# How Should a Large Donor Prioritize Cause Areas?

## Introduction

The Open Philanthropy Project has made some grants that look substantially less impactful than some of its others, and some people have questioned the choice. I want to discuss some reasons why these sorts of grants might plausibly be a good idea, and why I ultimately disagree.

I believe Open Phil’s grants on criminal justice and land use reform are much less effective in expectation1 than its grants on animal advocacy and global catastrophic risks. This would naively suggest that Open Phil should spend all its resources on these more effective causes, and none on the less effective ones. (Alternatively, if you believe that the grants on US policy do much more good than the grants on global catastrophic risk, then perhaps Open Phil should focus exclusively on the former.) There are some reasons to question this, but I believe that the naive approach is correct in the end.

Why give grants in cause areas that look much less effective than others? Why give grants to lots of cause areas rather than just a few? Let’s look at some possible explanations for these questions.

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# How Sentient Are Farm Animals?

I wrote this as a quick explanation of why I value non-human animals the way I do. It’s not particularly thorough, and my explanation has some clear holes; this is just a general outline.

When we’re considering charitable interventions that help animals, it’s important to have some sense of how valuable it is to help those animals, which means we want to know how sentient they are.

How sentient an animal is–that is, how strongly it experiences pleasure and pain–almost certainly relates to how its brain works. I see four reasonably plausible ways that sentience could relate to brain size:

1. Suffering is caused by certain fixed brain structures, and for certain types of physical pain (like what chickens experience on factory farms), humans and chickens have the same brain parts and therefore experience this pain equally.
2. Sentience is linear with brain size.
3. Sentience is sub-linearly related to brain size; for example, sentience may be logarithmic with brain size.
4. Less intelligent animals are generally more sentient because they “are [not] capable of intelligently working out what is good for [them], and what damaging events [they] should avoid”, so they need a stronger pain response to compensate.
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# The Myth that Reducing Wild Animal Suffering Is Intractable

Lots of people accept that wild animal suffering is a big problem, but they believe it’s completely intractable. I even see some people claim that it’s one of the biggest problems in the world, but we still shouldn’t try to do anything about it. Wild animal suffering is in fact much more tractable than most people believe.

If we think wild animal suffering is a pressing problem and we want to do something about it, what can we do?

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# Preventing Human Extinction, Now With Numbers!

Part of a series on quantitative models for cause selection.

## Introduction

Last time, I wrote about the most likely far future scenarios and how good they would probably be. But my last post wasn’t precise enough, so I’m updating it to present more quantitative evidence.

Particularly for determining the value of existential risk reduction, we need to approximate the probability of various far future scenarios to estimate how good the far future will be.

I’m going to ignore unknowns here–they obviously exist but I don’t know what they’ll look like (you know, because they’re unknowns), so I’ll assume they don’t change significantly the outcome in expectation.

Here are the scenarios I listed before and estimates of their likelihood, conditional on non-extinction:

*not mutually exclusive events

(Kind of hard to read; sorry, but I spent two hours trying to get flowcharts to work so this is gonna have to do. You can see the full-size image here or by clicking on the image.)

I explain my reasoning on how I arrived at these probabilities in my previous post. I didn’t explicitly give my probability estimates, but I explained most of the reasoning that led to the estimates I share here.

Some of the calculations I use make certain controversial assumptions about the moral value of non-human animals or computer simulations. I feel comfortable making these assumptions because I believe they are well-founded. At the same time, I recognize that a lot of people disagree, and if you use your own numbers in these calculations, you might get substantially different results.

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# Expected Value Estimates You Can (Maybe) Take Literally

Part of a series on quantitative models for cause selection.

Alternate title: Excessive Pessimism About Far Future Causes

## 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.