# 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.

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# Response to the Global Priorities Project on Human and Animal Interventions

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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# Cause Prioritization Research I Would Like to See

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?