I recently wrote about values spreading, and came out weakly in favor of focusing on global catastrophic risks over values spreading. However, I neglected an important consideration in favor of values spreading: feedback loops.

When we try to take actions that will benefit the long-term future but where we don’t get immediate feedback on our actions, it’s easy to end up taking actions that do nothing to achieve our goals. For instance, it is surprisingly difficult to predict in advance how effective a social intervention will be. This gives reason to be skeptical about the effectiveness of interventions with long feedback loops.

Interventions on global catastrophic risks have really, really bad feedback loops. It’s nearly impossible to tell if anything we do reduces the risk of a global pandemic or unfriendly AI. An intervention focused on spreading good values is substantially easier to test. An organization like Animal Ethics can produce immediate, measurable changes in people’s values. Measuring these changes is difficult, and evidence for the effectiveness of advocacy is a lot weaker than the evidence for, say, insecticide-treated bednets to prevent malaria. But short-term values spreading still has an advantage over GCR reduction in that it’s measurable in principle.

Still, will measurable short-term changes in values result in sustainable long-term changes? That’s a harder question to answer. It certainly seems plausible that values shifts today will lead to shifts in the long term; but, as mentioned above, interventions that sound plausible frequently turn out not to work. Values spreading may not actually have a stronger case here than GCR reduction.

We can find feedback loops on GCR reduction that measure proxy variables. This is particularly easy in the case of climate change, where we can measure whether an intervention reduces greenhouse gas levels in the atmosphere. But we can also find feedback loops for something like AI safety research: we might say MIRI is more successful if it publishes more technical papers. This is not a particularly direct metric of whether MIRI is reducing AI risk, but it’s still a place where we can get quick feedback.

Given that short-term value shifts don’t necessarily predict long-term shifts, and that we can measure proxy variables for global catastrophic risk reduction, it’s non-obvious that values spreading has better feedback loops than GCR reduction. There does seem to be some sense in which value shifts today and value shifts in a thousand years are more strongly linked than, say, number of AI risk papers published and a reduction in AI risk; although this might just be because both involve value shifts–they may not actually be that strongly tied, or tied at all.

Values spreading appears to have the advantage of short-term feedback loops. But it’s not clear that these changes have long-term effects, and this claim isn’t any easier to test than the claim that GCR work today reduces global catastrophic risk.

I have received some interest from a few people in donating to REG, and the main concern I’ve heard has been about whether REG could effectively use additional funding. I spent some more time learning about this. My broad conclusion is roughly the same as I wrote previously: REG can probably make good use of an additional $100,000 or so, and perhaps more but with less confidence.

Poker Market Saturation

Tobias from REG claims that about 70% of high-earning poker players have heard of REG, although many of those have had only limited engagement. He claims that they have had the most success convincing players to join through personal contact, and REG has not had contact with many of the players who have heard of it. This gives some reason to be optimistic that REG can expand substantially among high-earning poker players, although I would not be surprised if it started hitting rapidly diminishing returns once it grows to about 2x its current size.

To date, REG has not spent much effort on marketing to non-high-earning poker players. This field is much larger, but targeting lower-earning players should be less efficient because each individual player donates less money. To get a better sense of how important this is, I would have to know what the income distribution looks like for poker players, and getting this information is nontrivial.

REG would like to hire a new marketing person with experience in the poker world. They would probably be considerably better at marketing than any of the current REG employees. For this reason, additional funds to REG may actually be more effective than past funds, although this is difficult to predict in advance.

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Owen Cotton-Barratt of the Global Priorities Project wrote an article on comparing human and animal interventions. His major conclusions include:

1. Indirect long-term effects dominate considerations.
2. Changing behavior of far-future humans matters more than alleviating immediate animal suffering.
3. Helping humans has better flow-through effects than helping non-human animals.

The analysis effectively concludes that helping humans is more important than helping non-human animals but I believe it misses a few important considerations.

(These are fairly quick thoughts about which I have a lot of uncertainty; I’m publishing them here for the sake of making the conversation public.)

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Here are some research topics on cause prioritization that look important and neglected, in no particular order.

1. Look at historical examples of speculative causes (especially ones that were meant to affect the long-ish-term future) that succeeded or failed and examine why.
2. Try to determine how well picking winning companies translates to picking winning charities.
3. In line with 2, consider if there exist simple strategies analogous to value investing that can find good charities.
4. Find plausibly effective biosecurity charities.
5. Develop a rigorous model for comparing the value of existential risk reduction to values spreading.
6. Perform basic analyses of lots of EA-neglected or weird cause areas (e.g. depression, argument mapping, increasing savings, personal productivity–see here) and identify which ones look most promising.
7. Reason about the expected value of the far future.
8. Investigate neglected x-risk and meta charities (FHI, CSER, GPP, etc.).
9. Reason about expected value estimates in general. How accurate are they? Do they tend to be overconfident? How overconfident? Do some things predictably make them more reliable?

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In my recent post on cause selection, I constructed a model where I broke down by category all the charities REG has raised money for and gave each category a weight based on how much good I thought it did. I put a weight of 1 on my favorite object-level charity (MIRI) and gave other categories weights proportional to that. I put GiveWell-recommended charities at a weight of 0.1–that means I’m about indifferent between a donation of $1 to MIRI and $10 to the Against Malaria Foundation (AMF).

Buck criticized my model, claiming that my top charity, MIRI, is more than ten times better than AMF and I’m being too conservative. But I believe that this degree of conservatism is appropriate, and a substantially larger ratio would be epistemically immodest.

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Content note: Proofs involving computation and Turing machines. Whether you understand the halting problem is probably a good predictor of whether this post will make sense to you.

I use the terms “program” and “Turing machine” interchangeably.

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What is consciousness?

We can divide theories about consciousness into three categories:

1. Consciousness is a special non-physical property (dualism).
2. Consciousness is the result of the physical structures of the brain (identity theory).
3. Conscious mental states are the result of their functional role within a process (functionalism).

In particular, I want to talk about Turing machine functionalism, a specific form of functionalism which states that consciousness is computation on a Turing machine. I want to talk about Turing machine functionalism in particular because it is probably correct.

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Disclaimer: I am not a financial advisor. This is not financial advice.

Effective altruists often debate the question of whether to give now or later. One common approach is to give a regular donation each year. This approach makes a lot of sense: here Holden Karnofsky suggests a few reasons why we should give regularly.

But one problem arises with the “give regularly” strategy. If you’re young, and especially if you’re still in school, you probably aren’t earning much money right now, so you can’t donate much. You will earn a lot more money five or ten years from now, which means you’ll also be donating a lot more. If you’re currently a student and you follow the “donate however much I can afford every year” strategy, you end up leaning heavily toward giving more later.

This mirrors the problem described by Ayres and Nalebuff in Lifecycle Investing: if you’re saving for retirement, you end up saving a lot more money later in life. They recommend that most people leverage investments when they’re young and hold more bonds when they’re older in order to spread risk more evenly across their investing lifetimes (or, as they put it, to improve temporal diversification).

We can apply a similar principle to donations. If you don’t earn much now but expect to earn substantially more in the future, you can “leverage” your donations by donating more than you normally would given your income.

It’s not obvious how to do this. There are three basic methods I can see: taking out loans, foregoing savings, and donating trust fund savings. None of these is perfect, but they’re worth considering.

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Cross-posted to the EA Forum. If you want to leave a comment, you can post it there.

Last edited 2015-09-24.

In this essay, I provide my reasoning about the arguments for and against different causes and try to identify which one does the most good. I give some general considerations on cause selection and then lay out a list of causes followed by a list of organizations. I break up considerations on these causes and organizations into five categories: Size of Impact; Strength of Evidence; Tractability; Neglectedness/Room for More Funding; Learning Value. This roughly mirrors the traditional Importance; Tractability; Neglectedness criteria. I identify which cause areas look most promising. Then I examine a list of organizations working in these cause areas and narrow down to a few finalists. In the last section, I directly compare these finalists against each other and identify which organization looks strongest.

You can skip to Conclusions to see summaries of why I prioritize the finalists I chose, why I did not consider any of the other charities as finalists, and my decision about who to fund.

TL;DR

I chose these three finalists:

• Machine Intelligence Research Institute (MIRI) makes the strongest case for an extraordinarily high impact.
• Animal Charity Evaluators (ACE) makes a strong case for high impact and the strongest case for high learning value.
• Raising for Effective Giving (REG) produces a donation multiplier¹ for both MIRI and ACE, so it potentially has an even larger impact.

Based on everything I considered, REG looks like the strongest charity because it produces a large donation multiplier and it directs donations to both MIRI and ACE (as well as other effective charities).

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There are a few cause areas that are plausibly highly effective, but as far as I know, no one is working on them. If there existed a charity working on one of these problems, I might consider donating to it.

Happy Animal Farm

The closest thing we can make to a hedonium shockwave with current technology is a farm of many small animals that are made as happy as possible. Presumably the animals are cared for by people who know a lot about their psychology and welfare and can make sure they’re happy. One plausible species choice is rats, because rats are small (and therefore easy to take care of and don’t consume a lot of resources), definitively sentient, and we have a reasonable idea of how to make them happy.

I am not aware of any public discussion on this subject, so I will perform a quick ad-hoc effectiveness estimate.

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