Director of Research at CEARCH: https://exploratory-altruism.org/
I construct cost-effectiveness analyses of various cause areas, identifying the most promising opportunities for impactful work.
Previously a teacher in London, UK.
Stan Pinsent
Karma: 1,154
Hi Mike, thanks for taking the time to respond to another of my posts.
I think we might broadly agree on the main takeaway here, which is something like people should not assume that nuclear winter is proven—there are important uncertainties.
The rest is wrangling over details, which is important work but not essential reading for most people.
Comparing the estimates, the main cause of the differences in soot injection are if firestorms will form. Conditional on firestorms forming, my read of the literature is that at least significant lofting is likely to occur—this isn’t just from Rutgers.
Yes, I agree that the crux is whether firestorms will form. The difficulty is that we can only rely on very limited observations from Hiroshima and Nagasaki, plus modeling by various teams that may have political agendas.
I considered not modeling the detonation-soot relationship as a distribution, because the most important distinction is binary—would a modern-day countervalue nuclear exchange trigger firestorms? Unfortunately I could not figure out a way of converting the evidence base into a fair weighting of ‘yes’ vs. ‘no’, and the distributional approach I take is inevitably highly subjective.
Another approach I could have taken is modeling as a distribution the answer to the question “how specific do conditions have to be for firestorms to form?”. We know that a firestorm did form in a dense, wooden city hit by a small fission weapon in summer, with low winds. Firestorms are possible, but it is unclear how likely they are.
These charts are made up. The lower chart is an approximation of what my approach implies about firestorm conditions: most likely, firestorms are possible but relatively rare.
Los Alamos and Rutgers are not very helpful in forming this distribution: Los Alamos claim that firestorms are not possible anywhere. Rutgers claims that they are possible in dense cities under specific atmospheric conditions (and perhaps elsewhere). This gives us little to go on.
Fusion (Thermonuclear) weaponry is often at least an order of magnitude larger than the atomic bomb dropped on Hiroshima. This may well raise the probability of firestorms, although this is not easy to determine definitively.
Agreed. My understanding is that fusion weapons are not qualitatively different in any important way other than power.
Yet there is a lot of uncertainty—it has been proposed that large blast waves could smother much of the flammable materials with concrete rubble in modern cities. The height at which weapons are detonated also alters the effects of radiative heat vs blast, etc.
you only need maybe 100 or so firestorms to cause a serious nuclear winter. This may not be a high bar to reach with so many weapons in play.
Semi agree. Rutgers model the effects of a 100+ detonation conflict between India and Pakistan:
They find 1-2 degree cooling over cropland at the peak of the catastrophe. This would be unprecedented and really bad, but not serious compared to the nuclear winter we have in our imagination: I estimate about 100x less bad than the doomsday scenario with 10+ degrees cooling.
They are modeling 100 small fission weapons, so it would be worse with large weapons, or more detonations. But not as much worse as you might think: the 51st detonation is maybe 2-5x less damaging than the 5th.
Furthermore, they are assuming that each side targets for maximum firestorm damage. They assume that fuel loading is proportional to population density, and India & Pakistan have some of the world’s densest cities. So this is almost the worst damage you could do with 100 detonations.
Although 100 detonations sounds very small, this idealized conflict would be tapping into most of the firestorm potential of two countries in which 20% of the world’s population live—much more than live in the US and Russia.
It’s possible that 100 firestorms could trigger measurable cooling, but the conditions would have to be quite specific. 1000 firestorms seems much less likely still.
Conclusion
In the post I suggest that nuclear winter proponents may be guilty of inflating cooling effects by compounding a series of small exaggerations. I may be guilty of the same thing in the opposite direction!
I don’t see my model as a major step forward for the field of nuclear winter. It borrows results from proper climate models. But it is bolder than many other models, extending to annual risk and expected damage. And, unlike the papers which explore only the worst-case, it accounts for important factors like countervalue/force targeting and the number of detonations. I find that nuclear autumn is at least as great a threat as nuclear winter, with important implications for resilience-building.
The main thing I would like people to take away is that we remain uncertain what would be more damaging about a nuclear conflict: the direct destruction, or its climate-cooling effects.
I wonder whether it is worth for me to invest significant time in writing comments like mine above. It seems that they are often downvoted, and that I can sometimes tell before hand when this is going to be case. So, to the extent karma is a good proxy for what people value, I wonder whether I am just spending signicant time on doing something which has little value.
I am sad to see your comment getting downvotes as I do think it contributes a lot of value to the discussion.
I can guess why you might be getting them. You often respond to cause-prio posts with “what about corporate campaigns for chicken welfare?”, and many people now probably switch off and downvote when they see this. Maybe just keep the chicken comparison to one line and link to your original post/comment?
Also, you comment is 3200 words long—about 3x longer than the actual post. I think a 200-word summary-of-the-comment with bullet points would be really useful for readers who have only read this post and are unable to pick up the finer points of your modeling critique.
On animal welfare
I think that if you adopt RP’s moral weight estimates and reject speciesism, it is almost inevitable that the most cost-effective interventions to improve wellbeing will be animal welfare interventions.
My understanding is that CEARCH is not against evaluating animal welfare interventions in principle, but in practice we are not doing so while comparisons between human and animal welfare remain so shaky. Our research direction is also partly driven by the value of information, ie. how much resources we can plausibly redirect and the impact this will have. Maybe this is too deterministic of me, but I feel that banging the drum about corporate chicken campaigns will only open so many wallets.
Hi Mike,
Firstly, thanks to you and all of ALLFED for your willingness to let me prod and poke at your work in the past year.
You make some excellent points and I think they will help readers to decide where they stand on the important cruxes here.
We assign a higher probability that a nuclear conflict occurs compared to your estimates, and also assume that conditional on a nuclear conflict occurring that higher detonation totals are likely. This raises the likelihood and severity of nuclear winters versus your estimates.
For anyone wanting to get up to speed on my nuclear winter model, plus a quick intro to why nuclear cooling is so uncertain, see my just-released nuclear winter post.
Weightings of Metaculus, XPT vs individual estimates: we do place high weightings on individual’s estimates, which is not ideal. The main reason for this is that the Metaculus and XPT estimates are not calibrated to a 100+ detonation nuclear conflict involving US and/or Russia.
The Metaculus estimate still forms around 20% of the total weight. Overall, it seems credible that my final estimate (0.10%) is around a third of the Metaculus estimate (0.30%) for 100+ detonations, given that most experts I spoke to considered India/Pakistan to be one of the main threats of a 100+ detonation nuclear exchange.
The XPT estimate only forms around 2% of the total weight.
Distribution of the number of detonations in a 100+ detonation conflict. I have done my best to incorporate different sources of evidence. One expert I spoke to broadly agreed with you, Mike, and guessed that in a 100+ conflict involving US/Russia there would be 80-90% risk of over 1000 detonations. Another expert was less pessimistic and placed higher weight on the possibility of ‘moderate’ escalation involving hundreds of weapons.
I re-ran the numbers just looking at conflicts with over 1000 detonations and found that the cooling levels increase only modestly: the 75th-percentile cooling level increases from 0.66 degrees (100+ detonations) to 1.24 degrees (1000+ detonations). The initial assumption about soot volumes is far more important (see below)
Detonation-soot relationship: Yes, the results are extremely sensitive to the initial assumption about how much soot is produced in a small nuclear exchange targeting cities. When I re-ran the numbers under the more pessimistic assumption that soot levels were 10%-100% those predicted by Toon et al., 75th-percentile cooling increased from 0.66 degrees to 3 degrees.
We estimate that the expected mortality from supervolcanic eruptions (VEI 8+) would be comparable to VEI 7 eruptions, so their inclusion could increase cost effectiveness significantly.
I don’t exclude supereruptions; I estimate that the right tail of my volcanic cooling model already accounts for them.
We feel that you are selling short the importance of research in building resilience to nuclear winters in particular and ASRSs in general [...] Overall, we see research as the foundation on which you then build the policy work and other actions. Broadening and strengthening this foundation is therefore vital in allowing the work that finally effects change to occur—it isn’t an either/or.
I want to be clear that I recommend that funders prioritize policy advocacy over R&D on the margin at this point in time. I totally agree that advocacy on such an uncertain topic can only be effective if it is grounded in research, and that ALLFED’s research will very likely form the foundations of policy work in this area for years to come.
One key takeaway from my analysis is that mild and moderate scenarios form a larger proportion of the threat than the lore of nuclear winter might suggest. Resilient foods would likely have a role to play in these scenarios, but I think the calories at stake in distribution and adaptation are likely to be more pivotal.
One reason for my focus on resilient food pilot studies is that they are a possible next step for ALLFED if it were to receive a funding boost. ALLFED has been ticking along with modest but reliable core funding for some time now, and perhaps I am guilty of taking its theoretical research for granted.
Feel free to set the record straight and give some indication of the kinds of work ALLFED might be interested in accepting funding for.
Good point.
I looked at WTO agreements early in the research process and eventually decided that WTO advocacy was probably not, on the margin, the best way forward.
Food stockholding
I focused on the consequences of the AoA for food stockpiling (known as “stockholding”), the most urgent concern being that it may dissuade countries from stockholding as much as they otherwise would (as suggested by Adin Richards here). Although food reserves would never be big enough to get us through a full catastrophe, they would buy time for countries to adapt to the cooling shock.
The feedback I got from someone with experience at the WTO since the 1990′s was
The AoA probably isn’t holding countries back from stockholding. It does not make any restrictions on how much a country can hold, only on how much a country can spend on subsidizing domestically-produced stocks [while technically true, a lot of authors certainly seem to believe that the AoA affects stockholding, eg. here].
This AoA appears to be counterfactually reducing subsidies for only a handful of products in a few countries. India is the only country to have notified exceeding its limit, and only for rice.
The AoA seems intractable. The G33, a coalition of LMICs, have pushed for change with no success
On the other hand, basic amendment to the AoA seems obviously needed (imho). The original agreement does not properly allow for inflation. It does not make adequate exceptions for very low-income countries (whose market share is so low that allowing them to subsidize farmers would not be very distortionary).
Overall the WTO seems deadlocked at the moment and suffering a crisis of legitimacy. Tit-for-tat between US and China has led to a breakdown of trust in the organization.
If the US was on-side for AoA amendment, it is possible that the other dissenting countries would fall in line. But it is not clear that the US can be influenced on this. The US is doing fine with the system as it currently is, and has other ways of subsidizing domestic agriculture.
Other WTO theories of change
I don’t know much about the implications of the AoA beyond stockholding.
The most important things is that we ensure trade continues in a catastrophe, which seems congruent with the AoA. The second most important thing is that countries are able to quickly adapt their food systems in a crisis. In a major catastrophe I think all WTO rules would go out of the window. But could the AoA prevent countries from preparing?
It may hold them back from stockholding, or producing much more food than the market demands. But these measures would likely be way too expensive for governments to support in the name of preparing for a 1-in-400 event, anyway.
It may prevent them from using agri subsidies to foster emerging, resilient food sources that are not economically viable in normal times. But these food sources are at an early stage of development—pilot studies at most—and my guess is that they could be pursued under academic or innovation budgets.
I may well be missing something. Are there other ways the AoA could frustrate resilience work?
Hi Vasco. Thanks for all of your help giving feedback on the report and the modeling underpinning the CEA.
I am going to focus on the main points that you make. I hope to explain why I chose not to adopt the changes you mention in your comment and also to highlight some key weaknesses and limitations of my model.
Points I address (paraphrasing what you said):
By asking domain experts you probably got an overestimate for “probability that advocacy succeeds”. You should have also asked people in other fields.
Although you mention fungibility, you don’t account for it in cost-effectiveness estimates. You should be more explicit that fungibility undermines cost-effectiveness of grants that perform other, less effective interventions.
You overestimate the mortality effects of mild cooling events: if we apply your model to the 1815 eruption, we get higher mortality rates than actually occurred.
Poverty is a strong predictor of famine mortality. If your persistence estimate relied only on poverty & malnutrition burden indicators, the full-term benefits of policy advocacy would be significantly lower
By asking domain experts you probably got an overestimate for “probability that advocacy succeeds”. You should have also asked people in other fields.
I agree that domain experts are likely to overestimate the probability of successful policy advocacy in their space.
In my defence, only two of the seven experts I consulted for estimates worked in food resilience specifically. The geometric mean of their estimates was 30%; only slightly higher than the group average of 24%. The other experts would be best classified as GCR experts (so still likely to be overly optimistic)
The difficulty is that people who are not domain experts are (by definition) not well-informed. I don’t think people at GiveWell will have an accurate understanding of prospects for ASRS resilience policy advocacy. Especially because this is a very small field.
Although you mention fungibility, you don’t account for it in cost-effectiveness estimates. You should be more explicit that fungibility undermines cost-effectiveness of grants that perform other, less effective interventions.
I think that in ideal circumstances, fungibility should be accounted for in cost-effectiveness analysis. But since it depends on the organization receiving the funding, I decided not to do quantitative estimates of fungibility effects in this report. Maybe we will do so when we evaluate specific grants in this area.
I agree that funding to orgs who only do one highly cost-effective thing is generally less fungible.
You overestimate the mortality effects of mild cooling events: if we apply your model to the 1815 eruption, we get higher mortality rates than actually occurred.
I love this analysis, thanks for doing it.
First, let me say that yes, my model is very sensitive to mortality estimates in mild cooling scenarios. My estimate may be too high, but I believe there are compelling reasons not to be confident of this.
To illustrate my model for mortality in a mild cooling event (1-2.65 degrees cooling):
2% probability of 6% mortality (no adaptation, no food trade)
18% probability of 3% mortality (no food trade)
80% probability of no mortality
This gives an average of approximately 0.6% mortality
My counterarguments are as follows:
On a broad level, I think that a ‘panic’ scenario could include countries banning food exports to secure domestic supplies. This would be catastrophic for some food-importing countries even in normal climate conditions. The same cannot be said for the world of 1815, where almost all food was consumed locally and very few people lived far from agricultural areas.
I think the comparison with 1815 is well worth doing. However, there are a number of reasons why the validity of the comparison is limited:
A global 1% mortality event in 1815 may not have even been noticed. We have to patch together estimates of famine mortality in 1815 because there was almost no systematic documentation at the time. People were not aware of any global phenomenon, so nobody was trying to “join up the dots” and tally the full famine impact that year. Especially if much of the effects were felt in South Asia, East Asia or Africa, it is possible that a major-yet-distributed famine could have gone unnoticed
Relatively few people in the world of 1815 relied on food imports, as mentioned above[1]. Breakdown in international food trade is the main famine mechanism in modern-day agricultural catastrophes, but it barely applies to the agrarian economies of 1815.
Local famine effects may not have been worse in Indonesia. Undoubtedly, the effects of ash blanketing crops would have been worse near the eruption. But stratospheric soot quickly circulates around the world. Furthermore, Indonesia has a warm climate and would not have been at risk from completely failed harvests through unseasonal frost. Multiple annual harvests are common in the tropics; in higher latitudes, a failed crop leaves farmers without food for almost a year.
Cooling damage is highly superlinear. The Pinatubo eruption of 1991 caused 0.5 degrees of cooling and is not associated with important declines in agricultural productivity. Thus we might expect the expected burden of an 1815-level cooling event (0.8 to 1.3 degrees cooling) to be far lower than a 1-2.65 degree cooling event[2].
Poverty is a strong predictor of famine mortality. If your persistence estimate relied only on poverty & malnutrition burden indicators, the full-term benefits of policy advocacy would be significantly lower.
To push back:
A world with no extreme poverty and no nutritional diseases would still be vulnerable to global agricultural catastrophes. The advantage of my “grain production per capita” metric is that it has no such edge-case problems: more grain is always good for food security.
The famine/poverty relationship is strong in normal times. There are a number of reasons that it may become less strong in an agricultural catastrophe
Many of the global poor are subsistence farmers in warm countries. This demographic will be close to food supplies and away from the risk of frosts etc.. They may be better-positioned to survive than their middle-class compatriots in cities.
People at the bottom of the pile are the most at-risk of post-catastrophe famine, regardless of whether they are in absolute poverty. A wealthier world would simply have higher food prices, leaving the poorest without enough to eat.
As described above, the main cause of famine in my model is the breakdown of food trade. It is not clear that progress against poverty and nutritional diseases is an indication that populations are less dependent on food trade. If anything, development is probably associated with increased reliance on trade.
Thanks again for the detailed feedback!!
- ^
Admittedly, this would have made some people more vulnerable as it was difficult to relieve famine-stricken areas.
- ^
A counterargument could be that Western Europe appears to have had particularly bad summer cooling in 1816 - as well as better record-keeping than much of the world—and their famines were not so bad. On the other hand, spring cooling may be more important, as late frosts can ruin harvests of wheat, potatoes etc.
Great tool! I couldn’t figure how to go back when I mis-clicked an answer, and ended up losing my progress.
I’ll rewrite completely because I didn’t explain myself very clearly
10,000 participants is possible since they are using Whatsapp, in a large country, and recruiting users does not seem to be a bottleneck
10,000 participants is relevant as it represents the scale they might hope to expand to at the next stage
Presumably they used the number 10,000 to estimate the cost-per-treatment by finding the marginal cost per treatment and adding 1⁄10,000th of their expected fixed costs.
So if they were to expand to 100,000 or 1,000,000 participants, the cost-per-treatment would be even lower.
My guess is that a WhatsApp-based MH intervention would be almost arbitrarily scalable. 10 000 participants ($300,000) may reflect the scale of the grants they are looking for.
I would like to push back slightly on your second point: Secondly, isn’t it a massive problem that you only look at the 27% that completed the program when presenting results?
By restricting to the people who completed the program, we get to understand the effect that the program itself has. This is important for understanding its therapeutic value.
Retention is also important—it is usually the biggest challenge for online or self-help mental health interventions, and it is practically a given that many people will not complete the course of treatment. 27% tells us a lot about how “sticky” the program was. It lies between the typical retention rates of pure self-help interventions and face-to-face therapy, as we would expect for an in-between intervention like this.
More important than effect size and retention—I would argue—is the topline cost-effectiveness in depression averted per $1,000 or something like that. This we can easily estimate from retention rate, effect size and cost-per-treatment.
Zakat is paid on wealth, not income, so GDP is not a great proxy. Globally there appears to be $450tr in wealth and $100tr in GDP, so perhaps multiplying GDP by 4.5 gives a decent estimate for wealth.
Also global GDPincreased 43%between 2010 and 2022.
Thanks for sharing your calculations!
I’d recommend against thinking along the lines of what Muslim wealth would have to be to make the 1tr figure plausible, given that the figure seems to be made up. But I definitely agree with the idea of forming multiple estimates using different approaches.
Some ideas for getting a good figure:
The combined GDP of Organisation of Islamic Cooperation countries is 10tr, about 10% of global GDP. This does not include most muslims in countries where they are a minority (importantly, India, Europe and North America) but it does include non-muslims in OIC countries
Straight off the bat we can see that $1tr is implausible.
If we assume that OIC countries have 10% of global wealth, this would be $40-70Trn according to your estimate. This implies $1-1.75bn of wealth eligible for Zakat.
Where is all that wealth? Given that, as you say, a big chunk of wealth is help by the top 1%, I would guess that most of the wealth is in shares of companies, plus property and other physical assets. The value of such assets can easily be hidden or obfuscated by those who do not wish to pay Zakat on it.
It seems like Zakat is enforced by law in Saudi, and collected & used by the state(!) [source 1, 2]. So the $18bn figure is probably quite reliable.
Saudi GDP is about 1% of global GDP. Assuming Saudi therefore has 1% of global wealth (probably an underestimate because oil), and that 20% is a hard limit on the share of global wealth owned by muslims, I would guess 20*$18bn = $360bn is a very hard upper limit on global Zakat
More realistically I would assume that 10% of global wealth is owned by Muslims eligible for Zakat and that payment rates outside of Saudi are much lower—I’ll say 75% lower[1]. Then global Zakat would be $18bn*(1+9*0.25) = $85.5bn
Importantly, it seems like Zakat paid in Saudi is not influencable as it is kept by the state. Therefore my best estimate of the amount of influencable Zakat is $67.5bn worldwide.
As a sense-check: where are the Gaza billions? The war in Gaza was a huge huge issue in the Muslim world this past Ramadan, and presumably a lot of Zakat donations went to helping Gazans. If it was ins the tens or hundreds of billions (ie at least 10% of $200+bn), that would be $10,000s for each person in Gaza, which should be easy to spot. But it is not coming up in some brief googling.
- ^
I imagine the main factors are (1) simply underpaying or not paying because it must hurt to give away 2.5% of one’s wealth each year and (2) fudging by underrating one’s wealth (not counting or being naive about the value of one’s house, livestock, car etc.)
Great summary!
There is also a mental health benefit to averting unwanted pregnancy. A meta-analysis by Wang et al. (2021) found that women with an unplanned pregnancy were 62% more likely (28% vs. 17%) to suffer postpartum depression compared to women with a planned pregnancy. We don’t have good data on depression prevalence among women whose unplanned pregnancy was averted, but it seems probable that an unplanned pregnancy increases the risk of mental illness, even if only by adding another life stressor. It is very hard to measure, but I suspect that the wellbeing benefits of not having one’s education cut short / career & income disrupted / living with stigma of being a single mother or with a partner you didn’t choose to have a child with / etc. are high.
For us, high-impact clients include adolescents (under the age of 20), individuals living in multi-dimensional poverty, those who aren’t using or have never used contraception, and those who have no other options for the services we provide. Collectively, we aim for over 80% of our clients to be from one or more of these high-impact groups.
I think this is a good way of trying to secure strong counterfactual impact. I notice that your cost-effectiveness estimates imply that you prevent 200 maternal deaths per 100,000 averted pregnancies. How do you arrive at this figure, given the lack of maternal mortality data for your specific demographic (unwanted pregnancy, rural, underserved, low-income country)? Nigeria has the 2nd-worst maternal mortality rate in the world, at 112 per 100,000 - but your Nigeria numbers suggest something like 500 per 100,000.
Another interesting thing is that you avert a maternal death for $3,353 - which means maybe $70-100 per extra year of life that these women get—and you avert a DALY for $4.77. This implies that averted maternal deaths only account for ~5% of the health benefits you are measuring. Where are the other 95% coming from? Importantly, are any coming from the infant or from non-health benefits converted into DALYs—because counting these would be moral assumptions worth flagging.
This looks like a particularly strong crop of new orgs. I’m excited to see NOVAH and AMI get started, which (as far as I know) are pioneering totally new interventions. Good luck.
Thanks for the detailed response, Vasco! Apologies in advance that this reply is slightly rushed and scattershot.
I agree that you are right with the maths—it is 251x, not 63,000x.
I am not comparing the cost-effectiveness of preventing events of different magnitudes.
Instead, I am comparing the cost-effectiveness of saving lives in periods of different population losses.
OK, I did not really get this!
In your example on wars you say
As a consequence, if the goal is minimising war deaths[2], spending to save lives in wars 1 k times as deadly should be 0.00158 % (= (10^3)^(-1.6)) as large.
Can you give an example of what might count as “spending to save lives in wars 1k times as deadly” in this context?
I am guessing it is spending money now on things that would save lives in very deadly wars. Something like building a nuclear bunker vs making a bullet proof vest? Thinking about the amounts we might be willing to spend on interventions that save lives in 100-death wars vs 100k-death wars, it intuitively feels like 251x is a way better multiplier than 63,000. So where am I going wrong?
When you are thinking about the PDF of , are you forgetting that ∇ is not proportional to ∇?
To give a toy example: suppose .
Then if we have
If we have
The “height of the PDF graph” will not capture these differences in width. This won’t matter much for questions of 100 vs 100k deaths, but it might be relevant for near-existential mortality levels.
Using PDF rather than CDF to compare the cost-effectiveness of preventing events of different magnitudes here seems off.
You show that preventing (say) all potential wars next year with a death toll of 100 is 1000^1.6 = 63,000 times better in expectation than preventing all potential wars with a death toll of 100k.
More realistically, intervention A might decrease the probability of wars of magnitude 10-100 deaths and intervention B might decrease the probability of wars of magnitude 100,000 to 1,000,000 deaths. Suppose they decrease the probability of such wars over the next n years by the same amount. Which intervention is more valuable? We would use the same methodology as you did except we would use the CDF instead of the PDF. Intervention A would be only 1000^0.6 = 63 times as valuable.
As an intuition pump we might look at the distribution of military deaths in the 20th century. Should the League of Nations/UN have spent more effort preventing small wars and less effort preventing large ones?
The data actually makes me think that even the 63x from above is too high. I would say that in the 20th century, great-power conflict > interstate conflict > intrastate conflict should have been the order of priorities (if we wish to reduce military deaths). When it comes to things that could be even deadlier than WWII, like nuclear war or a pandemic, it’s obvious to me that the uncertainty about the death toll of such events increases at least linearly with the expected toll, and hence the “100-1000 vs 100k-1M” framing is superior to the PDF approach.
By incentives do you mean incentives for taking one job over another, like pay, benefits, type of work, etc.?
I welcome this idea! More love would be a good thing, and we would rather make this change earlier in the life course.
I think implementation is hard. This is a big “if”:
if we could come up with a healthy, inclusive (but respectful of differing opinions), curriculum that -encourages/rewards- love but does not impose it
As Joseph said, it is difficult to assess educational interventions. When it comes to knowledge transfer we can be generally confident that education is helping—calculus classes increase students’ aptitude for calculus. But love?
Recent research suggests that mindfulness interventions in schools were much less impactful than hoped. I suspect that something like mindfulness works well if you opt in—and is much less useful if you didn’t ask for it.
To explore this idea further, I recommend looking for comparable values-based educational experiments that have been tried in the past (maybe something about attitudes to sexuality, or religious tolerance, or positive thinking, or even campaigns to instil hate). Did they succeed in changing values? If they failed, why did they fail? If they succeeded, what can we learn from them?
[Update: the tool does capture diminishing marginal cost-effectiveness—see reply]
Cool tool!
I’d be interested to see how it performs if each project has diminishing marginal cost-effectiveness—presumably there would be a lot more diversification. As it stands, it seems better suited to individual decision-making.