This sounds really cool. Will have to read properly later. How would you recommend a time pressured reader to go through this? Are you planning a summary?
SoerenMind
Karma: 600
Just registering that I’m not convinced this justifies the title.
Yep, see reply to Lukas.
Agreed, I was assuming that the prior for the simulation hypothesis isn’t very low because people seem to put credence in it even before Will’s argument.
But I found it worth noting that Will’s inequality only follows from mine (the likelihood ratio) plus having a reasonably even prior odds ratio.
2.
For me, the HoH update is big enough to make a the simulation hypothesis a pretty likely explanation. It also makes it less likely that there are alternative explanations for “HoH seems likely”. See my old post here (probably better to read this comment though).
Imagine a Bayesian model with a variable S=”HoH seems likely” (to us) and 3 variables pointing towards it: “HoH” (prior: 0.001), “simulation” (prior=0.1), and “other wrong but convincing arguments” (prior=0.01). Note that it seems pretty unlikely there will be convincing but wrong arguments a priori (I used 0.01) because we haven’t updated on the outside view yet.
Further, assume that all three causes, if true, are equally likely to cause “HoH seems likely” (say with probability 1, but the probability doesn’t affect the posterior).
Apply Bayes rule: We’ve observed “HoH seems likely”. The denominator in Bayes rule is P(HoH seems likely) ~~ 0.111 (roughly the sum of the three priors because the priors are small). The numerator for each hypothesis H equals 1 * P(H).
Bayes rule gives an equal update (ca 1⁄0.111x = 9x) in favor of every hypothesis, bringing up the probability of “simulation” to nearly 90%.
Note that this probability decreases if we find, or think there are better explanations for “HoH seems likely”. This is plausible but not overwhelmingly likely because we already have a decent explanation with prior 0.1. If we didn’t have one, we would still have a lot of pressure to explain “HoH seems likely”. The existence of the plausible explanation “simulation” with prior 0.1 “explains away” the need for other explanations such as those falling under “wrong but convincing argument”.
This is just an example, feel free to plug in your numbers, or critique the model.
Both seem true and relevant. You could in fact write P(seems like HoH | simulation) >> P(seems like HoH | not simulation), which leads to the other two via Bayes theorem.
Important post!
I like your simulation update against HoH. I was meaning to write a post about this. Brian Tomasik has a great paper that quantitatively models the ratio of our influence on the short vs long-term. Though you’ve linked it, I think it’s worth highlighting it more.
How the Simulation Argument Dampens Future Fanaticism
The paper cleverly argues that the simulation argument combined with anthropics either strongly dampens the expected impact of far future altruism or strongly increases the impact of short-term altruism. That conclusion seems fairly robust to the choice of decision- and anthropic theory and uncertainty over some empirical parameters. He doesn’t directly discuss how the “seems like HoH” observation affects his conclusions, but I think it makes them stronger. (i recommend Brian’s simplified calculations here).
I assume this paper didn’t get as much discussion as it deserves because Brian posted it in the dark days of LW.
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.
Great analysis!
″...the bulk of suffering is concentrated in a small percentage of experiences...”
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!
So your prior says, unlike Will’s, that there are non-trivial probabilities of very early lock-in. That seems plausible and important. But it seems to me that your analysis not only uses a different prior but also conditions on “we live extremely early” which I think is problematic.
Will argues that it’s very weird we seem to be at an extremely hingy time. So we should discount that possibility. You say that we’re living at an extremely early time and it’s not weird for early times to be hingy. I imagine Will’s response would be “it’s very weird we seem to be living at an extremely early time then” (and it’s doubly weird if it implies we live in an extremely hingy time).
If living at an early time implies something that is extremely unlikely a priori for a random person from the timeline, then there should be an explanation. These 3 explanations seem exhaustive:
1) We’re extremely lucky.
2) We aren’t actually early: E.g. we’re in a simulation or the future is short. (The latter doesn’t necessarily imply that xrisk work doesn’t have much impact because the future might just be short in terms of people in our anthropic reference class).
3) Early people don’t actually have outsized influence: E.g. the hazard/hinge rate in your model is low (perhaps 1/N where N is the length of the future). In a Bayesian graphical model, there should be a strong update in favor of low hinge rates after observing that we live very early (unless another explanation is likely a priori).
Both 2) and 3) seem somewhat plausible a priori so it seems we don’t need to assume that a big coincidence explains how early we live.