Good links Max. I’ve often felt there is a conflict between ecosystems/species preservation and animal welfare and these are really useful for exploring that idea more.
However, I one point that I still get some cognitive dissonance from is the low-importance ascribed to (species) diversity. It seems like if resources are to be used to make more happy individuals (so using resources to improve the lives of unhappy individuals is not an option, maybe we’re in a utopia where the lives of all sentient individuals are already net-positive and we value totalist population ethics), then it could, for instance, be better to produce more happy rhinos than happy humans, as there are far fewer rhinos than humans (if our utopia has the same current species numbers as the world today), so we will get more increase in the diversity of happy experiences. A moral weighting should also be applied between humans and rhinos, but if there is a huge difference in relative population numbers then it would probably be the dominating factor. How do others value a world with 7,700,000,000 people and 40,000 rhinos vs. a world with 7,700,010,000 people and 30,000 rhinos (using rough current species numbers and assuming all were fairly happy)?
I think my intuition is to incorporate diminishing returns (for a given species) into multi-species population ethics, given that the experiences (phenomenology) of species differs, so they experience happiness in different ways. Does this make any sense, and is there a name for such ethical views? It works best for me from the totalist population ethics standpoint, and I probably wouldn’t extend this to saying we should help unhappy rhinos over unhappy humans, even given the current populations of both species.
I think that Point 1 will be difficult to test in this way. What you want to do sounds a bit like a regression discontinuity analysis, but (as I understand it) there isn’t really a sharp time point for when you started promoting EA more; the translations/meetings etc. increased steadily since Oct 2018, right? I think this will make it harder to see the effect during the first year that you are scaling up outreach (particularly if compared by month, as there is probably seasonal variation in both donation and outreach). Brazil has also had a fairly distinct set of news worthy events (i.e. election and major political change, arrest of two former presidents during ongoing corruption scandals, amazon fires, etc.) over the same time period you increased outreach. If these events influence donation behaviour, then comparisons to other countries might not be particularly relevant (and it further complicates your monthly comparison). I think a better way to try and observe a quantitative effect would be if you compare the total donations for three years: pre-Oct 2018, Oct 2018-Oct 2019, post-Oct 2019 (provided you keep your level of outreach similar for the next year, and are patient). Aggregating over year will remove the seasonal effect of donations and some of the effect of current events, and if this shows an increase for 2019-2020, then you could (cautiously) look at comparing the monthly donation behaviour (three years of data will be better to compensate for monthly variation).
At this point, I think tracking your impact more subjectively by using questionnaires and interviews would produce more useful information. Not sure if charities would link their donors to you (maybe getting the contact of Brazilians who report donating in the EA survey would be more likely), but you could also try adding a annual questionnaire link to your newsletter/facebook/site like 80,000 hours does. I’d specifically try to ask people who made their first donations, or who increased their donations, this year what motivated them to do so.
I read an article about using logic to fill in the gaps around sparse or weak data that reminded me of this post. The article is focused on health science, but I think the idea is relevant to development as well.
As far as I know all western universities take overheads, although the percentage varies a lot. I used to be at the Biology Department in Lund University and they took 50%!
But I think that refusing overheads is only really an option on the margin, for foundations and individual funders. Most researchers get the majority of their funding from government funding agencies (e.g. NIH, NSF) and as far as I know these all pay full overheads, but universities actually need these overheads to fund their operating expenses. I don’t have first hand knowledge of this, but my understanding is that if overheads are 50% and you get $100 grant that doesn’t pay overheads, the University actually has to source $50 from elsewhere in order to administer your grant.
I’ve never heard of a University turning down an grant without overheads, but I have heard that bringing in a majority of overhead free money reflects poorly on an academic during a career review for promotion/tenure/new job etc.
That’s interesting you mention the psychological aspect—I searched a lot of material on RSI, but don’t recall seeing this discussed before. When I initially developed RSI it didn’t bother me much, but as the physical symptoms progressed it upset me more and probably ultimately contributed to some moderate depression I developed (it didn’t help that my depression was related to difficulty reaching professional goals, and the RSI was slowing me down on achieving them). I put off treatment for both when they were at the mild stage and ultimately only treated the RSI after I treated the depression—maybe that was the wrong order to take.
Also, if you donate to researcher at a University, try to make sure it goes directly to them and their institution doesn’t take overheads from it.
OPP funds transformative basic science and might be able to make some suggestions about how to allocate the money.
I have wondered if species extinction should be treated as worse than simply the welfare/suffering of the last members of a species.
For example, I take it that most EAs would view the loss of the last 100 million humans as much worse than the 7.6 billion who might die before them in an existential catastrophe, particularly if the survivors still had a chance at re-building human civilizations. Likewise, if we lose a species, we lose any future value that was intrinsic to having that species in existence. And as most human value is likely to be in the far future this could also be true for animals, but this can only be realized if the species remains extant (i.e. future humans may wish to create zoo simulations or worlds after WBE or space colonization).
While I agree that a lot of both near- and long-term human related causes seem more important than protecting breeding populations of all endangered species, it could be that we are undervaluing the intrinsic benefit of biodiversity. A cheap way of safeguarding against the case we are currently under prioritizing species preservation would just be to take some genetic samples from those that are endangered (already being done). Then the opportunity exists to recreate extant species in the future if resources are available and we decide they should have been conserved.
Nice, I particularly like the table and bullet-point forms you used for curating your ideas—I often find myself with too many ideas to work on and this seems like a good way to take an objective overview.
During my PhD I read ‘Becoming a successful scientist’ - this presented a strategic approach to scientific discovery and problem selection (Section 3.1) that I haven’t really seen elsewhere. It focused on science, but the ideas of looking for contradictions, paradoxes, new viewpoints or different scales may also be helpful for generating research questions in philosophy/economics.
I have a PDF of the book I’m happy to send by email, PM me.
Another comment about the failings of peer-review and convoluted ways to circumvent them. It’s quite common that reviewers will suggest extra experiments, and often these can improve the quality of the paper.
However, a Professor in Cognitive Psychology once told me that reviewers in his field seem to feel obliged to suggest extra experiments and almost always do. Even if the experiments in the paper are already quite complete, the reviewer will usually suggest an unnecessary control or a tangential experiment. So this Professor’s strategy to speed things up was to do, but then leave out, a key control experiment when he wrote up his papers. Reviewers would then almost always pick up on this and only request this additional experiment, and so then he could easily include it and resubmit quickly.
Very interesting post! I have worked in life science up to the postdoc level and think that is generally a reasonable summary of how life sciences research works (disclosure, Guzey interviewed me for this study).
One question is I have is how generalizable is this description geographically and across Universities? Based on the Universities/funders referenced I’d assume your thinking about Tier 1 Research Universities in the US. But did the demographics of your interviewee demographics suggest this could be situation more broadly?
A few other comments to e on some of the points:Role of PIsAgreed that senior PIs with large labs tend not to do very much bench work themselves. However, they aren’t solely managing and writing grants—I think one of the most important things PIs do is knowledge synthesis through writing literature reviews. I haven’t really met any postdocs that have the depth and breadth of knowledge of their lab head, which allows the later to both provide a high-level summary of their fields in reviews and also propose new ways forward in their grants. A counterpoint I’ve come across is in mixed labs runs by a PI with a computational background who has postdocs and PhDs doing lab work while he works on using their biological results for computational modelling. From my perspective, these types of labs seem to function quite well as the PI usually relies on people coming into the lab to be well trained in the biological assays they’ll use, but then teaches them computational techniques that end up using themselves by the end of their project.
Peer reviewOne of the big drawbacks of peer review is the hugely variable quality of reviews that are provided. As an example simply in terms of the level of detail provided, I have had comments of one paragraph and three pages for the same article. I think a key reason for this is there isn’t really any standardized format or expectations for reviews nor is there much training or feedback for reviewers. One thought I’ve had is that paying peer-reviewers would allow journals to both enforce review consistency and quality—although publishers have such large profit margins that it this could be feasible, they have no incentive to do so as scientists accept the status quo. In the absence of paid peer-review, I think that disclosing reviewer names and comments helps prevent ‘niche guarding’ and encourage reviewers to provide a useful and honest review (eLife does this currently, not sure if any other journals do so).
Permanent researchers Agreed that letting postdocs move into staff scientist/researcher positions would be helpful—this has been discussed a bit in the Nature and Sciences career sections over the last few years (such as here). I’ve usually heard from postdocs who moved into staff scientist or lab/facility manager positions that they wanted to stop relying on grants for their employees and to get some job stability. But some then later regretted the move after finding the positions didn’t have many options for career advancement relative the professor track. The staff scientists role is a relatively new academic position (although it has been around for a long time in government and private research labs) that doesn’t yet have a lot of consistency between Universities—it would probably help to have more discussion and even formalize the roles expectations before a lot of people move into it.
Solo foundersThis is an interesting observation and I hadn’t thought about the individual lab head model in this way. I’d actually like to take this a step further and say that academia has a habit of breaking up good pairs of biologists. How so? In a few cases, I’ve seen two senior postdocs or a postdoc and junior PI (so essentially two researchers quite closely matched their level of experience and with complementary skills) work really well together and produce outstanding results over a few years, which will usually lead to one of the duo getting a permanent position. The other may be able to continue on as a postdoc for a while, but as their research speciality will overlap heavily with their colleague’s field and it’s unlikely that the hiring/promoting institution will open another position in a similar area for a few years, the postdoc will probably have to move elsewhere to continue their career. Although the two may continue to collaborate, the second person to be hired often starts working on different topics to show their intellectual independence (although the new topics may be less impactful than what they were working on as a pair). I only know of a few cases where duos separated in this way and I haven’t really followed their outcomes, but I’d assume that the productivity of both researchers declined afterwards. Allowing one to move into a staff researcher position would help in this respect.
Big labs vs. small labsAnother option is a cluster of small labs working on a similar theme (I was in one in Lund that worked on Vision, another in the department worked on Pheromones). This seems to be more common in Northern Europe where high salaries tend to limit the group sizes that are possible (often PI, 1-2 postdocs, 1-2 PhDs). Clusters seemed to have the benefits noted for larger labs, but meant there were a lot of PIs around to mentor students, and also allowed the cost of lab facilities and support staff to be shared.
Research nichesTerritorial PIs seem quite common, and as noted, the publication/grant review process allows them to be quite effective at delaying/blocking and even stealing ideas that encroach on their topic. A link was recently posted here to an economics paper taht even suggested new talent entering a field after the death of a gatekeeping PI could speed up research progress. If it seems that a gatekeeping PI is holding back research in an important field, I think that a confrontational grantmaking strategy could be used—whereby a grant agency offers to fund research on the topic but explicitly excludes the PI and his existing collaborators from applying and reviewing proposals.
Differing risk-aversion between PIs and studentsAlthough a PI may seem risk-loving, he benefits from being able to diversify his risk across all of his students and may only need one to get a great result to keep the funding coming. He’s unlikely to get all of his students working together on one hard problem, just like a student can’t spend all his time on a high-risk problem.I tend to think that developing the ability to judge a project’s risk is an important skill during a PhD, and a good supervisor should be able to make sure student has at least one ‘safe’ project that they can write up. Realistically it is possible to recover from a PhD where nothing worked well during a postdoc, but it is a setback (particularly in applying for ECR fellowships). I feel that postdocs are possibly where the highest risk projects get taken on at the individual level, both because they have the experience to pick an ambitious but achievable goal, and also because they want to publish something great to have a good chance at a faculty position.
A simple suggestion to mitigate these problems could already be trialled well before life extension is available. It is probably possible to identify niche field where star scientists are acting as gatekeepers (either from citation patterns or conversations with scientists in a variety of fields) - an agency interested in that field could then simply offer some large and long term grants for work in the field provided that does not involve any of the star scientist or any of his collaborators. Hopefully the promise of substantial funding would be enough to encourage new entrants to the field.
Admittedly, this would be a very confrontational approach that might lead the star scientist to try and block publications or other grants from people entering his field in this way, but academic rivalries already occur via other causes so it should hopefully work itself out. If funding scientific competition like this resulted in similar gains as this publication shows for the death of a star scientist then it is not only a solution to the situation, but also suggests funding competitors could prove more effective than funding the incumbent gatekeepers in some cases.
I think a lot of this comes down to social factors rather than star scientist’s productivity decreasing with age.
At least in neuroscience, and probably in the life sciences more broadly, PIs who are very influential in a subfield (or who start a new one) tend to be the go to people for a topic and often become the gatekeepers, so work on that topic is generally done in collaboration with them. Junior scientists (even ones trained by that PI) will usually try to establish a unique research focus that avoids conflict with the exisiting star PIs, even if that means they end up working in a less promising area.
I haven’t read the linked paper, but I assume that one factor leading to increase in productivity is simply an increase in good people working in a promising research field where the gatekeeper was removed. In principle, this doesn’t need the death of a star scientist to achieve.
Hi Ryan, do you know of anybody in the EA space working on BCI, either on development or ethical considerations. BCI is mentioned surprisingly infrequently here.
Interesting article Michael, thanks for linking to it. I haven’t thought much about measuring experience states before, but after briefly looking over Simon’s essay I think happiness/suffering must, at minimum, be possible to indicate on an ordinal scale. But while many factors that lead to happiness/suffering can probably be measured on a ratio scale (pain could be measured objectively as nociceptor activity), I doubt that how they influence valanced experience is consistent interpersonally, or even intrapersonally at different times/conditions.
Nonetheless, I think suffering the Weber-Fechner argument can still be made if suffering/happiness is measured on an ordinal scale. For instance, say a person is suffering immensely because of being in a lot of pain, vs. someone suffering mildly from minor pain. Our intuition would be to help the person in immense pain, but we will probably have to do much more to relieve their pain for them to even notice we’ve helped, compared to the person being in minor pain.
I’ve also just realized that intuitive problem with this argument is asymmetric, in that it indicates that we are better of doing a nice thing for somebody who has is in a neutral state vs. somebody who is already very happy which does intuitively makes sense (and is how the Weber-Fechner law is usually applied to finance—a poor person appreciates a $100 gift a lot more than a millionaire).
Does this mean that for a given link between a factor and intrinsic state (say pain to suffering), we are likely to get a greater change in subjective experience by working to improve that factor for individuals who are already close to neutral to start with? This seems counterintuitive...
I am not sure if absolute suffering/pleasure should be measured on a linear scale, but there the Weber-Fechner law suggests that relative changes are likely to be perceived less than linearly.
The Weber-Fechner law indicates that the perceived change in a stimulus is inversely proportional to the initial strength. Example:
Weber found that the just noticeable difference (JND) between two weights was approximately proportional to the weights. Thus, if the weight of 105 g can (only just) be distinguished from that of 100 g, the JND (or differential threshold) is 5 g. If the mass is doubled, the differential threshold also doubles to 10 g, so that 210 g can be distinguished from 200 g.
This is true for the 5 main senses in humans and some animals, but I’m not sure if its been tested for pain (which is already quite a subjective sense), or subjective/emotional states in response to stimuli.
So while I intuitively agree that one person experiencing 10 units of suffering is worse than ten people experiencing 1 unit of suffering, the Weber-Fechner law counterintuitively suggests that a person who goes from 1 to 0 suffering will experience more subjective relief than somebody going from 10 to 9.
Nice post! Agreed that hard problems (or at least those that are likely to take more than the usual academic funding cycle to produce results) are likely to be relatively neglected.
It would also be good to consider that interdisciplinary research tends to be hard to fund but often produces outsized results (tool development for basic biology often falls into this category). So some of the hard problems could be more tractable to an interdisciplinary group, but getting funding for one is often impractical. I don’t know enough about the priority areas you identify as neglected and important to know which might benefit from an such approach, but specifically allocating some funding for interdisciplinary work could might produce good results in these areas.
Both Bolsonaro and the Brazilian environment Minister Salles show strong support for loggers, even when the loggers are working illegally on (still) protected land. The Brazilian Institute for the Environment (IBAMA) does try to monitor and prevent illegal logging, but is limited in its ability to do so because of the threat of violence from loggers.
Unfortunately, IBAMA seems to receive little support from politicians - for instance, after loggers burned an IBAMA full tanker used to fuel helicopters that it was using to monitor illegal logging activities, Salles gave a speech to the loggers that seemed to generally support them more than his own department:
...there is a law that must be respected while it is still a law. On the other hand, there is the need for the products provided by the loggers...
(paywalled source and pdf copy—in Portuguese, and google translate doesn’t do a great job)
IBAMA looks to have a very uncertain future, but it does sound like their capabilities to monitor logging activity are quite limited at the moment (and I’m not sure what enforcement options they have).
A tractable intervention could be to provide more modern and scalable remote monitoring capabilities (UAVS/drones or even satellite imagery) and the skills to analyse data from them. I don’t know if IBAMA could receive such equipment directly as donations, or if the monitoring would be better done by a NGO that could then openly publish its results.