Jamie_Harris's Quick takes

Given that effective altruism is "a project that aims to find the best ways to help others, and put them into practice"^[1] it seems surprisingly rare to me that people actually do the hard work of:

1. (Systematically) exploring cause areas
2. Writing up their (working hypothesis of a) ranked or tiered list, with good reasoning transparency
3. Sharing their list and reasons publicly.^[2]

The lists I can think of that do this best are by 80,000 Hours, Open Philanthropy's, and CEARCH's list.

Related things I appreciate, but aren't quite what I'm envisioning:

• Tools and models like those by Rethink Priorities and Mercy For Animals, though they're less focused on explanation of specific prioritisation decisions.
• Longlists of causes by Nuno Sempere and CEARCH, though these don't provide ratings, rankings, and reasoning.
• Various posts pitching a single cause area and giving reasons to consider it a top priority without integrating it into an individual or organisation's broader prioritisation process.

There are also some lists of cause area priorities from outside effective altruism / the importance, neglectedness, tractability framework, although these often lack any explicit methodology, e.g. the UN, World Economic Forum, or the Copenhagen Consensus.

If you know of other public writeups and explanations of ranked lists, please share them in the comments!^[3]

1. ^{^}

   Of course, this is only one definition. But my impression is that many definitions share some focus on cause prioritisation, or first working out what doing the most good actually means.

2. ^{^}

   I'm a hypocrite of course, because my own thoughts on cause prioritisation are scattered across various docs, spreadsheets, long-forgotten corners of my brain... and not at all systematic or thorough. I think I roughly:

   - Came at effective altruism with a hypothesis of a top cause area based on arbitrary and contingent factors from my youth/adolescence (ending factory farming), 

   - Had that hypothesis worn down by various information and arguments I encountered and changed my views on the top causes

   - Didn't ever go back and do a systemic cause prioritisation exercise from first principles (e.g. evaluating cause candidates from a long-list that includes 'not-core-EA™-cause-areas' or based on criteria other than ITN).

   I suspect this is pretty common. I also worry people are deferring too much on what is perhaps the most fundamental question of the EA project.

3. ^{^}

   Rough and informal explanations welcome. I'd especially welcome any suggestions that come from a different methodology or set of worldviews & assumptions to 80k and Open Phil. I ask partly because I'd like to be able to share multiple different perspectives when I introduce people to cause prioritisation to avoid creating pressure to defer to a single list.

The value of graduate training for EA researchers: researchers seem to think it is worthwhile

Imagine the average "generalist" researcher employed by an effective altruist / longtermist nonprofit with a substantial research component (e.g. Open Philanthropy, Founders' Pledge, Rethink Priorities, Center on Long-Term Risk). Let's say that, if they start their research career with an undergraduate/bachelor's degree in a relevant field but no graduate training, each year of full-time work, they produce one "unit" of impact.

In a short Google Form, posted on the Effective Altruism Researchers and EA Academia Facebook groups,  I provided the above paragraph and then asked: "If, as well as an undergraduate/bachelor's degree, they start their research career at EA nonprofits with a master's degree in a relevant field, how many "units" of impact do you expect that they would produce each year for the first ~10 years of work?"* The average response, from the 8 respondents, was 1.7. 

I also asked: "If, as well as an undergraduate/bachelor's degree, they start their research career at EA nonprofits with a PhD in a relevant field, how many "units" of impact do you expect that they would produce each year for the first ~10 years of work?"* The average response was 3.9. 

I also asked people whether they were a researcher at a nonprofit, in academia, or neither, and whether they had graduate training themselves or not.** Unsurprisingly, researchers in academia rated the value of graduate training more highly than researchers in nonprofits (2.0 and 4.3 for each year with a master's and a PhD, respectively, compared to 1.2 and 1.7), as did respondents with graduate training themselves, relative to respondents without graduate training (2.0 and 5.2 compared to 1.2 and 1.7).

I asked a free-text response question: "Do you think that the value of graduate training would increase/compound, or decrease/discount, the got further into their career?" 4 respondents wrote that the value of graduate training would decrease/discount the got further into their career, but didn't provide any explanations for this reasoning. This was also my expectation; my reasoning was that one or more years' of graduate training, which would likely only be partly relevant to the nonprofit work that you would be doing, would become relatively less important later on, since your knowledge, skills, and connections would have increased through your work in nonprofits. 

However, two respondents argued that the value of graduate training would increase/compound. One added: "People without PhDs are sadly often overlooked for good research positions and also under-respected relative to their skill. If they don't have a PhD they will almost never end up in a senior research position." The other noted that it would "increase/compound, particularly if they do things other than anonymous research, e.g. they build an impressive CV, get invited to conferences because of their track record. If one doesn't have a PhD, the extent of this is limited, mostly unless one fits a high-credibility non-academic profile, e.g. founded an organization."

I did some simple modelling / back of the envelope calculations to estimate the value of different pathways, accounting for 1) the multipliers on the value of your output as discussed in the questions on the form and 2) the time lost on graduate education.*** Tldr; with the multiplier values suggested by the form respondents, graduate education clearly looks worthwhile for early career researchers working in EA nonprofits, assuming they will work in an EA research nonprofit for the rest of their career. It gets a little more complex if you try to work it out in financial terms, e.g. accounting for tuition fees.

For my own situation (with a couple of years of experience in an EA research role, no graduate training), I had guessed multipliers of 1.08 and 1.12 on the value of my research in the ~10 years after completing graduate training, for a master's and PhD, respectively. For the remaining years of a research career after that, I had estimated 1.01 and 1.02. Under these assumptions, the total output of a nonprofit research career with or without a master's looks nearly identical for me; the output after completing a PhD looks somewhat worse. However, with the average values from the Google form then the output looks much better with a master's than without and with a PhD than with just a master's. Using the more pessimistic values from other EA nonprofit researchers, or respondents without graduate training, the order is still undergrad only < master's < PhD, though the differences are smaller. In my case, tuition fees seem unlikely to affect these calculations much (see the notes on the rough models sheet).

Of course, which option is best for any individual also depends on numerous other career strategy considerations.  For example, let's think about "option value." Which options are you likely to pursue if research in EA nonprofits doesn't work out or you decide to try something else? Pursuing graduate training might enable you to test your fit with academia and pivot towards that path if it seems promising, but if your next best option is some role in a nonprofit that is unrelated to research (e.g. fundraising), then graduate education might not be as valuable.

I decided to post here partly in case others would benefit, and partly because I'm interested in feedback on/critiques of my reasoning, so please feel free to be critical in the comments!

*For both questions, I noted: "There are many complications and moderating factors for the questions below, but answering assuming the "average" for all other unspecified variables could still be helpful.)" and "1 = the same as if they just had a bachelor's; numbers below 1 represent reduced impact, numbers above 1 represent increased impact."
**These questions were pretty simplified, not permitting people to select multiple options.
*** Here, for simplicity,  I assumed that: 
- You would produce no value while doing your graduate training, which seems likely to be false, especially during (the later years of) a PhD. 
- The value of 1 year after your graduate education was the same as 1 year before retirement, which seems likely to be false.

Buying a house will probably save you lots of money, which you can later donate, but it might not make much difference (and may work out as negative) in terms of your ability to do good.

It seems like common sense that buying a house saves you from wasting money on rent and works out better, financially, in the long term. But earlier this year, John Halstead wrote a blogpost providing a bunch of reasons not to buy a house.

I had another look at John's calculations. I kept the basic calculations the same, but added a few considerations and re-checked the appropriate numbers for London (where I live). I also added various different tabs of the spreadsheet to compare things like variations in interest rates, property prices, timeframes for buying and selling, and other costs. In every scenario, unless there's a housing crash shortly after you buy, it looks like buying comes out as far, far better, from a financial perspective. In the best guess, realistic scenario, buying came out as about £550,000 better after 10 years. John has also had another look at his calculations since his post and seems more optimistic about buying. I haven't looked at figures and costs for countries other than the UK, but the differences are so large that I'd quite surprised if investing and renting came out as more favourable in (m)any countries.

This doesn't address the concerns about buying in John's blog post (e.g. that you will only be able access the money when you're older). But if you're interested in patient philanthropy, and are happy to donate more accumulated wealth in several decades' time (when you downsize or die) rather than having a strong preference for donating less sooner, then buying a house looks better. (For discussion, see "Giving now vs giving later" and "How becoming a ‘patient philanthropist’ could allow you to do far more good")

Despite the large raw difference between buying vs. renting and investing, these differences might mean surprisingly little, in terms of ability to do good in the world, if you apply a discount to the value of future money to calculate its net present value. If you apply a high discount rate, then the gains are practically zero. Indeed, some EA orgs express a strong preference for money sooner rather than later. I haven't worked this bit out properly, but if you take these numbers literally (and reject patient philanthropy) it might be better to just donate sooner rather than to save up for a deposit.

How did Nick Bostrom come up with the "Simulation argument"*? 

Below is an answer Bostrom gave in 2008. (Though note, Pablo shares a comment below that Bostrom might be misremembering this, and he may have taken the idea from Hans Moravec.)

"In my doctoral work, I had studied so-called self-locating beliefs and developed the first mathematical theory of observation selection effects, which affects such beliefs. I had also for many years been thinking a lot about future technological capabilities and their possible impacts on humanity. If one combines these two areas – observation selection theory and the study of future technological capacities – then the simulation argument is only one small inferential step away.

Before the idea was developed in its final form, I had for a couple of years been running a rudimentary version of it past colleagues at coffee breaks during conferences. Typically, the response would be “yeah, that is kind of interesting” and then the conversation would drift to other topics without anything having been resolved.

I was on my way to the gym one evening and was again pondering the argument when it dawned on me that it was more than just coffee-break material and that it could be developed in a more rigorous form. By the time I had finished the physical workout, I had also worked out the essential structure of the argument (which is actually very simple). I went to my office and wrote it up.

(Are there any lessons in this? That new ideas often spring from the combining of two different areas or cognitive structures, which one has previously mastered at sufficiently a deep level, is a commonplace. But an additional possible moral, which may not be as widely appreciated, is that even when we do vaguely realize something, the breakthrough often eludes us because we fail to take the idea seriously enough.)"

Context for this post:

• I'm doing some research on "A History of Robot Rights Research," which includes digging into some early transhumanist / proto-EA type content. I stumbled across this.
• I tend to think of researchers as contributing either more through being detail oriented -- digging into sources or generating new empirical data -- or being really inventive and creative. I definitely fall into the former camp, and am often amazed/confused by the process of how people in the latter camp do what they do. Having found this example, it seemed worth sharing quickly.

*Definition of the simulation argument: "The simulation argument was set forth in a paper published in 2003. A draft of that paper had previously been circulated for a couple of years. The argument shows that at least one of the following propositions is true: (1) the human species is very likely to go extinct before reaching a “posthuman” stage; (2) any posthuman civilization is extremely unlikely to run a significant number of simulations of their evolutionary history (or variations thereof); (3) we are almost certainly living in a computer simulation. It follows that the belief that there is a significant chance that we will one day become posthumans who run ancestor-simulations is false, unless we are currently living in a simulation. A number of other consequences of this result are also discussed. The argument has attracted a considerable amount of attention, among scientists and philosophers as well as in the media."