trentmkelly/LessWrong-43k huggingface dataset:
Exploring Botworld
by So8res — 2014-04-30T22:29:44.052Z

Many people have been asking me this question:

But what am I supposed to do with Botworld?

This indicates a failure of communication on my part. In this post, I'll try to resolve that question. As part of this attempt, I've made some updates to the Botworld code (which is now v1.1) which make Botworld a bit more approachable. A changelog and some documentation are included below.

Playing with Botworld

To clarify, Botworld was devised as a tool for reasoning about different AI formalisms. We're not trying to build a hotbed for programming competitions or anything of the sort. Rather, we need a concrete system with concrete games where we can study how a given agent deals with a given situation. As such, most of our work which references Botworld will describe complex agents playing simple games. A few such posts are in the pipeline.

Benja and I designed Botworld as a universe where we can distill many 'edge case' scenarios that an AI may face, such as having it's source code read or distributing itself across multiple machines. Such scenarios are often handled clumsily by existing AI formalisms, and it is quite useful to have simple, concrete scenarios where we can envision such events.

That's how we use Botworld: we throw different formalisms at different edge cases, figure out where their problems might lie, and visualize solutions. If we encounter problems, we use Botworld as a way to find a minimal example. You'll see one such example in an upcoming post.

So how are you supposed to use Botworld? Well, if you're studying AI formalisms, then you're encouraged to use Botworld to visualize and distill weird scenarios where different agents may run into trouble. Otherwise, we don't really expect you to get direct use out of it.

Don't get me wrong, you're more than welcome to play around, write amusing games, and design programs that do neat things. It's a flexible and fun little system. If you are just looking to entertain yourself, you're invited to:

Write some simple robots that cooperate in a Stag Hunt (now included in the games/ directory)

Design your own game

Write a program that wins in the Precommitment game (spoiler alert: a solution can be found in the Ideal.ct file)

This can definitely help you get a feel for Botworld, and will help you understand the upcoming posts more readily. That said, we don't have plans to make Bo

...
Discussion: How scientifically sound are MBAs?
by caffemacchiavelli — 2014-04-30T21:06:16.359Z

I'm finishing up the first year of my distance-learning MBA, which has been a very confusing experience.

I went into the course partially as insurance against "unknown unknowns", i.e., lacking concepts important to building or running a business because I didn't know about them or underestimated their importance.

The first surprise within the course was the relative lack of explaining the utility of the particular models presented. Apart from the section on financial reporting, which did explain how you'd be able to solve practical problems by using particular budgeting tools or costing methods, concepts where generally presented as "This well-known management thinker came up with this, why don't you play around with it a little".

Analysis and (subjective) discussion of course material was also the main focus of student assessment, which consisted of marked reports on a subject of the students' choice, usually a problem they'd be dealing with in their current position.

There were no quizzes, case studies with objectively correct answers (except for, again, financial reporting) or other problem-solving exercises.

Essentially, students are handed a lot of maps for whatever territory they're trying to analyze, but don't really receive any tool to judge the accuracy, let alone utility of said maps. For instance, I'd consider "Concept useful for describing why A does B well" and "Concept useful for helping A do B well" to be fundamentally different, but the course did not explicitly separate the two. (I've heard similar criticisms of Michael Porter's failed Monitor Group, so this may be a problem of business academics in general)

I fear that there will be a risk of training the stereotypically smug MBA, who has gained more confidence in their management skills even though they haven't proven their ability of making sound decisions.

I'm also starting to understand why so many entrepreneurs are dismissive of MBAs. Manoj Bhargava has a whole talk on the issue and Elon Musk isn't a fan, either. I'm not willing to argue that they're right, but on closer inspection, the field hasn't inspired much confidence in me. Even though I think that business administration should be taught in school, I can't help but feel that we're not quite there yet.

I still can't get the fog to clear completely and this post isn't as lucid as I want it to be, so I'd be happy to hear more ar...
Rebutting radical scientific skepticism
by asr — 2014-04-30T19:40:46.688Z

Suppose you distrusted everything you had ever read about science. How much of modern scientific knowledge could you verify for yourself, using only your own senses and the sort of equipment you could easily obtain? How about if you accept third-party evidence when many thousands of people can easily check the facts?

My purpose with the question isn't to cast radical doubt on science; rather, it's an entertaining game of trying to understand how we know what we know. Thinking through these sorts of questions also helped me notice interesting things in the history of science that I hadn't previously focused on. It might also be of interest from a science education perspective.

Some things are much easier to check than they used to be. As late as the 19th century, there were people who were publicly skeptical about the curvature of the earth. Skeptics and scientists did careful measurements (notably the Bedford Level Experiment) to observe the earth's curvature. Today, you can verify it by phoning a friend a few time zones away and noticing that the sun reaches the zenith at steadily later times as you move west. This only makes sense if the earth is curved.

Some things are still hard to check. I don't know an easy way to show that the Earth orbits the Sun. The direct way to show it would be to measure stellar parallax. But even the closest stars have a parallax of less than an arcsecond. My understanding is that very few amateurs are able to take measurements with that level of precision.

Some things are surprisingly easy. There are lots of easily accessible demonstrations of quantum phenomena. For example, a ten dollar spectroscope will show you that an incandescent light bulb has a continuous spectrum, and that LEDs and fluorescent bulbs don't. Bright-line spectra are very much a quantum mechanical phenomenon -- it's a sign that the atoms in the light source have fixed energy transition levels. Spectroscopy was one of the key early lines of evidence for quantum mechanics, and it blows my mind that it's something you can just see whenever you want, with a negligible equipment cost.

Pretty much all of modern chemistry and solid state physics rests on a quantum foundation, and you can test a great deal of chemistry pretty easily. If you are in doubt that water is a bonded compound of two gasses, you can do the electrolysis very easily yourself. You can ...
Mechanism Design: Constructing Algorithms for Strategic Agents
by badger — 2014-04-30T18:37:20.875Z

tl;dr Mechanism design studies how to design incentives for fun and profit. A puzzle about whether or not to paint a room is posed. A modeling framework is introduced, with lots of corresponding notation.

Mechanism design is a framework for constructing institutions for group interactions, giving us a language for the design of everything from voting systems to school admissions to auctions to crowdsourcing. Think of it as the engineering side of game theory, building algorithms for strategic agents. In game theory, the primary goal is to answer the question, “Given agents who can take some actions that will lead to some payoffs, what do we expect to happen when the agents strategically interact?” In other words, game theory describes the outcomes of fixed scenarios. In contrast, mechanism design flips the question around and asks, “Given some goals, what payoffs should agents be assigned for the right outcome to occur when agents strategically interact?” The rules of the game are ours to choose, and, within some design constraints, we want to find the best possible ones for a situation.

Although many people, even high-profile theorists, doubt the usefulness of game theory, its application in mechanism design is one of the major success stories of modern economics. Spectrum license auctions designed by economists paved the way for modern cell-phone networks and garnered billions in revenue for the US and European governments. Tech companies like Google and Microsoft employ theorists to improve advertising auctions. Economists like Al Roth and computer scientists like Tuomas Sandholm have been instrumental in establishing kidney exchanges to facilitate organ transplants, while others have been active in the redesign of public school admissions in Boston, Chicago, and New Orleans.

The objective of this post is to introduce all the pieces of a mechanism design problem, providing the setup for actual conclusions later on. I assume you have some basic familiarity with game theory, at the level of understanding the concept of a dominant strategies and Nash equilbria. Take a look at Yvain’s Game Theory Intro if you’d like to brush up.

Overly optimizing whether or not to paint an room

Let’s start with a concrete example of a group choice: Jack, an economics student, and Jill, a computer science student, are housemates. To...
[Sequence announcement] Introduction to Mechanism Design
by badger — 2014-04-30T16:21:31.652Z

Mechanism design is the theory of how to construct institutions for strategic agents, spanning applications like voting systems, school admissions, regulation of monopolists, and auction design. Think of it as the engineering side of game theory, building algorithms for strategic agents. While it doesn't have much to say about rationality directly, mechanism design provides tools and results for anyone interested in world optimization.

In this sequence, I'll touch on

The basic mechanism design framework, including the revelation principle and incentive compatibility.

The Gibbard-Satterthwaite impossibility theorem for strategyproof implementation (a close analogue of Arrow's Theorem), and restricted domains like single-peaked or quasilinear preference where we do have positive results.

The power and limitations of Vickrey-Clarke-Groves mechanisms for efficiently allocating goods, generalizing Vickrey's second-price auction.

Characterizations of incentive-compatible mechanisms and the revenue equivalence theorem.

Profit-maximizing auctions.

The Myerson-Satterthwaite impossibility for bilateral trade.

Two-sided matching markets à la Gale and Shapley, school choice, and kidney exchange.

As the list above suggests, this sequence is going to be semi-technical, but my foremost goal is to convey the intuition behind these results. Since mechanism design builds on game theory, take a look at Yvain's Game Theory Intro if you want to brush up.

Various resources:

For further introduction, you can start with the popular or more scholarly survey of mechanism design from the 2007 Nobel memoriam prize in economics.

Jeff Ely has lecture notes and short videos to accompany an undergraduate class in microeconomic theory from the perspective of mechanism design.

The textbook A Toolbox for Economic Design by Dimitrios Diamantaras is very accessible and comprehensive if you can get ahold of a copy.

Tilman Börgers has a draft textbook intended for graduate students.

Chapters 9-16 of Algorithmic Game Theory and chapters 10-11 of Multiagent Systems cover various topics in mechanism design from the perspective of computer scientists.

Video lectures introducing market design and computational aspects of mechanism design.

I plan on following up on this sequence with another focusing on group rationality and information aggregation, surveying scoring rules and p...
Meetup : London social meetup
by philh — 2014-04-30T13:34:43.181Z

Discussion article for the meetup : London social meetup

WHEN:
04 May 2014 02:00:00PM (+0100)

WHERE:
Shakespeare's Head, Holborn, WC2B 6BG

Our weekly meetings continue! As usual, we'll be meeting in the Shakespeare's Head in Holborn, from 2pm until we get bored (which is almost always after 6pm, but some people tend to leave sooner).

If you have difficulty finding us, you can contact me on 07792009646.

See also: London LW google group and facebook group.

Discussion article for the meetup : London social meetup
Summary of the first SoCal FAI Workshop
by Scott Garrabrant — 2014-04-30T07:35:52.711Z

Last Saturday, nine people met for the Southern California FAI Workshop. Unsurprisingly, we did not come up with any major results, but I know some people were curious about this experiment, so I a providing a summary anyway.

First, I would like to say that I consider this first meeting a success. The turnout was higher than I expected. We had 9 participants, and there were 2 other people who did not show up due to scheduling conflicts. We basically stayed on topic the entire 7 hours from 10:00 to 5:00, and then we had dinner at 5:00, generously provided by MIRI. We will be hosting these workshops again. In fact, we have decided to hold them monthly. We will probably follow a schedule of meeting the first Saturday of each month, starting in June. I will make another post announcing the second meetup once this date is finalized.

We talked about various ideas participants had about FAI, but most of our time was spent thinking about probability distributions on consistent theories. One thing we observed that if you view the space of all probability assignments to logical sentences as living inside the vector space of all functions from sentences to the real numbers, then the collection of coherent probability assignments (those which correspond to probability distributions on consistent theories), is an affine subspace. This is exciting, because we can set up an inner product on this vector space and orthogonally project probability assignments onto the closest point on this subspace (i.e. find a nearby coherent probability assignment to a given probability assignment). Further, while this projection is not computable, there exists a computable procedure which converges to this point. However, I am now convinced that this idea is a dead end, for the following reason: Just because the point you start with has all coordinates between 0 and 1, does not mean that the projection to the subspace containing coherent assignments still has all coordinates between 0 and 1. (Imagine a 3d unit cube, and imagine that a theory is coherent if x+y+z=1. If you project 1,1,0 onto this subspace, you get 2/3,2/3,-1/3, which is not a valid probability assignment) I am now convinced that this idea will not be fruitful.

However, we did get several good things out of the meeting. First, we introduced several new mathy people to the problems associated with FAI. Second, we set...
Link: Study finds that using a foreign language changes moral decisions
by Vladimir_Golovin — 2014-04-30T05:26:14.683Z

In the new study, two experiments using the well-known "trolley dilemma" tested the hypothesis that when faced with moral choices in a foreign language, people are more likely to respond with a utilitarian approach that is less emotional.

The researchers collected data from people in the U.S., Spain, Korea, France and Israel. Across all populations, more participants selected the utilitarian choice -- to save five by killing one -- when the dilemmas were presented in the foreign language than when they did the problem in their native tongue.

The article:
http://www.sciencedaily.com/releases/2014/04/140428120659.htm

The publication:
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0094842
Positive Queries - How Fetching
by Benquo — 2014-04-30T02:24:44.665Z

Help, having a brain blank. I can come up w examples of times something happened, but not times something didnt-happen. What heuristic?

— Kate Donovan (@donovanable) April 29, 2014

If I tell 100 people not to think of an elephant, what's the single thing they're all most likely to think about over the next five minutes, aside from sex?

An elephant, of course.

Negation and oppositeness are perfectly intelligible semantic concepts - in general, no one is confused about what "Don't think of an elephant" means - or, more generally, "Don't do [X]," where X is any intelligible behavior. And people would know how to comply, if [X] were a physical action like sitting down. But even if they wanted to, they don't know how to not think of an elephant - even though that's a behavior they exhibit most of their waking lives, and in some sense on purpose.

Even for physical actions we are not only admonished to refrain from, but have a strong personal interest in not doing, we feel an impulse to do them anyway. Standing on a narrow ledge, afraid of falling, you might feel a strong urge to jump. Why?

Because a part of your mind that is trying to take care of you is thinking, as hard as it can, "Don't jump!" And there's another part of your mind, whose job it is to fetch ideas related to the things you're interested in. This fetcher doesn't understand words like "don't," but it does understand that you're very interested in the idea of jumping off that ledge, so it helpfully suggests ways to do so.

Oops.

This can be a big problem if you're trying to find ways not to do something, or for something not to happen.

It is not possible to find ways for something not to happen.

Knowing this, how should we use our brains differently than we did before? For obvious reasons, I am not just going to tell you to avoid thinking of the things you want in terms of negations. Instead, I'm going to tell you some stories of how I used techniques designed with this in mind, to win at life.

The Case of the Missing Car Keys

A few days ago, I was on my way to an eagerly anticipated debate presided over by the incomparable Leah. I had gotten my scheduled prior weekend chores out of the way, and even had time to stop by the local Le Pain Quotidien for a leisurely brunch (for which the service was no more intolerably slow than usual, but this time they apologized without promptin...
Cognito Mentoring and zero-sum competition
by JonahS — 2014-04-29T23:06:53.387Z

I've recently made some posts ([1], [2], [3], [4]) geared toward helping high school and college students increase their expected future earnings, as a part of my work for Cognito Mentoring. A LWer recently asked how closely aligned this is with our goal of producing social value. Specifically, he noted that in some cases the effect of our advice will be to help people win zero-sum competitions. For example, to the extent that majoring in economics has signaling benefits, if somebody majors in economics based on our information, the signaling benefits to him or her will be counterbalanced by reduced signaling benefits for others. At first glance, this appears to lack social value, raising the question of why we're working to provide this information. We recognize the tension, and this post responds to this question.

There are a number of relevant considerations (which are largely unrelated to each other):

We need to provide a service that people find useful in order to help people create social value (whether by design or incidentally)

The primary reason that we're generating and offering advice that helps students win zero sum competitions is to gain traction so as to be better positioned to disseminate information that does have social value.

Our strategy for disseminating information that has social value is to become a central hub for intellectually talented students to find information on all subjects that are of personal interest to them. Some of what they're interested in is winning zero sum competitions, and we wouldn't be as attractive and visible to our target audience if we didn't provide them with relevant information.

We need to win people's trust and prove useful to them in order to have further impact. Some of the people will then be open to, and interested in, making decisions that contribute social value.

It's hard to help people without helping them win zero sum competitions, and that's ok

Even advice that superficially has no relation to zero sum competitions often has hidden zero sum elements. Consider the case of helping someone overcome depression. If one is successful, one (in expectation) enables the person to be a more productive worker, which will help him or her get better jobs over other job candidates, reducing their expected incomes. But helping someone overcome depression is a positive thing on balance, despite the fact tha...
The Universal Medical Journal Article Error
by PhilGoetz — 2014-04-29T17:57:09.854Z

TL;DR: When people read a journal article that concludes, "We have proved that it is not the case that for every X, P(X)", they generally credit the article with having provided at least weak evidence in favor of the proposition ∀x !P(x). This is not necessarily so.

Authors using statistical tests are making precise claims, which must be quantified correctly. Pretending that all quantifiers are universal because we are speaking English is one error. It is not, as many commenters are claiming, a small error. ∀x !P(x) is very different from !∀x P(x).

A more-subtle problem is that when an article uses an F-test on a hypothesis, it is possible (and common) to fail the F-test for P(x) with data that supports the hypothesis P(x). The 95% confidence level was chosen for the F-test in order to count false positives as much more expensive than false negatives. Applying it therefore removes us from the world of Bayesian logic. You cannot interpret the failure of an F-test for P(x) as being even weak evidence for not P(x).

I used to teach logic to undergraduates, and they regularly made the same simple mistake with logical quantifiers. Take the statement "For every X there is some Y such that P(X,Y)" and represent it symbolically:

∀x∃y P(x,y)

Now negate it:

!∀x∃y P(x,y)

You often don't want a negation to be outside quantifiers. My undergraduates would often just push it inside, like this:

∀x∃y !P(x,y)

If you could just move the negation inward like that, then these claims would mean the same thing:

A) Not everything is a raven: !∀x raven(x)

B) Everything is not a raven: ∀x !raven(x)

To move a negation inside quantifiers, flip each quantifier that you move it past.

!∀x∃y P(x,y) = ∃x!∃y P(x,y) = ∃x∀y !P(x,y)

Here's the findings of a 1982

article

[1] from JAMA Psychiatry (formerly Archives of General Psychiatry), back in the days when the medical establishment was busy denouncing the Feingold diet:

Previous studies have not conclusively demonstrated behavioral effects of artificial food colorings ... This study, which was designed to maximize the likelihood of detecting a dietary effect, found none.

Now pay attention; this is the part everyone gets wrong, including most of the commenters below.

The methodology used in th...