So, where did the money end up going?
Edit: On reflection, it almost doesn't matter, does it? Spending something like 800 person-hours to allocate a mere £10,000 seems somewhat crazy regardless of where it ultimately goes, given that you could instead take a minimum-wage job for those 800 hours, earn more than £10,000 in the process, add the earnings to the original £10,000, and then donate that much larger sum of money, well, basically anywhere—pick any even remotely effective charity or set of charities—and you'll have done more good than doing things this way.
And you say that £10,000 is a large grant, and suggest using this approach for even smaller amounts?! I do not understand the reasoning behind this.
I was a team member of OxPrio. To answer your question, we granted the money to 80,000 hours. I personally agree with your reasoning that the grant amount was too small compared to the effort put in; 800 person-hours is an underestimate (only counting the work of the two most-committed team members), and counterfactually the time could have been spent more productively than working minimum-wage. However, I don't follow why giving the money to "basically anywhere" would have been good enough, since "remotely effective charities" plausibly still vary significantly in cost-effectiveness: for our OxPrio shortlist, we saw two orders of magnitude difference in our model's cost-effectiveness estimates among the 4 options.
?? If you literally mean minimum-wage, I think that is less than 10,000 pounds... although agree with the general thrust of your point about the money being more valuanle than the time (but think you are missing the spirit of the exercise as outlined in the post).
The U.K.'s national minimum page seems to be £7.50/hr, so you are right and I stand corrected: it would only be about £6,000.
In order to earn at least £10,000, Jacob and Tom would have had to find work that paid at least £12.50/hr. I think this would be well within the scope of their abilities, as they both seem like smart enough young men, with one or two obvious employable skills, and no doubt more that we don't know about.
think you are missing the spirit of the exercise as outlined in the post
Perhaps. What is the spirit of the exercise as outlined in the post?
Points 1-5 at the beginning of the post are all primarily about community-building and personal development externalities of the project, and not about the donation itself.
Cross-posted to Tom's website.
The Oxford Prioritisation Project was a research group between January and May 2017. The team conducted research to allocate £10,000 in the most impactful way, and published all our work on our blog. Tom Sittler was the Project’s director and Jacob L was its secretary, closely supporting Tom. This document is our (Jacob and Tom’s) in-depth review and impact evaluation of the Project. Our main conclusions are that the Project was an exciting and ambitious experiment with a new form of EA volunteer work. Although its impact fell short of our expectations in many areas, we learned an enormous amount and produced useful quantitative models.
A number of paths for impact motivated this project, falling roughly into two categories: producing valuable research (both to inform and to inspire) and empowering people (by making them more knowledgeable, by improving the local community…).
We feel that the Project’s impact fell short of our expectations in many areas, especially in empowering people but also in producing research. Yet we are proud of the Project, which was an exciting and ambitious experiment with a new form of EA volunteer work. By launching into this unexplored space, we have provided significant value of information for ourselves and the EA community.
We believe that we increased the prioritisation skill of team members only to a small extent (and concentrated on one or two people), much less than we hoped. We encountered severe challenges with a heterogeneous team, and an eventual team breakdown that threatened the existence of the Project.
On the other hand, we feel confident that we learned an enormous amount through the Project, including some things we couldn’t have learned any other way. This goes from team management under strong time pressure and leadership in the face of uncertainty, to group epistemics and quantitative-model-building skills.
Research-wise, we are happy with our quantitative models, which we see as a moderately useful contribution. We are less excited about the rest of our output, which consumed a lot of time yet feels less relevant.
We’d like to thank everyone on the team for making the Project possible, as well as Owen Cotton-Barratt and Max Dalton for their valuable support.
In a document I wrote in January 2017, before the project started, I identified the following goals for the project:
In addition to these give object-level goals, a sixth goal:
Quantity-wise, this goal was achieved. We published 38 blog posts, including individuals describing their current views, minor technical contributions to bayesian probability theory, discussion transcripts and, most importantly, quantitative models.
However, the extent to which our content engaged with substantial prioritisation questions, and was intellectually useful to the wider EA community, was far less than we expected. Overall, we feel that our substantial intellectual contribution were our quantitative models. Yet these were extremely speculative and developed in the last few weeks of the Project, while most of the preceding work was far less useful.
Regarding “direct benefits for people who would learn from reading” our research: this is very difficult to evaluate, but our tentative feeling was that this was lower than we expected. We received less direct engagement with our research on the EA forum than we expected, and we believe few people read our models. Indirectly, the models were referenced in some newsletters (for example MIRI’s). However, since our writings will remain online, there may be a small but long-lasting trickle of benefits into the future, from people coming across our models.
Though we did not expect to break major new conceptual ground in prioritisation research, we believed that the EA community provides too many ‘considerations’-type and too few ‘weighing’-type1 arguments. Making an actual granting decision would hopefully force us to generate ‘weighing’-type arguments, and this was a major impetus for starting the Project. So, we reasoned, even though we might not go beyond the frontier of prioritisation research, we could nonetheless be useful to people with the most advanced EA knowledge, by producing work that helps aggregate existing research into an actionable ranking. We think we were moderately successful in this respect, thanks to our quantitative models.
This is technically too early to evaluate, but we are pessimistic about it: we do not think the project caused any member who otherwise would not have considered it to now consider prioritisation research as a career2. This is based on impressions of, and conversations with, members.
This goal was a major factor in our decisions of which applicants to admit to the project. We selected several people who had less experience with EA topics, but who were interested and talented, in order to increase our chance of achieving this sub-goal. In retrospect, this was clearly a mistake, since getting these people up to speed proved far more difficult than we expected, and we still don’t think we had a counterfactual impact on their careers. Looking back, we recognise that there was some evidence for this that we interpreted incorrectly at the time, so we made a mistake in expectation, but not an obvious one.
As often, we suspect the impact in this category was extremely skewed across individuals. While we think we had no impact on most members, we think there is a small (<5%)3 chance that we have counterfactually changed the interests and abilities of one team member, such that this person will in the future work in global priorities research.
This was not achieved, for two reasons. While at the outset, we believed that there were about 3 team members who might consider earning to give in the future, by the end we think only one of them has a >50% chance of choosing that career path. So even though we provided an opportunity to practice prioritisation thinking, and especially quantitative modelling, we don’t think we had an impact by improving the decisions of future earn-to-givers. Regardless, we believe that this practice failed to increase the prioritisation skill of our team (see previous sections), so we wouldn’t have had impact here anyway.
This goal was achieved. We designed and implemented a new form of object-level group engagement that could theoretically be replicated in other locations. However, it’s debatable whether the cost:benefit ratio of such replications is sufficiently high. See the section: “Should there be more similar projects? Lessons for replication”
This goal was not achieved.
One impulse for starting the project was a frustration about the lack of in-depth, object-level intellectual activity in the local student EA community, which we (Jacob and Tom) are both part of. Current activities look like:
We wanted to see more of:
We did not believe that the Project alone could have achieved any of these changes. But we were optimistic that it would help push in that direction. We thought members of the local group would become excited about the Project, discuss its technical details, and give feedback. We also thought that team members would socialise with local group members and discuss their work, and hoped that the Project would serve as a model inspiring other more intellectually focused activities. None of these happened.
The local community was largely indifferent to the Project, as evidenced by an attendance of no more than 10 people at our final decision announcement. Throughout the Project, there was little interaction between the community and team members. In retrospect, we think we could have done more to facilitate and encourage such interaction. But we were already very busy as things were, so this would have needed to trade off against another of our activities.
Overall we clearly didn’t achieve this goal.
This goal was arguably achieved, in the sense that the Project produced several unexpected results which carry important implications for future projects. The information gained included:
The Project was a huge learning experience for us both, and especially strongly for Tom. This was the first time Tom led a team. Running a group of prioritisation researchers was a very different task from academic projects or internships we had been involved with in the past.
Our guess is that between 25% and 75% of the value created by the Project was through our becoming wiser and more experienced. This admittedly subjective conclusion relies on a number of difficult-to-verbalise intuitions, to the effect that we came out of the project knowing more about our own strengths and weaknesses, and how people and groups work. Since we both plan to give a substantial weight to altruistic considerations in our career decisions, this could be impactful.
Throughout the Project, Tom kept a journal of specific learning points, mostly for his own benefit but also for others who would potentially be interested in replicating the Project. He originally planned to turn these notes into a well-structured and detailed retrospective, but completing this work now looks as though it would not be worth the time cost. Instead he is publishing his notes with minimal editing here. These files reflect Tom’s views at the time of writing (indicated on each document); he may not endorse them in full anymore. They cover the following topics, in alphabetical order:
Tom tracked 308 focused pomodoros (~ 150 hours) on this project, and estimates that the true number of focused hours was closer to 500. Tom also estimates he dedicated at least another 200 hours of less focused time to the Project.
Jacob estimates he spent 100 hours on the Project.
We would guess that the real costs of the £10,000 grant were low. At the outset, the probability was quite high that the money would eventually be granted to a high-impact organisation, with a cost-effectiveness not several times smaller than CEA’s counterfactual use of the money3. In fact, the grant was given to 80,000 Hours.
The costs of snacks and drinks for our meetings, and logistics for the final event were about £500, covered by CEA’s local group budget.
We very tentatively estimate that Owen Cotton-Barratt spent less than 5 hours, and Max Dalton about 15 hours, helping us with the Project over the six months in which it ran. We are very grateful to both for their valuable support.
We faced a number of challenges; we’ll describe only the biggest ones, taking them in rough chronological order.
Some team members were experienced with advanced EA topics, while others were beginners with an interest in cost-effective charity. This was in part because we explicitly aimed to include some less experienced team members at the recruitment stage (see above, “2: Prioritisation researchers”). But an equally important factor was that, before we met them in person, we overestimated some team members’ understanding of prioritisation research.
We selected the team exclusively with an online application form. Once the project started, and we began talking to them in person, it quickly became clear that we had overestimated many team members’ familiarity with the basic arguments, concepts, and stylised facts that constitute the groundwork of prioritisation work. Possible explanations for our mistake include:
A heterogenous team alone would not have been a major problem if we hadn’t also dramatically overestimated our ability to bring the less experienced members up to speed.
We had planned to spend several weeks at the beginning of the project working especially proactively with these team members to fill remaining gaps in their knowledge. We prepared a list of “prioritisation research concepts” and held a rotating series of short presentations on them and we gave specific team members relevant reading material. We expected that team members would learn quickly from each other and “learn by doing”, from trying their hand at prioritisation.
In fact, we made barely any progress. For all except one team member, we feel that we failed to bring them substantially closer to being able to meaningfully contribute to prioritisation research: everyone remained largely at their previous levels, some high, some low5.
This makes us substantially more pessimistic about the possibility of fostering EA research talent through proactive schemes rather than letting individuals learn organically. (EA Berkeley seemed more positive about their student-led EA class, calling it “very successful”, but we believe it was many times less ambitious). We feel more confident that there is a basic global prioritisation mindset, which is extremely rare and difficult to change by certain kinds of outside intervention, but essential for EA researchers.
We were struggling to create a cohesive team where everyone was able to contribute to the shared goal of optimally allocating the £10,000, and was motivated to do so. Meanwhile, some team members became less engaged, perhaps as a result of the lack of visible signs of progress. Meeting attendance began to decline, and the problem worsened until the end of the Project, at which point four out of nine team members had dropped out. After the project only 3 out of 7 team members took the post-project survey. The results have informed our estimates throughout this evaluation.
While understanding that the dropout rate for volunteer projects is typically high, we still perceived this as a frustrating failure. An unexpected number of team members encountered health or family problems, while others simply lost motivation. Starting around halfway through the Project, the majority our efforts were focused on averting a complete dissolution of the team, which would have ended the Project.
As a result, we decided to severely curtail the ambition of the Project by choosing our four shortlisted charities ourselves, without team input, and according to different criteria than those we had originally envisioned6. We had been planning to shortlist the organisations with the highest expected impact, as a team, in a principled way. Instead we (Jacob and Tom) took into account our hunches about expected impact as well as the intellectual value of producing a quantitative model of a particular organisation, in order to arrive at a highly subjective and under-justified judgement call.
We are satisfied with this decision; we believe that it allowed us to create most of the value that could still be captured at that stage, given the circumstances. With a smaller team and a more focused goal, we produced the four quantitative models which led to our final decision.
See above, “What were the costs of the project?”.
It’s important to make a distinction between the impacts of the Project from a purely impartial perspective, and the impacts according to my values, which give a much larger place to me and my friends’ well-being.
Given that the object-level impacts (see above, “The impact of the Project”) were, in my view, low, effects on Jacob’s and my personal trajectories (our academic performance, well-being, skill-building) could be important, even from an impartial point of view.
Against a counterfactual of “no Oxford Prioritisation Project” (say, if the idea had not been suggested to me, or if we had not received funding), I would guess with low confidence that the Project had negative (impartial) impact. Without the Project, I would have spent these 6+ months happier and less stressed, with more time to spend on my studies. I plan to give significant weight to altruistic considerations in my career decisions, so this alone could have made the project net-negative. In addition, I believe I would have spent significant time thinking about object-level prioritisation questions on my own, and published my thoughts in some form. On the other hand, I learned a lot about team management and my own strengths and weaknesses through the Project. All things considered, I suspect that the Project was a little bit less good than this counterfactual.
When it comes to my own values, I’m slightly more confident that the Project was negative against this counterfactual. If offered to go back in time to re-experience the same events, I would probably decline.
Both impartially and personally speaking, there are some nearby counterfactuals against which I am slightly more confident that the Project was negative. These mostly take the form of developing quantitative models with two or three close friends, in an informal setting, and iterating on them rapidly, with or without money to grant. However, these counterfactuals are unlikely; at the time I didn’t have the information to realise how good they would be.
Going back now to the impartial perspective: despite my weakly held view described above, there are several scenarios for positive impact from the Project which I find quite plausible. For example, I would consider the Project to have paid for itself relative to reasonable counterfactuals if:
Global prioritisation is very challenging for two reasons. First, the search space contains a large number of possible interventions and organisations. Second, the search space spans multiple very different focus areas, such as global health and existential risk reduction.
The Project aimed to tackle both of these challenges. This high level of ambition was a conscious decision; we were excited by the lack of artificial restrictions on the search space. Though we had no unrealistic hopes of finding the truly best intervention, or of breaking significant new ground in prioritisation research, we still felt that an unrestricted search space would make the task more valuable, it made it feel more real, and less like a student’s exercise. We implicitly predicted that other team members would also be more motivated by the ambitious nature of the Project, but this turned out not to be the case. If anything, motivation increased after we shifted to less ambitious goals.
Given that our initial goal proved too difficult, even given the talent pool available in Oxford, we would recommend that potential replications restrict the search space to eliminate one of the two challenges. This gives two options:
We would weakly recommend the former rather than the latter, because we already tried it with moderate success, and because it allows starting immediately with quantitative models of the shortlisted organisations (see below, “Focus on quantitative models from the beginning”).
Given the circumstances, we believe the Project was too long. A shorter project means less is lost if the Project fails, and the closer deadline could be motivating.
We would recommend one of two models:
Our most important work, building the actual quantitative models and deciding on their inputs, was done in about this amount of time. The large, early part of the project centering around learning and searching for candidate organizations, was marginally not very useful (see e.g. section “Continued heterogeneity” above).
We initially believed that the relatively large size of the grant (£10,000) would motivate team members not only to work hard, but, more importantly, to be epistemically virtuous – that is, to focus their efforts on actions to improve the final allocation rather than ones that felt fun or socially normative. We now believe that this effect is small, and does not depend much on the size of the grant. For more information, see section “More general updates about epistemics, teams and community”.
Where the £10,000 figure may have helped is through getting us more and better applicants by signalling serious intent. But we are very uncertain about this consideration and would give it low weight.
Overall, our view is that the benefits of a larger grant size are quite small, relative to other strategic decisions, and that a £1000-2000 grant might have achieved nearly all the same benefits7. On the other hand, the true monetary cost of the grant is low (see above, “CEA money and time”). Therefore our tentative recommendation is: a £10,000 grant may still be worth getting, but don’t worry too much about it. Be upfront to the funder about the small effect of the money, and consider going ahead anyway if they give you less.
One of the surprising updates from the Project was that we made much more progress, including the less experienced team members, once we began working on explicit quantitative models of specific organisations. (See section “More general updates about epistemics, teams and community”.) So we would recommend starting with quantitative models from the first day, even when they are very simple. This may sound surprising, but we urge you to try it8.
We severely overestimated the degree to which we could bring less experienced members of a heterogeneous team up to speed (see above, “Different levels of previous experience”). So we would recommend a homogenous team, with all members meeting a high threshold of experience.
We started with a model where, very roughly, people are either good, conscientious team members, or they get de-motivated and drop out of the team. Under this model, dropouts are not very costly. You lose a team member, but you also lose overhead in dealing with them. So that is a reason to have a bigger team to start with. However, what we actually observed is that demotivated people don’t like working, but the one thing they dislike more is dropping out. Without speculating about the underlying reasons, a common strategy while demotivated (that we ourselves have also been guilty of in the past) is to do the minimal amount of work required to avoid dropping out. Hence, future projects should start with a small team of fully dedicated members rather than a larger team hoping to provide some “buffer” should a team member drop out.
Having a smaller team also means expending more effort selecting team members; doing so should also help with the problem raised in the above subsection. Although it seemed to us that we had already put a lot of resources into finding the right team, we now see that much more could have been done here, for example:
We had several hypotheses about how a project like this would affect the members involved, as well as the larger effective altruism community in which it took place. Here are some of our updates. Of course, the Project is only a single data point. Nonetheless, we think it still carries evidence in the same sense that, say, an elaborate field-study might be important without being an RCT.
Update: substantially lower credence
There are at least two reasons why human intellectual interaction is often not truth-seeking. First, there are conflicting incentives. These can take the form of both internal, cognitive biases or external, structural incentives.
To some extent the money helped realign incentives. The looming £10,000 decision provided a Schelling point that could be used to end less useful discussions or sub-projects without the violation of social norms that this often entails otherwise.
Nonetheless, the atmosphere also suffered from many common cognitive biases. These includes things like deferring too much to perceived authorities, and discussing topics that one likes, feels comfortable with or know many interesting facts about. It is possible that the kind of disciplining “skin in the game” effect we were hoping for failed to occur since the grant was altruistic, and of little relevance to team members personally. In response to this, team members also pledged a secret amount of their own money to the eventual recipient (with pledges ranging from £0 to £200)9. It is difficult to disentangle the consequences of this personal decision from the donation at large, but it might still have suffered the same altruistic problem. Given how insensitive people are to astronomical differences in charity impact in general, the choice of which top charity one’s donations go to might not make a sufficient psychological difference to offset other incentives.
Second, truth-seeking interaction is partly difficult not because of misaligned incentives, but because it requires certain mental skills that have to be deliberately trained (see also “Different levels of previous experience”). Finding the truth, in general, is hard. For example, we strongly encouraged team members to center discussions around cruxes, but most often the cruxes members gave were not actually things that would change their minds about X, as opposed to generic evidence regarding X or evidence that would clearly falsify X but that they strongly never expected to be found. This was true for basically all members of the Project, often including ourselves.
Instead of the looming, large donation, the epistemic atmosphere seems to have been positively impacted by things like guiding disagreements in relation to which quantitative model input they would change, and working within a strict time limit (e.g. a set meeting ending). For more on this, see the section “Focus on quantitative models from the beginning”.
Update: lower credence
We nicknamed this the “pet charities” problem: participants start the project with some views about which grantees are most cost-effective, and see themselves as having to defend that view. They engage with contrary evidence, but only to argue against it, or to find some reason their original grantee is still superior.
This was hardly a problem, but something in the vicinity was. While people didn’t strongly defend a view, this was mostly because they didn’t feel comfortable engaging with competing views at all. Instead, participants strongly preferred to continue researching the area they already knew and cared most about, even as other participants were doing the same thing with a different area. Participants’ different choice of area implied disagreeing premises, but they proved extremely reluctant to attempt to resolve this disagreement. We might call this the “pet areas” problem or the problem of “lower bound propagation”. (Because participants may informally be using the heuristic: “consider only interventions better than X”, with very different Xs).
Another problem that proved bigger than pet charities was over-updating on authority opinion (such as Tom’s current ranking of grantees). We see this as linked with the lack of comfort or confidence mentioned above.
Update: false
We’re both socially close to the EA community in Oxford. We expected to more or less know all applicants personally: if someone was interested enough in EA to apply, we would have come across them somehow.
Instead, a large number of applications were from people we didn’t know at all, a few of which ended up being selected for the team. We update that, at least in Oxford, there are many “lurkers”: people who are interested in EA, but find the current offerings of the local group uninspiring, so that they don’t get involved at all. There appear to be many talented people who are only prepared to work on an EA project if it stands out to them as particularly interesting. Although we generally would advise caution, this could be one reason to be more optimistic about replications of the Project.
Written on October 12, 2017