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Did the next few posts Luke mentions would be about empathic metaethics ever get written? I don't see them anywhere.

2lukeprog12y

Now that I'm running MIRI, I'll probably never write it, but here's where I was headed.

this was covered here: http://lesswrong.com/lw/65/money_the_unit_of_caring/

"If the soup kitchen needed a lawyer, and the lawyer donated a large contiguous high-priority block of lawyering, then that sort of volunteering makes sense—that's the same specialized capability the lawyer ordinarily trades for money. But "volunteering" just one hour of legal work, constantly delayed, spread across three weeks in casual minutes between other jobs? This is not the way something gets done when anyone actually cares about it, or to state it near-equivalently, when money is involved."

Rationality Quotes May 2012

imbatman13y30

That's correct.

Rationality Quotes May 2012

imbatman13y20

"Contradictions do not exist. Whenever you think you are facing a contradiction, check your premises. You will find that one of them is wrong."

2BillyOblivion13y

Or that both of them (to reference a previous Rationality Quotes entry on arguments) are wrong.

2MinibearRex13y

source?

Oligopsony13y130

Or that you've made an invalid inference.

Evaluating Multiple Metrics (where not all are required)

imbatman13y00

Thanks. Do you think the vote downs have to do with the content? Is this not a relevant topic for this forum?

0Risto_Saarelma13y

I guess the downvotes might be a combination of the thing post being a lot more in the idea stage than a worked out solution stage and it being about rating movies, which as itself isn't a very relevant topic. The general idea of working out preferences using vectors instead of scalars does seem like a forum relevant topic to me, but your post leaves the details of making an actual working implementation, coming up with interesting use cases beyond movies and figuring out how the vector approach would be a significant improvement over a scalar approach in them up to the reader, so it's a bit thin as it stands.

Evaluating Multiple Metrics (where not all are required)

imbatman13y00

I tried visualizing but I don't know how that helps me construct a formula. I would imagine, in your example, the landscape would be mountainous. One movie may have both great suspense and great humor and be a great movie...another may have both great suspense and great humor and be just an okay movie. But then perhaps there is a movie with very low amounts of humor or suspense that is still a good movie for other reasons. So in that case neither of these metrics would be good predictors for that movie.

That's kind of the core of the issue, as your exer... (read more)

Evaluating Multiple Metrics (where not all are required)

imbatman13y10

I liked this idea, which carried the added bonus of only taking a few second to implement. Better?

0Risto_Saarelma13y

Looks good to me now.

Evaluating Multiple Metrics (where not all are required)

imbatman13y00

Yes! That helps. My question, then, is what to plug into that formula if a metric SOMETIMES matters.

e.g. If 9 9 9 9 isn't necessarily better than 9 9 9 0.

There are probably some additional questions to think of, but I'm not sure what they are. And I'm not entirely sure this is possible...that's why I brought it up.

0faul_sname13y

It is entirely possible, and feel free to ask more questions. I find that it's helpful to visualize the shape of the space I am operating in, which in this case is a 5-dimensional space (the dimensions are Plot, Acting, Humor, Suspense, and Overall Rating). However, many people find it difficult to visualize more than 3 dimensions, so I will describe only the interaction of Humor and Suspense on Overall Rating." In this case, let Humor (H) be the east/west direction, Suspense (S) be the north south direction, and Overall Rating (R) be the altitude. We can now visualize a landscape that corresponds to these variables. Here are some possible landscapes and what we can infer from them: *Flat, with no slope or features (The audience doesn't care about either H or S) *Sloped up as we go northeast (The audience likes humor and suspense together) *Saddle shaped with the high points to the northwest and southeast (The audience likes H or S independently, but not together) *Mountainous (The audience has complex tastes). You would then want to find the equation that best fit this terrain you have. Usually, the best fit is linear (which you would see as a sloped terrain). However, you can find better equations when it isn't. You do have to be careful not to over-fit: a good rule of thumb is that if it takes more information to approximate your data than is contained in the data itself, you're doing something wrong.

Evaluating Multiple Metrics (where not all are required)

imbatman13y00

I tried to acknowledge that the rankings in this case are completely subjective. Maybe it would help to think about it like this. Let's say instead we have a data set. We'll simplify to 4 metrics: Plot, Acting, Humor, and Suspense. We're given data for 3 movies, for each movie a ranking for these 4 metrics, respectively:

Groundhog Day 9 9 10 5 Terminator 8 8 6 9 Achorman 6 9 10 2

Based on this, what are some ways to evaluate this data? We're not satisfied that just summing the rankings for each metric comes up with an accurate ranking for the film overall. So how else can we do it?

3faul_sname13y

Empirically determine what formula most closely matches overall impressions in the real world, avoiding over-fitting by penalizing the formulas for complexity. The "sum the scores" would simply be P+A+H+S. A weighted sum would be k1P+k2A+k3H+k4S. Perhaps humor and suspense are found to correlate positively with rating when considered individually, but interfere negatively with each other. So we might go with k1P+k2A+k3H+k4S-k5(H*S). Each additional bit of complexity in the formula must double the predictive power of your formula (halve your error). We would start with the data and possible formulas (probably weighted by complexity). We would then plug in the data for each formula, seeing how well each one predicts it. The formula which most efficiently predicts movie ratings based on these dimensions is the one we would use.

Evaluating Multiple Metrics (where not all are required)

imbatman13y00

Just thought I would try to make it easier to follow. An alternative would have been to declare my terms, I guess. I haven't really developed a strategy for that -- just thought I'd try this.

6Risto_Saarelma13y

Bolding or italicizing each special term the first time it appears in the text and writing it in regular typeface afterwards would probably read better, while still drawing attention to the relevant special concept words. People can keep picking out the word better without the typeface once they've been primed by the first mention to assume the word denotes an important concept.

2faul_sname13y

Here are some possible definitions you might consider using. Class: A concentration of unusually high probability density in Thingspace. Type: A subclass. An even denser area of thingspace or conceptspace within a cluster of things. Metric: a scale you use to measure a single trait of something. In humans, that could be height, weight, hair color, etc. In order to be useful, a metric must give you further information about that thing as opposed to other things in its class/type (there must be significantly more variability along that dimension than others, in terms of thingspace). In regards to the article itself, it highlights the difficulty of projecting a multidimensional space (with the number of dimensions equal to the number of metrics you're using) and a complex distribution of "goodness" within that space to a single dimension of goodness with minimal complexity and minimal loss of information.

2Douglas_Knight13y

Why did you think that? Have you paid attention to your own experience reading things with bold? I recommend reading Razib Khan and paying attention. I find that his use of bold makes it more difficult to read the whole article, but easy to read just the bold passages, which is usually the right choice.

Rationality quotes January 2012

imbatman13y30

"A man's gotta know his limitations." - Dirty Harry

Rationality quotes January 2012

imbatman13y150

"A Confucian has stolen my hairbrush! Down with Confucianism!"

-GK Chesterton (on ad hominems)

Newcomb's Problem and Regret of Rationality

imbatman13y00

I liked the quote not because of any notion that Bayes will or should "go out the window," but because, coming from a devout (can I use that word?) Bayesian, it's akin to a mathematician saying that if 2+2 ceases to be 4, that equation goes out the window. I just like what this says about one's epistemology -- we don't claim to know with dogmatic certainty, but in varying degrees of certainty, which, to bring things full circle, is what Bayes seems to be all about (at least to me, a novice).

More concisely, I like the quote because it draws a line. We can rail against the crazy strict Empiricism that denies rationality, but we won't hold to a rationality so devoutly that it becomes faith.

2gwern13y

Duhem-Quine is just as much a problem there; from Ludwig Wittgenstein, Remarks on the Foundations of Mathematics: Indeed. To generalize, when we run into skeptical arguments employing the above circularity or fundamental uncertainties, I think of Kripke:

Newcomb's Problem and Regret of Rationality

imbatman13y20

Upvoted for this sentence:

"If it ever turns out that Bayes fails - receives systematically lower rewards on some problem, relative to a superior alternative, in virtue of its mere decisions - then Bayes has to go out the window."

This is such an important concept.

I will say this declaratively: The correct choice is to take only box two. If you disagree, check your premises.

"But it is agreed even among causal decision theorists that if you have the power to precommit yourself to take one box, in Newcomb's Problem, then you should do so. If y... (read more)

gwern13y100

"If it ever turns out that Bayes fails - receives systematically lower rewards on some problem, relative to a superior alternative, in virtue of its mere decisions - then Bayes has to go out the window."

This is such an important concept.

Yes, but like falsifiability, dangerous. This also goes for 'rationalists win', too.

'We' (Bayesians) face the Duhem-Quine thesis with a vengeance: we have often found situations where Bayes failed. And then we rescued it (we think) by either coming up with novel theses (TDT) or carefully analyzing the probl... (read more)

Welcome to Less Wrong! (2010-2011)

imbatman13y90

Hello All. I came across Less Wrong via Common Sense Atheism a few weeks ago. I have enjoyed it so far, but I have yet to put in the time to get up to speed on the sequences. Plan to, though.

I'm a Financial Accountant in Birmingham, AL. I'm not sure I would (yet) identify myself as a rationalist, but as for what I value, I value truth above all. And if I'm not mistaken, valuing truth seems a big step toward becoming a rationalist. I also value life, liberty, happiness, fun, music, pizza, and many other things.

LESSWRONG
LW

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