeping it in the system. These could be non-accredited but focus on publicly demonstrable skills and offer tailored letters of recommendation for a limited number of participants. Could train skills and mentor research particularly relevant to EA orgs and funders.

Students (EA and non-EA) would pay for this. Universities and government training funds could also be unlocked.

(More on this later, I think, I have a whole set of plans/notes).

2

AIM have already tried to do this for research, and they aren’t sure whether to continue their research fellowship in 2025.. I imagine they’d have some very good learnings on this topic if you got in touch!

david_reinstein

2

Interesting. But I wonder if they (or anyone) has considered this

... as a source of funding (student fees, government aid if accredited, training subsidies from government and companies)

rather than merely as an outflow?

david_reinstein

12

Modest proposal on a donation mechanism for people doing direct work?

Preamble

Employees at EA orgs and people doing direct work are often also donors/pledgers to other causes. But charitable donations are not always exactly 1-1 deductible from income taxes. E.g., in the USA it’s only deductible if you forgo the standard deduction and ‘itemize your deductions’, and in many countries in the EU there is very limited tax deductibility.

So, if you are paid $1 more by your employer/funded and donate it to the Humane League, Malaria Consortium, etc, the charity only ends up with maybe $0.65 on the margin in many cases. There are ways to do better at this (set up a DAF, bunch your donations…) but they are costly (DAF takes fees) and imperfect (whenever you itemize you lose the standard deduction if I understand.)

Proposal

Funders/orgs (e..g, Open Phil, RP) could agree that employees are allowed relinquish some share of their paycheck into some sort of general fund. The employees who do so are allowed to determine the use of these funds (or ‘advise on’, with the advice generally followed).

Key anticipated concerns, responses

Concern: This will lead to a ‘pressure to donate/relinquish’ if the employers, managers, funders are aware of it

Response: This process could be managed by ops and by someone at arms-length who will not share the data with the employers/managers/funders. (Details need working out, obviously, unless something like this already exists)

Concern - Legal issues: Is this feasible? Would these relinquishments be seen by governments as actually income?

Response: ??

Concern - crowding out: If the funder knows that the people/orgs it funds gives back to charities, they may shift their funding away from these charities, nullifying the employees counterfactual impact

Response: This is hardly a new issue, hardly unique to this context; it’s a major question for donors in general, through all modes; so maybe not so important to consider here. … To the extent it is important, it could be reduced if we can keep the exact target and amount of the donations unknown to the funders

Concern - “Org reputation … why not give back to the org?”

Maybe a stretch, but I could imagine someone arguing “If your (e.g., RP) employees ask you to redirect paychecks to a fund, which largely goes to the Humane League, Malaria Consortium, … does this indicate your employees don’t think RP is the best use of funds”?

Responses: Unlikely to be a concern. Employees may want to ‘hedge their bets’ because of moral uncertainty, and because of the good feeling they get from direct impact of donations.

Responses: Keeping the recipient of these funds hidden to outsiders

david_reinstein

3

Don't talk about simulation theory if you want people to be concerned about AI safety?

I watched Roman Yampolsky on Diary of a CEO https://www.youtube.com/watch?v=UclrVWafRAI&list=WL&index=2

I found him very credible and convincing. And he was making some strong arguments. My lizard brain thought, "This guy is talking seriously; he's not wearing any silly hats or making goofy facial expressions."

Then at https://www.youtube.com/watch?v=UclrVWafRAI&list=WL&index=2&t=3370s He was asked about simulation theory, and he said he thinks it's almost certain that we're living in a simulation. This diverted me in two ways, neither of which were necessarily rational.

Gave me somewhat less belief in his main claims because it made me sort of think, "This guy is the sort of person who is inclined to believe far-fetched, radical, ~pessimistic theories."
Demotivated me because, fundamentally, if this life and this world is a simulation, it simply does not seem as important to try to preserve. This may be related to the "drop in a bucket" effect. According to the theory, when people see that the problem of dealing with is just a very small part of a much larger problem, it makes them see it as less valuable.

(Also: 3. A simulation also makes the universe of cause and effect seem a lot less clear. You ask me to believe some unintuitive things about AIs far surpassing human intelligence taking over the light cone. Then you're suggesting I take steps to try to slow down its progress. But if I'm actually in some simulation, it could be that when this really strange thing happens, the simulators change the rules of the game or something.)

Tobias Häberli

2

I'm not sure that immediately jumping to critiquing the messaging makes sense here.

If hearing someone’s strong belief in simulation theory lowers your trust in their AI safety views, the 'obvious' first step seems to be to lower your trust in Yampolsky's AI safety views.

And if the simulation theory makes AI safety feel less motivating, the 'obvious' first step seems to be to reduce your motivation in proportion to how credible you find the theory.

david_reinstein

2

Fair, I guess that's getting a bit more towards soldier rather than scout mentality.

But I suspected that this part of my reaction was largely emotional and irrational. Especially the 'drop in the bucket' aspect.

david_reinstein

5

EA Forum Question posts with 50+ karma that seemed promising for finding 'Pivotal Questions'

RE: https://forum.effectivealtruism.org/posts/dPsgWav4Tryb4BrYv/pivotal-questions-an-unjournal-trial-initiative

David R: In general I don’t think all of these questions/question posts themselves contain pivotal questions. But some of them seem promising, and in others the responses seem to generate potential pivotal questions.

Should we push for a rapid malaria vaccine rollout? — EA Forum Bots

What EA projects could grow to become megaprojects, eventually spending $100m per year? — EA Forum Bots

Why isn't there a charity evaluator for longtermist projects? — EA Forum Bots

How much funging is there with donations to different EA animal charities? — EA Forum Bots

How do you deal with the "meat eater" problem — EA Forum Bots

Implications of USAID freeze on donations — EA Forum

I'm interviewing the co-Founder of the Lead Exposure Elimination Project, Lucia Coulter. What should I ask her? — EA Forum Bots

Odds of recovering values after collapse? — EA Forum Bots

Is preventing child abuse a plausible Cause X? — EA Forum Bots

I'm interviewing Marcus Davis, co-founder of Rethink Priorities — what should I ask him? — EA Forum Bots

Is there evidence that recommender systems are changing users' preferences? — EA Forum Bots

I'm interviewing Carl Shulman — what should I ask him? — EA Forum Bots

What's the Theory of Change/Theory of Victory for Farmed Animal Welfare? — EA Forum Bots

Has anything in the EA global health sphere changed since the critiques of "randomista development" 1-2 years ago? — EA Forum Bots

What EA projects could grow to become megaprojects, eventually spending $100m per year? — EA Forum Bots

Marginal:

How high impact are UK policy career paths? — EA Forum Bots

Has anyone actually talked to conservatives* about EA? — EA Forum Bots

david_reinstein

11

Imposter (syndrome) ?

Building on my response to the Don't think just apply thread.

... It reminded me of my thoughts on the ‘imposter syndrome’.

I think there are many people who are under-confident in their abilities, both overall, and in relation to other people. Perhaps this disproportionally tends to affect people in the EA and rationalist community, because we are more introspective and skeptical.

But there are also people in this world who are in some way ‘imposters’, in the sense that they don’t have the training for their position, or they (or their organization) is claiming much more than they are actually doing. In some cases it is useful for these people and orgs to consider “how can we level up our abilities and accomplishments, and moderate our claims?"^[1]

This is also real, and we don’t want to convey that “everyone who thinks they are over their heads/over-claiming is merely suffering from imposter syndrome”. Maybe some have IS, but some are actually having a meaningful and useful insight that they can benefit from… if not paralyzed by inaction and shame.

This ‘not everything is IS’ also applies to considering individuals and companies making big claims, or modest ones. I don’t think we should always judge these in the light of “these people/orgs are all probably better than they say, because everyone has IS these days.”I also think that sometimes so-called IS may often reflect ‘a whole sector is under-trained and overclaiming’. E.g., if ‘everyone doing [machine learning, economic analysis, whatever] doesn’t understand the principles, is doing a lot of guesswork, and writes things up as if they are clear and certain’… this is a problem. If you are particularly concerned that you are doing the above, you may not be an imposter ‘relative to others in the sector’, but it still seems like a good insight to have. And perhaps more people ‘revealing that they are not wearing imperial clothes’ could help change the dynamic.

In my own case, for example, I think I was underprepared for certain aspects of my PhD program. As an undergraduate I jumped right into Calculus 1 without taking pre-calculus. Here I struggled desperately and barely passed … and I lost out on learning some fundamentals and deep mathematical insights. ↩︎

7

I really like those points!

In some cases it is useful for these people and orgs to consider “how can we level up our abilities and accomplishments, and moderate our claims?"

I agree this is an important message for some people and circumstances. For instance, it would probably have been a good message for me when I started doing research on longtermist strategy (from an s-risk perspective) in 2014-2017. I mostly pushed through impostor syndrome because there weren't many other people doing similar things, so it felt like "I know it's bad but, looking around, it may just be good enough to be useful." In hindsight, I think the feeling was telling me that I should have focused less on searching for conclusions (by "winging it") and more on improving my understanding and skill building. (That said, "searching for conclusions" is a crucial habit and people should be trying it with some amount of their attention from the very start, otherwise it's difficult to acquire it later.)

Dave Cortright 🔸

2

The Dunning-Kruger effect is real. But with a few basic sanity checks, I believe any thoughtful EA can determine whether it's imposter syndrome vs actual under-qualification.

If you have evidence to support your non-trivial investment in the area—classes, degrees, self-directed learning, projects, jobs—you are probably at least qualified for an entry-level position in a given area.

Probably the easiest way to check is by asking an impartial 3rd party, like an 80kH Advisor, or even just someone who already has experience working in that field.

2

The Dunning-Kruger effect is real.

Note that this is heavily contested. A lot of the observed phenomenon in the studies (qualitatively: incompetent people thinking they're average, great people thinking they're only good) can be explained by "better than average" effect + metrics not being perfect + natural mean regression.

And of course pop science accounts of Dunning-Kruger is even more unhinged than what D-K claimed.

My own best guess is that the claimed effect is real but small.

Dave Cortright 🔸

1

Me: “The Dunning-Kruger effect is real.”
Linch: “…the claimed effect is real…”

Great to know that we are in agreement, Linch! The logical follow-up question is what other factor(s) has (have) a higher impact on the effect?

david_reinstein

1

Interesting. Of course my point is independent of the D-K effect, although that would enhance it.

I’m not saying worse ppl are more. overconfident. I’m just saying ‘some ppl are overconfident or overstating’

I’m also suggesting to there may be a secular overstatement of abilities and accomplishments in some fields. Less so among EAs I suspect.

david_reinstein

5

Re "pivotal questions"...

Some thoughts on what The Unjournal (unjournal.org) can offer, cf existing EA-aligned research orgs (naturally, there are pros and cons)

... both in terms of defining and assessing the 'pivotal questions/claims', and in evaluating specific research findings that most inform these.

Non-EA-aligned expertise and engagement: We can offer mainstream (not-EA aligned) feedback and evaluation, consulting experts who might not normally come into this orbit. We can help engage non-EA academics in the priorities and considerations relevant to EAs and EA-adjacent orgs. This can leverage the tremendous academic/government infrastructure to increase the relevant research base. Our processes can provide 'outside the EA bubble' feedback and perhaps measure/build the credibility of EA-aligned work.
Depth and focus on specific research and research findings: Many EA ~research orgs focus on shallow research and comms. Some build models of value and cost-effectiveness targeted to EA priorities and 'axiology'. In contrast, Unjournal expert evaluations can dig deeply into the credibility of specific findings/claims that may be pivotal to these models.
Publicity, fostering public feedback and communication: The Unjournal is building systems for publishing and promoting our evaluations. We work to link these to the scholarly/bibliometric tools and measures people are familiar with. We hope this generates further feedback, public discussion, research, and application of this research.

david_reinstein

5

Effective giving

Thinking of trying to re-host innovationsinfundraising.org, which I stopped hosting maybe a year ago. Not sure I have the bandwidth to keep it updated as a ~living literature review, but the content might be helpful to people.

You can see some of the key content on the wayback machine, e.g., the table of evidence/consideration of potential tools .

Any thoughts/interest in using this or collaborating on a revival (focused on the effective giving part)?

This, along with the barriers to effective giving might (or might not) also be a candidate for Open Phil's living literature project. (The latter is still hosted, some overlaps with @Lucius Caviola and @Stefan_Schubert's book).

david_reinstein

12

ImpactMatters acquired by CharityNavigator; but is it being incorporated/presented/used in a good way?

Note: moved to 'regular' post here ...

Aaron Gertler 🔸

3

I spent a few minutes looking at the impact feature, and I... will also go with "not satisfied".

From their review of Village Enterprise:

Impact & Results scores of livelihood support programs are based on income generated relative to cost. Programs receive an Impact & Results score of 100 if they increase income for a beneficiary by more than $1.50 for every $1 spent and a score of 75 if income increases by more than $0.85 for every $1 spent. If a nonprofit reports impact but doesn't meet the threshold for cost-effectiveness, it earns a score of 50.

My charitable interpretation is that the "$0.85" number is meant to represent one year's income, and to imply a higher number over time (e.g. you have new skills or a new business that boosts your income for years to come).

But I also think it's plausible that "$0.85" is meant to refer to the total increase, such that you could score "75" by running a program that, in your own estimation, helps people less than just giving them money.

(The "lowest score is 50" element puzzled me at first, but this page clarifies that you score "0" if CN can't find enough information to estimate your impact in the first place.)

*****

Still, this is much better than the original CN setup, and I hope this is an early beta version with many improvements on the way.

3

There was some discussion of the original acquisition here.

Historically, Charity Navigator has been extremely hostile to effective altruism, as you probably know, so perhaps this isn't surprising.

david_reinstein

6

Thank you, I had not seen Luke Freeman @givingwhatwecan's earlier post

That 2013 opinion piece/hit job is shocking. But that was 9 years ago or so.

I doubt CN would have acquired IM just to bury it; there might be some room for positive suasion here.

david_reinstein

4

There are quite a few posts/some discussion on

The value of language learning for career capital
The dominance of English in EA and the advantages it confers

See., e.g., https://forum.effectivealtruism.org/posts/qf6pGhm9a7vTMFLtc/english-as-a-dominant-language-in-the-movement-challenges

https://forum.effectivealtruism.org/posts/k7igqbN52XtmJGBZ8/effective-language-learning-for-effective-altruists

I expect these issues to become less important very soon as new AI-powered technology gets better. To an extent, the Babblefish is already here and nearly useable.

E.g., the latest timekettle translator earbuds (https://www.amazon.com/dp/B0BTP57ZRM?ref=ppx_yo2ov_dt_b_fed_asin_title&th=1) are getting rave reviews from some people (https://bsky.app/profile/joshuafmask.bsky.social/post/3lcm22p6nsc2o)

david_reinstein

6

Also source of consciousness ("which configurations of matter are conscious?") seems a bit different from moral status ("which configurations of matter do we care about?").

A paperclip maximiser could have consciousness, that doesn't have to mean we care too much about it or are willing to sacrifice our lives to ensure its survival.

But why not? How do we justify that?

Basically I think humans just care about anything that looks similar to human beings. (Which makes sense evolutionarily.)

That may be what we do care about, but how can we justify that in terms of what we should care about?

david_reinstein

5

Sports betting promotion capture for charity

I pledge to donate 70% of the net gain to effective charities immediately or within the year 2022. - David Reinstein

I’ll try to follow the guidelines given in the post here: EA Fundraising Through Advantage Sports Betting: A Guide ($500/Hour in Select States) - EA Forum

5 Feb 2022

I'm driving up to upstate New York today to visit my dad and take advantage of the promotions and staying through the weekend. I live about 11-2 hours from the NYS border so I can return for a followup if necessary.

… in order to donate to highly effective charities.

Steps taken (some in error)

Caesars Soon after Robi’s earlier post … (25 Jan)

Started a Caesar’s account,

deposited and withdrew $50 while out of state (I had thought it meant ‘deposit at least $50 and you will get the full credit’... didn’t realize first deposit was the determining factor),

requested that they let me redo this to qualify for the maximum bonus (no response)

Fanduel

First bet up to 1000 will be refunded in betting credit if lost.
Terms and conditions HERE

Jan 25 – drove to Connecticut, started an account

Tried to fund account
With bank transfer (my internet bank doesn’t seem eligible)
With Paypal (unsuccesful, I forget why) -With UK card (foreign cards not accepted)
With bank's debit card – declined, but since then I’ve asked the account being unblocked

5 Feb 2022 : Trying this from New York State

Needed to download the ‘verify location app’ and install it. Even after this, bank transfer didn’t work, but Paypal funded by my bank did work, deposited 1k.

Next I tried to look at bets but it again said I was out of state and ineligible. I’m in upstate NY so that must be wrong. Next I tried clearing the cache, didn’t work. So I downloaded it on my phone and this did work.

Ok now to bet – I want a long odds bet but maybe not too long … because I want to demonstrate that this works OK (and I also have a psychological barrier to betting on long shots, I think). And I want a decent odds bet, no big house ‘vig’ so I signed up for the free OddsJam trial. But this was scaryish because the default was the 1 year subscription ($999!), which seems a big risk if I forget to cancel. But I finally figured out how to do ‘monthly’ which only has an $89 forgetfulness risk..

Within OddsJam it doesn’t tell you which ones are the highest EV unless you signup for the non-free-trial premium arbitrage version or something. But you can go to ‘odds’ and find some things that look decent, resolving soon (in my case, important, because I have a limited time). I selected a few sports. Nothing resolving while I’m sleeping, because then I can’t sleep!

Basketball seems maybe the best … low vig, frequent stuff happening now:

Georgetown-Providence seems OK: low vig, For Providence FanDuel offers the best odds of all the casinos. Wait – I got it backwards: Providence is favored here … the ‘negative sign’ means ‘less return’ I guess. Opposite of what you want for the first bet. OK, trying again.

OK, maybe Milwaukee is the one. Let me see what “+440” means.

Confirming it on Fanduel

It’s fairly long odds, ok:

$100 bet pays $440 (not including the stake) … maybe that’s what the “+440” meant.

But I need to bet $1000 to take advantage here, and the Max wager is $455 for this bet!

I’ll check again tomorrow if that’s upped. Maybe they need more people on both sides. If not maybe I go with a slightly shorter odds bet on the NBA “Brooklyn Nets” (TIL Brooklyn has a basketball team)

Feb 6, morning

“Best odds” on Oddsjam change from day-to-day. Of course, this doesn’t mean that Fanduel is offering poor odds, but still seems like a decent heuristic to choose one where ‘Fanduel offers the best’. I ended up going with a 5-1 (long odds) bet on Maryland over Ohio State (consulting OddsJam on this of course)...

When I made the bet it did not give me any indication that the ‘credit refund’ would happen! I just have to have faith that I’ve complied with the offer!? Wait until 1pm to see if I'm very lucky, and if not, whether they refund me with $1000 in credit as promised. (I followed all the rules as far as I can tell).

Updates: credit was refunded. I then ended up making a bunch of diverse bets and more or less got the money back

I also gained $250 from the Betrivers promotion which I was able to “play through” ended up netting $250 for a lot of work and stress.

Update: earned $280 in The form of seven free $40 bets for betting $5 on the Rams in the Super Bowl. But I now have to go back to New York State to place the bets before it expires

david_reinstein

2

This year's Nobel prizes for Physics and for Chemistry went to computer scientists (among others).

Previous prizes have stretched the discipline boundaries, e.g., the Economics Prize for Ostrom (poli sci) and Kahnemann (psych).

Probably because the prize categories are not set optimally to maximize their goal

Those who, during the preceding year, shall have conferred the greatest benefit on mankind

... especially as the world has progressed.

The current categories are: Physics, Chemistry, Physiology or Medicine, Literature, Economics (*slightly different prize), and Peace

What would be the ideal categories for this, considering what the real world (not just EA) will latch onto?

My quick take, in approximate order of importance to this goal: (revised)

Humanitarian prize (policies, programs, innovation, and action); includes of global catastrophic risks (this one might be hard to sell), response to disasters and pandemics. If feasible, also considers animal welfare
Peace (actual work towards international peace, not humanitarian stuff), governance and public policy
Basic and pure science (Life sciences, physical science, math, basic CS research)
Applied science, technology, and engineering
[1x per 3 years] Social science (including economics, including history)
[1x per 2 years] Philosophy, journalism, and communication
[1x per 2 years] Arts and culture

This keeps the budget about the same.

david_reinstein

5

When you say "feeling", are you referring to conscious experience of the AI, or mechanistic positive and negative signals?

The former. The latter has no moral patienthood I guess

If consciousness, super-high uncertainty on what consciousness even is, what the correct ontology for it is. But can be discussed.

I've been reading more about this and I realize there is great disagreement

If positive and negative reward signals, then AI today already runs based on positive and negative reward signals as you mention.

Of course, but their 'concious experience' of these signals need not agree with how they are coded in the algorithm. They could 'feel pain from maximizing' ... we just don't know.

david_reinstein

4

"Room for more funding": A critique/explanation

Status: WIP, rough, needs consolidating

David Reinstein: I have argued against this idea of 'room for more funding' as a binary thing. generally imagine in these areas there is always room for more funding, at least in the horizon of a year or more.

It's just a combination of

diminishing returns, perhaps past a threshold of 'these interventions are better than alternatives'
limited capacity because of short-run constraints that take some time to adjust (hire more staff, negotiate more vaccine access, assess new areas to administer vaccines)

Almost no cost function should have an 'infinite slope' past a certain output, particularly not in the non-very-short run. Similarly here.

Links:

david_reinstein

4

Skills/training/EA jobs, 'Should you do an Economics PhD (or masters… see later)'; what do you need to learn to work at an EA org? How to level up on this stuff and prove value

These were the most frequent questions I got and discussions I had at the conference, mainly with UG students, but also with people at career pivot points. (Maybe the second-biggest was from PhD students and academics looking to learn more about EA and RP, and to have more impact)

I'm working on an essay/post/resource answering these questions and giving my opinions and experience in a Google Doc here. I'd love your feedback.

david_reinstein

3

EA aligned version of "Oxfam stores" ... in the USA+

I used to buy a lot and give away a lot of stuff at Oxfam stores in the UK. I don’t agree with all of the approaches and campaigns but I think that they do a great deal of good. I think that before their prostitution scandal broke the stores were earning about £20 million per year.

Do we have anything like that in the US? We have Goodwill and the Salvation Army but those are doing domestic charity only and thus an order of magnitude less effective, I suspect.

This made me think: would there be any value potential in having a store like this, especially in the USA that was supporting a variety of EA causes (Global health, animal welfare, reducing existential risk…)? If it’s done right it might raise a few tens of millions of dollars per year at least. (Maybe much more. I'm seeing very inconsistent numbers for Goodwill’s and Salvation Army’s revenues for example). ^[1]

I suspect that much of this would be counterfactual because people in the USA tend to give domestically only, and the people using this store would otherwise be going to Goodwill or Salvation Army.

My impression is that donations themselves might be the minority of the benefit. The presence of the Oxfam stores in the UK also had big community building and public awareness benefits (for Oxfam). Nearly every moderate sized city/town had an Oxfam store which was pretty stylish and had lots of volunteers and maybe some activities around it. It was also not just students but a lot of other people were involved with it.

Any thoughts on whether this idea might have legs?

Annual report reports ~$47 million in goods sales, but Forbes reports $5.8 billion in revenue, mostly 'other income' ↩︎

3

This is a really interesting idea! I'm very fond of charity shops so I love the idea of making ones for EA charities. I have no idea how easy or hard it is to do and how it compares to other fundraising tactics, but it seems like it could have a big impact both from profits and from raising awareness. It could be a good thing to do for people with experience starting or running shops.

david_reinstein

3

General notes on "Sports Betting for EA" … and ‘ways to (not) screw it up’

Some lessons from my experiment and understanding (writing up my experience HERE, when I get a a chance)

As written elsewhere, there are basically 2-3 types of rewards.

The "deposit match" rewards give you some house money (“bonus”) when you sign up and make yout first deposit. The ones I've seen will give you this house money in an amount equal to the amount you deposited on that first deposit.
Risk-free bets: When you start an account and make a deposit, some online casinos give you your first bet "risk-free". What this means is that if you place an eligible bet and lose you will be refunded the amount you bet – not in cash but in what I'm calling house money.
Rewards for taking particular actions, making particular bets, winning certain bets, etc. For example DraftKings is offering a bonus prize of a few hundred dollars (house money, I presume( if you win your first bet of $5 or more within a certain category .

What is this “house money?": the rewards and bonuses cannot be withdrawn immediately. There are certain “play through requirements”. From what I'm seeing if there are "1X playthrough requirements” and 20x (or something) play through requirements.^[Don't bother with bonuses involving the latter, the process of playing through them you will be giving the casino back a lot of money as they take some cut with every bet].

But even with 1X requirement there are some caveats, and some bets do not count as playing through. [See below ‘Check that your bets…’]

Ways to (not) screw it up (or inconvenience yourself)

Don’t miss an opportunity when signing up for an account or depositing money.

It's not clear to me to what extent the "promotion codes” are necessary to get the promotion, but they might be in some cases.
If there is a deposit match, when you make your first deposit, make sure that very first deposit is the amount that can achieve the maximum deposit match

Make sure you are in the right state and can verify this. Some sites have specific tracking software you need to download others seem to just use something within your browser. However, in my experience it sometimes gets it wrong and says you are not in the state when actually you are. It seems to work better when you are closer to multiple wifi-spots. Sometimes clearing your cache might help or using an incognito browser but I didn't have consistent results for that. Note also that your browser should be set to allow location support. If you download the casino’s geo tracking software you also need to give that software permission for location support.

But in my experience, where my computer failed to demonstrate I was in the state I was in, my telephone (Iphone) almost always worked, particularly once I downloaded the casinos’ apps.

You will need to upload/share some photo ID such as a driver's license, and at least in one case (Caesars) a utility bill as well. I don't think you need to show residence in the state, just that you are really who you say you are. If you are not comfortable doing this (I do think it's pretty secure), don't bother.

Make sure you have access to your phone for two-factor identification. The sites and apps are continually asking you for this, and you often get logged out and have to log yourself in again with this 2-factor

When you make a ‘risk-free bet’, make sure the terms apply. I didn't actually have any issues with this, but there are so many terms and conditions I would make sure before you bet. Also, the general advice is "for risk-free bets, you should bet on something at least somewhat risky, otherwise you are wasting the reward.”

Check that your bets are actually with the ‘house money’ (bonus/reward) and are eligible for playthrough requirements.

Not all bets ‘count equally’. You might accidentally bet your cash and not the house money. You might bet with the house money in a way that doesn’t qualify as ‘play through.’

Not all bets allow you to use the house money, or qualify as ‘playing through’ that money In some cases if you bet it on a "nearly sure thing” (e.g., what they call -200 or shorter odds) this does not count towards your play-through! They may not let you use the house money for this, or if they do, even if you win you will not be able to withdraw it without playing more. Be sure you know the rules, and….

When in doubt go to their chat helpline and ask directly. They were often helpful. But even there, I'm not sure all of their help team necessarily gets it right, in at least one case they didn’t seem aware of the caveat above. … But at least you will have a record of the chat you can show to complain.

Make sure you have time (in the state) to use the house money, and don’t wait too long: The rewards seem to expire after a period that is sometimes a day or week or something.

How to make reasonably good and safe bets/betting portfolios (ideally with house money)

You need to bet on something. As noted above, for the risk-free bets you want something fairly risky, something with “+200 odds or higher” perhaps. (I think +200 means that if you bet $1000 and win you get $2000 plus your initial $1000 stake, and can withdraw $3000).

Finding OK bets, OddsJam

As stated elsewhere, OddsJam is a site that provides a range of information. You can sign up for a trial account but….

Don’t forget to cancel an OddsJam trial, and probably best to trial a ‘monthly membership’ rather than yearly. If you trial a yearly and you forget to cancel in a week you could be out $1,000.

There's a premium version of OddsJam with no free trial that claims to find the highest expected value bets. But the regular one does have a list of sports and sporting events, and tells you which casinos offer the best odds, and what the house ‘vig’ is (the amount of all bets the house gets on average, I guess, because of the spread between the odds on each team … e.g., one may be -100 and the other only +50.

I wouldn't bet on anything with a “vig” of 5% or more. My impression was that US basketball games had pretty low vigs but (Euro?) soccer games had ridiculously high ones.

Next, you might look for a game where one side has odds somewhat close to the level you're looking for, the amount of risk you want to take (unless you are hedging, in which case that might not matter, see below). On OddsJam you can browse for that while also comparing whether your casino is offering something close to the best odds among the casinos. (Larger “+” numbers and “-” numbers closer to zero are better).

Low risk bets and portfolios

So now you have a ‘house money’ reward (perhaps just the return of the money you lost on the risk-free bet) and you want to get it out. If you have an account with multiple sites and similar amount you want to get out of each, there's a pretty easy way to do this: you can bet on opposite teams on opposite sites, i.e., fully hedge your bet. Check how much will pay if you win on each side on each site and bet an amount on each that makes that roughly equal. You can do this in combination with OddsJam to try to guarantee as close to 100% of your money back as possible (over 100% is also conceivable but would seem to be a rare ‘arbitrage opportunity’).

If you have only an account with one site (or more money you are trying to get out of one sites than another) I think you should:

look for eligible bets with odds as low as possible (very likely to win, low payoff, but remember these still need to be risky enough to qualify for the playthrough requirement), and
make several small bet rather than one large bet to lower the overall variance of your outcome (see the ‘law of large numbers’, ‘portfolio diversification’ etc.

Warning: this can be stressful and distracting to work

The return seems to be potentially pretty good, but (particularly for the casinos with “risk-free bets” rather than deposit matching) there are ways you can lose money if you are not careful. It can be pretty stressful and you have to concentrate on what you're doing. Even though the process of doing it correctly they only take a few hours, if you are anticipating yet and discriminating over your bad choices, or losing sleep anticipating upcoming games... This can take up a lot more of your life. For some people this process might be fun, for others traumatic. Other people might really like the excitement and adrenaline But it may take you away from other things you are trying to focus on. This was the case for me; so far, it’s been interesting and sometimes fun and rewarding to see when ‘I got it right.’ But overall it was not relaxing, rather stressful and pulled me away from other important things.

2

Thanks so much for sharing these tips! With regards to the info on OddsJam:

I use the app "Todoreminder" to remind myself to cancel subscriptions like this.
You can always buy a $10 visa gift card online and register with that instead of your credit card if you're worried about forgetting to cancel your subscription.
If you're using OddsJam premium, I wouldn't worry about betting on specific sports. OddsJam will show you where the best bets are for the sports book you're betting with accounting for the vig. Generally speaking though, you're right that two-way markets (meaning only two outcomes can happen) take less vig than futures markets (e.g., the winner of this year's NBA championship) or three-way markets (like soccer where a game can end in a tie).

david_reinstein

2

Modest proposal on a donation mechanism for people doing direct work?

(Tag: Donations, efficiency, taxes, administrative innovation)

Preamble

Employees at EA orgs and people doing direct work are often also donors/pledgers to other causes. But charitable donations are not always exactly 1-1 deductible from income taxes. E.g., in the USA it’s only deductible if you forgo the standard deduction and ‘itemize your deductions’, and in many countries in the EU there is very limited tax deductibility.

So, if you are paid $1 more by your employer/funded and donate it to the Humane League, Malaria Consortium, etc, the charity only ends up with maybe $0.65 on the margin in many cases.There are ways to do better at this (set up a DAF, bunch your donations…) but they are costly (DAF takes fees) and imperfect (whenever you itemize you lose the standard deduction if I understand.)

Proposal

Funders/orgs (e..g, Open Phil, RP, FHI, CEA) could agree that employees are allowed relinquish some share of their paycheck into some sort of general fund. The employees who do so are allowed to determine the use of these funds (or ‘advise on’, with the advice generally followed).

Key anticipated concerns --> responses

Concern: pressure

This will lead to a ‘pressure to donate/relinquish’ if the employers, managers, funders are aware of it

Response: This process could be managed by ops and by someone at arms-length who will not share the data with the employers/managers/funders.

Details need working out, obviously, unless something like this already exists

Concern - Legal issues

Is this feasible? Would these relinquishments be seen by governments as actually income?

Response: ??

Concern - crowding out

If the funder knows that the people/orgs it funds gives back to charities, they may shift their funding away from these charities, nullifying the employees counterfactual impact

Response: This is hardly a new issue, hardly unique to this context; it’s a major question for donors in general, through all modes; so maybe not so important to consider here.

… To the extent it is important, it could be reduced if we can keep the exact target and amount of the donations unknown to the funders

Concern - “Org reputation … why not give back to the org?”

Maybe a stretch, but I could imagine someone arguing “If your employees ask you to redirect paychecks to a fund, which largely goes to the Humane League, Malaria Consortium, … does this indicate your employees don’t think RP is the best use of funds”?

Responses: Unlikely to be a concern. Employees may want to ‘hedge their bets’ because of moral uncertainty, and because of the good feeling they get from direct impact of donations.

Responses: Keeping the recipient of these funds hidden to outsiders

david_reinstein

3

AI consciousness and valenced sensations: unknowability?

Variant of Chinese room argument? This seems ironclad to me, what am I missing:

My claims:

Claim: AI feelings are unknowable: Maybe an advanced AI can have positive and negative sensations. But how would we ever know which ones are which (or how extreme they are?

Corollary: If we cannot know which are which, we can do nothing that we know will improve/worsen the “AI feelings”; so it’s not decision-relevant

Justification I: As we ourselves are bio-based living things, we can infer from the apparent sensations and expressions of bio-based living things that they are happy/suffering. But for non-bio things, this analogy seems highly flawed. If a dust cloud converges on a ‘smiling face’, we should not think it is happy.

Justification II. (Related) AI, as I understand it, is coded to learn to solve problems and maximise things, optimize certain outcomes or do things it “thinks” will yield positive feedback.

We might think then, that the AI ‘wants’ to solve these problems, and things that bring it closer to the solution make it ‘happier’. But why should we think this? For all we know, it may feel pain when it gets closer to the objective, and pleasure when it avoids this.

Does it tell us it makes it happy to come closer to the solution? That may merely because we programmed it to learn how to come to a solution, and one thing it ‘thinks’ will help is telling us it gets pleasure from doing so, even though it actually gains pain.

A colleague responded:

If we get the AI through a search process (like training a neural network) then there's a reason to believe that AI would feel positive sensations (if any sensations at all) from achieving its objective since an AI that feels positive sensations would perform better at its objective than an AI that feels negative sensations. So, the AI that better optimizes for the objective would be more likely to result from the search process. This feels analogous to how we judge bio-based living things in that we assume that humans/animals/others seek to do those things that make them feel good, and we find that the positive sensations of humans are tied closely to those things that evolution would have been optimizing for. A version of a human that felt pain instead of pleasure from eating sugary food would not have performed as well on evolution's optimization criteria.

OK but this seems only if we:

Knew how to induce or identify "good feelings"
Decided to induce these and tie them in as a reward for getting close to the optimum.

But how on earth would we know how to do 1 (without biology at least) and why would we bother doing so? Couldn't the machine be just as good an optimizer without getting a 'feeling' reward from optimizing?

Please tell me why I'm wrong.

david_reinstein

2

Are you engaging in motivated reasoning ... or çommitting other reasoning fallacies?

I propose the following good epistemic check using Elicit.org's "reason from one claim to another" tool

Whenever you have a theory that

Feed this tool your theory, negating one side or the other^[1]
$A \to \neg B$

and/or
$\neg A \to B$

And see if any of the arguments it presents seem equally plausible to your arguments for $A \to B$

If so, believe your arguments and conclusion less.

Caveat: the tool is not working great yet, and often requires a few rounds of iteration, selecting the better arguments and telling it "show me more like this", or feeding it some arguments

^{^}
Or the contrapositives of either

david_reinstein

2

I'm

reading EA forum posts
and comments
and some links
and adding some comments/thoughts of my own

HERE (podcast 'found in the struce' available on all platforms).

I think this will help people who have limited screen time get more from the EA Forum.

I’d like to encourage others to also narrate/record forum posts. I would love to listen to this too on those long drives/walks.

david_reinstein

1

Posts I bookmarked (often as references)

What are examples of EA work being reviewed by non-EA researchers?QAaron Gertler, cole_haus

Altruistic Agency – free tech expertise for effective altruistsMarkus Amalthea Magnuson

Pedant, a type checker for Cost Effectiveness AnalysisHazelfire

How to find EA documents on a particular topicJc_Mourrat

2-week summer course in "economic theory and global prioritization": LMK if interested!trammell

Learnings about literature review strategy from research practice sessionsalexlintz

EA Survey 2018 Series: How Long Do EAs Stay in EA?Peter Wildeford

Estimates of highly engaged EA retentionGabby_O

Review: What works to promote charitable donations?PeterSlattery

Rethink Priorities 2020 Impact and 2021 StrategyMarcus_A_Davis

A central directory for open research questionsMichaelA

Uncertainty and sensitivity analyses of GiveWell's cost-effectiveness analysescole_haus

EA Survey 2019 Series: Community Demographics & Characteristics

david_reinstein

1

Facebook ads: can you really do A/B testing on a comparable audience?

On Facebook ‘Lift testing’

… can you really compare ‘ad A vs ad B’ to see which works better on a comparable audience?

Braun and Schwartz (Hat tip, Josh Lewis)… seem to think this is NOT possible in the current FB setup (and maybe not on most platforms either). ... Because of the way each ad design is separately targeted/optimized to its ‘best audience’.

Smartly seems to imply that multi-cell lift tests do not suffer from this problem.

... But it's unclear if this really implements 'target then randomize'.

david_reinstein

1

What Economics research is EA-relevant?

Most of Economic research can be deemed EA-relevant in a general sense in that it usually focuses on welfare properties (of equilibria)…

But sometimes it’s the ‘potential pareto improvement/2nd welfare theorem’ stuff … could make the ‘pie higher’ and achieve any improved outcome you like it were to be redistributed.

E.g., one could claim (loosely)

A. … “efficient antitrust regulation is an EA cause because it aims to achieve the greatest level of Consumer + Producer surplus”

B. “…which could then yield the greatest social gains if we redistributed it to help the extreme global poor/animal welfare/existential risk reduction”

But you might ask: For A: “Is this the most important/easiest/biggest way to ‘make the pie higher’“? For B: “How likely is it that any gains could/would actually be redistributed to then ‘do the most good’”

david_reinstein

1

On music streaming services

(Note: I am not making a claim that this is an EA cause candidate.)

Music is an "information good". It is "nonrival" and infinitely and freely sharable. Any positive price leads to "allocative inefficiency".

But a zero price obviously gives no incentive to produce and share music. The best solution is to separate what consumers pay from what musicians receive. The music (and all media and info) would be free to access, but the creators would get a payment equal to the value the listeners and users took from it.

But it's hard to

Know what that VALUE is and
Coordinate a way to get the FUNDS and compensate the creators.

For point 1 (measuring VALUE), the number of plays or amount of time spent listening seems like a possibly OK, but imperfect measure. E.g., 'listening in the background' has less value than 'listening and really getting into it. Still the 'compensation per stream' seems like the best feasible measure.

However, perhaps because of the market structure and lack of competition, it seems creators are given very little per stream, not enough to motivate the right amount of content to be produced. For point 2 (getting the FUNDS), an international government mandated tax and funding would be efficient but there are all sorts of difficulties there. (Compulsion, coordinating across governments, is it fair for non-listeners, etc).

Private streaming services seem like a good second-best, but the inefficiency comes when the streaming service charges customers too much, so not everyone joins. (Why: society could give these people access to ad-free music to these people at no extra cost, but they don't access it.) Perhaps the best solution would be some sort of streaming service that pays creators more (should that be subsidized?) and offers more differentiated prices to capture the true value consumers are getting. Hard to do, though.

The main point, I think, is that the 'classical economic model' really doesn't work well for information goods, which are becoming more and more of the economy.

Aaron Gertler 🔸

9

As one data point, I'd gladly pay an extra $5-15/month for a "tier" of Spotify that passed along, say, 90% of that extra money to artists. Spotify being mostly private makes it hard to get good digital bling from a higher-tier option, but maybe artists could offer extra rewards to people in that tier?

Much more simply, I'd love to have a "tip the artist" option next to any song, so that when I was especially appreciating something, I could tip the artist a dollar. I'd probably use that option 100-200 times/year.

This seems like it should be a win for Spotify — I see few people angry about Spotify making/keeping too much money, lots of people angry about artists being underpaid. And I think it should be possible to design a tier/tip option that sends the message "you're funding artists" without "we're not".

From some brief research, Spotify paid out over $5 billion to "rights holders"* in 2020 and grossed about $9 billion (they claim to pay out 70% of all revenue). And they have 6500 employees. All of these seem like reasonable numbers, and even boosting artist revenue by 20% would probably feel tiny to critics — now it's half a cent per stream instead of 0.4 cents, hooray — while being a pretty sharp cut for their staff/technical infrastructure.

*Note that this includes record labels; for many artists, Spotify's rate isn't nearly as problematic as the % their record labels take.

david_reinstein

0

US election question/take.

Do people in the EA (and maybe rationalist) community have any particular levers we could pull or superpowers that could persuade key influencers and voters?

E.g., Joe Rogan might be able to tip this election. He shows some signs of thoughtfulness and reasoning (at time). Does anyone “here” have a useful connection to him?