Modernity has made people quite averse to talking about and dealing with spirituality. I think maybe a big part of what’s going on is that while PR is a material concept, honor is a spiritual concept. It deals with meaning directly rather than only indirectly. Honor matters for its own sake (or can), matters to your soul. Whereas PR can only ever matter indirectly, only as a consequence of other things. No one has PR in their soul.
That would mean that people end up avoiding thinking about and relating to things like honor and reputation because it just feels weird. It feel like the sort of thing that you’re not supposed to deal with. It feels like something that science and technology have vaguely disproven.
jsalvatier
Luke, I think you often come across as defensive. I think it is difficult to avoid since you write a lot and thus put yourself out there for people to criticize and people do often comment in an aggressive fashion, but I think you should be aware of it anyway. I think avoiding seeming defensive would be useful to you because seeming defensive seems to make discussions more adversarial.
The phrase that gives me that impression here is
So now your objection is to my tone? You’ve reached DH2 on the disagreement heirarchy. I’ll take another look at my tone, but it’s not much of a disagreement if we’re disagreeing about tone.
I am a neutral observer of this conversation; I’ve only read the last two comments.
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- 20 Aug 2011 17:30 UTC; 2 points) 's comment on Are Deontological Moral Judgments Rationalizations? by (
I’m not talking about ‘covering fire’. If your goal is to win an argument or appear righteous, then your strategy is alright. If your goal is to actually get SIAI to change their behavior, then your language is hurting your cause. You want to make it as easy as possible for them to change their behavior, and it’s psychologically much easier to do something because an ally asks than because an adversary asks.
You have seen evidence: both Guy (link) and I (link) posted ‘lessons learned’ for the minicamp. You are right to say this is not especially strong evidence, but it is evidence. I think it would have been good to video tape some of the sessions and post them and post the exit surveys (they took testimonials too).
- 29 Aug 2011 18:51 UTC; 14 points) 's comment on Help Fund Lukeprog at SIAI by (
Wait; singing the alphabet song is still how I order letters. Is there a more efficient way?
I feel like it is useful to mention that because of efficient markets (which implies assets are “fairly priced”) and the benefits of diversification (lower risk), it’s almost always better to buy a low fee mutual fund than any particular stocks or bonds. In particular, Index Funds merely keep a portfolio which tracks a broad market index. These often have very low operating costs, so they are a pretty good way to invest. You can buy these as ETFs, or you can buy them through something like Vanguard.
I am uncomfortable with making the link between SIAI and LW so official (even though they sponsor LW).
I’ll just mention that if anyone needs a paper for LW related reasons, I (and others probably) will get it for you.
Upvoted for actually applying arithmetic!
These calculations are about Cryonics as charity rather than as something for yourself. I am somewhat altruistic, but I definitely don’t weight other people’s welfare equally with mine.
Update see my comment for new thoughts
Topic: Introductory Bayesian Statistics (as distinct from more advanced Bayesian statistics)
Recommendation: Data Analysis: A Bayesian Tutorial by Skilling and Sivia
Why: Sivia’s book is well suited for smart people who have not had little or no statistical training. It starts from the basics and covers a lot of important ground. I think it takes the right approach, first doing some simple examples where analytical solutions are available or it is feasible to integrate naively and numerically. Then it teaches into maximum likelihood estimation (MLE), how to do it and why it makes sense from a Bayesian perspective. I think MLE is a very very useful technique, especially so for engineers. I would overall recommend just Part I: The Essentials, I don’t think the second half is so useful, except perhaps the MLE extensions chapter. There are better places to learn about MCMC approximation.
Why not other books?
Bayesian Data Analysis by Gelman—Geared more for people who have done statistics before.
Bayesian Statistics by Bolstad—Doesn’t cover as much as Sivia’s book, most notably doesn’t cover MLE. Goes kinda slowly and spends a lot of time on comparing Bayesian statistics to Frequentist statistics.
The Bayesian Choice—more of a mathematical statistics book, not suited for beginners.
- The Best Textbooks on Every Subject by 16 Jan 2011 8:30 UTC; 709 points) (
- 25 Aug 2011 20:29 UTC; 4 points) 's comment on A History of Bayes’ Theorem by (
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Seeing SIAIs financials has made me more likely to donate to SIAI.
Does anyone have links to writing on what SIAI would do with increased funding? For example, “Allison Hu is a brilliant young Y and has come up with good ideas a,b,c. We would like to hire her, but we don’t have the funding to do so”. I’d like to see arguments about SIAIs marginal spending.
Also. Brandon! You should have talked about this at the meetup so we could all say what a great idea it was!
An example would help this comment.
Thanks for starting to make the case for SIAI’s marginal need for funding.
Upvote if you want such a thread. karma balance below (please downvote to correct the imbalance).
Maybe give her a hug, and tell her you love her and you’re the same daughter/son you always were and that you want to talk about this, but only if she’s calm and respectful about it. Then go to your room or otherwise leave to let her think about it.
(don’t take my advice too seriously, I don’t have any actual experience in this area).
In statistics the solution you describe is called Hierarchical or Multilevel Modeling. You assume that you data is drawn from a set of distributions which have their parameters drawn from another distribution. This automatically shrinks your estimates of the distributions towards the mean. I think it’s a pretty useful trick to know and I think it would be good to do a writeup but I think you might need to have a decent grasp of bayesian statistics first.
- 21 Jan 2020 11:49 UTC; 3 points) 's comment on Potential downsides of using explicit probabilities by (EA Forum;
One thing to keep in mind is that you have limited mental resources and can really only do a couple things effectively at a time. Thus you are hardly a bad person for concentrating on a couple of areas at once and ignoring other areas as they are for the time being.
To figure out what you want to concentrate on, you might sit down with someone make a list of all the different things you could try, and figure out which 1-3 seem the most promising and then make a plan to try those. Then, after an appropriate length of time you revisit the issue and find a couple more things to work on.
This GiveWell thread includes a transcript of a discussion between GiveWell and SIAI representatives.
It’s almost irresponsible to discuss this without addressing Efficient Markets, which more or less suggests that this is a bad, no good, terrible, rotten idea.
I only half mean to be rude, and don’t mean any personal offense.
As discussed in chapter 1, we have used a score of 27 on the APSD as our cut-off point for a classification of psychopathic tendencies in many of our studies.
I’ve noticed this elsewhere (looking into ADHD), Psychiatrists seem interested in developing a criteria which seems naturally continuous, and then using a cutoff without arguing for why that’s a good idea. I can easily imagine that some conditions are discrete, but many of them must be pretty continuous. It seems like they would lose a lot of statistical power with a cutoff approach.
Is this purely a historical accident? Is it because discrete judgments seem more authoritative? Is there an actual good reason that I can’t see? What’s going on here? This sort of thing makes me suspicious of the quality of psychiatry research.
Little or not at all off-putting.