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 Nuno Sempere
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98e2da71a4 | 1 year ago |
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Nuno Sempere
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Nuño Sempere
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Nuño Sempere
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Leopold Tal G
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Leopold Tal G
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Leopold Tal G
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Nuno Sempere
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Nuno Sempere
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.secret/
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sitemap.gz
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sitemap.txt
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@ -0,0 +1,45 @@
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// Helpers
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ss(arr) = SampleSet.fromList(arr)
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// Nuclear ukraine
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rusiaUsesNuclearWeaponsInUkraine = ss([0.27, 0.04, 0.02, 0.001, 0.09, 0.08, 0.07])// <- fill-in
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// Note that the period of time is left unspecified
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// Nuclear NATO
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escalationOutsideUkraineGivenUkraineWasNuked = ss([0.15, 0.09, 0.0013, 10^(-5), 0.01, 0.3, 0.05])// <- fill-in
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escalationToNATOUnconditional = rusiaUsesNuclearWeaponsInUkraine *
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escalationOutsideUkraineGivenUkraineWasNuked
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// Nuclear NATO to nuclear London/Washington
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bigUKUSCityNukedGivenEscalationOutsideUkraine = ss([0.4, 0.15, 0.9985, 0.05, 0.02, 0.002, 0.5])// <- fill-in
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bigUKUSCityUnconditional = escalationToNATOUnconditional *
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bigUKUSCityNukedGivenEscalationOutsideUkraine
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// Impact in lost hours
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remainlingLifeExpectancyInYears = 40 to 60 // <- change
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daysInYear= 365
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productiveHoursInDay = 6 to 18 // <- change
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ableToEscapeBefore = 0.5// <- fill-in
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proportionOfPeopleInLondonWhoDie = 0.7
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expectedLostHours = bigUKUSCityUnconditional *
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(1 - ableToEscapeBefore) *
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proportionOfPeopleInLondonWhoDie *
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remainlingLifeExpectancyInYears *
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daysInYear *
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productiveHoursInDay
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// Probably good to also estimate idiosyncratic factors such as
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// - Increased or decreased productivity in a city
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// - Increased or decreased impact in a city
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// - Value assigned to surviving in a world after a nuclear winter
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// - ...
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// Display
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{
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rusiaUsesNuclearWeaponsInUkraine: rusiaUsesNuclearWeaponsInUkraine,
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escalationToNATOUnconditional: escalationToNATOUnconditional,
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bigUKUSCityUnconditional: bigUKUSCityUnconditional,
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expectedLostHours: expectedLostHours
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}
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<form method="post" action="https://listmonk.nunosempere.com/subscription/form" class="listmonk-form">
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<div>
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<h3>Subscribe</h3>
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<input type="hidden" name="nonce" />
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<p><input type="email" name="email" required placeholder="E-mail" class="subscribe-input"/></p>
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<p><input type="text" name="name" placeholder="Name (optional)" class="subscribe-input"/></ap>
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<p>
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<input id="82ff8" type="checkbox" name="l" checked value="82ff889c-f9d9-4a45-bf9a-7e2696813021" />
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<label for="82ff8" style="font-size: 18px">nunosempere.com</label>
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</p>
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<p><input type="submit" value="Subscribe" class="subscribe-button"/></p>
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</div>
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</form>
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|
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@ -1,90 +0,0 @@
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Legalize acetylcysteine: An open letter to the UK's MHRA
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========================================================
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## Part I: Demagoguery
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This is the map of maximum Celtic expansion, in circa 270 BC, per [Wikipedia](https://upload.wikimedia.org/wikipedia/commons/0/08/Celtic_expansion_in_Europe.svg):
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Since then, the Spaniards have further developed into Gazpacho-drinking siesta-sleepers and the Britons have developed into tea-drinking weather-contemplators[^1]. Still, my understanding is that population differences are to a great degree cultural, and that the basic plumbing remains pretty much the same. My understanding is also that
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Imagine, then, my surprise, when in the middle of being sick in the UK, I find out that an extremely common medicine used to treat the cold in Spain throughout my childhood just wasn't commonly available in the UK. This medicine is [acetylcysteine](https://en.wikipedia.org/wiki/Acetylcysteine)—known in Spain under the brand name "Fluomicil". It's purpose is to decrease the thickness of the mucus so that it can be expelled, so that the patient can better breathe. In my experience, this is particularly crucial at night, because if the nose is blocked, you will breathe through the mouth and end up having a sore throat, and generally not sleep as well.
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Instead of using acetylcysteine, the UK uses other less efficaceous medicaments, such as nose sprays, which don't work as well through the night. They aren't as useful once the nose is already blocked. And they are more annoying to use, which means that people may forget or use them less.
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## Part II: Cost-effectiveness analysis
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I work as a forecaster, not as a doctor or as a medical researcher. So there are surely factors I'm missing. For instance, maybe living for two milenia under lousy weather has maybe made the population of Britain more immune to having blocked noses, and this could mean that nose sprays are a better tradeoff than acetylcysteine. I really wouldn't know, though it would surprise me.
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Still, as a forecaster I can offer the following estimation:
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Per the [NHS inform website](https://www.nhsinform.scot/illnesses-and-conditions/infections-and-poisoning/common-cold#colds-in-children):
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> Children get colds far more often than adults. While adults usually have two to four colds a year, children can catch as many as 8 to 12.
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According to the [latest data from the Office of National Statistics](https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/bulletins/annualmidyearpopulationestimates/mid202), the population pyramid of the UK looks as follows:
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meaning that there are 67,081,234 people, of which 20.1% (13,468,262) is under 16. I also estimate that acetylcysteine makes an illness somewhere between 1% and 10% better.
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Putting this together, I can [estimate the following](https://www.squiggle-language.com/playground#code=eNqFkV9rwjAUxb%2FKpbChnfPfnAPBR5%2FGXiZ7C4TYxBpIb7r0dlLE775E7aa1c2%2Fl9OR3TnJ2UbGx22WZZcJV0YxcqXoHaSE1WVcrGjVpYZafpU5To5bkNKbRLGI4GMCiIJ0JUrAqnVQIdg1SF0oUimFu89II0ha5XfOPV5jD9OXN687m1h30ZKONdP7cHIb98XAEHjke9kd3DMmSMFzI0hBPrJEFz5XjlRLOmztjIAuTLsRwlRJDZwSP0JLSram10AKeBPBo%2FAe5BfrDDChPuNH7AW7GM5Sl%2B8kL%2F8KLHfuEh5GiKuq08M23mja177xEDE3QeclD1m%2FTBmkAT9Nnhpfj6iwXCYVxRaKoMklVkNKowGLb8N7u7JfKFNKR3DgVxvZb%2B4v5qRmmQmPByfKV4htxeZlT2Rj%2BYZ4avysqHcJ96JIbUTHcMYQr3uxK6QVbo8isKXjTnmG0%2FwYbRy4m):
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|
||||
```
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// Estimate burden of disease
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population_of_UK = 67M
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proportion_children = 0.201 // 20.1%
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total_adult_colds_per_year = (2 to 4) * population_of_UK * (1 - proportion_children)
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total_children_colds_per_year = (4 to 12) * population_of_UK * proportion_children
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total_colds = total_adult_colds_per_year + total_children_colds_per_year
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duration_of_cold = 6 to 12 // days
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total_days_with_cold = total_colds * duration_of_cold
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total_cold_years = total_days_with_cold / 365
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// Estimate impact of acetylcysteine on burden of disease
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improvement_with_acetylcysteine = 0.01 to 0.1
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gains_to_be_had = total_cold_years * improvement_with_acetylcysteine
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// Return & display
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{
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total_cold_years: total_cold_years,
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gains_to_be_had: gains_to_be_had,
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}
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||||
```
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That is, I arrive at an estimate of 6M (1.7M to 9.2M) cummulative person-years spent having a cold in Britain:
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and a potential improvement from adopting acetylcysteine of 250,000 (53,000 to 640,000) "quality-adjusted-sickness-years"—an intutitive, ad-hoc unit that I just made up:
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The weakness of the method is that my subjective estimates of the 1% to 10% quality of life improvement might be off, or that my estimates of how often people are sick might be inaccurate—6M years of cold per year does seem a bit high. I'm also not really familiar with how potential alternatives, such as carbocisteine, are used in the UK. Still, I think that this rough calculation does show that having better medicaments is of great importance. And the Spanish doctors I've spoken expressed shock and disbelief that acetylcysteine was not available in the UK.
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But while an abstract argument may have been made, the action and followup remains. And it falls on the brave and hardworking souls at the [MHRA](https://www.gov.uk/government/organisations/medicines-and-healthcare-products-regulatory-agency) to send [a Message to Garcia](https://courses.csail.mit.edu/6.803/pdf/hubbard1899.pdf): Legalize acetylcysteine.
|
||||
|
||||
---
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||||
|
||||
Sources
|
||||
|
||||
https://www.nhs.uk/medicines/carbocisteine/#:~:text=A%20mucolytic%20helps%20you%20cough,chronic%20obstructive%20pulmonary%20disease%20(COPD)
|
||||
https://www.cochrane.org/CD003124/ARI_acetylcysteine-and-carbocysteine-to-treat-acute-upper-and-lower-respiratory-tract-infections-in-children-without-chronic-broncho-pulmonary-disease
|
||||
https://www.medicines.org.uk/emc/product/2916/smpc
|
||||
https://www.gov.uk/government/organisations/medicines-and-healthcare-products-regulatory-agency
|
||||
https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/bulletins/annualmidyearpopulationestimates/mid202
|
||||
https://www.boots.com/sitesearch?searchTerm=mucolytics
|
||||
https://www.drugs.com/drug-interactions/acetylcysteine-with-fenesin-dm-97-0-846-8166.html
|
||||
https://www.amazon.co.uk/N-Acetyl-Cysteine-Nutritional-Supplements/b?ie=UTF8&node=5977697031
|
||||
https://www.dbth.nhs.uk/wp-content/uploads/2017/10/Patient-Information-Leaflet-Acetylcysteine.pdf
|
||||
https://bnf.nice.org.uk/drugs/acetylcysteine/
|
||||
https://www.waymade.co.uk/contact-us/
|
||||
https://www.medicines.org.uk/emc/product/11366/smpc
|
||||
https://www.medicines.org.uk/emc/product/11366/smpc
|
||||
https://www.boots.com/boots-pharmaceuticals-effervescent-powder-10-sachets-10049868
|
||||
https://www.waymade.co.uk/wp-content/uploads/2021/05/PIL-Acetylcysteine-200mg.pdf
|
||||
https://mhraproducts4853.blob.core.windows.net/docs/1a9869bfeac300ad6cb531ff4434f1cf90243624
|
||||
https://products.mhra.gov.uk/search/?search=acetylcysteine&page=1&ter=UK&rerouteType=0
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[^1]: As in, "Isn't the weather nice today, darling?"
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Sometimes you give to the commons, and sometimes you take from the commons
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==========================================================================
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Sometimes you give to the commons, and sometimes you trade from the commons. And through this giving and taking, people are able to smooth consumption. This is good because getting more ressources from the commons when you temporarily have fewer of them is more positive than giving ressources away when you temporarily have more of them. <figure> <img src="https://i.imgur.com/eiwMyEI.jpg"><br><figcaption> Engraving depicting the curse of [Tantalus](https://en.wikipedia.org/wiki/Tantalus) </figcaption> </figure>
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Anyways, a phenomenon I've noticed is that sometimes, you can only give to the commons, but you can't take from the commons. This is dysfunctional, and defeats the whole purpose of the commons.
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Some examples, vaguely based on real life:
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- You generally have thoughtful opinions, but sometimes you make mistakes. Your aggregate effect is to make a group's models of the world better. One day you have an opinion that is wrong, and people pile on against you, without remembering previous times that you added information to the shared pool.
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- You generally give emotional support to people. But when you need emotional support, people don't give it to you.
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- You are glad to help people with your time, but when you need other people to lend you their time, they don't.
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- There is a shared pool of ressources that status-poor people are expected to fill, and high-status people are welcome to partake of.
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- Taking from the commons is socially punished, such that people *can't even think* of the idea of taking from the commons as an option that they have.
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Overall, there might be reasons for these kinds of dynamics. For example:
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- maybe there are types of people who would predictably take too much from the commons, and a group prevents those kinds of people from taking any part of the commons, as a preventative measure. Maybe people can smell the desesperation.
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- Or maybe there was a veil-of-ignorance type of deal going on, where some people only give to the commons, but would have received if they had had worse luck.
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- Or maybe there is a totally reasonable period between where one starts giving to the commons and when one can start taking from it, to disallow free-riders.
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But in practice, I think that the reasonable explanations aren't what's going on. And instead there are really weird effects where "for he that hath, to him shall be given: and he that hath not, from him shall be taken even that which he hath". So now, when I see this kind of dynamic around a supposed commons, I tend to run. And after seeing this kind of dynamic happening a few times, I've become more sympathetic about a cluster of ideas around self-sufficiency and libertarianism.
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@ -0,0 +1,79 @@
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Are flimsy evaluations worth it?
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================================
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I recently received a bit of grief over a [brief evaluations of the impact of the top-10 billionnaires](https://nunosempere.com/blog/2022/10/21/brief-evaluations-of-top-10-billionnaires/). It seems possible that this topic is worth discussing. In what follows I outline a few non-exhaustive considerations, as well as a few questions of interest.
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<figure><img src="https://imgs.xkcd.com/comics/duty_calls.png" class="img-frontpage-center"><br><figcaption>"Duty Calls"
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, by <a href="https://xkcd.com/386/">xkcd</a></figcaption></figure>
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### Value of flimsy evaluations
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Right now, I see the value of flimsy evaluations or estimations as coming from:
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#### 1. Value of experimentation
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There are many things we don't have estimates or evaluations for. Trying different evaluation methods and topics can be informative about which are more valuable. Individual flimsy evaluations can serve as a proof of concept that can be built upon if the preliminary version appears valuable, and as testing grounds for new evaluation methodologies.
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#### 2. Flimsy evaluations considered better than no evaluation
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When estimating a probability or a quantity, sometimes a quick BOTEC (back of the envelope calculation) or a Fermi estimate might be worth having despite its imprecision, because there isn't time or it isn't worth the effort to conjure a more complex estimate.
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For evaluations, oftentimes the tradeoff isn't between a flimsy evaluation and a more accurate in-depth evaluation, but rather between a flimsy evaluation and no evaluation at all.
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In particular, I don't think that the case of a ranking of billionnaires was that important. But the case of evaluations of EA organizations is. For example, a longstanding [annual evaluation of AI safety organizations](https://forum.effectivealtruism.org/posts/qdKhLcJmGQuYmzBoz/larks-s-shortform?commentId=e4h2yjCrK9kncfGTf) by Larks is not happening partly because it would be too expensive to produce. But then in that case we are getting no evaluation rather than an flimsier evaluation.
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|
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#### 3. Less sure: The world being complicated enough that epistemics is for now a community effort
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|
||||
I consider myself a reasonably knowledgeable individual, but I still regularly read things in the EA Forum and elsewhere that surprise me. Similarly, when forecasting, one usually gets a better result when combining different individual perspectives.
|
||||
|
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Adjacently, [Cunningham's law](https://meta.wikimedia.org/wiki/Cunningham%27s_Law) states that:
|
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|
||||
> the best way to get the right answer on the internet is not to ask a question; it's to post the wrong answer
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||||
So it doesn't seem crazy that for a given number of hours of research, a better answer can be found by posting a flimsy evaluation and relying on commenters to point flaws that would have been hard for the author to identify on their own.
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|
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This feels true, but too adversarial for my taste. If I was relying on this, I would explicitly signpost it.
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|
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### Disvalue of flimsy evaluation
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|
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#### 1. Reduced epistemics
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|
||||
In a previous post, a commenter mentioned:
|
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|
||||
> I think posting this was probably net negative EV but it was really funny
|
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> ...
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> Your methodology looks pretty flimsy but it looks like other EAs are taking it seriously
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> ...
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> I think the harm from posting things with flimsy methodology and get a lot of upvotes/uncritical comments is something like "lower epistemic rigour on the forum in general", rather than this article in particular causing a great deal of harm. I think the impact of this article whether positive or negative is likely to be small.
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|
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It's possible that factors such as these could be present for flimsy evaluations.
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|
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#### 2. People and organizations are really touchy about evaluations
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|
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People and organizations tend to get a bit angsty when being evaluated. I think this is a real cost. I also think that generally, it's a cost worth paying for communities to have better models of the world. But for very flimsy evaluations, it's very possible that the cost is just not worth paying.
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|
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#### 3. Evaluations having some chance of error
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|
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Evaluations have some rate of error that rises the flimsier they are. It's possible that negative errors are fairly harmful, e.g., by reducing an organization's ability to fundraise through no fault of their own.
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|
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### Discussion
|
||||
|
||||
Some questions:
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||||
|
||||
1. In which context are flimsy evaluations worth it?
|
||||
2. How should one signal that an evaluation could be flimsy?
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||||
3. Is there inflation of words going on? Open Philanthropy uses "shallow evaluations" for documents that can be a bit comprehensive
|
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4. What is the expected error rate before it's not worth publishing a flimsy evaluation? 1 in 20 seems to low, 1 in 2 too high.
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### Personal thoughts
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|
||||
Perhaps one likely conclusion could be that flimsy evaluations might be valuable if they clearly signal how much research has gone into them, and give an accurate impression of how flimsy they are.
|
||||
|
||||
One possible way of doing this would be to have a prediction about what the expected error rate is. For instance, one could have a prediction like: "I expect that there is a 5% chance of an eggregious error that switches the main conclusion, and 1 to 4 minor errors that flip secondary considerations".
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||||
|
||||
---
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||||
|
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<section id="isso-thread">
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<noscript>Javascript needs to be activated to view comments.</noscript>
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</section>
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@ -0,0 +1,815 @@
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||||
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Brief thoughts on my personal research strategy
|
||||
===============================================
|
||||
|
||||
Here are a few estimation related things that I can be doing:
|
||||
|
||||
1. In-house longtermist estimation: I estimate the value of speculative projects, organizations, etc.
|
||||
2. Improving marginal efficiency: I advise groups making specific decisions on how to better maximize expected value.
|
||||
3. *Building up estimation capacity*: I train more people, popularize or create tooling, create templates and acquire and communicate estimation know-how, and make it so that we can "estimate all the things".
|
||||
|
||||
Now, within the [Quantified Uncertainty Research Institute](https://quantifieduncertainty.org/) I have been kind of trying to do something like this:
|
||||
|
||||
<img src="https://i.imgur.com/jvSUjWX.png" class="img-frontpage-center">
|
||||
|
||||
That is, I have in theory been trying to directly aim for the jugular of growing evaluation capacity directly. I believe that this is valuable because once that capacity exists, it can be applied to estimate the many things for which estimation is currently unfeasible. However, although I buy the argument in the abstract, I have been finding that a bit demotivating. Instead, I would like to be doing something like:
|
||||
|
||||
|
||||
|
||||
I think I have the strong intuition that producing value in between scaling produces feedback that is valuable and that otherwise can't be accessed just by aiming for scaling. As a result, I have been trying to do things which also prove valuable in the meantime. This might have been to the slight irritation of my boss, who believes more in going for the yugular directly. Either way, I also think I will experience some tranquility from being more intentional about this piece of strategy.
|
||||
|
||||
In emotional terms, things that are aiming solely for scaling—like predicting when [a long list of mathematical theorems will be solved](https://en.wikipedia.org/wiki/List_of_unsolved_problems_in_mathematics)—feel "dry", "unengaging", "a drag", "disconnected", or other such emotional descriptors.
|
||||
|
||||
I can think of various things that could change my mind and my intuitions about this topic, such as:
|
||||
|
||||
- Past examples of people successfully aiming for an abstract goal and successfully delivering
|
||||
- An abstract reason or intuition why going for scaling is such that it's worth skipping feedback loops
|
||||
- etc.
|
||||
|
||||
<p><section id="isso-thread">
|
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<noscript>Javascript needs to be activated to view comments.</noscript>
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</section></p>
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||||
Tracking the money flows in forecasting
|
||||
=======================================
|
||||
|
||||
This list of forecasting organizations includes:
|
||||
|
||||
- A brief description of each organization
|
||||
- A monetary estimate of value. This can serve as a rough but hard-to-fake proxy of value. Sometimes this is a flow (e.g., budget per year), and sometimes this is an estimate of total value (e.g., valuation).
|
||||
- A more subjective, rough, and verbal estimate of how much value the organization produces. <p><figure><img src="https://i.imgur.com/gqCTHMq.png" class="img-frontpage-center"><br><figcaption>DALLE: "crystal ball surrounded by money, photorealistic"</figcaption></figure></p>
|
||||
|
||||
This started as a breadth first evaluation of the forecasting system, and to some extent it still is, i.e., it might be useful to get a rough sense of the ecosystem as a whole. After some discussion on whether very rough evaluations [are worth it](https://nunosempere.com/blog/2022/10/27/are-flimsy-evaluations-worth-it/) ([a](http://web.archive.org/web/20221031125900/https://nunosempere.com/blog/2022/10/27/are-flimsy-evaluations-worth-it/)), people who prefer their evaluations to have a high threshold of quality and polish might want to either ignore this post or just pay attention at the monetary estimates.
|
||||
|
||||
## Larger entities
|
||||
|
||||
### Flutter Entertainment
|
||||
|
||||
***What it is***: [Flutter Entertainment](https://en.wikipedia.org/wiki/Flutter_Entertainment) ([a](http://web.archive.org/web/20221013031739/https://en.wikipedia.org/wiki/Flutter_Entertainment)) is one of the largest gambling companies. It was created from the merger of [Betfair](https://en.wikipedia.org/wiki/Betfair) ([a](http://web.archive.org/web/20220913120222/https://en.wikipedia.org/wiki/Betfair)) and [Paddy Power](https://en.wikipedia.org/wiki/Paddy_Power) ([a](http://web.archive.org/web/20220809000831/https://en.wikipedia.org/wiki/Paddy_Power)). It has a large presence in the EU and UK.
|
||||
|
||||
***Monetary value (market cap)***: It has a ~$20B (~£18B) market cap.
|
||||
|
||||
***Social value***: Betfair and Paddy Power have some markets with public utilities, e.g., around elections, but focus mostly on sports. However, its social value is otherwise low or negative---e.g., by enabling or creating gambling addicts.
|
||||
|
||||
***Note***: There are many other betting companies, e.g., Draft Kings, which I will not review here because they tend to focus mostly on sports, though I will note that they tend to have a high market capitalization.
|
||||
|
||||
### Kalshi
|
||||
|
||||
***What it is***: Kalshi is a US company which aims to provide [prediction markets](https://en.wikipedia.org/wiki/Prediction_market) ([a](http://web.archive.org/web/20221028173838/https://en.wikipedia.org/wiki/Prediction_market)) to US consumers while complying and shaping US regulations on this topic.
|
||||
|
||||
***Monetary value (VC funding, valuation)***: [Kalshi](https://kalshi.com/) ([a](http://web.archive.org/web/20221101033945/https://kalshi.com/)) has received ~$30M in VC [funding](https://www.crunchbase.com/organization/kalshi/company_financials). My sense is that it's probably worth much more than that ($100M+) due to being the first prediction market regulated by the CFTC. Previous markets---like PredictIt or Iowa Electronic Markets---were only operating with a no-action letter.
|
||||
|
||||
***Social value***: There has been a bit of drama around Kalshi recently. In recent times, the CFTC withdrew its letter of no action from PredictIt, and fined and banned Polymarket from operating in the US. These are two of Kalshi's competitors, and my sense is that Kalshi probably contributed to these events. As such, I'd estimate Kalshi's impact so far to probably be negative, on account of these anti-competitive practices. In the future, however, if Kalshi contributes to the adoption of prediction markets in the US, and this leads to better decision-making, it could end up having a large positive value. It is also possible that their contracts have value as hedging instruments.
|
||||
|
||||
### Metaculus
|
||||
|
||||
***What it is***: [Metaculus](https://www.metaculus.com/) is a forecasting site and a public benefit corporation that spearheads forecasting initiatives.
|
||||
|
||||
***Monetary value (grant flow)***: Recently, Open Philanthropy granted them [$5.5M](https://www.openphilanthropy.org/grants/?q=Metaculus) ([a](https://web.archive.org/web/20221106215746/https://www.openphilanthropy.org/grants/?q=Metaculus)). With a 5% discount rate, and asuming the $5.5M grant is for 2 years will be indefinitely renewed, this makes[^1] Metaculus' discounted cashflow be worth $55M. Metaculus previously received [$308k](https://funds.effectivealtruism.org/funds/payouts/may-august-2021-ea-infrastructure-fund-grants) ([a](http://web.archive.org/web/20220804053231/https://funds.effectivealtruism.org/funds/payouts/may-august-2021-ea-infrastructure-fund-grants)) from the EA infrastructure Fund in 2021 and [$65k](https://funds.effectivealtruism.org/funds/payouts/april-2020-long-term-future-fund-grants-and-recommendations) ([a](http://web.archive.org/web/20220806101148/https://funds.effectivealtruism.org/funds/payouts/april-2020-long-term-future-fund-grants-and-recommendations)) from the EA Long-term Future Fund in 2020.
|
||||
|
||||
[^1]: This form of discounted cash flow might be useful for some very rough comparison between for-profits and not-for-profits. But they really are not the same.
|
||||
|
||||
***Social value***: With its $5.5M Open Philanthropy grant, Metaculus has been growing its already [12-person-strong team](https://www.metaculus.com/about/) ([a](http://web.archive.org/web/20220925082358/https://www.metaculus.com/about/)), and is hiring for [a number of positions](https://apply.workable.com/metaculus/) ([a](http://web.archive.org/web/20221105123354/https://apply.workable.com/metaculus/)). So it's one of the EA forecasting organizations most able to scale and deploy human capital in terms of sheer number. The community which makes up its forecasting site is also reasonably strong.
|
||||
|
||||
### Polymarket
|
||||
|
||||
***What it is:*** Polymarket is a crypto prediction market. It combined various technologies: [magic.link](https://magic.link/), [Polygon](https://polygon.technology/) ([a](http://web.archive.org/web/20221106192328/https://polygon.technology/)) and the [Gnosis conditional contract](https://blog.gnosis.pm/omen-and-the-next-generation-of-prediction-markets-2e7a2dd604e) in order to build a usable crypto prediction market at a time where the previous iteration, Augur, had become unusable in practice due to high fees on the Ethereum blockchain.
|
||||
|
||||
***Monetary value (VC funding)***: It has received [$4M in VC funding](https://www.crunchbase.com/organization/polymarket) ([a](http://web.archive.org/web/20220428173056/https://www.crunchbase.com/organization/polymarket)), according to Crunchbase. It's unclear to me what its valuation is.
|
||||
|
||||
***Social value***: Polymarket has had a range of public interest markets, on politics, COVID infection numbers, or the Russian invasion of Ukraine.
|
||||
|
||||
### Cultivate Labs
|
||||
|
||||
***What it is***: Cultivate Labs is a company which offers infrastructure for forecasting tournaments. They currently host Good Judgment Open, INFER, the Cosmic Bazaar, and a Czech Prediction platform---and probably a few more platforms that I don't know of.
|
||||
|
||||
***Monetary value (valuation)***: I'm pretty uncertain about how much they are worth, and I'd give a guess of $5M to $80M.
|
||||
|
||||
***Social value***: I used to view them as a bit clunky and outdated, but I've come to respect that they are one of the only few groups willing to offer a full package, with support for multiple years.
|
||||
|
||||
### Good Judgment
|
||||
|
||||
***What it is***: Good Judgment" I mean the set of organizations which administer the public prediction market tournament [GJOpen](https://www.gjopen.com/) ([a](http://web.archive.org/web/20221101071604/https://www.gjopen.com/)), and provide forecasts and training through the [Good Judgment brand](https://goodjudgment.io/) ([a](http://web.archive.org/web/20221021191540/https://goodjudgment.io/))
|
||||
|
||||
***Monetary value (valuation)***: I'm fairly uncertain how much they would be worth. Maybe a very wide interval would be $3M to $50M.
|
||||
|
||||
***Social value***: Good Judgment has helped build awareness of superforecasting practices. Their forecasts provided to their subscribers have probably influenced the decisions of their readers and of those who commissioned them. Their training at government and corporations probably marginally increased the decision quality of clients, but it's unclear to me to what extent and for how long.
|
||||
|
||||
### Manifold Markets
|
||||
|
||||
***What it is:*** Manifold Market is a play-money prediction market site with a really nice user experience.
|
||||
|
||||
***Monetary value (VC funding, valuation):*** Manifold Market has received $2M in funding, ([$2M in seed funding](https://manifold.markets/ManifoldMarkets/will-manifold-raise-2m-in-seed-fund) ([a](http://web.archive.org/web/20221031122017/https://manifold.markets/ManifoldMarkets/will-manifold-raise-2m-in-seed-fund)), of which $[1M from the FTX Future Fund's regrantor program](https://ftxfuturefund.org/our-regrants/?_search=manifold%20markets) ([a](https://web.archive.org/web/20221106215835/https://ftxfuturefund.org/our-regrants/?_search=manifold%20markets))), at a $15M valuation. They have a superb software engineering team, and one of the fastest development speeds, but on the other hand their user acquisition has been a bit [slow](https://manifold.markets/stats) ([a](http://web.archive.org/web/20221031003326/https://manifold.markets/stats)). My sense is that their valuation would be somewhere between $10M and $50M
|
||||
|
||||
***Social value:*** So far, by making an eminently usable site, they allow users to signal their beliefs and challenge others to do the same. They have also hosted a few tournaments, and at least the [Clearer Thinking tournament](https://manifold.markets/tournaments) ([a](https://web.archive.org/web/20221106215934/https://manifold.markets/tournaments)) was probably decision-relevant. But their value is linked to their future growth.
|
||||
|
||||
### Insight Prediction
|
||||
|
||||
***What it is***: [Insight Prediction](https://insightprediction.com/) ([a](http://web.archive.org/web/20221101064147/https://insightprediction.com/)) is a new prediction market which accepts crypto.
|
||||
|
||||
***Monetary value (valuation)***: This would depend on their usage numbers, which aren't public. I'd say it's worth at least $5M, but whether the upper bound is $50M or $500M probably depends on the team's execution over the coming year.
|
||||
|
||||
***Social value***: It is one of the few prediction sites which hosts real-money somewhat liquid markets on the Ukraine-Russia conflict.
|
||||
|
||||
### Tetlock's research group
|
||||
|
||||
***What it is***: Tetlock's research group is a loose collaboration of academics working in collaboration with [Phil Tetlock](https://scholar.google.com/citations?user=CJjf6H0AAAAJhttps://scholar.google.com/citations?user=CJjf6H0AAAAJ&hl=en&oi=ao) ([a](https://web.archive.org/web/20221106220007/https://scholar.google.com/citations?user=CJjf6H0AAAAJhttps://scholar.google.com/citations?user=CJjf6H0AAAAJ&hl=en&oi=ao))---author of the ***Superforecasting*** book.
|
||||
|
||||
***Monetary value (valuation, grant flow)***: They have received at least $2M from Open Philanthropy ([1](https://www.openphilanthropy.org/grants/university-of-pennsylvania-philip-tetlocks-making-conversations-smarter-faster-forecasting-project/) ([a](http://web.archive.org/web/20221017005338/https://www.openphilanthropy.org/grants/university-of-pennsylvania-philip-tetlocks-making-conversations-smarter-faster-forecasting-project/)), [2](https://www.openphilanthropy.org/grants/university-of-pennsylvania-philip-tetlock-on-forecasting/) ([a](http://web.archive.org/web/20221017005338/https://www.openphilanthropy.org/grants/university-of-pennsylvania-philip-tetlock-on-forecasting/))), and probably more than that through the University of Pennsylvania. It's unclear to me how much to value a research group, but I'd give a ballpark of $1M to $20M.
|
||||
|
||||
***Social value***: Tetlock's work on expert political judgment and on superforecasting was seminal work that kick-started the current forecasting community. In [recent times](https://scholar.google.com/citations?hl=en&user=CJjf6H0AAAAJ&view_op=list_works&sortby=pubdate), it seems likely that work stemming from their [work on estimating existential risk](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4001628) ([a](http://web.archive.org/web/20220812234335/https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4001628)) could be action-guiding for large organizations and governmental bodies, but most of the output from that research direction hasn't been published yet.
|
||||
|
||||
### PredictIt
|
||||
|
||||
***What it is***: PredictIt is a US-based prediction market. Historically, a community of sharp prediction market players coalesced around it. But in recent times, the CFTC has withdrawn its permission for it to operate in the US.
|
||||
|
||||
***Monetary value (valuation)***: Insight Prediction gives it only an [11% chance of it surviving](https://insightprediction.com/m/18505/will-predictit-survive) ([a](http://web.archive.org/web/20221005064000/https://insightprediction.com/m/18505/will-predictit-survive)). My guess is that it was worth $5M to $50M when alive, implying a $0.5M to $5M valuation right now.
|
||||
|
||||
***Social value***: PredictIt was fairly instrumental in the creation of a passionate if sometimes a bit cut-throat prediction market community. Its future value depends on its survival.
|
||||
|
||||
### Epoch
|
||||
|
||||
***What it is***: [Epoch](https://epochai.org/) ([a](http://web.archive.org/web/20221013183722/https://epochai.org/)) is a research team investigating AI progress.
|
||||
|
||||
***Monetary value (grant flow):*** They have received around [$2M](https://www.openphilanthropy.org/grants/epoch-general-support/) ([a](http://web.archive.org/web/20220922190346/https://www.openphilanthropy.org/grants/epoch-general-support/)) from Open Philanthropy.
|
||||
|
||||
***Social value***: My sense is that their research has usefully informed some OpenPhilanthropy decisions. Open Philanthropy donated around $80M in 2021 to the "Potential Risks from Advanced AI" cause area. A 5% improvement in that decision-making would imply a value of $4M/year.
|
||||
|
||||
### Swift Centre For Applied Forecasting
|
||||
|
||||
***What it is***: The [Swift Centre](https://www.swiftcentre.org/) ([a](http://web.archive.org/web/20221103141937/https://www.swiftcentre.org/)) publishes publicly available forecasts from accurate forecasters on topics of importance.
|
||||
|
||||
***Monetary value (grant flow):*** They have received [$2M from the FTX future fund regranting program](https://ftxfuturefund.org/our-regrants/?_search=Swift) ([a](https://web.archive.org/web/20221106220034/https://ftxfuturefund.org/our-regrants/?_search=Swift)).
|
||||
|
||||
***Social value***: Their [forecasts](https://www.swiftcentre.org/) ([a](http://web.archive.org/web/20221103141937/https://www.swiftcentre.org/)) lead to better decisions. My sense is that the organization is still getting up to speed, and that when running at full operational capacity, they'll be producing many more predictions.
|
||||
|
||||
### Quantified Uncertainty Research Institute (QURI)
|
||||
|
||||
***What it is***: QURI is a "new initiative to advance forecasting and epistemics to improve the long-term future of humanity. We write research and make software." In particular, we are developing [Squiggle](https://squiggle-language.com/), a web-capable programming language for better estimation, and doing research around scalable forecasting and estimation.
|
||||
|
||||
***Monetary value (grants flow)***: Overall we have received around $780k so far ([1](https://survivalandflourishing.fund/sff-2020-h1-recommendations) ([a](http://web.archive.org/web/20220703183731/https://survivalandflourishing.fund/sff-2020-h1-recommendations)), [2](https://survivalandflourishing.fund/sff-2022-h1-recommendations) ([a](http://web.archive.org/web/20220703184038/https://survivalandflourishing.fund/sff-2022-h1-recommendations)), [3](https://survivalandflourishing.fund/sff-2022-h1-recommendations) ([a](http://web.archive.org/web/20220703184038/https://survivalandflourishing.fund/sff-2022-h1-recommendations))), maybe a bit more.
|
||||
|
||||
***Social value***: Most of QURI's value is coming from the value of [Squiggle](https://www.squiggle-language.com/) ([a](http://web.archive.org/web/20221012160532/https://www.squiggle-language.com/)), which people are now starting to use for cost-effectiveness estimates, and from the value of [my own research](https://forum.effectivealtruism.org/users/nunosempere) ([a](http://web.archive.org/web/20221106162329/https://forum.effectivealtruism.org/users/nunosempere)), which might move the Effective Altruism movement more in the direction of using quantified estimates even for speculative stuff.
|
||||
|
||||
***Note***: This is the organization that I work for.
|
||||
|
||||
### Samotsvety Forecasting
|
||||
|
||||
***What it is***: [Samotsvety Forecasting](https://samotsvety.org/) ([a](http://web.archive.org/web/20221025004219/https://samotsvety.org/)) is a somewhat ad-hoc group of highly accurate forecasters of which I am a member.
|
||||
|
||||
***Monetary value***: My sense is that we could find some funder to sacrifice $200k to $5M if our group's existence depended on it. But I haven't actually tried that!
|
||||
|
||||
***Social value:*** We've published a variety of public forecasts, and we find ourselves more and more to be a part of Effective Altruism's general epistemic infrastructure.
|
||||
|
||||
### Czech Priorities
|
||||
|
||||
***What it is***: [Czech Priorities](https://www.ceskepriority.cz/) ([a](http://web.archive.org/web/20221102103806/https://www.ceskepriority.cz/)) is a Czech group working to advance forecasting within the Czech Republic.
|
||||
|
||||
***Monetary value:*** I couldn't find grantmaking information from a quick online search.
|
||||
|
||||
***Social value***: It's possible that they could find generalizable lessons about using forecasting to influence policy and improve decision-making that could then be extended to other countries. But this is not certain.
|
||||
|
||||
### Hedgehog Markets
|
||||
|
||||
***What it is***: [Hedghog Markets](https://hedgehog.markets/) ([a](http://web.archive.org/web/20221010202946/https://hedgehog.markets/)) is a crypto-prediction market
|
||||
|
||||
***Monetary value***: They raised [$3.5M in funding](https://www.crunchbase.com/organization/hedgehog-markets) back in the crypto bull days.
|
||||
|
||||
***Social value***: They have been particularly innovative around no-loss markets, and peer-to-peer markets. But value depends on future usage volume.
|
||||
|
||||
### INFER
|
||||
|
||||
***What it is***: INFER is a prediction market platform which seeks to influence US government policy around transformative technologies.
|
||||
|
||||
***Monetary value***: INFER is hosted at [ARLIS](https://www.arlis.umd.edu/home) ([a](http://web.archive.org/web/20221013111200/https://www.arlis.umd.edu/home)), at the University of Maryland, which received an ~[$8M grant from Open Philanthropy specifically for forecasting](https://www.openphilanthropy.org/grants/applied-research-laboratory-for-intelligence-and-security-forecasting-platforms/) ([a](http://web.archive.org/web/20220808081108/https://www.openphilanthropy.org/grants/applied-research-laboratory-for-intelligence-and-security-forecasting-platforms/)) (over two years, over two platforms), which I find very confusing because I don't think they have deployed much of that capital yet.
|
||||
|
||||
***Social value***: INFER aims to influence decisions by the US government around technology policy. To the extent that this is the case, it could be valuable indeed, but this is hard to evaluate from the outside.
|
||||
|
||||
## Smaller forecasting entities
|
||||
|
||||
### Nathan Young
|
||||
|
||||
[Nathan Young](https://twitter.com/NathanpmYoung) is a smallish forecasting influencer followed by 6k people on Twitter. He recently got [$182k from the FTX Future Fund regranting program](https://ftxfuturefund.org/our-grants/?_search=Nathan%20Young) ([a](https://web.archive.org/web/20221106220135/https://ftxfuturefund.org/our-grants/?_search=Nathan%20Young)) to build a platform for forecasting question generation, an alpha version of which can be seen [here](https://doubtful.app/) ([a](http://web.archive.org/web/20221031061542/https://doubtful.app/)).
|
||||
|
||||
### Sage
|
||||
|
||||
Sage is an organization dedicated to forecasting tooling and research. It got $700k from the FTX Future Fund, and has built the tools on [quantifiedintuitions.org](https://www.quantifiedintuitions.org/pastcasting) ([a](http://web.archive.org/web/20221013134558/https://www.quantifiedintuitions.org/pastcasting)). But it hasn't deployed most of its capital yet.
|
||||
|
||||
### Global Guessing
|
||||
|
||||
[Global Guessing](https://globalguessing.com/) ([a](http://web.archive.org/web/20221031010912/https://globalguessing.com/)) is a forecasting site which made and gathered forecasts early on the Russian invasion of Ukraine, but which seems pretty dormant since then. They have received [~$330k from the FTX Future Fund.](https://ftxfuturefund.org/our-regrants/?_search=Global%20Guessing) ([a](https://web.archive.org/web/20221106220157/https://ftxfuturefund.org/our-regrants/?_search=Global%20Guessing))
|
||||
|
||||
### Social Science Prediction Platform
|
||||
|
||||
The [Social Science Prediction Platform](https://socialscienceprediction.org/) ([a](http://web.archive.org/web/20221013155854/https://socialscienceprediction.org/)) collects forecasts from academics on upcoming papers. This can have a range of public benefits, such as estimates of how surprising results in the social sciences are. Personally I'd be hopeful about the ability to construct a more legible or objective Bayesian prior. I could quickly find that they have received [$346k](https://survivalandflourishing.fund/sff-2022-h1-recommendations) ([a](http://web.archive.org/web/20220703184038/https://survivalandflourishing.fund/sff-2022-h1-recommendations)) from the Survival and Flourishing Fund.
|
||||
|
||||
### Augur
|
||||
|
||||
[Augur](https://augur.net/) was a pioneering prediction market way back when, but isn't up to much these days. Somehow the implied [market cap](https://coinmarketcap.com/currencies/augur/) ([a](http://web.archive.org/web/20220619100141/https://coinmarketcap.com/currencies/augur/)) of their coin is still $74M (## ?!?).
|
||||
|
||||
### Confido
|
||||
|
||||
[Confido](https://confido.tools/) ([a](http://web.archive.org/web/20220711161543/https://confido.tools/)) is a three-person strong Czech organization which develops forecasting tooling. They received $190k from the [FTX Future Fund regranting program](https://ftxfuturefund.org/our-regrants/?_search=Confido) ([a](https://web.archive.org/web/20221106220218/https://ftxfuturefund.org/our-regrants/?_search=Confido)).
|
||||
|
||||
### Hypermind
|
||||
|
||||
[Hypermind ](https://www.hypermind.com/en/) ([a](http://web.archive.org/web/20221015015526/https://www.hypermind.com/en/))is a French forecasting organization which hosts some low-stakes markets with play money but real-money rewards.
|
||||
|
||||
### Replication Markets
|
||||
|
||||
[Replication Markets](https://replicationmarkets.com/) ([a](http://web.archive.org/web/20220930085636/https://replicationmarkets.com/)) was a super-interesting experiment in having forecasters predict the outcome of replications. It has now ended, and it's uncertain whether the creators will follow up with something as interesting. They had ~$150k in forecaster rewards.
|
||||
|
||||
### PredictionBook
|
||||
|
||||
[PredictionBook ](https://predictionbook.com/) ([a](http://web.archive.org/web/20221027154039/https://predictionbook.com/))is an old, old site used by people to keep track of their probabilities, with no rewards involved. Gwern still [uses it](https://predictionbook.com/users/gwern) ([a](http://web.archive.org/web/20221019042049/https://predictionbook.com/users/gwern)).
|
||||
|
||||
### Gnosis
|
||||
|
||||
Gnosis is much like Augur, but less successful. They also have a [GnosisDAO](https://gnosis.io/gnosisdao/) ([a](http://web.archive.org/web/20220825162029/https://gnosis.io/gnosisdao/)). The implied market cap of their token is worth [$330M](https://gnosis.io/gnosisdao/) ([a](http://web.archive.org/web/20220825162029/https://gnosis.io/gnosisdao/)), based on the [Gnosis safe](https://gnosis-safe.io/) ([a](http://web.archive.org/web/20221101042815/https://gnosis-safe.io/)). But it doesn't do much forecasting these days.
|
||||
|
||||
## Thoughts on this ranking
|
||||
|
||||
### On the money flows
|
||||
|
||||
Money flows are interesting, but flawed as a measure of value. The spending rate or VC money raised does not correspond to the value a project creates. The high valuations for crypto projects could be mostly illusory, and for the case of Gnosis and Augur, they probably are.
|
||||
|
||||
Still, a monetary ranking has some advantages
|
||||
|
||||
- There may be some ambiguity to it, but it allows one to differentiate between <$500k projects, $500k to $5M projects, and $5M to $50M projects.
|
||||
- It has an honesty to it, in that money is a somewhat hard-to-fake signal
|
||||
- It is a fast proxy to estimate
|
||||
|
||||
### On the estimate of social value
|
||||
|
||||
The estimate of social value isn't a quantified estimate, but rather a few lines of observations, and maybe a path to impact. I think I could translate this into a numerical relative value scale, but it would take a fair amount of time.
|
||||
|
||||
### On the different pathways to impact
|
||||
|
||||
I'm seeing mainly three pathways of impact for forecasting projects:
|
||||
|
||||
1. Providing consumer value, e.g., betting markets or hedging utilities that people want to use
|
||||
2. Providing informative markets about events of interest, which can apply pressure to decision-makers inside governments and corporations to make better decisions
|
||||
3. Providing tooling that people making decisions can make and incorporate as part of their decision-making
|
||||
|
||||
I'm mostly bullish on the third option, but this deserves more elaboration.
|
||||
|
||||
### On how this estimate could be improved
|
||||
|
||||
This estimate could be improved by having numerical estiamates of impact. This would allow for comparisons around efficiency &c. It could also be presented better, i.e., in a table, or more creatively, in a map.
|
||||
|
||||
<p><section id="isso-thread">
|
||||
<noscript>Javascript needs to be activated to view comments.</noscript>
|
||||
</section></p>
|
||||
</article>
|
||||
|
||||
|
After Width: | Height: | Size: 227 KiB |
|
After Width: | Height: | Size: 84 KiB |
|
After Width: | Height: | Size: 22 KiB |
@ -0,0 +1,280 @@
|
||||
## Description:
|
||||
|
||||
## Libraries
|
||||
|
||||
### Install
|
||||
# install.packages("ggplot2")
|
||||
# install.packages("readr")
|
||||
|
||||
### Load
|
||||
library("ggplot2")
|
||||
library("readr")
|
||||
library("ggthemes")
|
||||
library("magrittr")
|
||||
library("RColorBrewer")
|
||||
library("ggsci")
|
||||
|
||||
## Data import
|
||||
setwd("/home/loki/Documents/core/ea/fresh/misc/openphil-funding")
|
||||
data <- read.csv("grants.csv", header=TRUE, stringsAsFactors = FALSE)
|
||||
|
||||
## Data cleaning
|
||||
colnames(data)
|
||||
getYear <- function(dateRow){
|
||||
year = strsplit(dateRow, " ")[[1]][2]
|
||||
return(year)
|
||||
}
|
||||
getYear(data$Date[1])
|
||||
as.vector(sapply(data$Date, getYear))
|
||||
df <- list()
|
||||
df$year <- as.vector(sapply(data$Date, getYear))
|
||||
df$amount <- as.vector(sapply(data$Amount, parse_number))
|
||||
df$amount <- ifelse(is.na(df$amount), 0, df$amount)
|
||||
df$area <- as.vector(data$Focus.Area)
|
||||
df <- as.data.frame(df)
|
||||
df$area <- as.vector(data$Focus.Area) # not sure why this line is needed, but things break otherwise
|
||||
# View(df)
|
||||
|
||||
## Classify according to areas
|
||||
areas <- unique(df$area)
|
||||
ea_growth <- c("Effective Altruism Community Growth", "Effective Altruism Community Growth (Global Health and Wellbeing)")
|
||||
global_health <- c("South Asian Air Quality", "Human Health and Wellbeing", "GiveWell-Recommended Charities", "Global Aid Policy", "Global Health & Wellbeing", "Global Health & Development","Science for Global Health")
|
||||
longtermism <- c("Biosecurity & Pandemic Preparedness", "Potential Risks from Advanced AI", "Science Supporting Biosecurity and Pandemic Preparedness", "Longtermism")
|
||||
animal_welfare <- c("Farm Animal Welfare", "Broiler Chicken Welfare", "Cage-Free Reforms", "Alternatives to Animal Products")
|
||||
scientific_research <- c("Transformative Basic Science", "Scientific Research", "Other Scientific Research Areas", "Scientific Innovation: Tools and Techniques")
|
||||
politicy_advocacy <- c("Land Use Reform","Macroeconomic Stabilization Policy", "Criminal Justice Reform", "Immigration Policy")
|
||||
not_other <- c(ea_growth, global_health, longtermism, animal_welfare, scientific_research, politicy_advocacy)
|
||||
other <- areas[!(areas %in% not_other)]
|
||||
|
||||
df$area <- ifelse(df$area %in% ea_growth, "EA Community Building", df$area)
|
||||
df$area <- ifelse(df$area %in% global_health, "Global Health and Wellbeing", df$area)
|
||||
df$area <- ifelse(df$area %in% longtermism, "Longtermism & GCRs", df$area)
|
||||
df$area <- ifelse(df$area %in% animal_welfare, "Animal Welfare", df$area)
|
||||
df$area <- ifelse(df$area %in% scientific_research, "Scientific Research", df$area)
|
||||
df$area <- ifelse(df$area %in% politicy_advocacy, "Policy Advocacy", df$area)
|
||||
df$area <- ifelse(df$area %in% other, "Other", df$area)
|
||||
df$area
|
||||
|
||||
## Aggregate by year and area
|
||||
years <- c(2014: 2022)# as.vector(unique(df$year))
|
||||
num_years <- length(years)
|
||||
area_names <- as.vector(unique(df$area))
|
||||
num_areas <- length(area_names)
|
||||
|
||||
df2 <- list()
|
||||
df2$area <- sort(rep(area_names, num_years))
|
||||
df2$year <- rep(years, num_areas)
|
||||
df2 <- as.data.frame(df2)
|
||||
|
||||
getAmountForYearAreaPair <- function(a_df, target_year, target_area){
|
||||
filter = dplyr::filter
|
||||
# target_year = 2022
|
||||
# target_area = "Longtermism"
|
||||
rows = a_df %>% filter(year == target_year) %>% filter(area == target_area)
|
||||
return(sum(rows$amount))
|
||||
}
|
||||
getAmountForYearAreaPair(df, 2022, "Longtermism & GCRs")
|
||||
|
||||
getAmountForArea <- function(a_df, target_area){
|
||||
filter = dplyr::filter
|
||||
rows = a_df %>% filter(area == target_area)
|
||||
return(sum(rows$amount))
|
||||
}
|
||||
getAmountForArea(df, "Longtermism & GCRs")
|
||||
|
||||
amounts <- c()
|
||||
for(i in c(1:dim(df2)[1])){
|
||||
amount <- getAmountForYearAreaPair(df2, df2$year[i], df2$area[i])
|
||||
amounts <- c(amounts, amount)
|
||||
}
|
||||
df2$amount <- amounts
|
||||
|
||||
## Order by cummulative amount
|
||||
df2$cummulative_amount_for_its_area = sapply(df2$area, function(area) {
|
||||
return(getAmountForArea(df, area))
|
||||
})
|
||||
|
||||
## Plotting
|
||||
title_text="Open Philanthropy allocation by year and cause area"
|
||||
subtitle_text="with my own aggregation of categories"
|
||||
palette = "Classic Red-Blue"
|
||||
direction = -1
|
||||
open_philanthropy_plot <- ggplot(data=df2, aes(x=year, y=amount, fill=area, group = cummulative_amount_for_its_area))+
|
||||
geom_bar(stat="identity")+
|
||||
labs(
|
||||
title=title_text,
|
||||
subtitle=subtitle_text,
|
||||
x=element_blank(),
|
||||
y=element_blank()
|
||||
) +
|
||||
# scale_fill_wsj() +
|
||||
# scale_fill_tableau(dir =1) +
|
||||
# scale_fill_tableau(palette, dir=direction) +
|
||||
# scale_fill_viridis(discrete = TRUE) +
|
||||
# scale_fill_brewer(palette = "Set2") +
|
||||
|
||||
scale_fill_d3( "category20", alpha=0.8) +
|
||||
# scale_fill_uchicago("dark") +
|
||||
# scale_fill_startrek() +
|
||||
scale_y_continuous(labels = scales::dollar_format(scale = 0.000001, suffix = "M"), breaks = c(0:6)*10^8)+
|
||||
scale_x_continuous(breaks = years)+
|
||||
theme_tufte() +
|
||||
theme(
|
||||
legend.title = element_blank(),
|
||||
plot.title = element_text(hjust = 0.5),
|
||||
plot.subtitle = element_text(hjust = 0.5),
|
||||
legend.position="bottom",
|
||||
legend.box="vertical",
|
||||
axis.text.x=element_text(angle=60, hjust=1),
|
||||
legend.text=element_text(size=7, hjust = 0.5)
|
||||
) +
|
||||
guides(fill=guide_legend(nrow=3,byrow=TRUE))
|
||||
open_philanthropy_plot
|
||||
getwd() ## Working directory on which the file will be saved. Can be changed with setwd("/your/directory")
|
||||
height = 5
|
||||
width = 5
|
||||
ggsave(plot=open_philanthropy_plot, "open_philanthropy_grants_stacked.png", width=width, height=height, bg = "white")
|
||||
|
||||
## Including Dustin Moskovitz's wealth
|
||||
coeff <- 10^7*4
|
||||
wealth <- c(6, 8, 12, 15, 18, 12, 14, 19, 14)
|
||||
df2$wealth <- rep(wealth * coeff, num_areas)
|
||||
|
||||
make_fortune_plot <- function(show_fortune_legend = FALSE) {
|
||||
open_philanthropy_plot_with_fortune <- ggplot(data=df2, aes(x=year, y=amount, fill=area, group = cummulative_amount_for_its_area))+
|
||||
geom_bar(stat="identity")+
|
||||
geom_point(
|
||||
aes(x=year, y=wealth), size=2, color="darkblue", shape=4,
|
||||
show.legend=show_fortune_legend
|
||||
)+
|
||||
labs(
|
||||
title=title_text,
|
||||
subtitle=subtitle_text,
|
||||
x=element_blank(),
|
||||
y=element_blank()
|
||||
) +
|
||||
# scale_fill_wsj() +
|
||||
# scale_fill_tableau(dir =1) +
|
||||
# scale_fill_tableau(palette, dir=direction) +
|
||||
# scale_fill_viridis(discrete = TRUE) +
|
||||
# scale_fill_brewer(palette = "Set2") +
|
||||
|
||||
scale_fill_d3( "category20", alpha=0.8) +
|
||||
# scale_fill_uchicago("dark") +
|
||||
# scale_fill_startrek() +
|
||||
scale_y_continuous(
|
||||
labels = scales::dollar_format(scale = 0.000001, suffix = "M"),
|
||||
name="OpenPhil donations",
|
||||
breaks = c(0:5)*10^8,
|
||||
sec.axis = sec_axis(
|
||||
~.*1,
|
||||
name="Dustin Moskovitz's fortune\n(est. Bloomberg)",
|
||||
breaks = seq(0,20,by=5)*coeff,
|
||||
labels = c("$0B", "$5B","$10B","$15B", "$20B")
|
||||
),
|
||||
limits=c(0,8*10^8)
|
||||
)+
|
||||
scale_x_continuous(breaks = years)+
|
||||
theme_tufte() +
|
||||
theme(
|
||||
legend.title = element_blank(),
|
||||
plot.title = element_text(hjust = 0.5),
|
||||
plot.subtitle = element_text(hjust = 0.5),
|
||||
legend.position="bottom",
|
||||
legend.box="vertical",
|
||||
axis.text.x=element_text(angle=60, hjust=1),
|
||||
axis.title.y = element_text(vjust=3, hjust=0.25, size=10),
|
||||
axis.title.y.right = element_text(vjust=3, hjust=0.5, size=10),
|
||||
legend.text=element_text(size=8)
|
||||
) +
|
||||
guides(fill=guide_legend(nrow=4,byrow=TRUE))
|
||||
# open_philanthropy_plot_with_fortune
|
||||
|
||||
height = 6
|
||||
width = 5
|
||||
|
||||
filename = ifelse(
|
||||
show_fortune_legend,
|
||||
"open_philanthropy_plot_with_fortune.png",
|
||||
"open_philanthropy_plot_with_fortune_clean_labels.png"
|
||||
)
|
||||
ggsave(plot=open_philanthropy_plot_with_fortune, filename, width=width, height=height, bg = "white")
|
||||
}
|
||||
|
||||
make_fortune_plot(TRUE)
|
||||
make_fortune_plot(FALSE)
|
||||
|
||||
## Look at the different longtermist areas independently.
|
||||
longtermism <- c("Biosecurity & Pandemic Preparedness", "Potential Risks from Advanced AI", "Science Supporting Biosecurity and Pandemic Preparedness", "Longtermism")
|
||||
|
||||
df3 <- list()
|
||||
df3$year <- as.vector(sapply(data$Date, getYear))
|
||||
df3$amount <- as.vector(sapply(data$Amount, parse_number))
|
||||
df3$amount <- ifelse(is.na(df$amount), 0, df$amount)
|
||||
df3$area <- as.vector(data$Focus.Area)
|
||||
df3 <- as.data.frame(df3)
|
||||
df3$area <- as.vector(data$Focus.Area)
|
||||
df3 <- df3 %>% dplyr::filter(area %in% longtermism)
|
||||
# View(df3)
|
||||
|
||||
years <- c(2014: 2022) # as.vector(unique(df$year))
|
||||
num_years <- length(years)
|
||||
area_names <- longtermism
|
||||
num_areas <- length(area_names)
|
||||
|
||||
df4 <- list()
|
||||
df4$area <- sort(rep(area_names, num_years))
|
||||
df4$year <- rep(years, num_areas)
|
||||
df4 <- as.data.frame(df4)
|
||||
# View(df4)
|
||||
getAmountForYearAreaPair(df3, 2022, "Longtermism")
|
||||
|
||||
amounts <- c()
|
||||
for(i in c(1:dim(df4)[1])){
|
||||
amount <- getAmountForYearAreaPair(df3, df4$year[i], df4$area[i])
|
||||
amounts <- c(amounts, amount)
|
||||
}
|
||||
df4$amount <- amounts
|
||||
df4$cummulative_amount_for_its_area = sapply(df4$area, function(area) {
|
||||
return(getAmountForArea(df3, area))
|
||||
})
|
||||
## Plotting longtermist funding
|
||||
title_text="Open Philanthropy allocation by year and cause area"
|
||||
subtitle_text="restricted to longtermism & GCRs"
|
||||
palette = "Classic Red-Blue"
|
||||
direction = -1
|
||||
open_philanthropy_plot_lt <- ggplot(data=df4, aes(x=year, y=amount, fill=area, group=cummulative_amount_for_its_area))+
|
||||
geom_bar(stat="identity")+
|
||||
labs(
|
||||
title=title_text,
|
||||
subtitle=subtitle_text,
|
||||
x=element_blank(),
|
||||
y=element_blank()
|
||||
) +
|
||||
# scale_fill_wsj() +
|
||||
# scale_fill_tableau(dir =1) +
|
||||
# scale_fill_tableau(palette, dir=direction) +
|
||||
# scale_fill_viridis(discrete = TRUE) +
|
||||
# scale_fill_brewer(palette = "Set2") +
|
||||
|
||||
scale_fill_d3( "category20", alpha=0.8) +
|
||||
# scale_fill_uchicago("dark") +
|
||||
# scale_fill_startrek() +
|
||||
scale_y_continuous(labels = scales::dollar_format(scale = 0.000001, suffix = "M"))+
|
||||
scale_x_continuous(breaks = years)+
|
||||
theme_tufte() +
|
||||
theme(
|
||||
legend.title = element_blank(),
|
||||
plot.title = element_text(hjust = 0.5),
|
||||
plot.subtitle = element_text(hjust = 0.5),
|
||||
legend.position="bottom",
|
||||
legend.box="vertical",
|
||||
axis.text.x=element_text(angle=60, hjust=1),
|
||||
legend.text=element_text(size=7)
|
||||
) +
|
||||
guides(fill=guide_legend(nrow=3,byrow=TRUE))
|
||||
getwd() ## Working directory on which the file will be saved. Can be changed with setwd("/your/directory")
|
||||
height = 5
|
||||
width = 6
|
||||
## open_philanthropy_plot_lt
|
||||
ggsave(plot=open_philanthropy_plot_lt, "open_philanthropy_grants_lt.png", width=width, height=height, bg = "white")
|
||||
@ -0,0 +1,7 @@
|
||||
#!/bin/bash
|
||||
|
||||
cd /home/loki/Documents/core/ea/fresh/misc/openphil-funding
|
||||
|
||||
# wget https://www.openphilanthropy.org/wp-admin/admin-ajax.php?action=generate_grants&nonce=1920c9d172 -O grants.csv
|
||||
# rm admin-ajax.php?action=generate_grants
|
||||
Rscript analysis.R
|
||||
File diff suppressed because it is too large
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|
After Width: | Height: | Size: 35 KiB |
|
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|
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|
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|
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|
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|
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@ -0,0 +1,40 @@
|
||||
# Political ideologies in tension.
|
||||
|
||||
Currently, the various dominant ideological strains (neoliberalism, progressivism, etc.) are each able to point out each other's flaws, but not convince their opponents of their merits. I think that Goodhart's law and analogies to presocratic Greek philosophy are two fruitful lenses through which to view this situation. I conclude that better political technology is needed.
|
||||
|
||||
### The situation
|
||||
|
||||
[Goodhart's law](https://arxiv.org/abs/1803.04585)[^1] points out that when you chase a metric, you will end up getting high scores by breaking the relationship between the metric and what initially made you care about it. For example, a few years ago the Spanish government wanted to reduce deaths from traffic accidents—a laudable goal—and ended up achieving this by changing the calculation methodology for "traffic accident deaths" so as to not include people who died from traffic accidents in the hospital a few days later.
|
||||
|
||||
An enlightened progressive might correctly point out that a similar dynamic applies to letting laissez-faire market capitalism run amok. It will similarly lead to situations which maximize corporations' profits at the cost of human flourishing. On the less harmful side, this will result in addictive games or social media platforms. On the perverse side, this will lead to deals like the [Sumangali form of child labor](https://en.wikipedia.org/wiki/Sumangali_%28child_labour%29), where one side of the trade can't evaluate it and is thus reliably exploited. Therefore, labor protection laws and the paternal hand of the state is needed. To be clear, the argument isn't that laissez-faire market capitalism sometimes produces bad outcomes, but rather that in the absence of regulations, profit will be optimized for *at the expense* of human flourishing, and directly traded-off against it.
|
||||
|
||||
Conversely, neoliberals correctly see that relying on the paternal hand of the state has its own problems, like tremendous inefficiency compared to the private sector, [pork-barrel spending](https://en.wikipedia.org/wiki/Pork_barrel), myopic policies resulting from short election cycles, or corruption of the political process through [revolving doors](https://en.wikipedia.org/wiki/Revolving_door_%28politics%29) and ultimately [regulatory capture](https://en.wikipedia.org/wiki/Regulatory_capture). And market forces can indeed be a potent force to bring prosperity.
|
||||
|
||||
So we are in a situation where each side can make a convincing case that the other is wrong to its own followers, but isn't able to address the objections from the other side. This reminds me of presocratic philosophy, where Thale proposed that everything was made out of water and Anaximenes proposed that everything was made out of air. Like politics today, each could see that the other was wrong, but they couldn't convince the other side.
|
||||
|
||||
What is causing this situation is that both sides are wrong. More specifically, both sides have an [inner rethorical contradiction](https://en.wikipedia.org/wiki/Aporia#Definitions) in that they are strongly pushing for an imperfect mechanism that doesn't always aim for human flourishing, but pretending that the mechanism that they are pushing for will always result in human flourishing, no matter how strongly they push.
|
||||
|
||||
|
||||
|
||||
### Proposed solutions
|
||||
|
||||
At the individual level, a solution to this might involve something like following a Kantian categorical imperative: don't take actions—like profiting at the expense of other people's flourishing—that would make society shittier if other people did them as well. This might apply to personally working for a tobacco company, or personally making annoying advertisements.
|
||||
|
||||
At the societal level, the solution is less clear, and I think requires some ideation. Some solutions which I brainstorm were:
|
||||
- Rely on charismatic political figures who are able to live with the tension between ideologies, and recognize the value in both.
|
||||
- Come up with better mechanisms to align governments and human flourishing
|
||||
- Come up with better mechanisms to align markets and human flourishing
|
||||
- Periodically refound states and institutions to rid them of ideological cruft and realign them with human flourishing
|
||||
- Have more innovation around forms of government, perhaps within smaller states or with entities such as [Próspera](https://prospera.hn)
|
||||
- Read the literature on [regulatory economics](https://en.wikipedia.org/wiki/Regulatory_economics) and related areas.
|
||||
|
||||
Oddly enough, Effective Altruism (EA), a social movement that I am very sympathetic to, doesn't have great answers here. What it recommends is, greatly simplified, "rigorously rank problems in the world and start working on them in order of importance". But this isn't enough to run a state. In fact, it's even worse, because one of the main EA organizations, Open Philanthropy, doesn't trust itself enough to be able to specify "the good", and so employs a kludge called "worldview diversification" in the meantime.
|
||||
|
||||
Other ideologies also have their own ideas here. I like the vision sketched in [State Capacity Libertarianism](https://marginalrevolution.com/marginalrevolution/2020/01/what-libertarianism-has-become-and-will-become-state-capacity-libertarianism.html). My sense is that some recent feminist/progressive anthropology and social science also has the aim of highlighting or conceiving new types of societies in whose image we would improve our own society.
|
||||
|
||||
[^1]: In fact, [Goodhart's law](https://en.wikipedia.org/wiki/Goodhart%27s_law), also known as the [Lucas critique](https://en.wikipedia.org/wiki/Lucas_critique) says something narrower, and it's the
|
||||
|
||||
---
|
||||
|
||||
OP doesn't optimize too far because it doesn't believe it has good measures of the good, and so fears falling prey to Goodhart's law. But it could develop better measures which could allow it to deploy more optimization power.
|
||||
This explains their current attachment to worldview diversification
|
||||
@ -0,0 +1,44 @@
|
||||
Goodhart's law and aligning politics with human flourishing
|
||||
===========================================================
|
||||
|
||||
*Note: Written for someone I've been having political discussions with. For similarly introductory content, see [A quick note on the value of donations](https://nunosempere.com/blog/2022/04/06/note-donations/).*
|
||||
|
||||
The world's major ideologies, like neoliberalism and progressivism, are stuck in a stalemate. They're great at pointing out each other's flaws, but neither side can make a compelling case for itself the other can't poke holes into. To understand why, I want to look to Goodhart's law and presocratic Greek philosophy for insights. Ultimately, though, I think we need better political tools to better align governments and institutions with human flourishing.
|
||||
|
||||
### The situation
|
||||
|
||||
[Goodhart's law](https://arxiv.org/abs/1803.04585) cautions against chasing a metric. When you do, you will end up achieving high scores in a way which undermines the metric's original purpose. For example, a few years ago the Spanish government aimed to reduce deaths from traffic accidents—a worthy goal. But instead of actually saving lives, they simply changed the definition of "traffic accident deaths" to exclude people who died in the hospital days after the accident.
|
||||
|
||||
Progressives see this happening with unregulated capitalism. Without regulation, corporations will prioritize profits, even if it means sacrificing human well-being. For example, companies may create addictive games or social media platforms that monopolize our attention. In more extreme cases, they may exploit workers in ways that are outright abusive, such as the [Sumangali](https://en.wikipedia.org/wiki/Sumangali_%28child_labour%29) form of child labor. This is why we need labor protection laws and state intervention—to prevent such exploitation. The key point is that, without regulation, capitalism will inevitably prioritize profit over people.
|
||||
|
||||
Neoliberals, on the other hand, rightly point out that relying on the state has its own set of problems. Government inefficiency compared to the private sector, [pork-barrel spending](https://en.wikipedia.org/wiki/Pork_barrel), myopic policies resulting from short election cycles, or corruption of the political process through [revolving doors](https://en.wikipedia.org/wiki/Revolving_door_%28politics%29) and ultimately [regulatory capture](https://en.wikipedia.org/wiki/Regulatory_capture) can all have harmful effects. Neoliberals argue that market forces can be a powerful tool for generating prosperity, and that these issues should not be overlooked.[^1]
|
||||
|
||||
[^1]: To be honest, my personal experience has been that neoliberals are able to acknowledge that capitalism is an imperfect systems, but still defend that it's a better system than others humanity has tried. Progressives can also be more or less nuancedly anti-market. So the above two paragraphs are just a quick sketch.
|
||||
|
||||
So each side is skilled at articulating its opponent's weakeness, but is not as able to put forward an impregnable case for its own position. This stalemate is reminiscent of early presocratic philosophy. Thale and Anaximenes, for example, both put forth theories about the elemental composition of the universe—one claiming everything was made of water, the other asserting everything was made of air. Like politics today, each could see that the other was wrong, but they couldn't convince the other side.
|
||||
|
||||
The root of the problem is that both sides of this debate are in the wrong. Each side contains an [inherent rhetorical contradiction](https://en.wikipedia.org/wiki/Aporia#Definitions): they advocate for an imperfect mechanism that doesn't always prioritize human flourishing, while not realizing the degree to which it will not. This is my diagnosis for the current political stalemate.
|
||||
|
||||
|
||||
|
||||
### Proposed solutions
|
||||
|
||||
At the individual level, you could consciously avoid actions that make society shittier, like refusing job opportunities for a tobacco companies, or personally resisting creating irritating advertisements. You could also advocate for stronger social norms that discourage others from engaging in similar behaviors.
|
||||
|
||||
At the societal level, the solution is less clear, and I think requires some ideation. Some solutions which I brainstormed were to:
|
||||
|
||||
- Rely on political figures who can bridge ideological divides and understand the importance of multiple perspectives.
|
||||
- Come up with better mechanisms to align governments and human flourishing
|
||||
- Come up with better mechanisms to align markets and human flourishing
|
||||
- Periodically re-founding states and institutions to rid them of ideological cruft and realign them with human flourishing
|
||||
- Have more innovation around forms of government, perhaps within smaller states or with entities such as [Próspera](https://prospera.hn)
|
||||
- Read the literature on [regulatory economics](https://en.wikipedia.org/wiki/Regulatory_economics) and related areas.
|
||||
|
||||
The Effective Altruism (EA) movement doesn't have great answers here. It suggests to rigorously rank problems in the world and start working on them in order of importance. But this isn't a powerful enough answer to run a state. Other ideologies offer different embryonic approaches of how to proceed. The vision sketched in [State Capacity Libertarianism](https://marginalrevolution.com/marginalrevolution/2020/01/what-libertarianism-has-become-and-will-become-state-capacity-libertarianism.html) presents a libertarianism tweaked to deal with problems requiring large amounts of coordination, but is lacking in detail. My sense is also that some recent feminist/progressive anthropology and social science also has the aim of highlighting or conceiving new types of societies in whose image we would improve our own society.
|
||||
|
||||
All in all, the above analysis seems fairly rough. For one, it at time assumes that people share the same goals, and just differ in the mechanisms they think are best to achieve these common goals. Still, perhaps the above is clarifying to some readers.
|
||||
|
||||
|
||||
<p><section id="isso-thread">
|
||||
<noscript>Javascript needs to be activated to view comments.</noscript>
|
||||
</section></p>
|
||||
@ -0,0 +1,11 @@
|
||||
Example post with comments
|
||||
==========================
|
||||
|
||||
Bla bla
|
||||
|
||||
|
||||
---
|
||||
|
||||
<section id="isso-thread">
|
||||
<noscript>Javascript needs to be activated to view comments.</noscript>
|
||||
</section>
|
||||
@ -0,0 +1,15 @@
|
||||
<form method="post" action="https://listmonk.nunosempere.com/subscription/form" class="listmonk-form">
|
||||
<div>
|
||||
<h3>Subscribe</h3>
|
||||
<input type="hidden" name="nonce" />
|
||||
<p><input type="email" name="email" required placeholder="E-mail" /></p>
|
||||
<p><input type="text" name="name" placeholder="Name (optional)" /></p>
|
||||
|
||||
<p>
|
||||
<input id="82ff8" type="checkbox" name="l" checked value="82ff889c-f9d9-4a45-bf9a-7e2696813021" />
|
||||
<label for="82ff8">nunosempere.com</label>
|
||||
</p>
|
||||
|
||||
<p><input type="submit" value="Subscribe" /></p>
|
||||
</div>
|
||||
</form>
|
||||
Binary file not shown.
@ -1,212 +0,0 @@
|
||||
https://nunosempere.com/blog/
|
||||
https://nunosempere.com/blog/2019/
|
||||
https://nunosempere.com/blog/2019/06/
|
||||
https://nunosempere.com/blog/2019/06/13/
|
||||
https://nunosempere.com/blog/2019/06/13/EA_Mental_Health/
|
||||
https://nunosempere.com/blog/2019/10/
|
||||
https://nunosempere.com/blog/2019/10/04/
|
||||
https://nunosempere.com/blog/2019/10/04/Social_Movements/
|
||||
https://nunosempere.com/blog/2019/10/10/
|
||||
https://nunosempere.com/blog/2019/10/10/Shapley_Values_I/
|
||||
https://nunosempere.com/blog/2019/12/
|
||||
https://nunosempere.com/blog/2019/12/19/
|
||||
https://nunosempere.com/blog/2019/12/19/Amplification_I/
|
||||
https://nunosempere.com/blog/2019/12/20/
|
||||
https://nunosempere.com/blog/2019/12/20/Amplification_II/
|
||||
https://nunosempere.com/blog/2020/
|
||||
https://nunosempere.com/blog/2020/01/
|
||||
https://nunosempere.com/blog/2020/01/15/
|
||||
https://nunosempere.com/blog/2020/01/15/MIT_EDX_Review/
|
||||
https://nunosempere.com/blog/2020/03/
|
||||
https://nunosempere.com/blog/2020/03/01/
|
||||
https://nunosempere.com/blog/2020/03/01/Survey_Making/
|
||||
https://nunosempere.com/blog/2020/03/10/
|
||||
https://nunosempere.com/blog/2020/03/10/Shapley_Values_II/
|
||||
https://nunosempere.com/blog/2020/04/
|
||||
https://nunosempere.com/blog/2020/04/01/
|
||||
https://nunosempere.com/blog/2020/04/01/International_Supply_Chain/
|
||||
https://nunosempere.com/blog/2020/04/30/
|
||||
https://nunosempere.com/blog/2020/04/30/Forecasting_Newsletter_2020_04/
|
||||
https://nunosempere.com/blog/2020/05/
|
||||
https://nunosempere.com/blog/2020/05/31/
|
||||
https://nunosempere.com/blog/2020/05/31/forecasting-newsletter-may-2020/
|
||||
https://nunosempere.com/blog/2020/07/
|
||||
https://nunosempere.com/blog/2020/07/01/
|
||||
https://nunosempere.com/blog/2020/07/01/forecasting-newsletter-june-2020/
|
||||
https://nunosempere.com/blog/2020/08/
|
||||
https://nunosempere.com/blog/2020/08/01/
|
||||
https://nunosempere.com/blog/2020/08/01/forecasting-newsletter-july-2020/
|
||||
https://nunosempere.com/blog/2020/09/
|
||||
https://nunosempere.com/blog/2020/09/01/
|
||||
https://nunosempere.com/blog/2020/09/01/forecasting-newsletter-august-2020/
|
||||
https://nunosempere.com/blog/2020/10/
|
||||
https://nunosempere.com/blog/2020/10/01/
|
||||
https://nunosempere.com/blog/2020/10/01/forecasting-newsletter-september-2020/
|
||||
https://nunosempere.com/blog/2020/11/
|
||||
https://nunosempere.com/blog/2020/11/01/
|
||||
https://nunosempere.com/blog/2020/11/01/forecasting-newsletter-october-2020/
|
||||
https://nunosempere.com/blog/2020/11/10/
|
||||
https://nunosempere.com/blog/2020/11/10/incentive-problems-with-current-forecasting-competitions/
|
||||
https://nunosempere.com/blog/2020/11/15/
|
||||
https://nunosempere.com/blog/2020/11/15/announcing-the-forecasting-innovation-prize/
|
||||
https://nunosempere.com/blog/2020/11/22/
|
||||
https://nunosempere.com/blog/2020/11/22/predicting-the-value-of-small-altruistic-projects-a-proof-of/
|
||||
https://nunosempere.com/blog/2020/12/
|
||||
https://nunosempere.com/blog/2020/12/01/
|
||||
https://nunosempere.com/blog/2020/12/01/an-experiment-to-evaluate-the-value-of-one-researcher-s-work/
|
||||
https://nunosempere.com/blog/2020/12/01/forecasting-newsletter-november-2020/
|
||||
https://nunosempere.com/blog/2020/12/03/
|
||||
https://nunosempere.com/blog/2020/12/03/what-are-good-rubrics-or-rubric-elements-to-evaluate-and/
|
||||
https://nunosempere.com/blog/2020/12/25/
|
||||
https://nunosempere.com/blog/2020/12/25/big-list-of-cause-candidates/
|
||||
https://nunosempere.com/blog/2021/
|
||||
https://nunosempere.com/blog/2021/01/
|
||||
https://nunosempere.com/blog/2021/01/01/
|
||||
https://nunosempere.com/blog/2021/01/01/forecasting-newsletter-december-2020/
|
||||
https://nunosempere.com/blog/2021/01/10/
|
||||
https://nunosempere.com/blog/2021/01/10/2020-forecasting-in-review/
|
||||
https://nunosempere.com/blog/2021/01/13/
|
||||
https://nunosempere.com/blog/2021/01/13/a-funnel-for-cause-candidates/
|
||||
https://nunosempere.com/blog/2021/02/
|
||||
https://nunosempere.com/blog/2021/02/01/
|
||||
https://nunosempere.com/blog/2021/02/01/forecasting-newsletter-january-2021/
|
||||
https://nunosempere.com/blog/2021/02/19/
|
||||
https://nunosempere.com/blog/2021/02/19/forecasting-prize-results/
|
||||
https://nunosempere.com/blog/2021/03/
|
||||
https://nunosempere.com/blog/2021/03/01/
|
||||
https://nunosempere.com/blog/2021/03/01/forecasting-newsletter-february-2021/
|
||||
https://nunosempere.com/blog/2021/03/07/
|
||||
https://nunosempere.com/blog/2021/03/07/introducing-metaforecast-a-forecast-aggregator-and-search/
|
||||
https://nunosempere.com/blog/2021/03/16/
|
||||
https://nunosempere.com/blog/2021/03/16/relative-impact-of-the-first-10-ea-forum-prize-winners/
|
||||
https://nunosempere.com/blog/2021/04/
|
||||
https://nunosempere.com/blog/2021/04/01/
|
||||
https://nunosempere.com/blog/2021/04/01/forecasting-newsletter-march-2021/
|
||||
https://nunosempere.com/blog/2021/05/
|
||||
https://nunosempere.com/blog/2021/05/01/
|
||||
https://nunosempere.com/blog/2021/05/01/forecasting-newsletter-april-2021/
|
||||
https://nunosempere.com/blog/2021/06/
|
||||
https://nunosempere.com/blog/2021/06/01/
|
||||
https://nunosempere.com/blog/2021/06/01/forecasting-newsletter-may-2021/
|
||||
https://nunosempere.com/blog/2021/06/16/
|
||||
https://nunosempere.com/blog/2021/06/16/2018-2019-long-term-future-fund-grantees-how-did-they-do/
|
||||
https://nunosempere.com/blog/2021/06/16/what-should-the-norms-around-privacy-and-evaluation-in-the/
|
||||
https://nunosempere.com/blog/2021/06/24/
|
||||
https://nunosempere.com/blog/2021/06/24/shallow-evaluations-of-longtermist-organizations/
|
||||
https://nunosempere.com/blog/2021/07/
|
||||
https://nunosempere.com/blog/2021/07/01/
|
||||
https://nunosempere.com/blog/2021/07/01/forecasting-newsletter-june-2021/
|
||||
https://nunosempere.com/blog/2021/08/
|
||||
https://nunosempere.com/blog/2021/08/01/
|
||||
https://nunosempere.com/blog/2021/08/01/forecasting-newsletter-july-2021/
|
||||
https://nunosempere.com/blog/2021/09/
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https://nunosempere.com/blog/2021/09/01/
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https://nunosempere.com/blog/2021/09/01/forecasting-newsletter-august-2021/
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https://nunosempere.com/blog/2021/09/01/frank-feedback-given-to-very-junior-researchers/
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https://nunosempere.com/blog/2021/09/20/
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https://nunosempere.com/blog/2021/09/20/building-blocks-of-utility-maximization/
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https://nunosempere.com/blog/2021/10/
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https://nunosempere.com/blog/2021/10/01/
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https://nunosempere.com/blog/2021/10/01/forecasting-newsletter-september-2021/
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https://nunosempere.com/blog/2021/10/22/
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https://nunosempere.com/blog/2021/10/22/an-estimate-of-the-value-of-metaculus-questions/
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https://nunosempere.com/blog/2021/11/
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https://nunosempere.com/blog/2021/11/02/
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https://nunosempere.com/blog/2021/11/02/forecasting-newsletter-october-2021/
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https://nunosempere.com/blog/2021/11/08/
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https://nunosempere.com/blog/2021/11/08/a-model-of-patient-spending-and-movement-building/
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https://nunosempere.com/blog/2021/11/15/
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https://nunosempere.com/blog/2021/11/15/simple-comparison-polling-to-create-utility-functions/
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https://nunosempere.com/blog/2021/11/25/
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https://nunosempere.com/blog/2021/11/25/pathways-to-impact-for-forecasting-and-evaluation/
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https://nunosempere.com/blog/2021/12/
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https://nunosempere.com/blog/2021/12/02/
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https://nunosempere.com/blog/2021/12/02/forecasting-newsletter-november-2021/
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https://nunosempere.com/blog/2021/12/13/
|
||||
https://nunosempere.com/blog/2021/12/13/external-evaluation-of-the-ea-wiki/
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||||
https://nunosempere.com/blog/2021/12/31/
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||||
https://nunosempere.com/blog/2021/12/31/prediction-markets-in-the-corporate-setting/
|
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https://nunosempere.com/blog/2022/
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https://nunosempere.com/blog/2022/01/
|
||||
https://nunosempere.com/blog/2022/01/10/
|
||||
https://nunosempere.com/blog/2022/01/10/forecasting-newsletter-december-2021/
|
||||
https://nunosempere.com/blog/2022/01/27/
|
||||
https://nunosempere.com/blog/2022/01/27/forecasting-newsletter-looking-back-at-2021/
|
||||
https://nunosempere.com/blog/2022/02/
|
||||
https://nunosempere.com/blog/2022/02/03/
|
||||
https://nunosempere.com/blog/2022/02/03/forecasting-newsletter-january-2022/
|
||||
https://nunosempere.com/blog/2022/02/06/
|
||||
https://nunosempere.com/blog/2022/02/06/splitting-the-timeline-as-an-extinction-risk-intervention/
|
||||
https://nunosempere.com/blog/2022/02/08/
|
||||
https://nunosempere.com/blog/2022/02/08/we-are-giving-usd10k-as-forecasting-micro-grants/
|
||||
https://nunosempere.com/blog/2022/02/18/
|
||||
https://nunosempere.com/blog/2022/02/18/five-steps-for-quantifying-speculative-interventions/
|
||||
https://nunosempere.com/blog/2022/03/
|
||||
https://nunosempere.com/blog/2022/03/05/
|
||||
https://nunosempere.com/blog/2022/03/05/forecasting-newsletter-february-2022/
|
||||
https://nunosempere.com/blog/2022/03/10/
|
||||
https://nunosempere.com/blog/2022/03/10/samotsvety-nuclear-risk-forecasts-march-2022/
|
||||
https://nunosempere.com/blog/2022/03/17/
|
||||
https://nunosempere.com/blog/2022/03/17/valuing-research-works-by-eliciting-comparisons-from-ea/
|
||||
https://nunosempere.com/blog/2022/04/
|
||||
https://nunosempere.com/blog/2022/04/01/
|
||||
https://nunosempere.com/blog/2022/04/01/forecasting-newsletter-april-2222/
|
||||
https://nunosempere.com/blog/2022/04/05/
|
||||
https://nunosempere.com/blog/2022/04/05/forecasting-newsletter-march-2022/
|
||||
https://nunosempere.com/blog/2022/04/06/
|
||||
https://nunosempere.com/blog/2022/04/06/note-donations/
|
||||
https://nunosempere.com/blog/2022/04/07/
|
||||
https://nunosempere.com/blog/2022/04/07/openphil-allocation/
|
||||
https://nunosempere.com/blog/2022/04/16/
|
||||
https://nunosempere.com/blog/2022/04/16/optimal-scoring/
|
||||
https://nunosempere.com/blog/2022/04/17/
|
||||
https://nunosempere.com/blog/2022/04/17/simple-squiggle/
|
||||
https://nunosempere.com/blog/2022/05/
|
||||
https://nunosempere.com/blog/2022/05/01/
|
||||
https://nunosempere.com/blog/2022/05/01/ea-forum-lowdown-april-2022/
|
||||
https://nunosempere.com/blog/2022/05/10/
|
||||
https://nunosempere.com/blog/2022/05/10/forecasting-newsletter-april-2022/
|
||||
https://nunosempere.com/blog/2022/05/20/
|
||||
https://nunosempere.com/blog/2022/05/20/infinite-ethics-101/
|
||||
https://nunosempere.com/blog/2022/06/
|
||||
https://nunosempere.com/blog/2022/06/03/
|
||||
https://nunosempere.com/blog/2022/06/03/forecasting-newsletter-may-2022/
|
||||
https://nunosempere.com/blog/2022/06/14/
|
||||
https://nunosempere.com/blog/2022/06/14/the-tragedy-of-calisto-and-melibea/
|
||||
https://nunosempere.com/blog/2022/06/16/
|
||||
https://nunosempere.com/blog/2022/06/16/criminal-justice/
|
||||
https://nunosempere.com/blog/2022/07/
|
||||
https://nunosempere.com/blog/2022/07/04/
|
||||
https://nunosempere.com/blog/2022/07/04/cancellation-insurance/
|
||||
https://nunosempere.com/blog/2022/07/05/
|
||||
https://nunosempere.com/blog/2022/07/05/i-will-bet-on-your-success-or-failure/
|
||||
https://nunosempere.com/blog/2022/07/09/
|
||||
https://nunosempere.com/blog/2022/07/09/maximum-vindictiveness-strategy/
|
||||
https://nunosempere.com/blog/2022/07/12/
|
||||
https://nunosempere.com/blog/2022/07/12/forecasting-newsletter-june-2022/
|
||||
https://nunosempere.com/blog/2022/07/23/
|
||||
https://nunosempere.com/blog/2022/07/23/thoughts-on-turing-julia/
|
||||
https://nunosempere.com/blog/2022/07/27/
|
||||
https://nunosempere.com/blog/2022/07/27/how-much-to-run-to-lose-20-kilograms/
|
||||
https://nunosempere.com/blog/2022/08/
|
||||
https://nunosempere.com/blog/2022/08/04/
|
||||
https://nunosempere.com/blog/2022/08/04/usd1-000-squiggle-experimentation-challenge/
|
||||
https://nunosempere.com/blog/2022/08/08/
|
||||
https://nunosempere.com/blog/2022/08/08/forecasting-newsletter-july-2022/
|
||||
https://nunosempere.com/blog/2022/08/10/
|
||||
https://nunosempere.com/blog/2022/08/10/evolutionary-anchor/
|
||||
https://nunosempere.com/blog/2022/08/18/
|
||||
https://nunosempere.com/blog/2022/08/18/what-do-americans-mean-by-cutlery/
|
||||
https://nunosempere.com/blog/2022/08/20/
|
||||
https://nunosempere.com/blog/2022/08/20/fermi-introduction/
|
||||
https://nunosempere.com/blog/2022/08/31/
|
||||
https://nunosempere.com/blog/2022/08/31/on-cox-s-theorem-and-probabilistic-induction/
|
||||
https://nunosempere.com/forecasting/
|
||||
https://nunosempere.com/gossip/
|
||||
https://nunosempere.com/misc/
|
||||
https://nunosempere.com/misc/poetry/
|
||||
https://nunosempere.com/misc/test/
|
||||
https://nunosempere.com/misc/test/test-git/
|
||||
https://nunosempere.com/misc/test/test-git/README
|
||||
https://nunosempere.com/research/
|
||||
https://nunosempere.com/software/
|
||||
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Reference in new issue