Starting about seven years ago, every time I heard a song that I really liked that stuck with me for the rest of the day, I recorded it in my journal on a list of “Songs of the Day”.

This list shows how my musical tastes have shifted over the years. It isn’t entirely representative because there are plenty of songs I love that never made it onto this list.

Here’s the complete list up to the time of this writing. An asterisk means that I liked the song before it was Song of the Day, but I gained a new appreciation for it on that day. I have a corresponding Spotify playlist, although it only includes songs up to November 2015.

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Part of a series on quantitative models for cause selection.

Introduction

One major reason that effective altruists disagree about which causes to support is that they have different opinions on how strong an evidence base an intervention should have. Previously, I wrote about how we can build a formal model to calculate expected value estimates for interventions. You start with a prior belief about how effective interventions tend to be, and then adjust your naive cost-effectiveness estimates based on the strength of the evidence behind them. If an intervention has stronger evidence behind it, you can be more confident that it’s better than your prior estimate.

For a model like this to be effective, we need to choose a good prior belief. We start with a prior probability distribution P where P(x) gives the probability that a randomly chosen intervention¹ has utility x (for whatever metric of utility we’re using, e.g. lives saved). To determine the posterior expected value of an intervention, we combine this prior distribution with our evidence about how much good the distribution does.

For this to work, we need to know what the prior distribution looks like. In this essay, I attempt to determine what shape the prior distribution has and then estimate the values of its parameters.

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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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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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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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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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Part of a series on quantitative models for cause selection.

Alternate title: Excessive Pessimism About Far Future Causes

In my post on cause selection, I wrote that I was roughly indifferent between $1 to MIRI, $5 to The Humane League (THL), and $10 to AMF. I based my estimate for THL on the evidence and cost-effectiveness estimates for veg ads and leafleting. Our best estimates suggested that these are conservatively 10 times as cost-effective as malaria nets, but the evidence was fairly weak. Based on intuition, I decided to adjust this 10x difference down to 2x, but I didn’t have a strong justification for the choice.

Corporate outreach has a lower burden of proof (the causal chain is much clearer) and estimates suggest that it may be ten times more effective than ACE top charities’ aggregate activities¹. So does that mean I should be indifferent between $5 to ACE top charities and $0.50 to corporate campaigns? Or perhaps even less, because the evidence for corporate campaigns is stronger? But I wouldn’t expect this 10x difference to make corporate campaigns look better than AI safety, so I can’t say both that corporate campaigns are ten times better than ACE top charities and also that AI safety is only five times better. My previous model, in which I took expected value estimates and adjusted them based on my intuition, was clearly inadequate. How do I resolve this? In general, how can we quantify the value of robust, moderately cost effective interventions against non-robust but (ostensibly) highly cost effective interventions?

To answer that question, we have to get more abstract.

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If I’m wrong about anything, I want you to change my mind. I want to make that as easy as possible, so I’m going to give a list of charities/interventions and say what would convince me to support each of them.

Please try to change my mind! I prefer public discussions so the best thing to do is to comment on this post or on Facebook, but if you want to talk to me privately you can email me or message me on Facebook.

My Current Position

I discuss how I got to my current position here. Here’s a quick summary:

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Update 2016-05-18: I no longer entirely agree with this post. In particular, I believe GiveWell employees are more replaceable than this post suggests. I may write about my updated beliefs in the future.

Edited 2016-03-11 because I’ve adjusted my estimate of the value of global poverty charities downward, which makes working at GiveWell look worse.

Edited 2016-03-11 to add a new section.

Edited 2016-02-16 to update the model based on feedback I’ve received. Temporal replaceability doesn’t apply so I was underestimating the value of research analysts.

Summary: The value of working as a research analyst¹ at GiveWell is determined by:

• Temporal replaceability of employees
• How good you are relative to the counterfactual employee
• How much good GiveWell money moved does relative to where you could donate earnings
  • A lot if you care most about global poverty, not as much if you care about other cause areas
• How directly more employees translate into better recommendations and more money moved
  • This relationship looks strong for Open Phil and weak for GiveWell Classic

If you believe GiveWell top charities are the best place to donate, working at GiveWell is probably a really strong career option; if you believe other charities are substantially better (as I do) and you have good earning potential, earning to give is probably better.

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The common claim: Unlike more speculative interventions, GiveWell top charities have really strong evidence that they do good.

The problem: Thanks to flow-through effects, GiveWell top charities could be much better than they look or they could be actively harmful, and we have no idea how big their actual impact is or if it’s even net positive.

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