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You walk into a laboratory, and you read a set of instructions that tell you that your task is to decide how much of a $10 pie you want to give to an anonymous other person who signed up for the experimental session.
This describes, more or less, the Dictator Game, a staple of behavioral economics with a history dating back more than a quarter of a century. The Dictator Game (DG) might not be the drosophila melanogaster of behavioral economics – the Prisoner’s Dilemma can lay plausible claim to that prized analogy – but it could reasonably aspire to an only slightly more modest title, perhaps the e. coli of the discipline. Since the original work, more than 20,000 observations in the DG have been reported.
How much would participants in a Dictator Game give to the other person if they did not know they were in a Dictator Game study? Simply following me around during the day and recording how much cash I dispense won’t answer this question because in the DG, the money is provided by the experimenter. So, to build a parallel design, the method used must move money to subjects as a windfall so that we can observe how much of this “house money” they choose to give away.
And that is what Winking and Mizer did in a paper now in press and available online (paywall) in Evolution and Human Behavior, using participants, fittingly enough, in Las Vegas. Here’s what they did. Two confederates were needed. The first, destined to become the “recipient,” was occupied on a phone call near a bus stop in Vegas. The second confederate approached lone individuals at the bus stop, indicated that they were late for a ride to the airport, and asked the subject if they wanted the $20 in casino chips still in the confederate’s possession, scamming people into, rather than out of money, in sharp contradiction of the deep traditions of Las Vegas. The question was how many chips the fortunate subject transferred to the nearby confederate.
In a second condition, the confederate with the chips added a comment to the effect that the subject could “split it with that guy however you want,” indicating the first confederate. This condition brings the study a bit closer, but not much closer, to lab conditions, In a third condition, subjects were asked if they wanted to participate in a study, and then did so along the lines of the usual DG, making the treatment considerably closer to traditional lab-based conditions.
The difference between the first two treatments and the third treatments is interesting, but, as I said at the beginning, the DG should be thought of as a measuring tool. Figure 1 shows how many chips people give away in the DG in the three treatments. In conditions 1 and 2, the number of people (out of 60) who gave at least one chip to the second confederate was… zero. To the extent you think that this method answers the question, how much Dictator Game giving is due to people knowing they’re in an experiment, the answer is, “all of it.”
Link to paper (paywalled).
I attempt to figure out a way to dissolve the concepts of 'personal identity' and 'subjective expectation' down to the level of cognitive algorithms, in a way that would let one bite the bullets of the anthropic trilemma. I proceed by considering four clues which seem important: 1) the evolutionary function of personal identity, 2) a sense of personal identity being really sticky, 3) an undefined personal identity causing undefined behavior in our decision-making machinery, and 4) our decision-making machinery being more strongly grounded in our subjective expectation than in abstract models. Taken together, these seem to suggest a solution.
I ended up re-reading some of the debates about the anthropic trilemma, and it struck me odd that, aside for a few references to personal identity being an evolutionary adaptation, there seemed to be no attempt to reduce the concept to the level of cognitive algorithms. Several commenters thought that there wasn't really any problem, and Eliezer asked them to explain why the claim of there not being any problem regardless violated the intuitive rules of subjective expectation. That seemed like a very strong indication that the question needs to be dissolved, but almost none of the attempted answers seemed to do that, instead trying to solve the question via decision theory without ever addressing the core issue of subjective expectation. rwallace's I-less Eye argued - I believe correctly - that subjective anticipation isn't ontologically fundamental, but still didn't address the question of why it feels like it is.
Here's a sketch of a dissolvement. It seems relatively convincing to me, but I'm not sure how others will take it, so let's give it a shot. Even if others find it incomplete, it should at least help provide clues that point towards a better dissolvement.
Clue 1: The evolutionary function of personal identity.
Let's first consider the evolutionary function. Why have we evolved a sense of personal identity?
The first answer that always comes to everyone's mind is that our brains have evolved for the task of spreading our genes, which involves surviving at least for as long as it takes to reproduce. Simpler neural functions, like maintaining a pulse and having reflexes, obviously do fine without a concept of personal identity. But if we wish to use abstract, explicit reasoning to advance our own interests, we need some definition for exactly whose interests it is that our reasoning process is supposed to be optimizing. So evolution comes up with a fuzzy sense of personal identity, so that optimizing the interests of this identity also happens to optimize the interests of the organism in question.
That's simple enough, and this point was already made in the discussions so far. But that doesn't feel like it would resolve our confusion yet, so we need to look at the way that personal identity is actually implemented in our brains. What is the cognitive function of personal identity?
Clue 2: A sense of personal identity is really sticky.
Even people who disbelieve in personal identity don't really seem to disalieve it: for the most part, they're just as likely to be nervous about their future as anyone else. Even advanced meditators who go out trying to dissolve their personal identity seem to still retain some form of it. PyryP claims that at one point, he reached a stage in meditation where the experience of “somebody who experiences things” shattered and he could turn it entirely off, or attach it to something entirely different, such as a nearby flower vase. But then the experience of having a self began to come back: it was as if the brain was hardwired to maintain one, and to reconstruct it whenever it was broken. I asked him to comment on that for this post, and he provided the following:
I sometimes go around saying that the fundamental question of rationality is Why do you believe what you believe?
I was much impressed when they finally came out with a PC version of DragonBox, and I got around to testing it on some children I knew. Two kids, one of them four and the other eight years old, ended up blazing through several levels of solving first-degree equations while having a lot of fun doing so, even though they didn't know what it was that they were doing. That made me think that there has to be some way of making a computer game that would similarly teach rationality skills at the 5-second level. Some game where you would actually be forced to learn useful skills if you wanted to make progress.
After playing around with some ideas, I hit upon the notion of making a game centered around the Fundamental Question. I'm not sure whether this can be made to work, but it seems to have promise. The basic idea: you are required to figure out the solution to various mysteries by collecting various kinds of evidence. Some of the sources of evidence will be more reliable than others. In order to hit upon the correct solution, you need to consider where each piece of evidence came from, and whether you can rely on it.
Now, let's go into a little more detail. Let's suppose that the game has a character called Bob. Bob tells you that tomorrow, eight o'clock, there will be an assassination attempt on Market Square. The fact that Bob has told you this is evidence for the claim being true, so the game automatically records the fact that you have such a piece of evidence, and that it came from Bob.
(Click on the pictures in case you don't see them properly.)
But how does Bob know that? You ask, and it turns out that Alice told him. So next, you go and ask Alice. Alice is confused and says that she never said anything about any assassination attempt: she just said that something big is going to be happen at the Market Square at that time, she heard it from the Mayor. The game records two new pieces of evidence: Alice's claim of something big happening at the Market Square tomorrow (which she heard from the Mayor), and her story of what she actually told Bob. Guess that Bob isn't a very reliable source of evidence: he has a tendency to come up with fancy invented details.
Or is he? After all, your sole knowledge about Bob being unreliable is that Alice claims she never said what Bob says she said. But maybe Alice has a grudge against Bob, and is intentionally out to make everyone disbelieve him. Maybe it's Alice who's unreliable. The evidence that you have is compatible with both hypotheses. At this point, you don't have enough information to decide between them, but the game lets you experiment with setting either of them as "true" and seeing the implications of this on your belief network. Or maybe they're both true - Bob is generally unreliable, and Alice is out to discredit him. That's another possibility that you might want to consider. In any case, the claim that there will be an assassination tomorrow isn't looking very likely at the moment.
Actually, having the possibility for somebody lying should probably be a pretty late-game thing, as it makes your belief network a lot more complicated, and I'm not sure whether this thing should display numerical probabilities at all. Instead of having to juggle the hypotheses of "Alice lied" and "Bob exaggerates things", the game should probably just record the fact that "Bob exaggerates things". But I spent a bunch of time making these pictures, and they do illustrate some of the general principles involved, so I'll just use them for now.
So, to repeat the basic premise of the game, in slightly more words this time around: your task is to figure out something, and in order to do so, you need to collect different pieces of evidence. As you do so, the game generates a belief network showing the origin and history of the various pieces of evidence that you've gathered. That much is done automatically. But often, the evidence that you've gathered is compatible with many different hypotheses. In those situations, you can experiment with different ways of various hypotheses being true or false, and the game will automatically propagate the consequences of that hypothetical through your belief network, helping you decide what angle you should explore next.
Of course, people don't always remember the source of their knowledge, or they might just appeal to personal experiences. Or they might lie about the sources, though that will only happen at the more advanced levels.
As you proceed in the game, you will also be given access to more advanced tools that you can use for making hypothetical manipulations to the belief network. For example, it may happen that many different characters say that armies of vampire bats tend to move about at full moon. Since you hear that information from many different sources, it seems reliable. But then you find out that they all heard it from a nature documentary on TV that aired a few weeks back. This is reflected in your belief graph, as the game modifies it to show that all of those supposedly independent sources can actually be tracked back to a single one. That considerably reduces the reliability of the information.
But maybe you were already suspecting that the sources might not be independent? In that case, it would have been nice if the belief graph interface would let you postulate this beforehand, and see how big of an effect it would make on the plausibility of the different hypotheses if they were in fact reliant on each other. Once your character learns the right skills, it becomes possible to also add new hypothetical connections to the belief graph, and see how this would influence your beliefs. That will further help you decide what possibilities to explore and verify.
Because you can't explore every possible eventuality. There's a time limit: after a certain amount of moves, a bomb will go off, the aliens will invade, or whatever.
The various characters are also more nuanced than just "reliable" or "not reliable". As you collect information about the various characters, you'll figure out their mindware, motivations, and biases. Somebody might be really reliable most of the time, but have strong biases when it comes to politics, for example. Others are out to defame others, or invent fancy details to all the stories. If you talk to somebody you don't have any knowledge about yet, you can set a prior on the extent that you rely on their information, based on your experiences with other people.
You also have another source of evidence: your own intuitions and experience. As you get into various situations, a source of evidence that's labeled simply "your brain" will provide various gut feelings and impressions about things. The claim that Alice presented doesn't seem to make sense. Bob feels reliable. You could persuade Carol to help you if you just said this one thing. But in what situations, and for what things, can you rely on your own brain? What are your own biases and problems? If you have a strong sense of having heard something at some point, but can't remember where it was, are you any more reliable than anyone else who can't remember the source of their information? You'll need to figure all of that out.
As the game progresses to higher levels, your own efforts will prove insufficient for analyzing all the necessary information. You'll have to recruit a group of reliable allies, who you can trust to analyze some of the information on their own and report the results to you accurately. Of course, in order to make better decisions, they'll need you to tell them your conclusions as well. Be sure not to report as true things that you aren't really sure about, or they will end up drawing the wrong conclusions and focusing on the wrong possibilities. But you do need to condense your report somewhat: you can't just communicate your entire belief network to them.
Hopefully, all of this should lead to player learning on a gut level things like:
- Consider the origin of your knowledge: Obvious.
- Visualizing degrees of uncertainty: In addition to giving you a numerical estimate about the probability of something, the game also color-codes the various probabilities and shows the amount of probability mass associated with your various beliefs.
- Considering whether different sources really are independent: Some sources which seem independent won't actually be that, and some which seem dependent on each other won't be.
- Value of information: Given all the evidence you have so far, if you found out X, exactly how much would it change your currently existing beliefs? You can test this and find out, and then decide whether it's worth finding out.
- Seek disconfirmation: A lot of things that seem true really aren't, and acting on flawed information can cost you.
- Prefer simpler theories: Complex, detailed hypotheses are more likely to be wrong in this game as well.
- Common biases: Ideally, the list of biases that various characters have is derived from existing psychological research on the topic. Some biases are really common, others are more rare.
- Epistemic hygiene: Pass off wrong information to your allies, and it'll cost you.
- Seek to update your beliefs: The game will automatically update your belief network... to some extent. But it's still possible for you to assign mutually exclusive events probabilities that sum to more than 1, or otherwise have conflicting or incoherent beliefs. The game will mark these with a warning sign, and it's up to you to decide whether this particular inconsistency needs to be resolved or not.
- Etc etc.
It's not enough for the game to be educational: if somebody downloads the game because it teaches rationality skills, that's great, but we want people to also play it because it's fun. Some principles that help ensure that, as well as its general utility as an educational aid, include:
- Provide both short- and medium-term feedback: Ideally, there should be plenty of hints for how to find out the truth about something by investigating just one more thing: then the player can find out whether your guess was correct. It's no fun if the player has to work through fifty decisions before finding out whether they made the right move: they should get constant immediate feedback. At the same time, the player's decisions should be building up to a larger goal, with uncertainty about the overall goal keeping them interested.
- Don't overwhelm the player: In a game like this, it would be easy to throw a million contradictory pieces of evidence at the player, forcing them to go through countless of sources of evidence and possible interactions and have no clue of what they should be doing. But the game should be manageable. Even if it looks like there is a huge messy network of countless pieces of contradictory evidence, it should be possible to find the connections which reveal the network to be relatively simple after all. (This is not strictly realistic, but necessary for making the game playable.)
- Introduce new gameplay concepts gradually: Closely related to the previous item. Don't start out with making the player deal with every single gameplay concept at once. Instead, start them out in a trusted and safe environment where everyone is basically reliable, and then begin gradually introducing new things that they need to take into account.
- No tedium: A game is a series of interesting decisions. The game should never force the player to do anything uninteresting or tedious. Did Alice tell Bob something? No need to write that down, the game keeps automatic track of it. From the evidence that has been gathered so far, is it completely obvious what hypothesis is going to be right? Let the player mark that as something that will be taken for granted and move on.
- No glued-on tasks: A sign of a bad educational game is that the educational component is glued on to the game (or vice versa). Answer this exam question correctly, and you'll get to play a fun action level! There should be none of that - the educational component should be an indistinguishable part of the game play.
- Achievement, not fake achievement: Related to the previous point. It would be easy to make a game that wore the attire of rationality, and which used concepts like "probability theory", and then when your character leveled up he would get better probability attacks or whatever. And you'd feel great about your character learning cool stuff, while you yourself learned nothing. The game must genuinely require the player to actually learn new skills in order to get further.
- Emotionally compelling: The game should not be just an abstract intellectual exercise, but have an emotionally compelling story as well. Your choices should feel like they matter, and characters should be in risk of dying if you make the wrong decisions.
- Teach true things: Hopefully, the players should take the things that they've learned from the game and apply them to their daily lives. That means that we have a responsibility not to teach them things which aren't actually true.
- Replayable: Practice makes perfect. At least part of the game world needs to be randomly generated, so that the game can be replayed without a risk of it becoming boring because the player has memorized the whole belief network.
What you've just read is a very high-level design, and a quite incomplete one at that: I've spoken on the need to have "an emotionally compelling story", but said nothing about the story or the setting. This should probably be something like a spy or detective story, because that's thematically appropriate for a game which is about managing information; and it might be best to have it in a fantasy setting, so that you can question the widely-accepted truths of that setting without needing to get on anyone's toes by questioning widely-accepted truths of our society.
But there's still a lot of work that remains to be done with regard to things like what exactly does the belief network look like, what kinds of evidence can there be, how does one make all of this actually be fun, and so on. I mentioned the need to have both short- and medium-term feedback, but I'm not sure of how that could be achieved, or whether this design lets you achieve it at all. And I don't even know whether the game should show explicit probabilities.
And having a design isn't enough: the whole thing needs to be implemented as well, preferably while it's still being designed in order to take advantage of agile development techniques. Make a prototype, find some unsuspecting testers, spring it on them, revise. And then there are the graphics and music, things for which I have no competence for working on.
I'll probably be working on this in my spare time - I've been playing with the idea of going to the field of educational games at some point, and want the design and programming experience. If anyone feels like they could and would want to contribute to the project, let me know.
EDIT: Great to see that there's interest! I've created a mailing list for discussing the game. It's probably easiest to have the initial discussion here, and then shift the discussion to the list.
For the first time in history, it has become possible for a limited group of a few thousand people to threaten the absolute destruction of millions.
-- Norbert Wiener (1956), Moral Reflections of a Mathematician.
Today, the general attitude towards scientific discovery is that scientists are not themselves responsible for how their work is used. For someone who is interested in science for its own sake, or even for someone who mostly considers research to be a way to pay the bills, this is a tempting attitude. It would be easy to only focus on one’s work, and leave it up to others to decide what to do with it.
But this is not necessarily the attitude that we should encourage. As technology becomes more powerful, it also becomes more dangerous. Throughout history, many scientists and inventors have recognized this, and taken different kinds of action to help ensure that their work will have beneficial consequences. Here are some of them.
This post is not arguing that any specific approach for taking responsibility for one's actions is the correct one. Some researchers hid their work, others refocused on other fields, still others began active campaigns to change the way their work was being used. It is up to the reader to decide which of these approaches were successful and worth emulating, and which ones were not.
… I do not publish nor divulge [methods of building submarines] by reason of the evil nature of men who would use them as means of destruction at the bottom of the sea, by sending ships to the bottom, and sinking them together with the men in them.
People did not always think that the benefits of freely disseminating knowledge outweighed the harms. O.T. Benfey, writing in a 1956 issue of the Bulletin of the Atomic Scientists, cites F.S. Taylor’s book on early alchemists:
Alchemy was certainly intended to be useful .... But [the alchemist] never proposes the public use of such things, the disclosing of his knowledge for the benefit of man. …. Any disclosure of the alchemical secret was felt to be profoundly wrong, and likely to bring immediate punishment from on high. The reason generally given for such secrecy was the probable abuse by wicked men of the power that the alchemical would give …. The alchemists, indeed, felt a strong moral responsibility that is not always acknowledged by the scientists of today.
With the Renaissance, science began to be viewed as public property, but many scientists remained cautious about the way in which their work might be used. Although he held the office of military engineer, Leonardo da Vinci (1452-1519) drew a distinction between offensive and defensive warfare, and emphasized the role of good defenses in protecting people’s liberty from tyrants. He described war as ‘bestialissima pazzia’ (most bestial madness), and wrote that ‘it is an infinitely atrocious thing to take away the life of a man’. One of the clearest examples of his reluctance to unleash dangerous inventions was his refusal to publish the details of his plans for submarines.
Later Renaissance thinkers continued to be concerned with the potential uses of their discoveries. John Napier (1550-1617), the inventor of logarithms, also experimented with a new form of artillery. Upon seeing its destructive power, he decided to keep its details a secret, and even spoke from his deathbed against the creation of new kinds of weapons.
But only concealing one discovery pales in comparison to the likes of Robert Boyle (1627-1691). A pioneer of physics and chemistry and possibly the most famous for describing and publishing Boyle’s law, he sought to make humanity better off, taking an interest in things such as improved agricultural methods as well as better medicine. In his studies, he also discovered knowledge and made inventions related to a variety of potentially harmful subjects, including poisons, invisible ink, counterfeit money, explosives, and kinetic weaponry. These ‘my love of Mankind has oblig’d me to conceal, even from my nearest Friends’.
As Risto Saarelma pointed out on IRC, "Volcano Lair Doom Institute" would have been cooler, but this is pretty good too. As the word "Singularity" has pretty much lost its meaning, it's better to have a name that doesn't give a new person all kinds of weird initial associations as their first impression. And "Machine Intelligence Research Institute" is appropriately descriptive while still being general enough.
The beginning of a new year is a customary time to take a look back and consider what has happened during the last 12 months. And while the time for doing so is admittedly rather arbitrary - after all, "years" do not really exist in the universe, just in our heads - it is useful and fun to review one's accomplishments every now and then. And a time when everyone else is doing it gives us a nice Schelling point for joining in, so we can pretend that it's not quite that arbitrary.
So what might be some noteworthy things that happened on Less Wrong in 2012 that could be worth mentioning?
First, I would like to say "thank you" to all the people working on keeping this site running and helping it make increasingly more awesome! This obviously includes pretty much everyone who comments, posts and writes here, but particularly also the folks at Trikeapps, and everyone who contributes updates to the site's codebase. There were several site upgrades in 2012, four of which were major enough to get separate announcements:
Less Wrong's new front page was rolled out in March, thanks to work by matt. One can easily access a number of site features from the brain graphic, and there's a convenient introduction under it, together with links to featured articles and recent promoted articles. Hopefully, this has made it easier for newcomers to get familiar with the site.
The "Best" sorting system for comments was introduced in July. The work was done by John Simon, and integrated by Wes. Whereas the old default sorting system, "Top", favored old comments that had already floated to the top and were thus more likely to get even more upvotes, "Best" attempts to give newer comments a fairer chance.
In August we got the ability to show parent comments on /comments. The work was done by John Simon, and integrated by wmoore. This change makes it far easier to grasp the context of things seen on the recent comments page, given that we now see the old comment that the new comments are replying to.
And finally, starting from September, we have been able to write comments that contain polls! Work on the code was originally began by jimrandomh, finished later by John Simon, and deployed by wmoore and matt. Although people had long been taking advantage of comment vote counts as a crude way of creating their own polls, this change makes things far easier.
In June, we published the How to Run a Successful Less Wrong Meetup booklet, which I wrote together with lukeprog, and which got its graphical design from Stanislaw Boboryk. Numerous other people also helped, both by providing advice and by contributing pictures to it. In addition to general advice on running a meetup, it contains various games and exercises as well as case studies and examples from real meetup groups from around the world.
Index of original research
Starting from October, lukeprog has maintained a curated index of Less Wrong posts containing significant original research. It contains numerous posts, organized under categories such as general philosophy, decision theory / AI architectures / mathematical logic, ethics, and AI risk strategy. Last updated on December 17th, it now links to a total of 78 different posts.
Who are we?
In November and December, Yvain continued his hard work in holding the yearly survey. Among other interesting details, around 90% of us are male, 55% are from the USA, 41% are students and 31% are doing for-profit work. See the 2012 survey results for many more details.
Most popular posts of 2012
On LW, people tend to judge the popularity of a post by the number of upvotes that it has. But this only reflects the opinion of the registered users who care enough to vote. For purposes of this article, we were interested in finding out the posts that had made the biggest impact on the whole Internet. Although it's not a perfect measure either, we decided to measure popularity by the number of unique pageviews, as reported by Google Analytics.
Overall, in 2012 Less Wrong had over eight million unique pageviews and close to two million unique visitors (8,225,509 and 1,756,899, respectively). Of the posts that were written in 2012, the most popular ones were...
#10: Get Curious, in which lukeprog suggests that one of the most important rationality skills is being genuinely curious about things, instead of just jumping to the first answer that comes to your mind and leaving it at that. He suggests a three-step approach for actually becoming more curious: first, feel that you don't already know the answer, then start wanting to know the answer, and finally sprint headlong to reality. Together with a number of exercises intended to make you better at these steps, this article made a lot of folks curious about Less Wrong and caused people to sprint headlong to the post 10,850 times.
#9: Being curious about things means that you genuinely want to know the truth. That makes it useful to have a good grasp of The Useful Idea of Truth. This article by Eliezer Yudkowsky starts the Highly Advanced Epistemology 101 for Beginners sequence by explaining what exactly it means for something to be "true". In order to avoid spoiling the article's "meditations" for anyone who hasn't read it yet, I will not attempt to summarize the answer. I'll only suggest that one definition for "truth" could be the correctness of the claim that this post was viewed 11,161 times.
#8: Having defined truth, we can move on to ask, what are numbers? And in what sense is "2 + 2 = 4" meaningful or true? Eliezer Yudkowsky's Logical Pinpointing attempts to answer this question, partially through the cute device of conversing with an imaginary logician who understands logic perfectly but has no grasp of numbers. As they converse, they define the rules according to which arithmetic works. I'm going to skip the obvious pun due to it being too obvious, and only say that this article was viewed 12,606 times.
#7: Now that we're curious and understand both the meaning of truth and of numbers, it stands to reason that we should Be Happier than before. Or maybe not, since Klevador's article does not actually mention "understand obscure philosophy" as a way of getting happier. What it does mention is a big list of other things that have been shown to increase happiness. We first get a list of brief recommendations a few sentences long, and then somewhat longer excerpts of the relevant literature. There's also a full list of references. Let's hope that the 14,178 views that this post got made someone happier.
#6: Getting into more controversial territory, lukeprog advises us to Train Philosophers with Pearl and Kahneman, not Plato and Kant. Philosophy is getting increasingly diseased and irrelevant, he argues, and the cure for that involves incorporating more actual science and rationality into the standard philosopher curriculum. If the discussion on Hacker News is any indication, this post got a lot of people incensed, which might help explain why it got 14,334 views.
#5: Now that we got started on calling whole disciplines diseased, let's look at Diseased disciplines: the strange case of the inverted chart. Morendil's post begins with a hypothetical example of numerous academics all citing a particular source, which doesn't actually contain the intended reference... and then the intended source doesn't actually have the data to back up its claim, either. But that's just a hypothetical example, right? Well, not really, which helped this post get 17,385 views.
#4: Interestingly, our fourth-most-popular post isn't actually an original contribution as such. Grognor's transcript of Richard Feynman on Why Questions discusses the nature of explanations, and the fact that there are some things which simply cannot be adequately explained in terms of pre-existing knowledge. Instead, one has to learn entirely new concepts in order to comprehend them. Hopefully, at least this much was understood on the 18,402 times that the post was viewed.
#3: From physics to neuroscience: kalla724's Attention control is critical for changing/increasing/altering motivation explores the effect of attention on neural plasticity, including the plasticity of motivation. It explains that paying attention to something can increase the amount of brain circuitry dedicated to processing that something, generally by repurposing nearby less-used circuitry. This also has practical applications, such as in helping to explain why Cognitive Behavioral Therapy works. That earned the post 21,136 views.
#2: I should be writing this post instead of browsing Facebook. Fortunately, lukeprog has a post titled My Algorithm for Beating Procrastination. Based on the equation of Motivation = (Expectancy * Value) / (Impulsiveness * Delay), the algorithm involves first noticing that you are procrastinating, then guessing which part of the motivation equation is causing you the most trouble, and then trying several methods for attacking that specific problem. I guess that a lot of people shared this on Facebook where other procrastinators saw it, because the article got 38,637 views.
#1: And finally... the most read 2012 article on the site was Yvain's The noncentral fallacy - the worst argument in the world?, where he defined the noncentral fallacy as "X is in a category whose archetypal member gives us a certain emotional reaction. Therefore, we should apply that emotional reaction to X, even though it is not a central category member." Which sounds pretty abstract, but the political examples in the post should make it clearer. The politics probably helped contribute to this post's achievement of 41,932 views.
Most popular all-time posts
In addition to looking at only the posts that were made in 2012, people might be interested in knowing which posts were the most viewed in 2012 overall. The top three ones were all written by lukeprog, and we can see that two of them were closely related to the top-scorers which were written last year.
How to be Happy is LW's run-away favorite article and was viewed more than every page on LW except the home page and the discussion homepage. That is, 228,747 times! The Best Textbooks on Every Subject comes as a distant second at 98,011 views. And the third one is How to Beat Procrastination, at 66,587 views.
So I guess the take-home message is: people want to be happier, smarter, and more productive. Let's keep becoming those things in 2013!
Moral uncertainty (or normative uncertainty) is uncertainty about how to act given the diversity of moral doctrines. For example, suppose that we knew for certain that a new technology would enable more humans to live on another planet with slightly less well-being than on Earth. An average utilitarian would consider these consequences bad, while a total utilitarian would endorse such technology. If we are uncertain about which of these two theories are right, what should we do? (LW wiki)
I have long been slightly frustrated by the existing discussions about moral uncertainty that I've seen. I suspect that the reason has been that they've been unclear on what exactly they mean when they say that we are "uncertain about which theory is right" - what is uncertainty about moral theories? Furthermore, especially when discussing things in an FAI context, it feels like several different senses of moral uncertainty get mixed together. Here is my suggested breakdown, with some elaboration:
Descriptive moral uncertainty. What is the most accurate way of describing my values? The classical FAI-relevant question, this is in a sense the most straightforward one. We have some set values, and although we can describe parts of them verbally, we do not have conscious access to the deep-level cognitive machinery that generates them. We might feel relatively sure that our moral intuitions are produced by a system that's mostly consequentialist, but suspect that parts of us might be better described as deontologist. A solution to descriptive moral uncertainty would involve a system capable of somehow extracting the mental machinery that produced our values, or creating a moral reasoning system which managed to produce the same values by some other process.
Epistemic moral uncertainty. Would I reconsider any of my values if I knew more? Perhaps we hate the practice of eating five-sided fruit and think that everyone who eats five-sided fruit should be thrown to jail, but if we found out that five-sided fruit made people happier and had no averse effects, we would change our minds. This roughly corresponds to the "our wish if we knew more, thought faster" part of Eliezer's original CEV description. A solution to epistemic moral uncertainty would involve finding out more about the world.
Intrinsic moral uncertainty. Which axioms should I endorse? We might be intrinsically conflicted between different value systems. Perhaps we are trying to choose whether to be loyal to a friend or whether to act for the common good (a conflict between two forms of deontology, or between deontology and consequentialism), or we could be conflicted between positive and negative utilitarianism. In its purest form, this sense of moral uncertainty closely resembles what would otherwise be called a wrong question, one where
you cannot even imagine any concrete, specific state of how-the-world-is that would answer the question. When it doesn't even seem possible to answer the question.
But unlike wrong questions, questions of intrinsic moral uncertainty are real ones that you need to actually answer in order to make a choice. They are generated when different modules within your brain generate different moral intuitions, and are essentially power struggles between various parts of your mind. A solution to intrinsic moral uncertainty would involve somehow tipping the balance of power in favor of one of the "mind factions". This could involve developing an argument sufficiently persuasive to convince most parts of yourself, or self-modifying in such a way that one of the factions loses its sway over your decision-making. (Of course, if you already knew for certain which faction you wanted to expunge, you wouldn't need to do it in the first place.) I would roughly interpret the "our wish ... if we had grown up farther together" part of CEV to be an attempt to model some of the social influences on our moral intuitions and thereby help resolve cases of intrinsic moral uncertainty.
This is a very preliminary categorization, and I'm sure that it could be improved upon. There also seem to exist cases of moral uncertainty which are hybrids of several categories - for example, ontological crises seem to be mostly about intrinsic moral uncertainty, but to also incorporate some elements of epistemic moral uncertainty. I also have a general suspicion that these categories still don't cut reality that well at the joints, so any suggestions for improvement would be much appreciated.
The widespread prevalence and persistence of misinformation in contemporary societies, such as the false belief that there is a link between childhood vaccinations and autism, is a matter of public concern. For example, the myths surrounding vaccinations, which prompted some parents to withhold immunization from their children, have led to a marked increase in vaccine-preventable disease, as well as unnecessary public expenditure on research and public-information campaigns aimed at rectifying the situation.
We first examine the mechanisms by which such misinformation is disseminated in society, both inadvertently and purposely. Misinformation can originate from rumors but also from works of fiction, governments and politicians, and vested interests. Moreover, changes in the media landscape, including the arrival of the Internet, have fundamentally influenced the ways in which information is communicated and misinformation is spread.
We next move to misinformation at the level of the individual, and review the cognitive factors that often render misinformation resistant to correction. We consider how people assess the truth of statements and what makes people believe certain things but not others. We look at people’s memory for misinformation and answer the questions of why retractions of misinformation are so ineffective in memory updating and why efforts to retract misinformation can even backfire and, ironically, increase misbelief. Though ideology and personal worldviews can be major obstacles for debiasing, there nonetheless are a number of effective techniques for reducing the impact of misinformation, and we pay special attention to these factors that aid in debiasing.
We conclude by providing specific recommendations for the debunking of misinformation. These recommendations pertain to the ways in which corrections should be designed, structured, and applied in order to maximize their impact. Grounded in cognitive psychological theory, these recommendations may help practitioners—including journalists, health professionals, educators, and science communicators—design effective misinformation retractions, educational tools, and public-information campaigns.
This is a fascinating article with many, many interesting points. I'm excerpting some of them below, but mostly just to get you to read it: if I were to quote everything interesting, I'd have to pretty much copy the entire (long!) article.
Rumors and fiction
[...] A related but perhaps more surprising source of misinformation is literary fiction. People extract knowledge even from sources that are explicitly identified as fictional. This process is often adaptive, because fiction frequently contains valid information about the world. For example, non-Americans’ knowledge of U.S. traditions, sports, climate, and geography partly stems from movies and novels, and many Americans know from movies that Britain and Australia have left-hand traffic. By definition, however, fiction writers are not obliged to stick to the facts, which creates an avenue for the spread of misinformation, even by stories that are explicitly identified as fictional. A study by Marsh, Meade, and Roediger (2003) showed that people relied on misinformation acquired from clearly fictitious stories to respond to later quiz questions, even when these pieces of misinformation contradicted common knowledge. In most cases, source attribution was intact, so people were aware that their answers to the quiz questions were based on information from the stories, but reading the stories also increased people’s illusory belief of prior knowledge. In other words, encountering misinformation in a fictional context led people to assume they had known it all along and to integrate this misinformation with their prior knowledge (Marsh & Fazio, 2006; Marsh et al., 2003).
The effects of fictional misinformation have been shown to be stable and difficult to eliminate. Marsh and Fazio (2006) reported that prior warnings were ineffective in reducing the acquisition of misinformation from fiction, and that acquisition was only reduced (not eliminated) under conditions of active on-line monitoring—when participants were instructed to actively monitor the contents of what they were reading and to press a key every time they encountered a piece of misinformation (see also Eslick, Fazio, & Marsh, 2011). Few people would be so alert and mindful when reading fiction for enjoyment. These links between fiction and incorrect knowledge are particularly concerning when popular fiction pretends to accurately portray science but fails to do so, as was the case with Michael Crichton’s novel State of Fear. The novel misrepresented the science of global climate change but was nevertheless introduced as “scientific” evidence into a U.S. Senate committee (Allen, 2005; Leggett, 2005).
Writers of fiction are expected to depart from reality, but in other instances, misinformation is manufactured intentionally. There is considerable peer-reviewed evidence pointing to the fact that misinformation can be intentionally or carelessly disseminated, often for political ends or in the service of vested interests, but also through routine processes employed by the media. [...]
Assessing the Truth of a Statement: Recipients’ Strategies
Misleading information rarely comes with a warning label. People usually cannot recognize that a piece of information is incorrect until they receive a correction or retraction. For better or worse, the acceptance of information as true is favored by tacit norms of everyday conversational conduct: Information relayed in conversation comes with a “guarantee of relevance” (Sperber & Wilson, 1986), and listeners proceed on the assumption that speakers try to be truthful, relevant, and clear, unless evidence to the contrary calls this default into question (Grice, 1975; Schwarz, 1994, 1996). Some research has even suggested that to comprehend a statement, people must at least temporarily accept it as true (Gilbert, 1991). On this view, belief is an inevitable consequence of—or, indeed, precursor to—comprehension.
Although suspension of belief is possible (Hasson, Simmons, & Todorov, 2005; Schul, Mayo, & Burnstein, 2008), it seems to require a high degree of attention, considerable implausibility of the message, or high levels of distrust at the time the message is received. So, in most situations, the deck is stacked in favor of accepting information rather than rejecting it, provided there are no salient markers that call the speaker’s intention of cooperative conversation into question. Going beyond this default of acceptance requires additional motivation and cognitive resources: If the topic is not very important to you, or you have other things on your mind, misinformation will likely slip in." [...]
Is the information compatible with what I believe?
As numerous studies in the literature on social judgment and persuasion have shown, information is more likely to be accepted by people when it is consistent with other things they assume to be true (for reviews, see McGuire, 1972; Wyer, 1974). People assess the logical compatibility of the information with other facts and beliefs. Once a new piece of knowledge-consistent information has been accepted, it is highly resistant to change, and the more so the larger the compatible knowledge base is. From a judgment perspective, this resistance derives from the large amount of supporting evidence (Wyer, 1974); from a cognitive-consistency perspective (Festinger, 1957), it derives from the numerous downstream inconsistencies that would arise from rejecting the prior information as false. Accordingly, compatibility with other knowledge increases the likelihood that misleading information will be accepted, and decreases the likelihood that it will be successfully corrected.
When people encounter a piece of information, they can check it against other knowledge to assess its compatibility. This process is effortful, and it requires motivation and cognitive resources. A less demanding indicator of compatibility is provided by one’s meta-cognitive experience and affective response to new information. Many theories of cognitive consistency converge on the assumption that information that is inconsistent with one’s beliefs elicits negative feelings (Festinger, 1957). Messages that are inconsistent with one’s beliefs are also processed less fluently than messages that are consistent with one’s beliefs (Winkielman, Huber, Kavanagh, & Schwarz, 2012). In general, fluently processed information feels more familiar and is more likely to be accepted as true; conversely, disfluency elicits the impression that something doesn’t quite “feel right” and prompts closer scrutiny of the message (Schwarz et al., 2007; Song & Schwarz, 2008). This phenomenon is observed even when the fluent processing of a message merely results from superficial characteristics of its presentation. For example, the same statement is more likely to be judged as true when it is printed in high rather than low color contrast (Reber & Schwarz, 1999), presented in a rhyming rather than nonrhyming form (McGlone & Tofighbakhsh, 2000), or delivered in a familiar rather than unfamiliar accent (Levy-Ari & Keysar, 2010). Moreover, misleading questions are less likely to be recognized as such when printed in an easy-to-read font (Song & Schwarz, 2008).
As a result, analytic as well as intuitive processing favors the acceptance of messages that are compatible with a recipient’s preexisting beliefs: The message contains no elements that contradict current knowledge, is easy to process, and “feels right.”
Is the story coherent?
Whether a given piece of information will be accepted as true also depends on how well it fits a broader story that lends sense and coherence to its individual elements. People are particularly likely to use an assessment strategy based on this principle when the meaning of one piece of information cannot be assessed in isolation because it depends on other, related pieces; use of this strategy has been observed in basic research on mental models (for a review, see Johnson-Laird, 2012), as well as extensive analyses of juries’ decision making (Pennington & Hastie, 1992, 1993).
A story is compelling to the extent that it organizes information without internal contradictions in a way that is compatible with common assumptions about human motivation and behavior. Good stories are easily remembered, and gaps are filled with story-consistent intrusions. Once a coherent story has been formed, it is highly resistant to change: Within the story, each element is supported by the fit of other elements, and any alteration of an element may be made implausible by the downstream inconsistencies it would cause. Coherent stories are easier to process than incoherent stories are (Johnson-Laird, 2012), and people draw on their processing experience when they judge a story’s coherence (Topolinski, 2012), again giving an advantage to material that is easy to process. [...]
Is the information from a credible source?
[...] People’s evaluation of a source’s credibility can be based on declarative information, as in the above examples, as well as experiential information. The mere repetition of an unknown name can cause it to seem familiar, making its bearer “famous overnight” (Jacoby, Kelley, Brown, & Jaseschko, 1989)—and hence more credible. Even when a message is rejected at the time of initial exposure, that initial exposure may lend it some familiarity-based credibility if the recipient hears it again.
Do others believe this information?
Repeated exposure to a statement is known to increase its acceptance as true (e.g., Begg, Anas, & Farinacci, 1992; Hasher, Goldstein, & Toppino, 1977). In a classic study of rumor transmission, Allport and Lepkin (1945) observed that the strongest predictor of belief in wartime rumors was simple repetition. Repetition effects may create a perceived social consensus even when no consensus exists. Festinger (1954) referred to social consensus as a “secondary reality test”: If many people believe a piece of information, there’s probably something to it. Because people are more frequently exposed to widely shared beliefs than to highly idiosyncratic ones, the familiarity of a belief is often a valid indicator of social consensus. But, unfortunately, information can seem familiar for the wrong reason, leading to erroneous perceptions of high consensus. For example, Weaver, Garcia, Schwarz, and Miller (2007) exposed participants to multiple iterations of the same statement, provided by the same communicator. When later asked to estimate how widely the conveyed belief is shared, participants estimated consensus to be greater the more often they had read the identical statement from the same, single source. In a very real sense, a single repetitive voice can sound like a chorus. [...]
The extent of pluralistic ignorance (or of the false-consensus effect) can be quite striking: In Australia, people with particularly negative attitudes toward Aboriginal Australians or asylum seekers have been found to overestimate public support for their attitudes by 67% and 80%, respectively (Pedersen, Griffiths, & Watt, 2008). Specifically, although only 1.8% of people in a sample of Australians were found to hold strongly negative attitudes toward Aboriginals, those few individuals thought that 69% of all Australians (and 79% of their friends) shared their fringe beliefs. This represents an extreme case of the false-consensus effect. [...]
The Continued Influence Effect: Retractions Fail to Eliminate the Influence of Misinformation
We first consider the cognitive parameters of credible retractions in neutral scenarios, in which people have no inherent reason or motivation to believe one version of events over another. Research on this topic was stimulated by a paradigm pioneered by Wilkes and Leatherbarrow (1988) and H. M. Johnson and Seifert (1994). In it, people are presented with a fictitious report about an event unfolding over time. The report contains a target piece of information: For some readers, this target information is subsequently retracted, whereas for readers in a control condition, no correction occurs. Participants’ understanding of the event is then assessed with a questionnaire, and the number of clear and uncontroverted references to the target (mis-)information in their responses is tallied.
A stimulus narrative commonly used in this paradigm involves a warehouse fire that is initially thought to have been caused by gas cylinders and oil paints that were negligently stored in a closet (e.g., Ecker, Lewandowsky, Swire, & Chang, 2011; H. M. Johnson & Seifert, 1994; Wilkes & Leatherbarrow, 1988). Some participants are then presented with a retraction, such as “the closet was actually empty.” A comprehension test follows, and participants’ number of references to the gas and paint in response to indirect inference questions about the event (e.g., “What caused the black smoke?”) is counted. In addition, participants are asked to recall some basic facts about the event and to indicate whether they noticed any retraction.
Research using this paradigm has consistently found that retractions rarely, if ever, have the intended effect of eliminating reliance on misinformation, even when people believe, understand, and later remember the retraction (e.g., Ecker, Lewandowsky, & Apai, 2011; Ecker, Lewandowsky, Swire, & Chang, 2011; Ecker, Lewandowsky, & Tang, 2010; Fein, McCloskey, & Tomlinson, 1997; Gilbert, Krull, & Malone, 1990; Gilbert, Tafarodi, & Malone, 1993; H. M. Johnson & Seifert, 1994, 1998, 1999; Schul & Mazursky, 1990; van Oostendorp, 1996; van Oostendorp & Bonebakker, 1999; Wilkes & Leatherbarrow, 1988; Wilkes & Reynolds, 1999). In fact, a retraction will at most halve the number of references to misinformation, even when people acknowledge and demonstrably remember the retraction (Ecker, Lewandowsky, & Apai, 2011; Ecker, Lewandowsky, Swire, & Chang, 2011); in some studies, a retraction did not reduce reliance on misinformation at all (e.g., H. M. Johnson & Seifert, 1994).
When misinformation is presented through media sources, the remedy is the presentation of a correction, often in a temporally disjointed format (e.g., if an error appears in a newspaper, the correction will be printed in a subsequent edition). In laboratory studies, misinformation is often retracted immediately and within the same narrative (H. M. Johnson & Seifert, 1994). Despite this temporal and contextual proximity to the misinformation, retractions are ineffective. More recent studies (Seifert, 2002) have examined whether clarifying the correction (minimizing misunderstanding) might reduce the continued influence effect. In these studies, the correction was thus strengthened to include the phrase “paint and gas were never on the premises.” Results showed that this enhanced negation of the presence of flammable materials backfired, making people even more likely to rely on the misinformation in their responses. Other additions to the correction were found to mitigate to a degree, but not eliminate, the continued influence effect: For example, when participants were given a rationale for how the misinformation originated, such as, “a truckers’ strike prevented the expected delivery of the items,” they were somewhat less likely to make references to it. Even so, the influence of the misinformation could still be detected. The wealth of studies on this phenomenon have documented its pervasive effects, showing that it is extremely difficult to return the beliefs of people who have been exposed to misinformation to a baseline similar to those of people who were never exposed to it.
Multiple explanations have been proposed for the continued influence effect. We summarize their key assumptions next. [...]
Concise recommendations for practitioners
[...] We summarize the main points from the literature in Figure 1 and in the following list of recommendations:
Consider what gaps in people’s mental event models are created by debunking and fill them using an alternative explanation.
Use repeated retractions to reduce the influence of misinformation, but note that the risk of a backfire effect increases when the original misinformation is repeated in retractions and thereby rendered more familiar.
To avoid making people more familiar with misinformation (and thus risking a familiarity backfire effect), emphasize the facts you wish to communicate rather than the myth.
Provide an explicit warning before mentioning a myth, to ensure that people are cognitively on guard and less likely to be influenced by the misinformation.
Ensure that your material is simple and brief. Use clear language and graphs where appropriate. If the myth is simpler and more compelling than your debunking, it will be cognitively more attractive, and you will risk an overkill backfire effect.
Consider whether your content may be threatening to the worldview and values of your audience. If so, you risk a worldview backfire effect, which is strongest among those with firmly held beliefs. The most receptive people will be those who are not strongly fixed in their views.
If you must present evidence that is threatening to the audience’s worldview, you may be able to reduce the worldview backfire effect by presenting your content in a worldview-affirming manner (e.g., by focusing on opportunities and potential benefits rather than risks and threats) and/or by encouraging self-affirmation.
You can also circumvent the role of the audience’s worldview by focusing on behavioral techniques, such as the design of choice architectures, rather than overt debiasing.
Here's the biggest thing that I've been working on for the last several months:
Responses to Catastrophic AGI Risk: A Survey
Kaj Sotala, Roman Yampolskiy, and Luke Muehlhauser
Abstract: Many researchers have argued that humanity will create artificial general intelligence (AGI) within the next 20-100 years. It has been suggested that this may become a catastrophic risk, threatening to do major damage on a global scale. After briefly summarizing the arguments for why AGI may become a catastrophic risk, we survey various proposed responses to AGI risk. We consider societal proposals, proposals for constraining the AGIs’ behavior from the outside, and for creating AGIs in such a way that they are inherently safe.
This doesn't aim to be a very strongly argumentative paper, though it does comment on the various proposals from an SI-ish point of view. Rather, it attempts to provide a survey of all the major AGI-risk related proposals that have been made so far, and to provide some thoughts on their respective strengths and weaknesses. Before writing this paper, we hadn't encountered anyone who'd have been familiar with all of these proposals - not to mention that even we ourselves weren't familiar with all of them! Hopefully, this should become a useful starting point for anyone who's at all interested in AGI risk or Friendly AI.
The draft will be public and open for comments for one week (until Nov 23rd), after which we'll incorporate the final edits and send it off for review. We're currently aiming to have it published in the sequel volume to Singularity Hypotheses.
EDIT: I've now hidden the draft from public view (so as to avoid annoying future publishers who may not like early drafts floating around before the work has been accepted for publication) while I'm incorporating all the feedback that we got. Thanks to everyone who commented!
In late spring this year, Luke tasked me with writing a summary and analysis of the Hanson-Yudkowsky FOOM debate, with the intention of having it eventually published in somewhere. Due to other priorities, this project was put on hold for the time being. Because it doesn't look like it will be finished in the near future, and because Curiouskid asked to see it, we thought that we might as well share the thing.
I have reorganized the debate, presenting it by topic rather than in chronological order: I start by providing some brief conceptual background that's useful for understanding Eliezer's optimization power argument, after which I present his argument. Robin's various objections follow, after which there is a summary of Robin's view of how the Singularity will be like, together with Eliezer's objections to that view. Hopefully, this should make the debate easier to follow. This summary also incorporates material from the 90-minute live debate on the topic that they had in 2011. The full table of contents:
- The optimization power argument
- Conceptual background
- The argument: Yudkowsky
- Recursive self-improvement
- Hard takeoff
- Questioning optimization power: the question of abstractions
- Questioning optimization power: the historical record
- Questioning optimization power: the UberTool question
- Hanson's Singularity scenario
- Architecture vs. content, sharing of information
- Modularity of knowledge
- Local or global singularity?
Here's the link to the current draft, any feedback is welcomed. Feel free to comment if you know of useful references, if you think I've misinterpreted something that was said, or if you think there's any other problem. I'd also be curious to hear to what extent people think that this outline is easier to follow than the original debate, or whether it's just as confusing.
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