That fair, I made a mathematical error there. The cluster headache math convinces me that a large chunk of total suffering goes to few people there due to lopsided frequencies. Do you have other examples? I particularly felt that the relative frequency of extreme compared to less extreme pain wasn’t well supported.
Your 4 cluster headache groups contribute about equally to the total number of cluster headaches if you multiply group size by # of CH’s. (The top 2% actually contribute a bit less). That’s my entire point. I’m not sure if you disagree?
To the second half of your comment, I agree that extreme suffering can be very extreme and I think this is an important contribution. Maybe we have a misunderstanding about what ‘the bulk’ of suffering refers to. To me it means something like 75-99% and to you it means something like 45% as stated above? I should also clarify that by frequency I mean the product of ‘how many people have it’, ‘how often’ and ‘for how long’.
“the people in the top 10% of sufferers will have 10X the amount, and people in the 99% [I assume you mean top 1%?] will have 100X the amount”
I’m confused, you seem to be suggesting that every level of pain accounts for the _same_ amount of total suffering here.
To elaborate, you seem to be saying that at any level of pain, 10x worse pain is also 10x less frequent. That’s a power law with exponent 1. I.e. the levels of pain have an extreme distribution, but the frequencies do too (mild pains are extremely common). I’m not saying you’re wrong—just that I’ve seen also seems consistent with extreme pain being less than 10% of the total. I’m excited to see more data :)
Aside from my concern about extreme pain being rarer than ordinary pain, I also would find the conclusion that
″...the bulk of suffering is concentrated in a small percentage of experiences...”
very surprising. Standard computational neuroscience decision-making views such as RL models would say that if this is true, animals would have to spend most of their everyday effort trying to avoid extreme pain. But that seems wrong. E. g. we seek food to relieve mild hunger and get a nice taste and not because we once had a an extreme hunger experience that we learned from.
You could argue that the learning from extreme pain doesn’t track the subjective intensity of pain. But then people would be choosing e. g. a subjectively 10x worse pain over a <10x longer pain. In this cause I’d probably say that the subjective impression is misguided or ethically irrelevant, though that’s an ethical judgment.
Thanks. I was actually asking about a different frequency distribution. You’re talking about the frequency of extreme pain among people with extreme pain which has no bearing on the quote above. I’m talking about the frequency of extreme pain experiences among all pain experiences (i. e. is extreme pain it lmuch less prevalent). Hence the example about mild discomfort.
This seems like your core implication. But it requires an argument about intensity distribution and frequency distribution. There’s only arguments about the first one if I haven’t missed anything? To illustrate, I have mild discomfort about 8000s/day on average but extreme pain perhaps 0.02s/day, if I get 1h of extreme pain in my life (and many people don’t get any at all).
Echoing your second point, I had the same reaction.
Great work! I wonder if there are any ways to track quality adjusted engagement since that what we’ve mostly been optimizing for the last few years. E. g. if low-quality page views/joins/listeners are going down it seems hard to compensate with an equal number of high quality ones because they’re harder to create. 80k’s impact adjusted plan changes metric is the only suitable metric I can think of.
PMed you the paywalled review. There seems to be some agreement that evidence transfers between different tendons FYI, e. g. some studies are about Achilles tendons. The specific review on golfer arm (seen by my doc as nearly equivalent to RSI on the hand-facing tendons) is also in my message. If you want to talk to an expert about the evidence you can probably ask to skype him for a fee.
PMed, and yes. The exercise the doc gave me was to hold it with both hands facing down and then alternatingly bend into an inverted / normal u-shape. This hits both flexors and extensors and it’s both eccentric and concentric combined.
Many policies are later revoked and aren’t about trading off present vs future resources (e. g. income redistribution). So those who are still alive when a policy’s effects stop got more than their fair share of voting power under this proposal if I understand correctly. E. g. if I’m 80 when a policy against redistribution comes into effect, and it’s revoked when I die at 84, my 1x vote weighting seems unfair because everyone else was also just affected for 4 years.
Retracted because I’m no longer sure if “then” instead of “the” was intended. I still emphasize that it’s a very nice read!
Very nicely written! Typo: “and the taking action on that basis”
This post seems to be missing the therapy with the best evidence basis—heavy loaded eccentric training. See e. g. https://www.uptodate.com/contents/overview-of-the-management-of-overuse-persistent-tendinopathy (paywalled). The combination with concentric training is almost as well supported and easier to do. The only tool needed is a flexbar. 3x 15 reps twice daily for 3 months should bring results.
The website painscience.com is a great SSC-like read but I’ve found it lacking from time to time, for instance by omitting eccentric training.
I can also recommend a professor in Germany who specializes in tendon problems and charges ca 150eur for a 30-60m session plus email support. I could even imagine him doing a skype session with some convincing but he’ll want to get an ultra sound and strength test. He was recommended to me by a paid service in Germany (betterdoc) that asks a council of medical experts for the leading expert for a desease. The professor website: http://www.sportpraxis-knobloch.de/
Possible ambiguity in the survey question: If the person stops working “for you or anyone for 3 years” that plausibly negates most of their life’s impact, unless they find a great way to build career capital without working for anyone. So with this interpretation, the answers would be something close to the NPV of their life’s impact divided by 3 (ignoring discounting).
Also, did you control for willingness to accept vs pay?
Sorry if this addressed, I skimmed the post.
To cover more content that’s not new but important, you could use a new source on one topic to summarize the state of that topic. I like that papers do this in the introduction and literature review and I think more posts and the like should do it.
Google scholar also lists recommended new papers on its homepage.
Why not just pay Russia an (arguably fair) reparation?
What sort of decision timeline can applicants expect? The existing opportunities are often slow compared to e.g. VC funding which is bad for planning.
Re 4): Correlation or similarity between agents is not really necessary condition for cooperation in the open source PD. LaVictoire et al. (2012) and related papers showed that ‘fair’ agents with completely different implementations can cooperate. A fair agent, roughly speaking, has to conform to any structure that implements “I’ll cooperate with you if I can show that you’ll cooperate with me”. So maybe that’s the measure you’re looking for.
A population of fair agents is also typically a Nash equilibrium in such games so you might expect that they sometimes do evolve.
LaVictoire, P., Fallenstein, B., Yudkowsky, E., Barasz, M., Christiano, P., & Herreshoff, M. (2014, July). Program equilibrium in the prisoner’s dilemma via Löb’s theorem. In AAAI Multiagent Interaction without Prior Coordination workshop.