= 27d567bc2d48d9f40e9ccc20ea370b15)
Vote for MIRI to be donated a share of reddit's advertising revenue
http://www.reddit.com/donate?organization=582565917
"Today we are announcing that we will donate 10% of our advertising revenue receipts in 2014 to non-profits chosen by the reddit community. Whether it’s a large ad campaign or a $5 sponsored headline on reddit, we intend for all ad revenue this year to benefit not only reddit as a platform but also to support the goals and causes of the entire community."
How to learn soft skills
Acquiring some skills is mostly about deliberate, explicit information transfer. For example, one might explicitly learn the capital of Missouri, or the number of miles one can drive before needing an oil change, or how to use the quadratic formula to solve quadratic equations.
For other skills, practitioners' skill rests largely on semi-conscious, non-explicit patterns of perception and action. I have in mind here such skills as:
- Managing your emotions and energy levels;
- Building strong relationships;
- Making robust plans;
- Finding angles of attack on a mathematical problem;
- Writing persuasively;
- Thinking through charged subjects without bias;
and so on. Experts in these skills will often be unable to accurately and explicitly describe how to do what they do, but they will be skilled nonetheless.
I'd like to share some thoughts on how to learn such "soft skills".
Attempted Telekinesis
Related to: Compartmentalization in epistemic and instrumental rationality; That other kind of status.
Intrapersonal comparisons: you might be doing it wrong.
Nothing weighty or profound today, but I noticed a failure mode in myself which other people might plausibly suffer from so I thought I'd share it.
Basically, I noticed that sometimes when I discovered a more effective way of doing something -- say, going from conventional flashcards to Anki -- I found myself getting discouraged.
I realized that it was because each time I found such a technique, I automatically compared my current self to a version of me that had had access to the technique the whole time. Realizing that I wasn't as far along as I could've been resulted in a net loss of motivation.
Now, I deliberately compare two future versions of myself, one armed with the technique I just discovered and one without. Seeing how much farther along I will be results in a net gain of motivation.
A variant of this exercise is taking any handicap you might have and wildly exaggerating it. I suffer from mild Carpal Tunnel (or something masquerading as CT) which makes progress in programming slow. When I feel down about this fact I imagine how hard programming would be without hands.
Sometimes I go as far as to plan out what I might do if I woke up tomorrow with a burning desire to program and nothing past my wrists. Well, I'd probably figure out a way to code by voice and then practice mnemonics because I wouldn't be able to write anything down. Since these solutions exist I can implement one or both of them the moment my carpal tunnel gets bad enough.
With this realization comes a boost in motivation knowing I can go a different direction if required.
CFAR fundraiser far from filled; 4 days remaining
We're 4 days from the end of our matching fundraiser, and still only about 1/3rd of the way to our target (and to the point where pledged funds would cease being matched).
If you'd like to support the growth of rationality in the world, do please consider donating, or asking me about any questions/etc. you may have. I'd love to talk. I suspect funds donated to CFAR between now and Jan 31 are quite high-impact.
As a random bonus, I promise that if we meet the $120k matching challenge, I'll post at least two posts with some never-before-shared (on here) rationality techniques that we've been playing with around CFAR.
How subjective is attractiveness?
Consider the two statements:
- There is a universal standard for beauty.
- Beauty is in the eye of the beholder.
Most people would agree that there's some truth to each of these statements. At Thing of Things Ozy wrote:
As for the beauty thing… well, yeah, everyone’s beautiful in the sense that everyone is sexually attractive to someone, and that human bodies in general are pretty cool-looking. But conventional attractiveness is still a thing. While I’m fairly conventionally attractive (thin, white, clear skin, symmetrical features), I doubt hairy legs, bound chests, and haircuts that make one look like a teenage boy are going to be all the rage at Cosmo any time soon.
This post explores the question of the extent to which each of the two statements is true, using data from a study of speed dating events conducted by Raymond Fisman and Sheena Iyengar.
The basic facts that I describe here are:
- Attractiveness as defined by group consensus can be modeled well using a normal distribution.
- The group consensus on somebody's attractiveness accounted for roughly 60% of the variance in people's perceptions of the person's relative attractiveness.
- The distribution of people's perceptions of the relative attractiveness of a fixed person can be modeled well using a normal distribution. Moreover, the standard deviations of these distributions tend to be quite close to one another (across different people), so that it's often possible to approximate the entire distribution of perceptions of somebody's relative attractiveness using only the mean of the distribution, which is just the group consensus on the person's attractiveness.
There's much more to say about how to interpret the group consensus and its implications, which I'll go into in a later post.
Low Hanging fruit for buying a better life
What can I purchase with $100 that will be the best thing I can buy to make my life better?
I've decided to budget some regular money to improving my life each month. I'd like to start with low hanging fruit for obvious reasons - but when I sat down to think of improvements, I found myself thinking of the same old things I'd already been planning to do anyway... and I'd like out of that rut.
Constraints/more info:
- be concrete. I know - "spend money on experiences" is a good idea - but what experiences are the best option to purchase *first*
- "better" is deliberately left vague - choose how you would define it, so that I'm not constrained just by ways of "being better" that I'd have thought of myself.
- please assume that I have all my basic needs met (eg food, clothing, shelter) and that I have budgeted separately for things like investing for my financial future and for charity.
- apart from the above, assume nothing - Especially don't try and tailor solutions to anything you might know and/or guess about me specifically, because I think this would be a useful resource for others who might have just begun.
- don't constrain yourself to exactly $100 - I could buy 2-3 things for that, or I could save up over a couple of months and buy something more expensive... I picked $100 because it's a round number and easy to imagine.
- it's ok to add "dumb" things - they can help spur great ideas, or just get rid of an elephant in the room.
- try thinking of your top-ten before reading any comments, in order not to bias your initial thinking. Then come back and add ten more once you've been inspired by what everyone else came up with.
Background:
This is a question I recently posed to my local Less Wrong group and we came up with a few good ideas, so I thought I'd share the discussion with the wider community and see what we can come up with. I'll add the list we came up with later on in the comments...
It'd be great to have a repository of low-hanging fruit for things that can be solved with (relatively affordable) amounts of money. I'd personally like to go through the list - look at candidates that sound like they'd be really useful to me and then make a prioritised list of what to work on first.
2014 Survey Results
Thanks to everyone who took the 2014 Less Wrong Census/Survey. Extra thanks to Ozy, who did a lot of the number crunching work.
This year's results are below. Some of them may make more sense in the context of the original survey questions, which can be seen here. Please do not try to take the survey as it is over and your results will not be counted.
I. Population
There were 1503 respondents over 27 days. The last survey got 1636 people over 40 days. The last four full days of the survey saw nineteen, six, and four responses, for an average of about ten. If we assume the next thirteen days had also gotten an average of ten responses - which is generous, since responses tend to trail off with time - then we would have gotten about as many people as the last survey. There is no good evidence here of a decline in population, although it is perhaps compatible with a very small decline.
II. Demographics
Sex
Female: 179, 11.9%
Male: 1311, 87.2%
Gender
F (cisgender): 150, 10.0%
F (transgender MtF): 24, 1.6%
M (cisgender): 1245, 82.8%
M (transgender FtM): 5, 0.3%
Other: 64, 4.3%
Sexual Orientation
Asexual: 59, 3.9%
Bisexual: 216, 14.4%
Heterosexual: 1133, 75.4%
Homosexual: 47, 3.1%
Other: 35, 2.3%
[This question was poorly worded and should have acknowledged that people can both be asexual and have a specific orientation; as a result it probably vastly undercounted our asexual readers]
Relationship Style
Prefer monogamous: 778, 51.8%
Prefer polyamorous: 227, 15.1%
Uncertain/no preference: 464, 30.9%
Other: 23, 1.5%
Number of Partners
0: 738, 49.1%
1: 674, 44.8%
2: 51, 3.4%
3: 17, 1.1%
4: 7, 0.5%
5: 1, 0.1%
Lots and lots: 3, 0.2%
Relationship Goals
Currently not looking for new partners: 648, 43.1%
Open to new partners: 467, 31.1%
Seeking more partners: 370, 24.6%
[22.2% of people who don’t have a partner aren’t looking for one.]
Relationship Status
Married: 274, 18.2%
Relationship: 424, 28.2%
Single: 788, 52.4%
[6.9% of single people have at least one partner; 1.8% have more than one.]
Living With
Alone: 345, 23.0%
With parents and/or guardians: 303, 20.2%
With partner and/or children: 411, 27.3%
With roommates: 428, 28.5%
Children
0: 1317, 81.6%
1: 66, 4.4%
2: 78, 5.2%
3: 17, 1.1%
4: 6, 0.4%
5: 3, 0.2%
6: 1, 0.1%
Lots and lots: 1, 0.1%
Want More Children?
Yes: 549, 36.1%
Uncertain: 426, 28.3%
No: 516, 34.3%
[418 of the people who don’t have children don’t want any, suggesting that the LW community is 27.8% childfree.]
Country
United States, 822, 54.7%
United Kingdom, 116, 7.7%
Canada, 88, 5.9%
Australia: 83, 5.5%
Germany, 62, 4.1%
Russia, 26, 1.7%
Finland, 20, 1.3%
New Zealand, 20, 1.3%
India, 17, 1.1%
Brazil: 15, 1.0%
France, 15, 1.0%
Israel, 15, 1.0%
Lesswrongers Per Capita
Finland: 1/271,950
New Zealand: 1/223,550
Australia: 1/278,674
United States: 1/358,390
Canada: 1/399,545
Israel: 1/537,266
United Kingdom: 1/552,586
Germany: 1/1,290,323
France: 1/ 4,402,000
Russia: 1/ 5,519,231
Brazil: 1/ 13,360,000
India: 1/ 73,647,058
Race
Asian (East Asian): 59. 3.9%
Asian (Indian subcontinent): 33, 2.2%
Black: 12. 0.8%
Hispanic: 32, 2.1%
Middle Eastern: 9, 0.6%
Other: 50, 3.3%
White (non-Hispanic): 1294, 86.1%
Work Status
Academic (teaching): 86, 5.7%
For-profit work: 492, 32.7%
Government work: 59, 3.9%
Homemaker: 8, 0.5%
Independently wealthy: 9, 0.6%
Nonprofit work: 58, 3.9%
Self-employed: 122, 5.8%
Student: 553, 36.8%
Unemployed: 103, 6.9%
Profession
Art: 22, 1.5%
Biology: 29, 1.9%
Business: 35, 4.0%
Computers (AI): 42, 2.8%
Computers (other academic): 106, 7.1%
Computers (practical): 477, 31.7%
Engineering: 104, 6.1%
Finance/Economics: 71, 4.7%
Law: 38, 2.5%
Mathematics: 121, 8.1%
Medicine: 32, 2.1%
Neuroscience: 18, 1.2%
Philosophy: 36, 2.4%
Physics: 65, 4.3%
Psychology: 31, 2.1%
Other: 157, 10.2%
Other “hard science”: 25, 1.7%
Other “social science”: 34, 2.3%
Degree
None: 74, 4.9%
High school: 347, 23.1%
2 year degree: 64, 4.3%
Bachelors: 555, 36.9%
Masters: 278, 18.5%
JD/MD/other professional degree: 44, 2.9%
PhD: 105, 7.0%
Other: 24, 1.4%
III. Mental Illness
535 answer “no” to all the mental illness questions. Upper bound: 64.4% of the LW population is mentally ill.
393 answer “yes” to at least one mental illness question. Lower bound: 26.1% of the LW population is mentally ill. Gosh, we have a lot of self-diagnosers.
Depression
Yes, I was formally diagnosed: 273, 18.2%
Yes, I self-diagnosed: 383, 25.5%
No: 759, 50.5%
OCD
Yes, I was formally diagnosed: 30, 2.0%
Yes, I self-diagnosed: 76, 5.1%
No: 1306, 86.9%
Autism spectrum
Yes, I was formally diagnosed: 98, 6.5%
Yes, I self-diagnosed: 168, 11.2%
No: 1143, 76.0%
Bipolar
Yes, I was formally diagnosed: 33, 2.2%
Yes, I self-diagnosed: 49, 3.3%
No: 1327, 88.3%
Anxiety disorder
Yes, I was formally diagnosed: 139, 9.2%
Yes, I self-diagnosed: 237, 15.8%
No: 1033, 68.7%
BPD
Yes, I was formally diagnosed: 5, 0.3%
Yes, I self-diagnosed: 19, 1.3%
No: 1389, 92.4%
[Ozy says: RATIONALIST BPDERS COME BE MY FRIEND]
Schizophrenia
Yes, I was formally diagnosed: 7, 0.5%
Yes, I self-diagnosed: 7, 0.5%
No: 1397, 92.9%
IV. Politics, Religion, Ethics
Politics
Communist: 9, 0.6%
Conservative: 67, 4.5%
Liberal: 416, 27.7%
Libertarian: 379, 25.2%
Social Democratic: 585, 38.9%
[The big change this year was that we changed "Socialist" to "Social Democratic". Even though the description stayed the same, about eight points worth of Liberals switched to Social Democrats, apparently more willing to accept that label than "Socialist". The overall supergroups Libertarian vs. (Liberal, Social Democratic) vs. Conservative remain mostly unchanged.]
Politics (longform)
Anarchist: 40, 2.7%
Communist: 9, 0.6%
Conservative: 23, 1.9%
Futarchist: 41, 2.7%
Left-Libertarian: 192, 12.8%
Libertarian: 164, 10.9%
Moderate: 56, 3.7%
Neoreactionary: 29, 1.9%
Social Democrat: 162, 10.8%
Socialist: 89, 5.9%
[Amusing politics answers include anti-incumbentist, having-well-founded-opinions-is-hard-but-I’ve-come-to-recognize-the-pragmatism-of-socialism-I-don’t-know-ask-me-again-next-year, pirate, progressive social democratic environmental liberal isolationist freedom-fries loving pinko commie piece of shit, republic-ist aka read the federalist papers, romantic reconstructionist, social liberal fiscal agnostic, technoutopian anarchosocialist (with moderate snark), whatever it is that Scott is, and WHY ISN’T THERE AN OPTION FOR NONE SO I CAN SIGNAL MY OBVIOUS OBJECTIVITY WITH MINIMAL EFFORT. Ozy would like to point out to the authors of manifestos that no one will actually read their manifestos except zir, and they might want to consider posting them to their own blogs.]
American Parties
Democratic Party: 221, 14.7%
Republican Party: 55, 3.7%
Libertarian Party: 26, 1.7%
Other party: 16, 1.1%
No party: 415, 27.6%
Non-Americans who really like clicking buttons: 415, 27.6%
Voting
Yes: 881, 58.6%
No: 444, 29.5%
My country doesn’t hold elections: 5, 0.3%
Religion
Atheist and not spiritual: 1054, 70.1%
Atheist and spiritual: 150, 10.0%
Agnostic: 156, 10.4%
Lukewarm theist: 44, 2.9%
Deist/pantheist/etc.: 22,, 1.5%
Committed theist: 60, 4.0%
Religious Denomination
Christian (Protestant): 53, 3.5%
Mixed/Other: 32, 2.1%
Jewish: 31, 2.0%
Buddhist: 30, 2.0%
Christian (Catholic): 24, 1.6%
Unitarian Universalist or similar: 23, 1.5%
[Amusing denominations include anti-Molochist, CelestAI, cosmic engineers, Laziness, Thelema, Resimulation Theology, and Pythagorean. The Cultus Deorum Romanorum practitioner still needs to contact Ozy so they can be friends.]
Family Religion
Atheist and not spiritual: 213, 14.2%
Atheist and spiritual: 74, 4.9%
Agnostic: 154. 10.2%
Lukewarm theist: 541, 36.0%
Deist/Pantheist/etc.: 28, 1.9%
Committed theist: 388, 25.8%
Religious Background
Christian (Protestant): 580, 38.6%
Christian (Catholic): 378, 25.1%
Jewish: 141, 9.4%
Christian (other non-protestant): 88, 5.9%
Mixed/Other: 68, 4.5%
Unitarian Universalism or similar: 29, 1.9%
Christian (Mormon): 28, 1.9%
Hindu: 23, 1.5%’
Moral Views
Accept/lean towards consequentialism: 901, 60.0%
Accept/lean towards deontology: 50, 3.3%
Accept/lean towards natural law: 48, 3.2%
Accept/lean towards virtue ethics: 150, 10.0%
Accept/lean towards contractualism: 79, 5.3%
Other/no answer: 239, 15.9%
Meta-ethics
Constructivism: 474, 31.5%
Error theory: 60, 4.0%
Non-cognitivism: 129, 8.6%
Subjectivism: 324, 21.6%
Substantive realism: 209, 13.9%
V. Community Participation
Less Wrong Use
Lurker: 528, 35.1%
I’ve registered an account: 221, 14.7%
I’ve posted a comment: 419, 27.9%
I’ve posted in Discussion: 207, 13.8%
I’ve posted in Main: 102, 6.8%
Sequences
Never knew they existed until this moment: 106, 7.1%
Knew they existed, but never looked at them: 42, 2.8%
Some, but less than 25%: 270, 18.0%
About 25%: 181, 12.0%
About 50%: 209, 13.9%
About 75%: 242, 16.1%
All or almost all: 427, 28.4%
Meetups
Yes, regularly: 154, 10.2%
Yes, once or a few times: 325, 21.6%
No: 989, 65.8%
Community
Yes, all the time: 112, 7.5%
Yes, sometimes: 191, 12.7%
No: 1163, 77.4%
Romance
Yes: 82, 5.5%
I didn’t meet them through the community but they’re part of the community now: 79, 5.3%
No: 1310, 87.2%
CFAR Events
Yes, in 2014: 45, 3.0%
Yes, in 2013: 60, 4.0%
Both: 42, 2.8%
No: 1321, 87.9%
CFAR Workshop
Yes: 109, 7.3%
No: 1311, 87.2%
[A couple percent more people answered 'yes' to each of meetups, physical interactions, CFAR attendance, and romance this time around, suggesting the community is very very gradually becoming more IRL. In particular, the number of people meeting romantic partners through the community increased by almost 50% over last year.]
HPMOR
Yes: 897, 59.7%
Started but not finished: 224, 14.9%
No: 254, 16.9%
Referrals
Referred by a link: 464, 30.9%
HPMOR: 385, 25.6%
Been here since the Overcoming Bias days: 210, 14.0%
Referred by a friend: 199, 13.2%
Referred by a search engine: 114, 7.6%
Referred by other fiction: 17, 1.1%
[Amusing responses include “a rationalist that I follow on Tumblr”, “I’m a student of tribal cultishness”, and “It is difficult to recall details from the Before Time. Things were brighter, simpler, as in childhood or a dream. There has been much growth, change since then. But also loss. I can't remember where I found the link, is what I'm saying.”]
Blog Referrals
Slate Star Codex: 40, 2.6%
Reddit: 25, 1.6%
Common Sense Atheism: 21, 1.3%
Hacker News: 20, 1.3%
Gwern: 13, 1.0%
VI. Other Categorical Data
Cryonics Status
Don’t understand/never thought about it: 62, 4.1%
Don’t want to: 361, 24.0%
Considering it: 551, 36.7%
Haven’t gotten around to it: 272, 18.1%
Unavailable in my area: 126, 8.4%
Yes: 64, 4.3%
Type of Global Catastrophic Risk
Asteroid strike: 64, 4.3%
Economic/political collapse: 151, 10.0%
Environmental collapse: 218, 14.5%
Nanotech/grey goo: 47, 3.1%
Nuclear war: 239, 15.8%
Pandemic (bioengineered): 310, 20.6%
Pandemic (natural): 113. 7.5%
Unfriendly AI: 244, 16.2%
[Amusing answers include ennui/eaten by Internet, Friendly AI, “Greens so weaken the rich countries that barbarians conquer us”, and Tumblr.]
Effective Altruism (do you self-identify)
Yes: 422, 28.1%
No: 758, 50.4%
[Despite some impressive outreach by the EA community, numbers are largely the same as last year]
Effective Altruism (do you participate in community)
Yes: 191, 12.7%
No: 987, 65.7%
Vegetarian
Vegan: 31, 2.1%
Vegetarian: 114, 7.6%
Other meat restriction: 252, 16.8%
Omnivore: 848, 56.4%
Paleo Diet
Yes: 33, 2.2%
Sometimes: 209, 13.9%
No: 1111, 73.9%
Food Substitutes
Most of my calories: 8. 0.5%
Sometimes: 101, 6.7%
Tried: 196, 13.0%
No: 1052, 70.0%
Gender Default
I only identify with my birth gender by default: 681, 45.3%
I strongly identify with my birth gender: 586, 39.0%
Books
<5: 198, 13.2%
5 - 10: 384, 25.5%
10 - 20: 328, 21.8%
20 - 50: 264, 17.6%
50 - 100: 105, 7.0%
> 100: 49, 3.3%
Birth Month
Jan: 109, 7.3%
Feb: 90, 6.0%
Mar: 123, 8.2%
Apr: 126, 8.4%
Jun: 107, 7.1%
Jul: 109, 7.3%
Aug: 120, 8.0%
Sep: 94, 6.3%
Oct: 111, 7.4%
Nov: 102, 6.8%
Dec: 106, 7.1%
[Despite my hope of something turning up here, these results don't deviate from chance]
Handedness
Right: 1170, 77.8%
Left: 143, 9.5%
Ambidextrous: 37, 2.5%
Unsure: 12, 0.8%
Previous Surveys
Yes: 757, 50.7%
No: 598, 39.8%
Favorite Less Wrong Posts (all > 5 listed)
An Alien God: 11
Joy In The Merely Real: 7
Dissolving Questions About Disease: 7
Politics Is The Mind Killer: 6
That Alien Message: 6
A Fable Of Science And Politics: 6
Belief In Belief: 5
Generalizing From One Example: 5
Schelling Fences On Slippery Slopes: 5
Tsuyoku Naritai: 5
VII. Numeric Data
Age: 27.67 + 8.679 (22, 26, 31) [1490]
IQ: 138.25 + 15.936 (130.25, 139, 146) [472]
SAT out of 1600: 1470.74 + 113.114 (1410, 1490, 1560) [395]
SAT out of 2400: 2210.75 + 188.94 (2140, 2250, 2320) [310]
ACT out of 36: 32.56 + 2.483 (31, 33, 35) [244]
Time in Community: 2010.97 + 2.174 (2010, 2011, 2013) [1317]
Time on LW: 15.73 + 95.75 (2, 5, 15) [1366]
Karma Score: 555.73 + 2181.791 (0, 0, 155) [1335]
P Many Worlds: 47.64 + 30.132 (20, 50, 75) [1261]
P Aliens: 71.52 + 34.364 (50, 90, 99) [1393]
P Aliens (Galaxy): 41.2 + 38.405 (2, 30, 80) [1379]
P Supernatural: 6.68 + 20.271 (0, 0, 1) [1386]
P God: 8.26 + 21.088 (0, 0.01, 3) [1376]
P Religion: 4.99 + 18.068 (0, 0, 0.5) [1384]
P Cryonics: 22.34 + 27.274 (2, 10, 30) [1399]
P Anti-Agathics: 24.63 + 29.569 (1, 10, 40) [1390]
P Simulation 24.31 + 28.2 (1, 10, 50) [1320]
P Warming 81.73 + 24.224 (80, 90, 98) [1394]
P Global Catastrophic Risk 72.14 + 25.620 (55, 80, 90) [1394]
Singularity: 2143.44 + 356.643 (2060, 2090, 2150) [1177]
[The mean for this question is almost entirely dependent on which stupid responses we choose to delete as outliers; the median practically never changes]
Abortion: 4.38 + 1.032 (4, 5, 5) [1341]
Immigration: 4 + 1.078 (3, 4, 5) [1310]
Taxes : 3.14 + 1.212 (2, 3, 4) [1410] (from 1 - should be lower to 5 - should be higher)
Minimum Wage: 3.21 + 1.359 (2, 3, 4) [1298] (from 1 - should be lower to 5 - should be higher)
Feminism: 3.67 + 1.221 (3, 4, 5) [1332]
Social Justice: 3.15 + 1.385 (2, 3, 4) [1309]
Human Biodiversity: 2.93 + 1.201 (2, 3, 4) [1321]
Basic Income: 3.94 + 1.087 (3, 4, 5) [1314]
Great Stagnation: 2.33 + .959 (2, 2, 3) [1302]
MIRI Mission: 3.90 + 1.062 (3, 4, 5) [1412]
MIRI Effectiveness: 3.23 + .897 (3, 3, 4) [1336]
[Remember, all of these are asking you to rate your belief in/agreement with the concept on a scale of 1 (bad) to 5 (great)]
Income: 54129.37 + 66818.904 (10,000, 30,800, 80,000) [923]
Charity: 1996.76 + 9492.71 (0, 100, 800) [1009]
MIRI/CFAR: 511.61 + 5516.608 (0, 0, 0) [1011]
XRisk: 62.50 + 575.260 (0, 0, 0) [980]
Older siblings: 0.51 + .914 (0, 0, 1) [1332]
Younger siblings: 1.08 + 1.127 (0, 1, 1) [1349]
Height: 178.06 + 11.767 (173, 179, 184) [1236]
Hours Online: 43.44 + 25.452 (25, 40, 60) [1221]
Bem Sex Role Masculinity: 42.54 + 9.670 (36, 42, 49) [1032]
Bem Sex Role Femininity: 42.68 + 9.754 (36, 43, 50) [1031]
Right Hand: .97 + 0.67 (.94, .97, 1.00)
Left Hand: .97 + .048 (.94, .97, 1.00)
VIII. Fishing Expeditions
[correlations, in descending order]
SAT Scores out of 1600/SAT Scores out of 2400 .844 (59)
P Supernatural/P God .697 (1365)
Feminism/Social Justice .671 (1299)
P God/P Religion .669 (1367)
P Supernatural/P Religion .631 (1372)
Charity Donations/MIRI and CFAR Donations .619 (985)
P Aliens/P Aliens 2 .607 (1376)
Taxes/Minimum Wage .587 (1287)
SAT Score out of 2400/ACT Score .575 (89)
Age/Number of Children .506 (1480)
P Cryonics/P Anti-Agathics .484 (1385)
SAT Score out of 1600/ACT Score .480 (81)
Minimum Wage/Social Justice .456 (1267)
Taxes/Social Justice .427 (1281)
Taxes/Feminism .414 (1299)
MIRI Mission/MIRI Effectiveness .395 (1331)
P Warming/Taxes .385 (1261)
Taxes/Basic Income .383 (1285)
Minimum Wage/Feminism .378 (1286)
P God/Abortion -.378 (1266)
Immigration/Feminism .365 (1296)
P Supernatural/Abortion -.362 (1276)
Feminism/Human Biodiversity -.360 (1306)
MIRI and CFAR Donations/Other XRisk Charity Donations .345 (973)
Social Justice/Human Biodiversity -.341 (1288)
P Religion/Abortion -.326 (1275)
P Warming/Minimum Wage .324 (1248)
Minimum Wage/Basic Income .312 (1276)
P Warming/Basic Income .306 (1260)
Immigration/Social Justice .294 (1278)
P Anti-Agathics/MIRI Mission .293 (1351)
P Warming/Feminism .285 (1281)
P Many Worlds/P Anti-Agathics .276 (1245)
Social Justice/Femininity .267 (990)
Minimum Wage/Human Biodiversity -.264 (1274)
Immigration/Human Biodiversity -.263 (1286)
P Many Worlds/MIRI Mission .263 (1233)
P Aliens/P Warming .262 (1365)
P Warming/Social Justice .257 (1262)
Taxes/Human Biodiversity -.252 (1291)
Social Justice/Basic Income .251 (1281)
Feminism/Femininity .250 (1003)
Older Siblings/Younger Siblings -.243 (1321)
Charity Donations/Other XRisk Charity Donations .240 (957
P Anti-Agathics/P Simulation .238 (1312)
Abortion/Minimum Wage .229 (1293)
Feminism/Basic Income .227 (1297)
Abortion/Feminism .226 (1321)
P Cryonics/MIRI Mission .223 (1360)
Immigration/Basic Income .208 (1279)
P Many Worlds/P Cryonics .202 (1251)
Number of Current Partners/Femininity: .202 (1029)
P Warming/Immigration .202 (1260)
P Warming/Abortion .201 (1289)
Abortion/Taxes .198 (1304)
Age/P Simulation .197 (1313)
Political Interest/Masculinity .194 (1011)
P Cryonics/MIRI Effectiveness .191 (1285)
Abortion/Social Justice .191 (1301)
P Simulation/MIRI Mission .188 (1290)
P Many Worlds/P Warming .188 (1240)
Age/Number of Current Partners .184 (1480)
P Anti-Agathics/MIRI Effectiveness .183 (1277)
P Many Worlds/P Simulation .181 (1211)
Abortion/Immigration .181 (1304)
Number of Current Partners/Number of Children .180 (1484)
P Cryonics/P Simulation .174 (1315)
P Global Catastrophic Risk/MIRI Mission -.174 (1359)
Minimum Wage/Femininity .171 (981)
Abortion/Basic Income .170 (1302)
Age/P Cryonics -.165 (1391)
Immigration/Taxes .165 (1293)
P Warming/Human Biodiversity -.163 (1271)
P Aliens 2/Warming .160 (1353)
Abortion/Younger Siblings -.155 (1292)
P Religion/Meditate .155 (1189)
Feminism/Masculinity -.155 (1004)
Immigration/Femininity .155 (988)
P Supernatural/Basic Income -.153 (1246)
P Supernatural/P Warming -.152 (1361)
Number of Current Partners/Karma Score .152 (1332)
P Many Worlds/MIRI Effectiveness .152 (1181)
Age/MIRI Mission -.150 (1404)
P Religion/P Warming -.150 (1358)
P Religion/Basic Income -.146 (1245)
P God/Basic Income -.146 (1237)
Human Biodiversity/Femininity -.145 (999)
P God/P Warming -.144 (1351)
Taxes/Femininity .142 (987)
Number of Children/Younger Siblings .138 (1343)
Number of Current Partners/Masculinity: .137 (1030)
P Many Worlds/P God -.137 (1232)
Age/Charity Donations .133 (1002)
P Anti-Agathics/P Global Catastrophic Risk -.132 (1373)
P Warming/Masculinity -.132 (992)
P Global Catastrophic Risk/MIRI and CFAR Donations -.132 (982)
P Supernatural/Singularity .131 (1148)
God/Taxes -.130 (1240)
Age/P Anti-Agathics -.128 (1382)
P Aliens/Taxes .127(1258)
Feminism/Great Stagnation -.127 (1287)
P Many Worlds/P Supernatural -.127 (1241)
P Aliens/Abortion .126 (1284)
P Anti-Agathics/Great Stagnation -.126 (1248)
P Anti-Agathics/P Warming .125 (1370)
Age/P Aliens .124 (1386)
P Aliens/Minimum Wage .124 (1245)
P Aliens/P Global Catastrophic Risk .122 (1363)
Age/MIRI Effectiveness -.122 (1328)
Age/P Supernatural .120 (1370)
P Supernatural/MIRI Mission -.119 (1345)
P Many Worlds/P Religion -.119 (1238)
P Religion/MIRI Mission -.118 (1344)
Political Interest/Social Justice .118 (1304)
P Anti-Agathics/MIRI and CFAR Donations .118 (976)
Human Biodiversity/Basic Income -.115 (1262)
P Many Worlds/Abortion .115 (1166)
Age/Karma Score .114 (1327)
P Aliens/Feminism .114 (1277)
P Many Worlds/P Global Catastrophic Risk -.114 (1243)
Political Interest/Femininity .113 (1010)
Number of Children/P Simulation -.112 (1317)
P Religion/Younger Siblings .112 (1275)
P Supernatural/Taxes -.112 (1248)
Age/Masculinity .112 (1027)
Political Interest/Taxes .111 (1305)
P God/P Simulation .110 (1296)
P Many Worlds/Basic Income .110 (1139)
P Supernatural/Younger Siblings .109 (1274)
P Simulation/Basic Income .109 (1195)
Age/P Aliens 2 .107 (1371)
MIRI Mission/Basic Income .107 (1279)
Age/Great Stagnation .107 (1295)
P Many Worlds/P Aliens .107 (1253)
Number of Current Partners/Social Justice .106 (1304)
Human Biodiversity/Great Stagnation .105 (1285)
Number of Children/Abortion -.104 (1337)
Number of Current Partners/P Cryonics -.102 (1396)
MIRI Mission/Abortion .102 (1305)
Immigration/Great Stagnation -.101 (1269)
Age/Political Interest .100 (1339)
P Global Catastrophic Risk/Political Interest .099 (1295)
P Aliens/P Religion -.099 (1357)
P God/MIRI Mission -.098 (1335)
P Aliens/P Simulation .098 (1308)
Number of Current Partners/Immigration .098 (1305)
P God/Political Interest .098 (1274)
P Warming/P Global Catastrophic Risk .096 (1377)
In addition to the Left/Right factor we had last year, this data seems to me to have an Agrees with the Sequences Factor-- the same people tend to believe in many-worlds, cryo, atheism, simulationism, MIRI’s mission and effectiveness, anti-agathics, etc. Weirdly, belief in global catastrophic risk is negatively correlated with most of the Agrees with Sequences things. Someone who actually knows how to do statistics should run a factor analysis on this data.
IX. Digit Ratios
After sanitizing the digit ratio numbers, the following correlations came up:
Digit ratio R hand was correlated with masculinity at a level of -0.180 p < 0.01
Digit ratio L hand was correlated with masculinity at a level of -0.181 p < 0.01
Digit ratio R hand was slightly correlated with femininity at a level of +0.116 p < 0.05
Holy #@!$ the feminism thing ACTUALLY HELD UP. There is a 0.144 correlation between right-handed digit ratio and feminism, p < 0.01. And an 0.112 correlation between left-handed digit ratio and feminism, p < 0.05.
The only other political position that correlates with digit ratio is immigration. There is a 0.138 correlation between left-handed digit ratio and believe in open borders p < 0.01, and an 0.111 correlation between right-handed digit ratio and belief in open borders, p < 0.05.
No digit correlation with abortion, taxes, minimum wage, social justice, human biodiversity, basic income, or great stagnation.
Okay, need to rule out that this is all confounded by gender. I ran a few analyses on men and women separately.
On men alone, the connection to masculinity holds up. Restricting sample size to men, left-handed digit ratio corresponds to masculinity with at -0.157, p < 0.01. Left handed at -0.134, p < 0.05. Right-handed correlates with femininity at 0.120, p < 0.05. The feminism correlation holds up. Restricting sample size to men, right-handed digit ratio correlates with feminism at a level of 0.149, p < 0.01. Left handed just barely fails to correlate. Both right and left correlate with immigration at 0.135, p < 0.05.
On women alone, the Bem masculinity correlation is the highest correlation we're going to get in this entire study. Right hand is -0.433, p < 0.01. Left hand is -0.299, p < 0.05. Femininity trends toward significance but doesn't get there. The feminism correlation trends toward significance but doesn't get there. In general there was too small a sample size of women to pick up anything but the most whopping effects.
Since digit ratio is related to testosterone and testosterone sometimes affects risk-taking, I wondered if it would correlate with any of the calibration answers. I selected people who had answered Calibration Question 5 incorrectly and ran an analysis to see if digit ratio was correlated with tendency to be more confident in the incorrect answer. No effect was found.
Other things that didn't correlate with digit ratio: IQ, SAT, number of current partners, tendency to work in mathematical professions.
...I still can't believe this actually worked. The finger-length/feminism connection ACTUALLY WORKED. What a world. What a world. Someone may want to double-check these results before I get too excited.
X. Calibration
There were ten calibration questions on this year's survey. Along with answers, they were:
1. What is the largest bone in the body? Femur
2. What state was President Obama born in? Hawaii
3. Off the coast of what country was the battle of Trafalgar fought? Spain
4. What Norse God was called the All-Father? Odin
5. Who won the 1936 Nobel Prize for his work in quantum physics? Heisenberg
6. Which planet has the highest density? Earth
7. Which Bible character was married to Rachel and Leah? Jacob
8. What organelle is called "the powerhouse of the cell"? Mitochondria
9. What country has the fourth-highest population? Indonesia
10. What is the best-selling computer game? Minecraft
I ran calibration scores for everybody based on how well they did on the ten calibration questions. These failed to correlate with IQ, SAT, LW karma, or any of the things you might expect to be measures of either intelligence or previous training in calibration; they didn't differ by gender, correlates of community membership, or any mental illness [deleted section about correlating with MWI and MIRI, this was an artifact].
Your answers looked like this:
The red line represents perfect calibration. Where answers dip below the line, it means you were overconfident; when they go above, it means you were underconfident.
It looks to me like everyone was horrendously underconfident on all the easy questions, and horrendously overconfident on all the hard questions. To give an example of how horrendous, people who were 50% sure of their answers to question 10 got it right only 13% of the time; people who were 100% sure only got it right 44% of the time. Obviously those numbers should be 50% and 100% respectively.
This builds upon results from previous surveys in which your calibration was also horrible. This is not a human universal - people who put even a small amount of training into calibration can become very well calibrated very quickly. This is a sign that most Less Wrongers continue to neglect the very basics of rationality and are incapable of judging how much evidence they have on a given issue. Veterans of the site do no better than newbies on this measure.
XI. Wrapping Up
To show my appreciation for everyone completing this survey, including the arduous digit ratio measurements, I have randomly chosen a person to receive a $30 monetary prize. That person is...the person using the public key "The World Is Quiet Here". If that person tells me their private key, I will give them $30.
I have removed 73 people who wished to remain private, deleted the Private Keys, and sanitized a very small amount of data. Aside from that, here are the raw survey results for your viewing and analyzing pleasure:
(as Excel)
Estimating the cost-effectiveness of research
At a societal level, how much money should we put into medical research, or into fusion research? For individual donors seeking out the best opportunities, how can we compare the expected cost-effectiveness of research projects with more direct interventions?
Over the past few months I've been researching this area for the Global Priorities Project. We've written a variety of articles which focus on different parts of the question. Estimating the cost-effectiveness of research is the central example here, but a lot of the methodology is also applicable to other one-off projects with unknown difficulty (perhaps including political lobbying). I don't think it's all solved, but I do think we've made substantial progress.
I think people here might be interested, so I wanted to share our work. To help you navigate and find the most appropriate pieces, here I collect them, summarise what's contained in each, and explain how they fit together.
- I gave an overview of my thinking at the Good Done Right conference, held in Oxford in July 2014. The slides and audio of my talk are available; I have developed more sophisticated models for some parts of the area since then.
- How to treat problems of unknown difficulty introduces the problem: we need to make decisions about when to work more on problems such as research into fusion where we don't know how difficult it will be. It builds some models which allow principled reasoning about how we should act. These models are quite crude but easy to work with: they are intended to lower the bar for Fermi estimates and similar, and provide a starting point for building more sophisticated models.
- Estimating cost-effectiveness for problems of unknown difficulty picks up from the models in the above post, and asks what they mean for the expected cost-effectiveness of work on the problems. This involves building a model of the counterfactual impact, as solvable research problems are likely to be solved eventually, so the main effect is to move the solution forwards. This post includes several explicit formulae that you can use to produce estimates; it also explains analogies between the explicit model we derive and the qualitative 'three factor' model that GiveWell and 80,000 Hours have used for cause selection.
- Estimating the cost-effectiveness of research into neglected diseases is an investigation by Max Dalton, which uses the techniques for estimating cost-effectiveness to provide ballpark figures for how valuable we should expect research into vaccines or treatments for neglected diseases to be. The estimates suggest that, if carefully targeted, such research could be more cost-effective than the best direct health interventions currently available for funding.
- The law of logarithmic returns discusses the question of returns to resources into a field rather than on a single question. With some examples, it suggests that as a first approximation it is often reasonable to assume that diminishing marginal returns take a logarithmic form.
- Theory behind logarithmic returns explains how some simple generating mechanisms can produce roughly logarithmic returns. This is a complement to the above article: we think having both empirical and theoretical justification for the rule helps us to have higher confidence in it, and to better understand when it's appropriate to generalise to new contexts. In this piece I also highlight areas for further research on the theoretical side, into when the approximation will break down, and what we might want to use instead in these cases.
- How valuable is medical research? written with Giving What We Can, applies the logarithmic returns model together with counterfactual reasoning to produce an estimate for the cost-effectiveness of medical research as a whole.
Using machine learning to predict romantic compatibility: empirical results
Overview
For many people, having a satisfying romantic relationship is one of the most important aspects of life. Over the past 10 years, online dating websites have gained traction, and dating websites have access to large amounts of data that could be used to build predictive models to achieve this goal. Such data is seldom public, but Columbia business school professors Ray Fisman and Sheena Iyengar compiled a rich and relevant data set for their paper Gender Differences in Mate Selection: Evidence From a Speed Dating Experiment. Their main results were:
Women put greater weight on the intelligence and the race of partner, while men respond more to physical attractiveness. Moreover, men do not value women’s intelligence or ambition when it exceeds their own. Also, we find that women exhibit a preference for men who grew up in affluent neighborhoods. Finally, male selectivity is invariant to group size, while female selectivity is strongly increasing in group size.
I found the study through Andrew Gelman’s blog, where he wrote:
What I really want to do with these data is what I suggested to Ray and Sheena several years ago when they first told me about the study: a multilevel model that allows preferences to vary by person, not just by sex. Multilevel modeling would definitely be useful here, since you have something like 10 binary observations and 6 parameters to estimate for each person.
Several months ago I decided to pursue a career in data science, and with a view toward building my skills, I worked to build a model to predict when an individual participant will express interest in seeing a given partner again. Along with the goal of learning, I had the dual intent of contributing knowledge that had the potential, however slight, to help people find satisfying romantic relationships.
It’s unlikely that what I did will have practical applications (as basic research seldom does), but I did learn a great deal about many things, most having to do with data science methodology in general, but also some about human behavior.
This is the first of a series of posts where I report on my findings. A linear narrative would degenerate to a sprawling blog post that would be of little interest to anybody but me. In this post, I’ll restrict focus to the question: how much predictive power can we get by estimating the generic selectivity and desirability of the people involved, without using information about the interactions between their traits?
I’ll ultimately go into the details of the methodology that I used, including discussion of statistical significance, the rationale for making the decisions that I did the, and links to relevant code, but here I’ll suppress technical detail in favor of relegating it to separate blog posts that might be of interest to a more specialized audience. In several places I speculate as to the meaning of the results. I’ve made efforts to subject my reasoning to cross checks, but haven’t gotten almost any external feedback yet, and I’d welcome counter considerations, alternative hypotheses, etc. I’m aware that there are places where claims that I make don’t logically follow from what precedes them, and I’m not so much looking for examples of this in general as much as I am instances where there’s a sizable probability that I’ve missed something that alters the bottom line conclusions.
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