trentmkelly/LessWrong-43k huggingface dataset:
Sequence Announcement: Applied Causal Inference
by Anders_H — 2014-07-30T20:55:41.741Z

Applied Causal Inference for Observational Research

This sequence is an introduction to basic causal inference. It was originally written as auxiliary notes for a course in Epidemiology, but it is relevant to almost any kind of applied statistical research, including econometrics, sociology, psychology, political science etc. I would not be surprised if you guys find a lot of errors, and I would be very grateful if you point them out in the comments. This will help me improve my course notes and potentially help me improve my understanding of the material.

For mathematically inclined readers, I recommend skipping this sequence and instead reading Pearl's book on Causality. There is also a lot of good material on causal graphs on Less Wrong itself. Also, note that my thesis advisor is writing a book that covers the same material in more detail, the first two parts are available for free at his website.

Pearl's book, Miguel's book and Eliezer's writings are all more rigorous and precise than my sequence. This is partly because I have a different goal: Pearl and Eliezer are writing for mathematicians and theorists who may be interested in contributing to the theory. Instead, I am writing for consumers of science who want to understand correlation studies from the perspective of a more rigorous epistemology.

I will use Epidemiological/Counterfactual notation rather than Pearl's notation. I apologize if this is confusing. These two approaches refer to the same mathematical objects, it is just a different notation. Whereas Pearl would use the "Do-Operator" E[Y|do(a)], I use counterfactual variables E[Ya]. Instead of using Pearl's "Do-Calculus" for identification, I use Robins' G-Formula, which will give the same results.

For all applications, I will use the letter "A" to represent "treatment" or "exposure" (the thing we want to estimate the effect of), Y to represent the outcome, L to represent any measured confounders, and U to represent any unmeasured confounders.

Outline of Sequence:

I hope to publish one post every week. I have rough drafts for the following eight sections, and will keep updating this outline with links as the sequence develops:

Part 0: Sequence Announcement / Introduction (This post)

Part 1: Basic Terminology and the Assum...
Continuity axiom of vNM
by Stuart_Armstrong — 2014-07-30T16:27:54.434Z

In a previous post, I left a somewhat cryptic comment on the continuity/Archimedean axiom of vNM expected utility.

(Continuity/Achimedean) This axiom (and acceptable weaker versions of it) is much more subtle that it seems; "No choice is infinity important" is what it seems to say, but " 'I could have been a contender' isn't good enough" is closer to what it does. Anyway, that's a discussion for another time.

Here I'll explain briefly what I mean by it. Let's drop that axiom, and see what could happen. First of all, we could have a utility function with non-standard real value. This allows some things to be infinitely more important than others. A simple illustration is lexicographical ordering; eg my utility function consists of the amount of euros I end up owning, with the amount of sex I get serving as a tie-breaker.

There is nothing wrong with such a function! First, because in practice it functions as a standard utility function (I'm unlikely to be able to indulge in sex in a way that has absolutely no costs or opportunity costs, so the amount of euros will always predominate). Secondly because, even if it does make a difference... it's still expected utility maximisation, just a non-standard version.

But worse things can happen if you drop the axiom. Consider this decision criteria: I will act so that, at some point, there will have been a chance of me becoming heavy-weight champion of the world. This is compatible with all the other vNM axioms, but is obviously not what we want as a decision criteria. In the real world, such decision criteria is vacuous (there is a non-zero chance of me becoming heavyweight champion of the world right now), but it certainly could apply in many toy models.

That's why I said that the continuity axiom is protecting us from "I could have been a contender (and that's all that matters)" type reasoning, not so much from "some things are infinitely important (compared to others)".

Also notice that the quantum many-worlds version of the above decision criteria - "I will act so that the measure of type X universe is non-zero" - does not sound quite as stupid, especially if you bring in anthropics.
How to treat problems of unknown difficulty
by owencb — 2014-07-30T11:27:33.014Z

Crossposted from the Global Priorities Project

This is the first in a series of posts which take aim at the question: how should we prioritise work on problems where we have very little idea of our chances of success. In this post we’ll see some simple models-from-ignorance which allow us to produce some estimates of the chances of success from extra work. In later posts we’ll examine the counterfactuals to estimate the value of the work. For those who prefer a different medium, I gave a talk on this topic at the Good Done Right conference in Oxford this July.

Introduction

How hard is it to build an economically efficient fusion reactor? How hard is it to prove or disprove the Goldbach conjecture? How hard is it to produce a machine superintelligence? How hard is it to write down a concrete description of our values?

These are all hard problems, but we don’t even have a good idea of just how hard they are, even to an order of magnitude. This is in contrast to a problem like giving a laptop to every child, where we know that it’s hard but we could produce a fairly good estimate of how much resources it would take.

Since we need to make choices about how to prioritise between work on different problems, this is clearly an important issue. We can prioritise using benefit-cost analysis, choosing the projects with the highest ratio of future benefits to present costs. When we don’t know how hard a problem is, though, our ignorance makes the size of the costs unclear, and so the analysis is harder to perform. Since we make decisions anyway, we are implicitly making some judgements about when work on these projects is worthwhile, but we may be making mistakes.

In this article, we’ll explore practical epistemology for dealing with these problems of unknown difficulty.

Definition

We will use a simplifying model for problems: that they have a critical threshold D such that the problem will be completely solved when D resources are expended, and not at all before that. We refer to this as the difficulty of the problem. After the fact the graph of success with resources will look something like this:

Of course the assumption is that we don’t know D. So our uncertainty about where the threshold is will smooth out the curve in expectation. Our expectation beforehand for success with resources will end up looking something like ...
Gaming Democracy
by Froolow — 2014-07-30T09:45:31.271Z

I live in the UK, which has a very similar voting structure to the US for the purposes of this article. Nevertheless, it may differ on the details, for which I am sorry. I also use a couple of real-life political examples which I hope are uncontroversial enough not to break the unofficial rules here. If they are not, I can change them, because this is a discussion of gaming democracy by exploiting swing seats to push rationalist causes.

Cory Doctrow writes in the Guardian about using Kickstarter-like thresholds to encourage voting for minority parties:

http://www.theguardian.com/technology/2014/jul/24/how-the-kickstarter-model-could-transform-uk-elections

He points out that nobody votes for minority parties because nobody else votes for them; if you waste your vote on Yellow then it is one fewer vote that might stop the hated Blue candidate getting in by voting for the not-quite-so-bad Green. He argues that you could use the internet to inform people when some pre-set threshold had been triggered with respect to voting for a minor party and thus encourage them to get out and vote. So for example if the margin of victory was 8000 votes and 9000 people agreed with the statement, “If more than 8000 people agree to this statement, then I will go to the polls on election day and vote for the minority Yellow party”, the minority Yellow party would win power even though none of the original 9000 participants would have voted for Yellow without the information-coordinating properties of the internet.

I’m not completely sure of the argument, but I looked into some of the numbers myself. There are 23 UK seats (roughly equivalent to Congressional Districts for US readers) with a margin of 500 votes or fewer. So to hold the balance of power in these seats you need to find either 500 non-voters who would be prepared to vote the way you tell them, or 250 voters with the same caveats (voters are worth twice as much as non-voters to the aspiring seat-swinger, since a vote taken from the Blues lowers the margin by one, and a vote given to the Greens lowers the margin by one, and every voter is entitled to both take a vote away from the party they are currently voting for and award a vote to any party of their choice). I’ll call the number of votes required to swing a seat the ‘effective voter’ count, which allows for the fact that so...
Meetup : Perth, Australia: Introducing Less Wrong
by  — 2014-07-30T05:43:21.468Z

Discussion article for the meetup : Perth, Australia: Introducing Less Wrong

WHEN:
05 August 2014 06:00:00PM (+0800)

WHERE:
Sync Labs, 6/663 Newcastle Street, Leederville WA 6007, Australia

What is Less Wrong? What are its central ideas? What is the Less Wrong community?

If you're in Perth and new to Less Wrong, this meeting is for you: I'll give an overview and answer any questions you might have. If you're already familiar with Less Wrong, please come and share your knowledge!

You'll also get to make some tea and chat with other members of the group; we've snagged some friendly, smart, and interesting people.

You can RSVP here: http://www.meetup.com/Perth-Less-Wrong/events/197893912/

Discussion article for the meetup : Perth, Australia: Introducing Less Wrong
Tentative tips for people engaged in an exercise that involves some form of prediction or forecasting
by VipulNaik — 2014-07-30T05:24:35.963Z

Note: This is the concluding post of my LessWrong posts related to my forecasting work for MIRI. There are a few items related to forecasting that I didn't get time to look into and might return to later. I might edit this post to include references to those posts if I get to them later.

I've been looking at forecasting in different domains as part of work for the Machine Intelligence Research Institute (MIRI). I thought I'd draw on whatever I've learned to write up advice for people engaged in any activity that involves making forecasts. This could include a wide range of activities, including those that rely on improving the accuracy of predictions in highly circumscribed contexts (such as price forecasting or energy use forecasting) as well as those that rely on trying to determine the broad qualitative contours of possible scenarios.

The particular application of interest to MIRI is forecasting AI progress, leading up to (but not exclusively focused on) the arrival of AGI. I will therefore try to link my general tips with thoughts on how it applies to forecasting AI progress. That being said, I hope that what I say here will have wider interest and appeal.

If you're interested in understanding the state of the art with respect to forecasting AI progress specifically, consider reading Luke Muehlhauser's summary of the state of knowledge on when AI will be created. The post was written in May 2013, and there have been a couple of developments since then, including:

A paper by Vincent C. Müller and Nick Bostrom that describes a poll of artificial intelligence experts on future progress in the area

An update by Paul Christiano and Katja Grace to the data collected in the paper by Stuart Armstrong and Kaj Sotala on how we're predicting AI (or failing to)

#1: Appreciate that forecasting is hard

It's hard to make predictions, especially about the future (see also more quotes here). Forecasting is a difficult job along many dimensions. Apart from being difficult, it's also a job where feedback is far from immediate. This holds more true as the forecasting horizon becomes wider (for lists of failed predictions made in the past, see here and here). Fortunately, a fair amount has been discovered about forecasting in general, and you can learn from the experience of people trying to make forecasts in many different domains.

Philip Tetlock's work on expert politica...
Meditations On Moloch
by Scott Alexander — 2014-07-30T04:00:06.000Z

[Content note: Visions! omens! hallucinations! miracles! ecstasies! dreams! adorations! illuminations! religions!]

I.

Allan Ginsberg’s famous poem, Moloch:

What sphinx of cement and aluminum bashed open their skulls and ate up their brains and imagination?

Moloch! Solitude! Filth! Ugliness! Ashcans and unobtainable dollars! Children screaming under the stairways! Boys sobbing in armies! Old men weeping in the parks!

Moloch! Moloch! Nightmare of Moloch! Moloch the loveless! Mental Moloch! Moloch the heavy judger of men!

Moloch the incomprehensible prison! Moloch the crossbone soulless jailhouse and Congress of sorrows! Moloch whose buildings are judgment! Moloch the vast stone of war! Moloch the stunned governments!

Moloch whose mind is pure machinery! Moloch whose blood is running money! Moloch whose fingers are ten armies! Moloch whose breast is a cannibal dynamo! Moloch whose ear is a smoking tomb!

Moloch whose eyes are a thousand blind windows! Moloch whose skyscrapers stand in the long streets like endless Jehovahs! Moloch whose factories dream and croak in the fog! Moloch whose smoke-stacks and antennae crown the cities!

Moloch whose love is endless oil and stone! Moloch whose soul is electricity and banks! Moloch whose poverty is the specter of genius! Moloch whose fate is a cloud of sexless hydrogen! Moloch whose name is the Mind!

Moloch in whom I sit lonely! Moloch in whom I dream Angels! Crazy in Moloch! Cocksucker in Moloch! Lacklove and manless in Moloch!

Moloch who entered my soul early! Moloch in whom I am a consciousness without a body! Moloch who frightened me out of my natural ecstasy! Moloch whom I abandon! Wake up in Moloch! Light streaming out of the sky!

Moloch! Moloch! Robot apartments! invisible suburbs! skeleton treasuries! blind capitals! demonic industries! spectral nations! invincible madhouses! granite cocks! monstrous bombs!

They broke their backs lifting Moloch to Heaven! Pavements, trees, radios, tons! lifting the city to Heaven which exists and is everywhere about us!

Visions! omens! hallucinations! miracles! ecstasies! gone down the American river!

Dreams! adorations! illuminations! religions! the whole boatload of sensitive bullshit!

Breakthroughs! over the river! flips and crucifixions! gone down the flood! Highs! Epiphanies! Despairs! Ten years’ animal screams and suicides! Minds! New loves! Mad generation! down on the rocks of Time

...
Meetup : Washington DC: Encore (or Goal Factoring)
by RobinZ — 2014-07-28T22:53:00.078Z

Discussion article for the meetup : Washington DC: Encore (or Goal Factoring)

WHEN:
03 August 2014 03:00:00PM (-0400)

WHERE:
Navy Memorial, Washington D.C.

Weather permitting, we will be meeting at the Navy Memorial to play the singing game Encore. Current plan is to congregate between 3:00 and 3:30 p.m. to start playing at 3:30. Board Game Geek estimates that a game takes 45 minutes, but it could go faster or slower depending on the players; in all likelihood, we would play one or two games and the Encore part of the meetup would finish between 5 and 6.
If the day looks unsuitable for meeting outdoors, our backup location is the courtyard of the National Portrait Gallery (8th and F Sts or 8th and G Sts NW, go straight past the information desk from either entrance), where we will talk about goal factoring.

Discussion article for the meetup : Washington DC: Encore (or Goal Factoring)
Open thread, July 28 - August 3, 2014
by polymathwannabe — 2014-07-28T20:27:12.810Z

If it's worth saying, but not worth its own post (even in Discussion), then it goes here.

Notes for future OT posters:

1. Please add the 'open_thread' tag.

2. Check if there is an active Open Thread before posting a new one.

3. Open Threads should be posted in Discussion, and not Main.

4. Open Threads should start on Monday, and end on Sunday.
Expected utility, unlosing agents, and Pascal's mugging
by Stuart_Armstrong — 2014-07-28T18:05:00.094Z

Still very much a work in progress

EDIT: model/existence proof of unlosing agents can be found here.

Why do we bother about utility functions on Less Wrong? Well, because of results of the New man and the Morning Star, which showed that, essentially, if you make decisions, you better use something equivalent to expected utility maximisation. If you don't, you lose. Lose what? It doesn't matter, money, resources, whatever: the point is that any other system can be exploited by other agents or the universe itself to force you into a pointless loss. A pointless loss being a lose that give you no benefit or possibility of benefit - it's really bad.

The justifications for the axioms of expected utility are, roughly:

(Completeness) "If you don't decide, you'll probably lose pointlessly."

(Transitivity) "If your choices form loops, people can make you lose pointlessly."

(Continuity/Achimedean) This axiom (and acceptable weaker versions of it) is much more subtle that it seems; "No choice is infinity important" is what it seems to say, but " 'I could have been a contender' isn't good enough" is closer to what it does. Anyway, that's a discussion for another time.

(Independence) "If your choice aren't independent, people can expect to make you lose pointlessly."

Equivalency is not identity

A lot of people believe a subtlety different version of the result:

If you don't have a utility function, you'll lose pointlessly.

This is wrong. The correct result is:

If you don't lose pointlessly, then your decisions are equivalent with having a utility function.

What's the difference? I'll illustrate with Eliezer's paraphrase of Omohundro:

If you would rather be in Oakland than San Francisco, and you would rather be in San Jose than Oakland, and you would rather be in San Francisco than San Jose, you're going to spend an awful lot of money on taxi rides.

If you believed the first bullet point, then you would decide which you preferred between the three cities (and by how much), this would be you "utility function", and you'd them implement it and drive to the top city. But you could just as well start off in San Francisco, drive to Oakland, then to San Jose, be tempted to drive to San Francisco again, realise that that's stupid, and stay put. No intransitivity. Or, even better, notice the cycle, and choose to stay put in the San Francisco.

Phrased that way,...
"All natural food" as an constrained optimisation problem
by Stuart_Armstrong — 2014-07-28T17:57:29.981Z

A look at all natural foods through the lenses of Bayesianism, optimisation, and friendly utility functions.

How should we consider foods that claim to be "all natural"? Or, since that claim is a cheap signal, foods that have few ingredients, all of them easy to recognise and all "natural"? Or "GM free"?

From the logical point of view, the case is clear: valuing these foods is nothing more that the appeal to nature fallacy. Natural products include many pernicious things (such as tobacco, hemlock, belladonna, countless parasites, etc...). And the difference between natural and not-natural isn't obvious: synthetic vitamin C is identical to the "natural" molecule, and gene modifications are just advanced forms of selective breeding.

But we're not just logicians, we're Bayesians. So let's make a few prior assumptions:

There are far more possible products in the universe that are bad to eat than are good.

Products that humans have been consuming for generations are much more likely to be good to eat that than a random product.

Now let's see the food industry as optimising along a few axis:

Cost. This should be low.

Immediate consumer satisfaction (including taste, appearance, texture, general well-being for a week or so). This should be high.

Long term damage to the consumer's health. This should be low.

The last point is the weakest one, though. Long term health impacts are fiendishly hard to measure (by anyone), and consumers' memories are short. The main things that the food industry wants to avoid are blaring headlines like "salmon saturated with mercury", "sugar linked to cancer", or "food industry coverup". Even there, journalists can create scandals out of very little (or ignore major ones if something more juicy comes along), so the pressure to actually prioritise long term consumer health is very weak. So most optimisation pressure is along the first two axis.

So the food industry will strongly push to decrease cost and increase satisfaction, while mostly taking a random walk on long term consumer health. Given the assumptions above, this means that we'd expect the long term health impacts to worsen (because there are far more negative products than good one). This somewhat similar to the importance of programming everything of value into a friendly AI, lest the things not programmed get squeezed out.

Now, what does "all natural" mean in this setting? It...
Meetup : Rationality Dojo - Aversions
by luminosity — 2014-07-28T10:27:53.760Z

Discussion article for the meetup : Rationality Dojo - Aversions

WHEN:
03 August 2014 04:00:00PM (+1000)

WHERE:
Humanist House, 10 Shepherd St Chippendale

Following on from the last dojo covering agency, this one will be discussing dealing with aversions. 4pm - 6pm, with the opportunity to hang around for dinner afterwards.

Discussion article for the meetup : Rationality Dojo - Aversions