Hi, does anyone from the US want to donation-swap with me to a German tax-deductible organization? I want to donate $2410 to the Berkeley Genomics Project via Manifund.

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Assuming that interventions have log-normally distributed impact, compromising on interventions for the sake of public perception is not worth it unless it brings in exponentially more people.

Depending on the relationship between brain size and moral weight, different animals may be more or less ethical to farm.

A common assumption in effective altruism is that moral weight is marginally decreasing in number of neurons (i.e. small brains matter more per neuron). This implies that we'd want to avoid putting many small animals into factory farms, and prefer few big ones, especially if smaller animals have faster subjective experience.

A reductio ad absurdum of this view would be to (on the margin) advocate for the re-introduction of whaling, but this would be blocked by optics concerns and moral uncertainty (if we value something like sapience and culture of animals).

If factory farming can't be easily replaced with clean meat in the forseeable future, one might want to look for animals that are least unethicl to farm, mostly by them fulfilling the following conditions:

• Small brain & low number of neurons
• Easy to breed & fast reproduction cycle
• Low behavioral complexity
• Large body, high-calorie meat
• Palatable to consumers
• Stopped evolving early (if sentience evolved late in evolutionary history)

In conversation with various LLMs^[1], three animals were suggested as performing well on those trade-offs. My best guess is that current factory farming can't be beat with these animals in effectiveness.

Ostriches

Advantages: Already farmed, very small brain for large body mass

Disadvantages: Fairly late in evolutionary history

Arapaima

Advantages: Very large for small brain size (up to 3m in length), fast-growing, simple neurology, already farmed, can be raised herbivorously, lineage is ~200 mio. years old bony fishes

Disadvantages: Tricky to breed

Tilapia

Advantages: Very easy to breed, familiarity to consumers, small neuron count

Disadvantages: Fairly small, not as ancient as the arapaima

epistemic status: I saw the best minds of my generation

Limply balancing mile-deep cocktail glasses and clamoring for laughable Thielbucks on the cramped terraces of Berkely,
who invite you to the luring bay, promising to pay for your entire being while elegantly concealing the scarlet light evaporating from their tearducts and nostrils,
who down 3¾ bottles of Huel™ and sprint to their next fellowship or retreat or meetup or workshop, in the pore-showingly lit hallways with whiteboards and whiteboards and whiteboards and whiteboards,
who want to crown random nerds with aureal sigmas fished from the manifold crevaces of self-denying deities,
who write epics of institutionalized autists bent on terraforming these overpopulated hypothetical hells,
pointing out dynamic inconsistency in the comments and melting hectoton steel-marbles around civilizational recipes,
who improve their aim hurling yeeting philosophical tomes at unsuspecting passerbys and ascend urban lampposts to loudly deduce their mechanical liturgies,
who cut down oaks and poppies in the parks to make oil and space for those ones yet to come and who hunker down in fortified coalmines in Montana and the Ruhrgebiet, propelled into the rock by a Ordite reactor, a Bostromite dynamo,
who exitedly pour & plaster tabulated exponents into webscripted canisters just below the teal-tinded ceiling,
who can name the 11 AI forecasting organisations and the 4 factors of successful nonprofits and the 7 noble ways of becoming more agentic and might even find Rwanda on a map not made in Silicon Valley,
contemplating hemispherectomies to purify their nascent idealism on the verge of a hope-ash London dawn,
who catch a feral heart in the garden behind the A100 rack and save it into a transparent domicile, injecting it with 17000 volts to illuminate all the last battery cages equally,
who empty out their pockets with uncountable glaring utilons onto innocent climate activists, promising to make them happy neutron stars one day,
Microscopically examining the abyssal monstrosities their oracles conjure up out of the lurching empty chaos,
who fever towards silver tendrils bashing open their skulls and eating up their brains and imaginations, losslessly swallowed into that ellipsoid strange matter gut pulsing out there between the holes

I was curious how the "popularity" of the ITN factors has changed in EA recently. In short: Mentions of "importance" have become slightly more popular, and both "neglectedness" and "tractability" have become slightly less popular, by ~2-6 percentage points.

I don't think this method is strong enough to make conclusions, but it does track my perception of a vibe-shift towards considering importance more than the other two factors.

Searching the EA forum for the words importance/neglectedness/tractability (in quotation marks for exact matches) in the last year yields 454/87/110 (in percentages 69%/13%/17%) results, for important/neglected/tractable it's 1249/311/152 (73%/18%/9%).

When searching for all time the numbers for importance/neglectedness/tractability are 2824/761/858 (in percentages 63%/17%/19%) results, for important/neglected/tractable it's 7956/2002/1129 (71%/18%/10%). I didn't find a way to exclude results from the last year, unfortunately.

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epistemic status: Borderline schizopost, not sure I'll be able to elaborate much better on this, but posting anyway, since people always write that one should post on the forum. Feel free to argue against. But: Don't let this be the only thing you read that I've written.

Effective Altruism is a Pareto Frontier of Truth and Power

In order to be effective in the world one needs to coordinate (exchange evidence, enact plans in groups, find shared descriptions of the world) and interact with hostile entities (people who lie, people who want to steal your resources, subsystems of otherwise aligned people who want to do those things, engage in public relations or zero-sum conflict). Solving those often requires trading off truth for "power" on the margin, e.g. by nudging members to "just accept" conclusions for action believed to be a basis for effective action (since making elaborate arguments common knowledge is costly and agreement converges slowly to ε difference with $O\left(\frac{1}{{\epsilon }^2}\right)$ bits on evidence-sharing), by misrepresenting beliefs to other actors to make them more favorable towards effective altruism, or by choosing easy-communicable Schelling categories that minmax utility to the lowest-bounded agents.

On the one side of the Pareto frontier one would have an even more akrasia-plagued version of the rationality community with excellent epistemics but which would be universally hated, on the other hand one would have the attendants of this party.

Members of effective altruism seem not explicitely aware of this tradeoff or tension between truth-seeking and effectiveness/power (maybe for power-related reasons?) or at least don't talk about it, even though it appears to be relevant.

In general, the thinking having come out of Lesswrong in the last couple of years strongly suggests that while (for ideal agents) there's no such tension in individual rationality (because true beliefs are convergently instrumental), this does not hold for groups of humans (and maybe also not for groups of bounded agents in general, although there's some people who believe strong coordination is easy for highly capable bounded agents).

Bad Information Hazard Disclosure for Effective Altruists (Bad I.D.E.A)

Epistemic effort: Four hours of armchair thinking, and two hours of discussion. No literature review, and the equations are intended as pointers rather than anything near conclusive.

Currently, the status quo in information sharing is that a suboptimally large amount of information hazards are likely being shared. In order to decrease infohazard sharing, we have modeled out a potential system for achieving such a goal. As with all issues related to information hazards, we strongly discourage unilateral action. Below you find a rough outline of such a possible system and descriptions of its downsides. We furthermore currently believe that the for the described system, in the domain of biosecurity the disadvantages likely outweigh the advantages (that’s why we called it Bad IDEA), and in the domain of AI capabilities research the advantages outweigh the disadvantages (due to suboptimal sharing norms such as “publishing your infohazard on arXiv”). It’s worth noting that there are potentially many more downside risks that neither author thought of.

Note: We considered using the term sociohazard/outfohazard/exfohazard, but decided against it for reasons of understandability.

Current Situation

• Few incentives not to publish dangerous information
  • Based in previous known examples 
• We’d like a system to incentivize people not to publish infohazards

Model

• Researcher discovers infohazard
• Researcher writes up description of infohazard (longer is better)
• Researcher computes cryptographic hash of infohazard
• Researcher sends hash of description to IDEA
• Bad IDEA stores hash
• Two possibilities:
  • Infohazard gets published
    • Researcher sends in description of infohazard
    • Bad IDEA computes the cryptographic hash and compares the two
    • Bad IDEA estimates the badness of the infohazard
    • Researcher gets rewarded according to the reward function
    • Bad IDEA deletes the hash function and description of infohazard from their database
  • Researcher wants intermediate payout
    • All of the above steps, except IDEA doesn’t delete the hash function from the database, but does delete the the description of the infohazard

Reward Function Desiderata

We know it’ll be Goodharted, but we can at least try.

• Reward function is dependent on
  • Danger of the infohazard (d)
    • Reward higher if danger is higher
  • Time between discovery & cashin (t)
    • Reward higher if time between discovery & cashin is longer
  • The number of people who found it (n)
    • Lower payout if more people found it, to discourage sharing of the idea
      • Reward being total_payout/discoveries? Or something that increases total_payout as a function of independent discoveries?
        • Latter case would make sense, since that indicates the idea is “easier” to find (or at least the counterfactual probability of discovery is higher)
  • Counterfactual probability of it being discovered (p)
    • This is really hard to estimate
    • If counterfactual discovery probability is high, we want to reward higher than if it’s low.
    • How difficult the idea is to discover 
      • Ideas that are “very” difficult to discover would be given less than “easy” ideas. This could potentially discourage strong efforts to research new information hazards

Individual payout for a researcher then is

$f(d,t,n,p)=\frac{d\cdot t}{n}+p\cdot \sqrt{d}$

Alternative version punishes actively for looking for infohazards

$f(d,t,n,p)=\frac{d\cdot t}{n}-(1-p)\cdot \sqrt{d}$

Advantages

• Base rate of information hazard discovery -> publication rate 

Disadvantages

• Incentive for people to research & create infohazards
  • Might be counteracted by the right reward function which incorporates counterfactual discovery probability
  • Gently sharing existence of IDEA with trusted actors 
• Bad IDEA observers might remember infohazard
  • Repository for malign actors to go & recruit
  • This could be solved by (unrealistically)
    • Real-world amnestics
    • AI systems trained to estimate badness
    • Em spurs estimating badness
• Estimating danger of information hazard is quite difficult
  • Could be overcome through rough estimates on how many people would be capable of engaging with idea to do harm + how damaging it would be
• Estimating difference between ideas are difficult
• Attracting attention to the concept of infohazard

Argument against longtermism:

Longtermism seems to rely on zero discount rates for the value of future lives. But per moral uncertainty, we probably have a probability distribution over discount rates. This probability distribution is very likely skewed towards having positive discount rates (there are much more plausible reasons why future lives are worth less than current lives, but very few (none?) why they should be more important ceteris paribus).

Therefore, expected discount rate is positive, and longtermism loses some of its bite.

Possible counterarguments

• Discount rates are not part of moral uncertainty, but different kind of normativity (decision theoretic?), over which we ought not have uncertainty
• Equally plausible reasons for positive as for negative discount rates (although I don't know which ones?)
• Complete certainty in 0 discount rate (seems way overconfident imho)

Main inspiration from the chapter on practical implications of moral uncertainty from MacAskill, Bykvist & Ord 2020. I remember them discussing very similar implications, but not this one – why?

Consider the problem of being automated away in a period of human history with explosive growth, and having to subsist on one's capital. Property rights are respected, but there is no financial assistance by governments or AGI corporations.

How much wealth does one need to have to survive, ideally indefinitely?

Finding: If you lose your job at the start of the singularity, with monthly spending of $1k, you need ~$71k in total of capital. This number doesn't look very sensitive to losing one's job slightly later.

At the moment, the world economy is growing at a pace that leads to doublings in GWP every 20 years, steadily since ~1960. Explosive growth might instead be hyperbolic (continuing the trend we've seen seen through human history so far), with the economy first doubling in 20, then in 10, then in 5, then 2.5, then 15 months, and so on. I'll assume that the smallest time for doublings is 1 year.

    initial_doubling_time=20
    final_doubling_time=1
    initial_growth_rate=2^(1/(initial_doubling_time*12))
    final_growth_rate=2^(1/(final_doubling_time*12))

    function generate_growth_rate_array(months::Int)
        growth_rate_array = zeros(Float64, years)
        growth_rate_step = (final_growth_rate - initial_growth_rate) / (years - 1)

        current_growth_rate = initial_growth_rate

        for i in 1:years
            growth_rate_array[i] = current_growth_rate
            current_growth_rate += growth_rate_step
        end

        return growth_rate_array
    end

We can then define the doubling sequence:

    years=12*ceil(Int, 10+5+2.5+1.25+final_doubling_time)
    economic_growth_rate = generate_growth_rate_array(years)
    economic_growth_rate=cat(economic_growth_rate, repeat([final_growth_rate], 60*12-size(economic_growth_rate)[1]), dims=1)

And we can then write a very simple model of monthly spending to figure out how our capital develops.

    capital=collect(1:250000)
    monthly_spending=1000 # if we really tighten our belts

    for growth_rate in economic_growth_rate
            capital=capital.*growth_rate
            capital=capital.-monthly_spending
    end

capital now contains the capital we end up with after 60 years. To find the minimum amount of capital we need to start out with to not lose out we find the index of the number closest to zero:

    julia> findmin(abs.(capital))
    (1.1776066747029436e13, 70789)

So, under these requirements, starting out with more than $71k should be fine.

But maybe we'll only lose our job somewhat into the singularity already! We can simulate that as losing a job when initial doubling times are 15 years:

    initial_doubling_time=15
    initial_growth_rate=2^(1/(initial_doubling_time*12))
    years=12*ceil(Int, 10+5+2.5+1.25+final_doubling_time)
    economic_growth_rate = generate_growth_rate_array(years)
    economic_growth_rate=cat(economic_growth_rate, repeat([final_growth_rate], 60*12-size(economic_growth_rate)[1]), dims=1)

    capital=collect(1:250000)
    monthly_spending=1000 # if we really tighten our belts

    for growth_rate in economic_growth_rate
            capital=capital.*growth_rate
            capital=capital.-monthly_spending
    end

The amount of initially required capital doesn't change by that much:

    julia> findmin(abs.(capital))
    (9.75603002635271e13, 68109)

If agents are usually suffering then negative sum games might be good via destructive warfare that removes negentropy, which is one consideration in cooperation-focused agendas for suffering-focused longtermism.