Ego depletion is quite a narrow psychological effect. If the idea that people’s moment to moment fatigue saps moment to moment willpower is debunked, that’s far from showing that akrasia isn’t a thing in general.
In a world where general-sense akrasia was not a thing there would be a far higher rate of people being ripped like movie stars, a far lower rate of smoking, a much high rate of personal savings etc than there is in the world we inhabit.
Nice point.
‘I also wish we didn’t accidentally make donating to AMF or GiveDirectly so uncool.’
This reminds me of the pattern where we want to do something original, so we don’t take the obvious solution.
This is a nice piece of accessible scholarship. It would perhaps benefit from an explicit note on why the question is interesting in this context and to this audience.
Would suggest at least forming a ‘control group’, performing the same analysis and looking at differences in the sets of popular feeds. Following Obama doesn’t tell you much about a person.
One would also need to figure out what it is about those accounts that separates them from other , similar accounts ’IPW’s could have followed but didn’t.
The willpower argument is actually quite good. There are ways to reduce the amount of willpower required, but the kernel of the argument applies.
My prediction for people who constantly feel bad for not living up to an exacting standard is that a majority will fall off the boat entirely.
Well, on the basis of the description in the SEP article:
The idea behind this view is that the value of adding worthwhile lives to a population varies with the number of already existing lives in such a way that it has more value when the number of these lives is small than when it is large
It’s not the same thing, since above we’re saying that each individual’s utility is a function of the whole setup. So when you add new people you change the existing population’s utilities. The SEP description instead sounds like changing only what happens at the margin.
The main argument above is more or less technical, rather than ‘verbal’. And reliance on verbal argument is pretty much the root of the original issue.
In the event someone else said something similar some other time, there’s still value in a rederivation from a different starting position. I’m not so much concerned with credit for coming up with an idea than that I less frequently encounter instances of this issue.
As long as the core focuses on unusual priorities – which using neglectedness as a heuristic for prioritization will mean is likely – there’s a risk that new members get surprised when they find out about these unusual priorities
Perhaps there are also some good reasons that people with different life experience both a) don’t make it to ‘core’ and b) prioritize more near term issues.
There’s an assumption here that weirdness alone is off-putting. But, for example, technologists are used to seeing weird startup ideas and considering the contents.
This suggests a next thing to find out is: who disengages and why.
This has the ring of truth to it.
So, what do you think of the idea that aiming for high expected returns in long term investments might not be the best thing to do, given the skewed distribution? This is, we want to ensure that most futures are ‘good’; not just a few that are ‘excellent’ lost in a mass of ‘meh’ or worse.
BTW, I did like the podcast—it does take something to make me tap out forum posts :)
Thanks for the response. To clarify: in the second model both the drift and the diffusion term impact on the expected returns. If you substitute in a model return e^{q + sz}, with z a standard normal:
E[V(1)] = E[e^{q + s z}] = E[e^{sz}]e^q = e^{s^2/2} e^q > e^q
So, if we have fixed from some source that E[V(1)]=1.07=e^r then we cannot set q=r in the model with randomness while maintaining the equality. Where the equality cashes out as ‘the expected rate of return a year from now is 7%’.
Empirically estimated long run rates already take into account the effects of randomness since they are typically some sort of mean of observed returns. If this were not the case one would always have to, at least, quote the parameters in pairs (drift=such and such, vol=such and such) and perform a calculation in order to get out the expected returns.
> How difficult should we expect AI alignment to be?
With many of the AI questions, one needs to reason backwards rather than pose the general question.
Suppose we all die because unaligned AI. What form did the unaligned AI take? How did it work? Which things that exist now were progenitors of it, and what changed to make it dangerous? How could those problems have been avoided, technically? Organisationally?
I don’t see how useful alignment research can be done quite separately to capabilities research. Otherwise we’ll get will be people coming in at the wrong time with a bunch of ideas that lack technical purchase.
Similarly, the questions about what applications we’ll see first are already hinted at in capabilities research.
That being the case, it will take way more energy than 1 year for someone to upskill because they actually need to understand something about capabilities work.
GDP is a very leaky measure for growth in this context. To see this, consider a utopian scenario with dirt cheap fusion energy powered Star Trek replicators. The marginal cost of most traded goods drops to near zero and GDP tanks (setting aside services). You have for traditional industry writ large a similar dynamic to that napster triggered for the music industry.
Assuming we don’t all die sometime soon and things ‘carry on’ the solution is likely to lie at least in part, eventually, in giving up on trying to summarise all of technology in a scalar value.
On another note, as a piece of futurism we see here that technology / the economy is predicted to either:
Get much bigger.
Get much smaller.
Stay about the same.
Do something else.
This covers just about every possibility, so it can’t constitute much evidence on which to update.
[edit: tidied up]
I also think it would be a lot more helpful to walk through how this mistake could happen in some real scenarios in the context of EA
Hopefully, we’ll get there! It’ll be mostly Bayesian though :)
Thanks—that last link was one I’d come across and liked when looking for previous coverage. My sole previous blog post was about Pascal’s Wager. I’d found though when speaking about it that I was assuming too much for some of the audience I wanted to bring along; notwithstanding my sloppy writing :D So, I’m going to attempt to stay focused and incremental.
Ouch.