trentmkelly/LessWrong-43k huggingface dataset:
What does your accuracy tell you about your confidence interval?
by HonoreDB — 2011-11-02T19:21:42.249Z

Yvain's 2011 Less Wrong Census/Survey is still ongoing throughout November, 2011. If you haven't taken it, please do before reading on, or at least write down your answers to the calibration questions so they won't get skewed by the following discussion.

The survey includes these questions:

Calibration YearWithout checking a source, in what year do you estimate [redacted event happened]?

Calibration AnswerWithout checking a source, estimate the probability that the answer you just gave is within 15 years either way of the correct answer.

In the comments, several people including myself wondered what our level of accuracy in the first question said about the calibration of our answer to the second question. If your guess for the first question was really close to correct, but your probability for the second question was low, were you underconfident? If you were far off, but your probability was high, were you overconfident?

We could test our calibration by simply answering a lot of these pairs of questions, then applying a proper scoring rule. But that seems like throwing out information. Surely we could calibrate faster if we're allowed to use our accuracy as evidence?

I suspect there are people on here with the tools to work this out trivially. Here's my try at it:

Suppose you state a p-confidence interval of ±a around your guess x of the true value X. Then you find that, actually, |X - x| = b. What does this say about your confidence interval?

As a first approximation, we can represent your confidence interval as a claim that the answer is uniformly randomly placed within an interval of ±(a/p), and that you have guessed uniformly within the same interval. If this is the case, your guess should on average be ±(1/3 * a/p) off, following a triangular distribution. It should be in the range (1/3 ± 3/16)(a/p) half the time. It should be less than 1/3(3 - sqrt(6)), or about .18, 1/3 of the time, and greater than 1-1/(sqrt(3), or about .42, 1/3 of the time.

So, here's a rule of thumb for evaluating your confidence intervals based on how close you're getting to the actual answer. Again, a is the radius of your interval, and p is the probability you assigned that the answer is in that interval.

1. Determine how far you were of

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[Link] Active tactile exploration using a brain–machine–brain interface
by Logos01 — 2011-11-02T10:38:27.083Z

ICMS (intracortical microstimulation) was successfully demonstrated for inducing artificial sensory stimulation in rhesus monkeys. (This is a significant -- albeit miniscule -- step forward for data-in brain-computer interfaces.)

-- http://www.nature.com/nature/journal/vaop/ncurrent/full/nature10489.html --
Anthropic Decision Theory II: Self-Indication, Self-Sampling and decisions
by Stuart_Armstrong — 2011-11-02T10:03:07.981Z

A near-final version of my Anthropic Decision Theory paper is available on the arXiv. Since anthropics problems have been discussed quite a bit on this list, I'll be presenting its arguments and results in this, subsequent, and previous posts 1 2 3 4 5 6.

In the last post, we saw the Sleeping Beauty problem, and the question was what probability a recently awoken or created Sleeping Beauty should give to the coin falling heads or tails and it being Monday or Tuesday when she is awakened (or whether she is in Room 1 or 2). There are two main schools of thought on this, the Self-Sampling Assumption and the Self-Indication Assumption, both of which give different probabilities for these events.

The Self-Sampling Assumption

The self-sampling assumption (SSA) relies on the insight that Sleeping Beauty, before being put to sleep on Sunday, expects that she will be awakened in future. Thus her awakening grants her no extra information, and she should continue to give the same credence to the coin flip being heads as she did before, namely 1/2.

In the case where the coin is tails, there will be two copies of Sleeping Beauty, one on Monday and one on Tuesday, and she will not be able to tell, upon awakening, which copy she is. She should assume that both are equally likely. This leads to SSA:

All other things being equal, an observer should reason as if they are randomly selected from the set of all actually existent observers (past, present and future) in their reference class.

There are some issues with the concept of 'reference class', but here it is enough to set the reference class to be the set of all other Sleeping Beauties woken up in the experiment.

Given this, the probability calculations become straightforward:

PSSA(Heads) = 1/2

PSSA(Tails) = 1/2

PSSA(Monday|Heads) = 1

PSSA(Tuesday|Head) = 0

PSSA(Monday|Tails) = 1/2

PSSA(Tuesday|Tails) = 1/2

By Bayes' theorem, these imply that:

PSSA(Monday) = 3/4

PSSA(Tuesday) = 1/4

The Self-Indication Assumption

There is another common way of doing anthropic probability, namely to use the self-indication assumption (SIA). This derives from the insight that being woken up on Monday after a heads, being woken up on Monday after a tails, and being woken up on Tuesday are all subjectively indistinguishable events, which each have a probability 1/2 of happening, therefore ...
AGI Quotes
by lukeprog — 2011-11-02T08:25:53.179Z

Similar to the monthly Rationality Quotes threads, this is a thread for memorable quotes about Artificial General Intelligence.

Please post all quotes separately, so that they can be voted up/down separately. (If they are strongly related, reply to your own comments. If strongly ordered, then go ahead and post them together.)

Do not quote yourself.

Do not quote comments/posts on LW/OB.
Review of Sharot & Dolan, 'Neuroscience of Preference and Choice' (2011)
by lukeprog — 2011-11-02T06:52:24.623Z

The new edited volume Neuroscience of Preference and Choice includes chapters from a good chunk of the leading researchers in neuroeconomics. If you read only two books on neuroeconomics, they should be Glimcher (2010) and Neuroscience of Preference and Choice.

First, let me review the main conclusions from my Crash Course in the Neuroscience of Human Motivation:

Utilities [aka 'decision values'] are real numbers ranging from 0 to 1,000 that take action potentials per second as their natural units.

Mean utilities are mean firing rates of specific populations of neurons in the final common path of human choice circuits.

Mean utilities predict choice stochastically, similar to random utility models from economics.

Utilities are encoded cardinally in firing rates relative to neuronal baseline firing rates. (This is opposed to post-Pareto, ordinal notions of utility.)

The choice circuit takes as its input a firing rate that encodes relative (normalized) stochastic expected utility.

As the choice set size grows, so does the error rate.

Final choice is implemented by an argmax function or a reservation price mechanism.

Much of Neuroscience of Preference and Choice repeats and reinforces these conclusions. In this review, I'll focus on the major results that are discussed in Neuroscience of Preference and Choice but not in my 'Crash Course'. The editors of Neuroscience of Preference and Choice see two major results from the work reviewed in the book:

Contrary to traditional decision-making theories, which assume choices are based on relatively steady preferences, preferences are in fact highly volatile and susceptible to the context in which the alternatives are presented. Moreover, our preferences are modified by the mere act of choosing and altered by changing choice sets.

Regardless of whether we are selecting a musical tune, a perfume, or a new car – the brain uses similar computational principles to compute the value of our options, which are tracked by common neural systems.

I will highlight three additional important results:

There are three competing valuation systems, and they respond to different stimuli. The first is "model-based" control, associated with goal-directed behavior. The second is "model-free" control, associated with reinforcement learning. The third is "Pavlovian control," which imposes hard-wired motivati

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[SEQ RERUN] Truly Part of You
by MinibearRex — 2011-11-02T03:30:20.580Z

Today's post, Truly Part Of You was originally published on 21 November 2007. A summary (taken from the LW wiki):

Any time you believe you've learned something, you should ask yourself, "Could I re-generate this knowledge if it were somehow deleted from my mind, and how would I do so?" If the supposed knowledge is just empty buzzwords, you will recognize that you can't, and therefore that you haven't learned anything. But if it's an actual model of reality, this method will reinforce how the knowledge is entangled with the rest of the world, enabling you to apply it to other domains, and know when you need to update those beliefs. It will have become "truly part of you", growing and changing with the rest of your knowledge.

Discuss the post here (rather than in the comments to the original post).
This post is part of the Rerunning the Sequences series, where we'll be going through Eliezer Yudkowsky's old posts in order so that people who are interested can (re-)read and discuss them. The previous post was Artificial Addition, and you can use the sequence_reruns tag or rss feed to follow the rest of the series.
Sequence reruns are a community-driven effort. You can participate by re-reading the sequence post, discussing it here, posting the next day's sequence reruns post, or summarizing forthcoming articles on the wiki. Go here for more details, or to have meta discussions about the Rerunning the Sequences series.
Do we have it too easy?
by Klao — 2011-11-02T01:53:01.625Z

I am worried that I have it too easy. I recently discovered LessWrong for myself, and it feels very exciting and very important and I am learning a lot, but how do I know that I am really on a right way? I have some achievements to show, but there are some worrisome signs too.

I need some background to explain what I mean. I was raised in an atheist/agnostic family, but some time in my early teens I gave a mysterious answer to a mysterious question, and... And long story short, influenced by everything I was reading at the time I became a theist. I wasn't religious in the sense that I never followed any established religion, but I had my own "theological model" (heavily influenced by theosophy and other western interpretations of eastern religions). I believed in god, and it was a very important part of my life (around the end of high school, beginning of college I started talking about it with my friends and was quite open and proud about it).

Snip 15-20 years. This summer I started lurking on LessWrong, reading mostly Eliezer's sequences. One morning, walking to the train station, thinking about something I read, my thoughts wondered to how this all affects my faith. And I noticed myself flinching away, and thought “Isn't this what Eliezer calls "flinching away"?” I didn't resolve my doubts there and then, but there was no turning back and couple of days later I was an atheist. This is my first "achievement". The second is: when I got to the "free will" sequence, I stopped before reading any spoilers, gave myself a weekend and I figured it out! (Not perfectly, but at least one part I figured out very clearly, and got important insights into the other part.) Which I would have never thought I would be able to do, because as it happens, this was the original mysterious question on which I got so confused as a teenager. (Another, smaller "achievement" I documented here.)

Maybe these are not too impressive, but they are not completely trivial either (actually, I am a bit proud of myself :)). But, I get a distinct feeling that something is off. Take the atheism: I think, one of the reasons I so easily let go of my precious belief, was that I had something to replace it with. And this is very-very scary, that I sometimes get the same feeling of amazing discovery reading Eliezer as when I was 13, and my mind just accepts it all unconditionally!...
Myers-Briggs / MLPTI personality-type conversion chart
by PhilGoetz — 2011-11-01T20:08:15.110Z

While psychology wonks have been going on for years about the statistical rigor and calibration of the Big Five, most people have just carried on using the Myers-Briggs type indicator (MBTI), which may not be statistical or scientific but is able to categorize people without insulting them.

A serious critique of the MBTI is the Myers-Briggs entropy distribution paradox (or, "Why are there 16 personality types when everyone I know is an INTJ?") A new personality test which has been gaining ground recently, the MLPTI, does not break up the INTJ into multiple categories; but does reduce the number of bothersome non-INTJ personality types and thus ameliorates the entropy paradox. For those not yet familiar with it, here is a rough translation between MLPTI and MBTI types.

MLP type

Traits
M-B types

TS
conscientious, introverted, self-conscious

INTJ

RD
impulsive, activity-oriented, high stimulation threshold

ESFJ, ESFP

PP
creative, un-self-conscious

ENFP

AJ
pragmatic, disciplined, outcome-oriented

ISTJ

FS
introverted, empathetic, anxious
ISFJ, INFJ

R
extroverted, creative, status-seeking

ENFJ

The loss of half of the MBTI categories is not a serious problem, as demonstrated by the fact that you can't even name the ones that were left out without going back and looking. Seriously, when was the last time you met an ENTP?
You Are Not So Smart (Pop-Rationality Book) 
by betterthanwell — 2011-11-01T19:42:37.243Z

Journalist David McRaney has very recently published a popular book on human rationality. The book, You Are Not So Smart, is currently the 3rd best selling book in Nonfiction/Philosophy on Amazon.com after less than a week on the market. (Eighth best selling book in Nonfiction/Education)

The tag-line of the project is: "A celebration of self-delusion." As such the book seems less an attempt at giving advice on how to act and decide, than an attempt to reveal, chapter by chapter, the folly of common sense.

Topics include: Hindsight Bias, Confirmation bias, The Sunk Cost Fallacy, Anchoring Effect, The Illusion of Transparency, The Just World Fallacy, Representativeness Heuristic, The Perils of Introspection, The Dunning-Kruger Effect, The Monty Hall Problem, The Bystander Effect, Placebo Buttons, Groupthink, Conformity, Social Loafing, Helplessness, Cults, Change Blindness, Self-Fulfilling Prophecies, Self Handicapping, Availability Heuristic, Self-Serving Bias, The Ultimatum Game, Inattentional Blindness.

These are topics we enjoy learning about, pride ourself in knowing a lot about, and, we profess, we would want more people to know about. A popular book on this subject is now out. This sounds like a good thing.

I will note that the blog features at least one direct quote from LessWrong.

We always know what we mean by our words, and so we expect others to know it too. Reading our own writing, the intended interpretation falls easily into place, guided by our knowledge of what we really meant. It’s hard to empathise with someone who must interpret blindly, guided only by the words.

- Eliezer Yudowsky from Lesswrong.com

One one hand, You Are Not So Smart could bee a boon to Eliezer's popular rationality book by priming the market. His writings on a given topic have rarely been described as redundant. On the other hand, it seems to me that this book closely covers a number of topics, seemingly in a similar style to the treatments that were published on this site and Overcoming Bias. Intended to be published in book form at a later date. I will try to refrain from speculation here.

Sample blook chapters from YouAreNotSoSmart:

Procrastination

Misattribution of Arousal

Texas Sharpshooter Fallacy

For more material, here's a list of all posts at youarenotsosmart.com

I'll save the rest of my review until I have ...
Rationality Quotes November 2011
by Jayson_Virissimo — 2011-11-01T18:28:38.290Z

Here's the new thread for posting quotes, with the usual rules:

Please post all quotes separately, so that they can be voted up/down separately. (If they are strongly related, reply to your own comments. If strongly ordered, then go ahead and post them together.)

Do not quote yourself.

Do not quote comments/posts on LW/OB.

No more than 5 quotes per person per monthly thread, please.
2011 Less Wrong Census / Survey
by Scott Alexander — 2011-11-01T18:28:22.220Z

The final straw was noticing a comment referring to "the most recent survey I know of" and realizing it was from May 2009. I think it is well past time for another survey, so here is one now.

Click here to take the survey

I've tried to keep the structure of the last survey intact so it will be easy to compare results and see changes over time, but there were a few problems with the last survey that required changes, and a few questions from the last survey that just didn't apply as much anymore (how many people have strong feelings on Three Worlds Collide these days?)

Please try to give serious answers that are easy to process by computer (see the introduction). And please let me know as soon as possible if there are any security problems (people other than me who can access the data) or any absolutely awful questions.

I will probably run the survey for about a month unless new people stop responding well before that. Like the last survey, I'll try to calculate some results myself and release the raw data (minus the people who want to keep theirs private) for anyone else who wants to examine it.

Like the last survey, if you take it and post that you took it here, I will upvote you, and I hope other people will upvote you too.
Anthropic decision theory I: Sleeping beauty and selflessness
by Stuart_Armstrong — 2011-11-01T11:41:32.962Z

A near-final version of my Anthropic Decision Theory paper is available on the arXiv. Since anthropics problems have been discussed quite a bit on this list, I'll be presenting its arguments and results in this and subsequent posts 1 2 3 4 5 6.

Many thanks to Nick Bostrom, Wei Dai, Anders Sandberg, Katja Grace, Carl Shulman, Toby Ord, Anna Salamon, Owen Cotton-barratt, and Eliezer Yudkowsky.

The Sleeping Beauty problem, and the incubator variant

The Sleeping Beauty problem is a major one in anthropics, and my paper establishes anthropic decision theory (ADT) by a careful analysis it. Therefore we should start with an explanation of what it is.

In the standard setup, Sleeping Beauty is put to sleep on Sunday, and awoken again Monday morning, without being told what day it is. She is put to sleep again at the end of the day. A fair coin was tossed before the experiment began. If that coin showed heads, she is never reawakened. If the coin showed tails, she is fed a one-day amnesia potion (so that she does not remember being awake on Monday) and is reawakened on Tuesday, again without being told what day it is. At the end of Tuesday, she is put to sleep for ever. This is illustrated in the next figure:

The incubator variant of the problem, due to Nick Bostrom, has no initial Sleeping Beauty, just one or two copies of her created (in different, identical rooms), depending on the result of the coin flip. The name `incubator' derived from the machine that was to do the birthing of these observers. This is illustrated in the next figure:

The question then is what probability a recently awoken or created Sleeping Beauty should give to the coin falling heads or tails and it being Monday or Tuesday when she is awakened (or whether she is in Room 1 or 2).

Selfishness, selflessness and altruism

I will be using these terms in precise ways in ADT, somewhat differently from how they are usually used. A selfish agent is one whose preferences are only about their own personal welfare; a pure hedonist would be a good example. A selfless agent, on the other hand is one that cares only about the state of the world, not about their own personal welfare - or anyone else's. They might not be nice (patriots are - arguably - selfless), but they do not care about their own welfare as a terminal goal.

Altruistic agents, on the other hand, care about the welfare of everyone, not just thems...
Thinking Statistically [ebook]
by Dreaded_Anomaly — 2011-11-01T03:36:16.554Z

Uri Bram, a recent Princeton graduate, has just published an ebook called Thinking Statistically. The book is aimed at conveying a few important statistical concepts (selection bias, endogeneity and correlation vs. causation, Bayes theorem and base rate neglect) to a general audience. The official product description:

This book will show you how to think like a statistician, without worrying about formal statistical techniques. Along the way we'll see why supposed Casanovas might actually be examples of the Base Rate Fallacy; how to use Bayes' Theorem to assess whether your partner is cheating on you; and why you should never use Mark Zuckerberg as an example for anything. See the world in a whole new light, and make better decisions and judgements without ever going near a t-test. Think. Think Statistically.

Less Wrong members will be familiar with these topics, but we should keep this book in mind as a convenient method of getting friends, relatives, acquaintances, and others interested in understanding rationality.

Eliezer's An Intuitive Explanation of Bayes' Theorem gets a shout-out in the Recommended Reading at the end.
[SEQ RERUN] Artificial Addition
by MinibearRex — 2011-11-01T02:51:16.252Z

Today's post, Artificial Addition was originally published on 20 November 2007. A summary (taken from the LW wiki):

If you imagine a world where people are stuck on the "artifical addition" (i.e. machine calculator) problem, the way people currently are stuck on artificial intelligence, and you saw them trying the same popular approaches taken today toward AI, it would become clear how silly they are. Contrary to popular wisdom (in that world or ours), the solution is not to "evolve" an artificial adder, or invoke the need for special physics, or build a huge database of solutions, etc. -- because all of these methods dodge the crucial task of understanding what addition involves, and instead try to dance around it. Moreover, the history of AI research shows the problems of believing assertions one cannot re-generate from one's own knowledge.

Discuss the post here (rather than in the comments to the original post).
This post is part of the Rerunning the Sequences series, where we'll be going through Eliezer Yudkowsky's old posts in order so that people who are interested can (re-)read and discuss them. The previous post was Conjuring an Evolution to Serve You, and you can use the sequence_reruns tag or rss feed to follow the rest of the series.
Sequence reruns are a community-driven effort. You can participate by re-reading the sequence post, discussing it here, posting the next day's sequence reruns post, or summarizing forthcoming articles on the wiki. Go here for more details, or to have meta discussions about the Rerunning the Sequences series.