Less Wrong is a community blog devoted to refining the art of human rationality. Please visit our About page for more information.
[I'm unsure how much this rehashes things 'everyone knows already' - if old hat, feel free to downvote into oblivion. My other motivation for the cross-post is the hope it might catch the interest of someone with a stronger mathematical background who could make this line of argument more robust]
Many outcomes of interest have pretty good predictors. It seems that height correlates to performance in basketball (the average height in the NBA is around 6'7"). Faster serves in tennis improve one's likelihood of winning. IQ scores are known to predict a slew of factors, from income, to chance of being imprisoned, to lifespan.
What is interesting is the strength of these relationships appear to deteriorate as you advance far along the right tail. Although 6'7" is very tall, is lies within a couple of standard deviations of the median US adult male height - there are many thousands of US men taller than the average NBA player, yet are not in the NBA. Although elite tennis players have very fast serves, if you look at the players serving the fastest serves ever recorded, they aren't the very best players of their time. It is harder to look at the IQ case due to test ceilings, but again there seems to be some divergence near the top: the very highest earners tend to be very smart, but their intelligence is not in step with their income (their cognitive ability is around +3 to +4 SD above the mean, yet their wealth is much higher than this) (1).
The trend seems to be that although we know the predictors are correlated with the outcome, freakishly extreme outcomes do not go together with similarly freakishly extreme predictors. Why?
Too much of a good thing?
One candidate explanation would be that more isn't always better, and the correlations one gets looking at the whole population doesn't capture a reversal at the right tail. Maybe being taller at basketball is good up to a point, but being really tall leads to greater costs in terms of things like agility. Maybe although having a faster serve is better all things being equal, but focusing too heavily on one's serve counterproductively neglects other areas of one's game. Maybe a high IQ is good for earning money, but a stratospherically high IQ has an increased risk of productivity-reducing mental illness. Or something along those lines.
I would guess that these sorts of 'hidden trade-offs' are common. But, the 'divergence of tails' seems pretty ubiquitous (the tallest aren't the heaviest, the smartest parents don't have the smartest children, the fastest runners aren't the best footballers, etc. etc.), and it would be weird if there was always a 'too much of a good thing' story to be told for all of these associations. I think there is a more general explanation.
The simple graphical explanation
[Inspired by this essay from Grady Towers]
Suppose you make a scatter plot of two correlated variables. Here's one I grabbed off google, comparing the speed of a ball out of a baseball pitchers hand compared to its speed crossing crossing the plate:
It is unsurprising to see these are correlated (I'd guess the R-square is > 0.8). But if one looks at the extreme end of the graph, the very fastest balls out of the hand aren't the very fastest balls crossing the plate, and vice versa. This feature is general. Look at this data (again convenience sampled from googling 'scatter plot') of quiz time versus test score:
Given a correlation, the envelope of the distribution should form some sort of ellipse, narrower as the correlation goes stronger, and more circular as it gets weaker:
The thing is, as one approaches the far corners of this ellipse, we see 'divergence of the tails': as the ellipse doesn't sharpen to a point, there are bulges where the maximum x and y values lie with sub-maximal y and x values respectively:
So this offers an explanation why divergence at the tails is ubiquitous. Providing the sample size is largeish, and the correlation not to tight (the tighter the correlation, the larger the sample size required), one will observe the ellipses with the bulging sides of the distribution (2).
Hence the very best basketball players aren't the tallest (and vice versa), the very wealthiest not the smartest, and so on and so forth for any correlated X and Y. If X and Y are "Estimated effect size" and "Actual effect size", or "Performance at T", and "Performance at T+n", then you have a graphical display of winner's curse and regression to the mean.
An intuitive explanation of the graphical explanation
It would be nice to have an intuitive handle on why this happens, even if we can be convinced that it happens. Here's my offer towards an explanation:
The fact that a correlation is less than 1 implies that other things matter to an outcome of interest. Although being tall matters for being good at basketball, strength, agility, hand-eye-coordination matter as well (to name but a few). The same applies to other outcomes where multiple factors play a role: being smart helps in getting rich, but so does being hard working, being lucky, and so on.
For a toy model, pretend these height, strength, agility and hand-eye-coordination are independent of one another, gaussian, and additive towards the outcome of basketball ability with equal weight.(3) So, ceritus paribus, being taller will make one better at basketball, and the toy model stipulates there aren't 'hidden trade-offs': there's no negative correlation between height and the other attributes, even at the extremes. Yet the graphical explanation suggests we should still see divergence of the tails: the very tallest shouldn't be the very best.
The intuitive explanation would go like this: Start at the extreme tail - +4SD above the mean for height. Although their 'basketball-score' gets a massive boost from their height, we'd expect them to be average with respect to the other basketball relevant abilities (we've stipulated they're independent). Further, as this ultra-tall population is small, this population won't have a very high variance: with 10 people at +4SD, you wouldn't expect any of them to be +2SD in another factor like agility.
Move down the tail to slightly less extreme values - +3SD say. These people don't get such a boost to their basketball score for their height, but there should be a lot more of them (if 10 at +4SD, around 500 at +3SD), this means there is a lot more expected variance in the other basketball relevant activities - it is much less surprising to find someone +3SD in height and also +2SD in agility, and in the world where these things were equally important, they would 'beat' someone +4SD in height but average in the other attributes. Although a +4SD height person will likely be better than a given +3SD height person, the best of the +4SDs will not be as good as the best of the much larger number of +3SDs
The trade-off will vary depending on the exact weighting of the factors, which explain more of the variance, but the point seems to hold in the general case: when looking at a factor known to be predictive of an outcome, the largest outcome values will occur with sub-maximal factor values, as the larger population increases the chances of 'getting lucky' with the other factors:
So that's why the tails diverge.
Endnote: EA relevance
I think this is interesting in and of itself, but it has relevance to Effective Altruism, given it generally focuses on the right tail of various things (What are the most effective charities? What is the best career? etc.) It generally vindicates worries about regression to the mean or winner's curse, and suggests that these will be pretty insoluble in all cases where the populations are large: even if you have really good means of assessing the best charities or the best careers so that your assessments correlate really strongly with what ones actually are the best, the very best ones you identify are unlikely to be actually the very best, as the tails will diverge.
This probably has limited practical relevance. Although you might expect that one of the 'not estimated as the very best' charities is in fact better than your estimated-to-be-best charity, you don't know which one, and your best bet remains your estimate (in the same way - at least in the toy model above - you should bet a 6'11" person is better at basketball than someone who is 6'4".)
There may be spread betting or portfolio scenarios where this factor comes into play - perhaps instead of funding AMF to diminishing returns when its marginal effectiveness dips below charity #2, we should be willing to spread funds sooner.(4) Mainly, though, it should lead us to be less self-confident.
1. One might look at the generally modest achievements of people in high-IQ societies as further evidence, but there are worries about adverse selection.
2. One needs a large enough sample to 'fill in' the elliptical population density envelope, and the tighter the correlation, the larger the sample needed to fill in the sub-maximal bulges. The old faithful case is an example where actually you do get a 'point', although it is likely an outlier.
3. If you want to apply it to cases where the factors are positively correlated - which they often are - just use the components of the other factors that are independent of the factor of interest. I think, but I can't demonstrate, the other stipulations could also be relaxed.
4. I'd intuit, but again I can't demonstrate, the case for this becomes stronger with highly skewed interventions where almost all the impact is focused in relatively low probability channels, like averting a very specified existential risk.
This is a thread for rationality-related or LW-related jokes and humor. Please post jokes (new or old) in the comments.
Q: Why are Chromebooks good Bayesians?
A: Because they frequently update!
A super-intelligent AI walks out of a box...
Q: Why did the psychopathic utilitarian push a fat man in front of a trolley?
A: Just for fun.
The official story: "Fifty Shades of Grey" was a Twilight fan-fiction that had over two million downloads online. The publishing giant Vintage Press saw that number and realized there was a huge, previously-unrealized demand for stories like this. They filed off the Twilight serial numbers, put it in print, marketed it like hell, and now it's sold 60 million copies.
The reality is quite different.
Still very much a work in progress
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.
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.
This is an exposition of some of the main ideas in the paper Robust Cooperation. My goal is to make the ideas and proofs seem natural and intuitive - instead of some mysterious thing where we invoke Löb's theorem at the right place and the agents magically cooperate. Also I hope it is accessible to people without a math or CS background. Be warned, it is pretty cheesy ok.
In a small quirky town, far away from other cities or towns, the most exciting event is a game called (for historical reasons) The Prisoner's Dilemma. Everyone comes out to watch the big tournament at the end of Summer, and you (Alice) are especially excited because this year it will be your first time playing in the tournament! So you've been thinking of ways to make sure that you can do well.
The way the game works is this: Each player can choose to cooperate or defect with the other player. If you both cooperate, then you get two points each. If one of you defects, then that player will get three points, and the other player won't get any points. But if you both defect, then you each get only one point. You have to make your decisions separately, without communicating with each other - however, everyone is required to register the algorithm they will be using before the tournament, and you can look at the other player's algorithm if you want to. You also are allowed to use some outside help in your algorithm.
Now if you were a newcomer, you might think that no matter what the other player does, you can always do better by defecting. So the best strategy must be to always defect! Of course, you know better, if everyone tried that strategy, then they would end up defecting against each other, which is a shame since they would both be better off if they had just cooperated.
But how can you do better? You have to be able to describe your algorithm in order to play. You have a few ideas, and you'll be playing some practice rounds with your friend Bob soon, so you can try them out before the actual tournament.
Your first plan:
I'll cooperate with Bob if I can tell from his algorithm that he'll cooperate with me. Otherwise I'll defect.
For your first try, you'll just run Bob's algorithm and see if he cooperates. But there's a problem - if Bob tries the same strategy, he'll have to run your algorithm, which will run his algorithm again, and so on into an infinite loop!
So you'll have to be a bit more clever than that... luckily you know a guy, Shady, who is good at these kinds of problems.
You call up Shady, and while you are waiting for him to come over, you remember some advice your dad Löb gave you.
(Löb's theorem) "If someone says you can trust them on X, well then they'll just tell you X."
If (someone tells you If [I tell you] X, then X is true)
Then (someone tells you X is true)
(See The Cartoon Guide to Löb's Theorem[pdf] for a nice proof of this)
Here's an example:
Sketchy watch salesman: Hey, if I tell you these watches are genuine then they are genuine!
You: Ok... so are these watches genuine?
Sketchy watch salesman: Of course!
It's a good thing to remember when you might have to trust someone. If someone you already trust tells you you can trust them on something, then you know that something must be true.
On the other hand, if someone says you can always trust them, well that's pretty suspicious... If they say you can trust them on everything, that means that they will never tell you a lie - which is logically equivalent to them saying that if they were to tell you a lie, then that lie must be true. So by Löb's theorem, they will lie to you. (Gödel's second incompleteness theorem)
Despite his name, you actually trust Shady quite a bit. He's never told you or anyone else anything that didn't end up being true. And he's careful not to make any suspiciously strong claims about his honesty.
So your new plan is to ask Shady if Bob will cooperate with you. If so, then you will cooperate. Otherwise, defect. (FairBot)
It's game time! You look at Bob's algorithm, and it turns out he picked the exact same algorithm! He's going to ask Shady if you will cooperate with him. Well, the first step is to ask Shady, "will Bob cooperate with me?"
Shady looks at Bob's algorithm and sees that if Shady says you cooperate, then Bob cooperates. He looks at your algorithm and sees that if Shady says Bob cooperates, then you cooperate. Combining these, he sees that if he says you both cooperate, then both of you will cooperate. So he tells you that you will both cooperate (your dad was right!)
Let A stand for "Alice cooperates with Bob" and B stand for "Bob cooperates with Alice".
From looking at the algorithms, and .
So combining these, .
Then by Löb's theorem, .
Since that means that Bob will cooperate, you decide to actually cooperate.
Bob goes through an analagous thought process, and also decides to cooperate. So you cooperate with each other on the prisoner's dilemma! Yay!
That night, you go home and remark, "it's really lucky we both ended up using Shady to help us, otherwise that wouldn't have worked..."
Your dad interjects, "Actually, it doesn't matter - as long as they were both smart enough to count, it would work. This doesn't just say 'I tell you X', it's stronger than that - it actually says 'Anyone who knows basic arithmetic will tell you X'. So as long as they both know a little arithmetic, it will still work - even if one of them is pro-axiom-of-choice, and the other is pro-axiom-of-life. The cooperation is robust." That's really cool!
But there's another issue you think of. Sometimes, just to be tricky, the tournament organizers will set up a game where you have to play against a rock. Yes, literally just a rock that holding the cooperate button down. If you played against a rock with your current algorithm, well you start by asking Shady if the rock will cooperate with you. Shady is like, "well yeah, duh." So then you cooperate too. But you could have gotten three points by defecting! You're missing out on a totally free point!
You think that it would be a good idea to make sure the other player isn't a complete idiot before you cooperate with them. How can you check? Well, let's see if they would cooperate with a rock placed on the defect button (affectionately known as 'DefectRock'). If they know better than that, and they will cooperate with you, then you will cooperate with them.
The next morning, you excitedly tell Shady about your new plan. "It will be like before, except this time, I also ask you if the other player will cooperate with DefectRock! If they are dumb enough to do that, then I'll just defect. That way, I can still cooperate with other people who use algorithms like this one, or the one from before, but I can also defect and get that extra point when there's just a rock on cooperate."
Shady get's an awkward look on his face, "Sorry, but I can't do that... or at least it wouldn't work out the way you're thinking. Let's say you're playing against Bob, who is still using the old algorithm. You want to know if Bob will cooperate with DefectRock, so I have to check and see if I'll tell Bob that DefectRock will cooperate with him. I would have say I would never tell Bob that DefectRock will cooperate with him. But by Löb's theorem, that means I would tell you this obvious lie! So that isn't gonna work."
Notation, if X cooperates with Y in the prisoner's dilemma (or = D if not).
You ask Shady, does ?
Bob's algorithm: only if .
So to say , we would need .
This is equivalent to , since is an obvious lie.
By Löb's theorem, , which is a lie.
<Extra credit: does the fact that Shady is the one explaining this mean you can't trust him?>
<Extra extra credit: find and fix the minor technical error in the above argument.>
Shady sees the dismayed look on your face and adds, "...but, I know a guy who can vouch for me, and I think maybe that could make your new algorithm work."
So Shady calls his friend T over, and you work out the new details. You ask Shady if Bob will cooperate with you, and you ask T if Bob will cooperate with DefectRock. So T looks at Bob's algorithm, which asks Shady if DefectRock will cooperate with him. Shady, of course, says no. So T sees that Bob will defect against DefectRock, and lets you know. Like before, Shady tells you Bob will cooperate with you, and thus you decide to cooperate! And like before, Bob decides to cooperate with you, so you both cooperate! Awesome! (PrudentBot)
If Bob is using your new algorithm, you can see that the same argument goes through mostly unchanged, and that you will still cooperate! And against a rock on cooperate, T will tell you that it will cooperate with DefectRock, so you can defect and get that extra point! This is really great!!
(ok now it's time for the really cheesy ending)
It's finally time for the tournament. You have a really good feeling about your algorithm, and you do really well! Your dad is in the audience cheering for you, with a really proud look on his face. You tell your friend Bob about your new algorithm so that he can also get that extra point sometimes, and you end up tying for first place with him!
A few weeks later, Bob asks you out, and you two start dating. Being able to cooperate with each other robustly is a good start to a healthy relationship, and you live happily ever after!
In decision theory, we often talk about programs that know their own source code. I'm very confused about how that theory applies to people, or even to computer programs that don't happen to know their own source code. I've managed to distill my confusion into three short questions:
1) Am I uncertain about my own source code?
2) If yes, what kind of uncertainty is that? Logical, indexical, or something else?
3) What is the mathematically correct way for me to handle such uncertainty?
Don't try to answer them all at once! I'll be glad to see even a 10% answer to one question.
I'd like to gauge interest in an (english-language) Tokyo area meetup - given Tokyo's size, if a couple people are interested, it would be good to pick a location/day that's convenient for everybody. Otherwise I will announce a date and time and wait in a cafe with a book hoping that somebody will turn up.
I have been to several LW gatherings and have met consistently awesome and nice people, so if any Tokyo lurkers are reading this, I can assure you it's totally worth it to come! Please make yourself heard in the comments if you are interested.
There is a site dedicated to the story at hpmor.com, which is now the place to go to find the authors notes and all sorts of other goodies. AdeleneDawner has kept an archive of Author’s Notes. (This goes up to the notes for chapter 76, and is now not updating. The authors notes from chapter 77 onwards are on hpmor.com.)Spoiler Warning: this thread is full of spoilers. With few exceptions, spoilers for MOR and canon are fair game to post, without warning or rot13. More specifically:
You do not need to rot13 anything about HP:MoR or the original Harry Potter series unless you are posting insider information from Eliezer Yudkowsky which is not supposed to be publicly available (which includes public statements by Eliezer that have been retracted).
If there is evidence for X in MOR and/or canon then it’s fine to post about X without rot13, even if you also have heard privately from Eliezer that X is true. But you should not post that “Eliezer said X is true” unless you use rot13.
I recently stumbled upon an article from early 2003 in Physics World outlining a bit of evidence that some of the constants in nature may change over time. In this particular case, researchers studying quasars noticed that the fine-structure constant (α) might have fluctuated a bit billions of years ago, in both directions (bigger and smaller) with significance 4.1 sigma. What intrigues me about this is that I’ve previously pondered if something like this might be found, albeit for very different reasons.
Back in the 90s I read a book that made a case for the universe as a computer simulation. That particular book wasn’t all that compelling to me, but I’ve never been completely satisfied with arguments against that model and tend to think of the universe generally in those terms anyway. Can I still call myself an atheist if I allow the possibility of a creator in this context? A non-practicing atheist maybe?
If this universe is a computer-generated simulation, programmed by another life form, perhaps the search for extraterrestrial intelligence (SETI) should be expanded to include life forms beyond our universe. It sounds nonsensical, but is it?
If I was to design and code an environment sophisticated enough to allow a species of life to evolve in that environment, I am not convinced that I would have many tools at my disposal to truly be able to understand and evaluate that species very well. Sure, I may be able to see them generating patterns that indicate intelligent life within my simulation, but this life form evolved and exists in an environment completely alien to me. I might have only limited methods at my disposal through which to communicate with them. They would exist in a place that to me is not exactly real and vice-versa.
I’ve always imagined it would be more like evaluating patterns and data readouts or viewing cells through a microscope more than say something like, The Sims. Having designed and implemented the very laws of their universe though, the fundamental constants of the universe could act as a sort of communication channel – one that allows me to at the very least let them know I existed (assuming they were intelligent and were looking). I could modify those constants in such a way over time in much the same manner that we might try to communicate with the more local and familiar concept of alien.
I realize this is all just rambling, but because the alpha is so closely related to those parts of nature that allow for our own existence, it made me take notice, and wonder if this could be some sort of alpha mail. The thought of being able to communicate with an external intelligence is thought provoking enough for me that I decided to write this as my first post here. Who knows? If it ever was confirmed, perhaps we could turn out to be the paper clip maximizer, and we should start looking for our ticket out of here.
Green Martians and Blue Martians have one thing in common: They both derive a tremendous amount of utility from tickling humans behind the ears, using their soft, feathery tentacles. In fact, the utility that they derive from this is so intense that most scientists believe at some time in the recent evolutionary past, there must have been a large selection pressure directed at ensuring that Martians were motivated to tickle humans.
There are numerous differences between Green and Blue Martians. One of those differences is that whereas the feathery tentacles of Green Martians contain stinging hairs similar to nettles, the analogous anatomic part of the Blue Martian contains a safe drug with an euphoric effect. Therefore, humans who are tickled by green martians experience a moderate stinging pain, whereas those who are tickled by blue martians experience mild to moderate pleasure.
Human ethicists have long struggled to come up with a coherent ethical theory that determines whether tickling humans is morally acceptable. Some have suggested that tickling humans behind the ear is ethically permissible if and only if you are a blue martian. However, many other thinkers are worried that this line of thinking results in an unjust world, where the ethics of an act is determined by characteristics of the Martian that they cannot be held responsible for.
However, human ethicists are not very familiar with Martian physiology, and the situation is actually even more complicated than they suspect. In fact, all Martians are born Green. They can shed their green shell and become blue Martians only after they have perfected the art of tickling humans with their feathery tentacles. All Martians aspire to one day become blue, but the amount of practicing it takes to reach perfection is highly variable - some martians reach perfection at their first attempt, whereas others keep trying their whole life without making any discernible progress. Therefore, if the ethical code says that green martians are prohibited from tickling humans, ethical Martians will be unable to reach their full potential in life, and will be stuck as Green Martians forever. Under this ethical code, only unethical Martians will be able to metamorphose.
Making the situation even more complicated, is the fact that a group of recently metamorphosed Blue Martians are vocally spreading information on the internet about tickling techniques. These techniques are sometimes effective, but if used imperfectly they increase the sting of the stinging hairs fourfold. Importantly, it seems that part of the reason some young Green Martians are naturally better ticklers and therefore metamorphose earlier, is that they intuitively understand these techniques, and are able to apply them without increasing the sting of their tentacles. Moreover, while the tickling technique has empirical support, the theory behind it relies heavily on speculation about human evolutionary history that may not be true, and which is offensive to humans.
This raises a number of additional ethical questions: Is it unethical for a Green Martian to attempt to metamorphose? Does this depend on whether they believe themselves to be fast or slow learners? Should only the small subset of Martians who intuitively understand the tickling techniques be allowed to use them? Is spreading explicit information about the techniques unethical?
(Note : This parable is obviously an allegory for something. Discussing whether the allegory is valid is interesting, but will lead to mindkill. I would prefer if the discussion could stay focused on the Martians, so that we can discuss the ethics of a hypothetical scenario that may not be relevant in real life. I am genuinely confused about the ethics of this, and I think this can lead to an interesting question regardless of whether it is applicable to humans)
How much AI technique could it possibly take for google (or something better) to do a decent job with
speechby:obama attitude:positive "Saul Alinsky".
I.e. "speechby:" and "attitude:" don't exist, but could, I believe be implemented pretty accurately, to see in this case if we can find any instances of Obama praising Saul Alinsky.
An article: "Bill Ayers and Obama Both Quote Alinsky" claims such quotes exist, but their one attempt to demonstrate it is laughable -- something vaguely like a paraphrase of an Alinsky statement, but which has, in fact the reverse sense of what the supposed "original" meant. Yet I think most of the world, and not just conservatives, if they have any idea who Alinsky is, will tend not to question Obama's "debt" to Alinski -- just for the sheer number of times it's been said or implied. For the other shoe dropping, false quotes that help demonize Alinsky, see tinyurl.com/qa6fglk.
The point isn't to defend Obama. It is that I think the world would work better if the ratio of
ability to find verifiable facts pertinent to political discussion
supply of highly opinionated and slanted "news".
could be raised by, say, an order of magnitude.
So many assertions are made that are likely not true, but are incredibly difficult for the average person to disprove. In this Internet era, the personal cost to write some almost free associative screed about a political point is very low, while the personal cost of finding quite a lot of pertinent facts is awfully high.
This is not to say the "average person" will look for facts to confirm or contradict what they read, but much of what they read is written by bloggers some of whom are sincere and would become users of such resources, and I do believe the emotional rewards of finding a nugget of truth versus the current pain of often fruitless search would have an effect on people's thinking habits -- maybe small at first but growing over time.
The particular proposal merely illustrates one of many sorts of resource that are missing or hard to find. Ideas for other such resources would be welcome.
Summary: Is there demand for writing posts about this aspect of decision-making?
And of course, is there offer? Because I didn't see any post about it.
Topics I intended to cover include:
- How much is worth 100$ in few years? Why? Why is it useful?
- Risk-return relationship.
- How is it useful in life outside finance?
And topic I would like, but I am not sure if i should cover:
- How can we apply it to death? (in sense, should I live a happy life or struggle to live endlessly?)
I found that missing in decision analysis, and I think it is very important thing to know, since we don't always choose between "I take A" or "I take B", but also between "I take A" or "I take B in two years", or "should i give A to gain B every year next 100 years?"
Why not simply redirect to some other source?
Well, that can be done either way, but I thought clear basics would not harm and would be useful to people who want to invest less time in it.
[QUESTION]: Looking for insights from machine learning that helped improve state-of-the-art human thinking
This question is a follow-up of sorts to my earlier question on academic social science and machine learning.
Machine learning algorithms are used for a wide range of prediction tasks, including binary (yes/no) prediction and prediction of continuous variables. For binary prediction, common models include logistic regression, support vector machines, neural networks, and decision trees and forests.
Now, I do know that methods such as linear and logistic regression, and other regression-type techniques, are used extensively in science and social science research. Some of this research looks at the coefficients of such a model and then re-interprets them.
I'm interesting in examples where knowledge of the insides of other machine learning techniques (i.e., knowledge of the parameters for which the models perform well) has helped provide insights that are of direct human value, or perhaps even directly improved unaided human ability. In my earlier post, I linked to an example (courtesy Sebastian Kwiatkowski) where the results of naive Bayes and SVM classifiers for hotel reviews could be translated into human-understandable terms (namely, reviews that mentioned physical aspects of the hotel, such as "small bedroom", were more likely to be truthful than reviews that talked about the reasons for the visit or the company that sponsored the visit).
PS: Here's a very quick description of how these supervised learning algorithms work. We first postulate a functional form that describes how the output depends on the input. For instance, the functional form in the case of logistic regression outputs the probability as the logistic function applied to a linear combination of the inputs (features). The functional form has a number of unknown parameters. Specific values of the parameters give specific functions that can be used to make predictions. Our goal is to find the parameter values.
We use a huge amount of labeled training data, plus a cost function (which itself typically arises from a statistical model for the nature of the error distribution) to find the parameter values. In the crudest form, this is purely a multivariable calculus optimization problem: choose parameters so that the total error function between the predicted function values and the observed function values is as small as possible. There are a few complications that need to be addressed to get to working algorithms.
So what makes machine learning problems hard? There are a few choice points:
- Feature selection: Figuring out the inputs (features) to use in predicting the outputs.
- Selection of the functional form model
- Selection of the cost function (error function)
- Selection of the algorithmic approach used to optimize the cost function, addressing the issue of overfitting through appropriate methods such as regularization and early stopping.
Of these steps, (1) is really the only step that is somewhat customized by domain, but even here, when we have enough data, it's more common to just throw in lots of features and see which ones actually help with prediction (in a regression model, the features that have predictive power will have nonzero coefficients in front of them, and removing them will increase the overall error of the model). (2) and (3) are mostly standardized, with our choice really being between a small number of differently flavored models (logistic regression, neural networks, etc.). (4) is the part where much of the machine learning research is concentrated: figuring out newer and better algorithms to find (approximate) solutions to the optimization problems for particular mathematical structures of the data.
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.
- [Atlanta] MIRIxAtlanta: 19 July 2014: 19 July 2014 06:00PM
- Frankfurt: Goal Factoring: 20 July 2014 02:00PM
- Houston, TX: 19 July 2014 12:16AM
- Upper Canada LW Megameetup: Ottawa, Toronto, Montreal, Waterloo, London: 18 July 2014 07:00PM
The remaining meetups take place in cities with regular scheduling, but involve a change in time or location, special meeting content, or simply a helpful reminder about the meetup:
- Brussels - August (topic TBD): 09 August 2014 01:00PM
- Canberra: Intro to Anthropic Reasoning: 25 July 2014 06:00PM
- Sydney Meetup - July: 23 July 2014 07:00PM
- Washington DC: Short Talks: 20 July 2014 03:00PM
Locations with regularly scheduled meetups: Austin, Berkeley, Berlin, Boston, Brussels, Buffalo, Cambridge UK, Canberra, Columbus, London, Madison WI, Melbourne, Mountain View, New York, Philadelphia, Research Triangle NC, Salt Lake City, Seattle, Sydney, Toronto, Vienna, Washington DC, Waterloo, and West Los Angeles. There's also a 24/7 online study hall for coworking LWers.
Claim: Scenario planning is preferable to quantitative forecasting for understanding and coping with AI progress
As part of my work for MIRI on forecasting, I'm considering the implications of what I've read up for the case of thinking about AI. My purpose isn't to actually come to concrete conclusions about AI progress, but more to provide insight into what approaches are more promising and what approaches are less promising for thinking about AI progress.
I've written a post on general-purpose forecasting and another post on scenario analysis. In a recent post, I considered scenario analyses for technological progress. I've also looked at many domains of forecasting and at forecasting rare events. With the knowledge I've accumulated, I've shifted in the direction of viewing scenario analysis as a more promising tool than timeline-driven quantitative forecasting for understanding AI and its implications.
I'll first summarize what I mean by scenario analysis and quantitative forecasting in the AI context. People who have some prior knowledge of the terms can probably skim through the summary quickly. Those who find the summary insufficiently informative, or want to delve deeper, are urged to read my more detailed posts linked above and the references therein.
Quantitative forecasting and scenario analysis in the AI context
The two approaches I am comparing are:
- Quantitative forecasting: Here, specific predictions or forecasts are made, recorded, and later tested against what actually transpired. The forecasts are made in a form where it's easy to score whether they happened. Probabilistic forecasts are also included. These are scored using one of the standard methods to score probabilistic forecasts (such as logarithmic scoring or quadratic scoring).
- Scenario analysis: A number of scenarios of how the future might unfold are generated in considerable detail. Predetermined elements, common to the scenario, are combined with critical uncertainties, that vary between the scenarios. Early indicators that help determine which scenario will transpire are identified. In many cases, the goal is to choose strategies that are robust to all scenarios. For more, read my post on scenario analysis.
Quantitative forecasts are easier to score for accuracy, and in particular offer greater scope for falsification. This has perhaps attracted rationalists more to quantitative forecasting, as a way of distinguishing themselves from what appears to be the more wishy-washy realm of unfalsifiable scenario analysis. In this post, I argue that, given the considerable uncertainty surrounding progress in artificial intelligence, scenario analysis is a more apt tool.
There are probably some people on LessWrong who have high confidence in quantitative forecasts. I'm happy to make bets (financial or purely honorary) on such subjects. However, if you're claiming high certainty while I am claiming uncertainty, I do want to have odds in my favor (depending on how much confidence you express in your opinion), for reasons similar to those that Bryan Caplan described here.
Below, I describe my reasons for preferring scenario analysis to forecasting.
#1: Considerable uncertainty
Proponents of the view that AI is scheduled to arrive in a few decades typically cite computing advances such as Moore's law. However, there's considerable uncertainty even surrounding short-term computing advances, as I described in my scenario analyses for technological progress. When it comes to the question of progress in AI, we have to combine uncertainties in hardware progress with uncertainties in software progress.
Quantitative forecasting methods, such as trend extrapolation, tend to do reasonably well, and might be better than nothing. But they are not foolproof. In particular, the impending death of Moore's law, despite the trend staying quite robust for about 50 years, should make us cautious about too naive an extrapolation of trends. Arguably, simple trend extrapolation is still the best choice relative to other forecasting methods, at least as a general rule. But acknowledging uncertainty and considering multiple scenarios could prepare us a lot better for reality.
In a post in May 2013 titled When Will AI Be Created?, MIRI director Luke Muehlhauser (who later assigned me the forecasting project) looked at the wide range of beliefs about the time horizon for the arrival of human-level AI. Here's how Luke described the situation:
To explore these difficulties, let’s start with a 2009 bloggingheads.tv conversation between MIRI researcher Eliezer Yudkowsky and MIT computer scientist Scott Aaronson, author of the excellent Quantum Computing Since Democritus. Early in that dialogue, Yudkowsky asked:
It seems pretty obvious to me that at some point in [one to ten decades] we’re going to build an AI smart enough to improve itself, and [it will] “foom” upward in intelligence, and by the time it exhausts available avenues for improvement it will be a “superintelligence” [relative] to us. Do you feel this is obvious?
The idea that we could build computers that are smarter than us… and that those computers could build still smarter computers… until we reach the physical limits of what kind of intelligence is possible… that we could build things that are to us as we are to ants — all of this is compatible with the laws of physics… and I can’t find a reason of principle that it couldn’t eventually come to pass…
The main thing we disagree about is the time scale… a few thousand years [before AI] seems more reasonable to me.
Those two estimates — several decades vs. “a few thousand years” — have wildly different policy implications.
After more discussion of AI forecasts as well as some general findings on forecasting, Luke continues:
Given these considerations, I think the most appropriate stance on the question “When will AI be created?” is something like this:
We can’t be confident AI will come in the next 30 years, and we can’t be confident it’ll take more than 100 years, and anyone who is confident of either claim is pretending to know too much.
How confident is “confident”? Let’s say 70%. That is, I think it is unreasonable to be 70% confident that AI is fewer than 30 years away, and I also think it’s unreasonable to be 70% confident that AI is more than 100 years away.
This statement admits my inability to predict AI, but it also constrains my probability distribution over “years of AI creation” quite a lot.
I think the considerations above justify these constraints on my probability distribution, but I haven’t spelled out my reasoning in great detail. That would require more analysis than I can present here. But I hope I’ve at least summarized the basic considerations on this topic, and those with different probability distributions than mine can now build on my work here to try to justify them.
I believe that in the face of this considerable uncertainty, considering multiple scenarios, and the implications of each scenario, can be quite helpful.
#2: Isn't scenario analysis unfalsifiable, and therefore unscientific? Why not aim for rigorous quantitative forecasting instead, that can be judged against reality?
First off, just because a forecast is quantitative doesn't mean it is actually rigorous. I think it's worthwhile to elicit and record quantitative forecasts. These can have high value for near-term horizons, and can provide a rough idea of the range of opinion for longer timescales.
However, simply phoning up experts to ask them for their timelines, or sending them an Internet survey, is not too useful. Tetlock's work, described in Muehlhauser's post and in my post on historical evaluations of forecasting, shows that unaided expert judgment has little value. Asking people who haven't thought through the issue to come up with numbers can give a fake sense of precision with little accuracy (and little genuine precision, either, if we consider the diverse range of responses from different experts). On the other hand, eliciting detailed scenarios from experts can force them to think more clearly about the issues and the relationships between them. Note that there are dangers to eliciting detailed scenarios: people may fall into their own make-believe world. But I think the trade-off with the uncertainty in quantitative forecasting still points in favor of scenario analysis.
Explicit quantitative forecasts can be helpful when people have an opportunity to learn from wrong forecasts and adjust their methodology accordingly. Therefore, I argue that if we want to go down the quantitative forecasting route, it's important to record forecasts about the near and medium future instead of or in addition to forecasts about the far future. Also, providing experts some historical information and feedback at the time they make their forecasts can help reduce the chances of them simply saying things without reflecting. Depending on the costs of recording forecasts, it may be worthwhile to do so anyway, even if we don't have high hopes that the forecasts will yield value. Broadly, I agree with Luke's suggestions:
- Explicit quantification: “The best way to become a better-calibrated appraiser of long-term futures is to get in the habit of making quantitative probability estimates that can be objectively scored for accuracy over long stretches of time. Explicit quantification enables explicit accuracy feedback, which enables learning.”
- Signposting the future: Thinking through specific scenarios can be useful if those scenarios “come with clear diagnostic signposts that policymakers can use to gauge whether they are moving toward or away from one scenario or another… Falsifiable hypotheses bring high-flying scenario abstractions back to Earth.”13
- Leveraging aggregation: “the average forecast is often more accurate than the vast majority of the individual forecasts that went into computing the average…. [Forecasters] should also get into the habit that some of the better forecasters in [an IARPA forecasting tournament called ACE] have gotten into: comparing their predictions to group averages, weighted-averaging algorithms, prediction markets, and financial markets.” See Ungar et al. (2012) for some aggregation-leveraging results from the ACE tournament.
But I argue that the bulk of the effort should go into scenario generation and scenario analysis. Even here, the problem of absence of feedback is acute: we can design scenarios all we want for what will happen over the next century, but we can't afford to wait a century to know if our scenarios transpired. Therefore, it makes sense to break the scenario analysis exercises into chunks of 10-15 years. For instance, one scenario analysis could consider scenarios for the next 10-15 years. For each of the scenarios, we can have a separate scenario analysis exercise that considers scenarios for the 10-15 years after that. And so on. Note that the number of scenarios increases exponentially with the time horizon, but this is simply a reflection of the underlying complexity and uncertainty. In some cases, scenarios could "merge" at later times, as scenarios with slow early progress and fast later progress yield the same end result that scenario with fast early progress and slow later progress do.
#3: Evidence from other disciplines
Explicit quantitative forecasting is common in many disciplines, but the more we look at longer time horizons, and the more uncertainty we are dealing with, the more common scenario analysis becomes. I considered many examples of scenario analysis in my scenario analysis post. As you'll see from the list there, scenario analysis, and variants of it, have become influential in areas ranging from climate change (as seen in IPCC reports) to energy to macroeconomic and fiscal analysis to land use and transportation analysis. And big consulting companies such as McKinsey & Company use scenario analysis frequently in their reports.
It's of course possible to argue that the use of scenario analyses is a reflection of human failing: people don't want to make single forecasts because they are afraid of being proven wrong, or of contradicting other people's beliefs about the future. Or maybe people are shy of thinking quantitatively. I think there is some truth to such a critique. But until we have human-level AI, we have to rely on the failure-prone humans for input on the question of AI progress. Perhaps scenario analysis is superior to quantitative forecasting because humans are insufficiently rational, but to the extent it's superior, it's superior.
Addendum: What are the already existing scenario analyses for artificial intelligence?
I had a brief discussion with Luke Muehlhauser and some of the names below were suggested by him, but I didn't run the final list by him. All responsibility for errors is mine.
To my knowledge (and to the knowledge of people I've talked to) there are no formal scenario analyses of Artificial General Intelligence structured in a manner similar to the standard examples of scenario analyses. However, if scenario analysis is construed sufficiently loosely as a discussion of various predetermined elements and critical uncertainties and a brief mention of different possible scenarios, then we can list a few scenario analyses:
- Nick Bostrom's book Superintelligence (released in the UK and on Kindle, but not released as a print book in the US at the time of this writing) discusses several scenarios for paths to AGI.
- Eliezer Yudkowsky's report on Intelligence Explosion Microeconomics (93 pages, direct PDF link) can be construed as an analysis of AI scenarios.
- Robin Hanson's forthcoming book on em economics discusses one future scenario that is somewhat related to AI progress.
- The Hanson-Yudkowsky AI Foom debate includes a discussion of many scenarios.
The above are scenario analyses for the eventual properties and behavior of an artificial general intelligence, rather than scenario analyses for the immediate future. The work of Ray Kurzwzeil can be thought of as a scenario analysis that lays out an explicit timeline from now to the arrival of AGI.
I have trouble expressing myself in such a way that my ideas come out even remotely like they sound in my head. So please apply the principle of charity and try to read how you think I thought of it.
Tit for Tat
Tit for Tat is usually presented in a game between two players where each chooses to either cooperate or defect. The real world game however differs in two important ways.
First, it's not a two player game. We make choices not only on our single instance of interaction but also on observed interactions between other players. Thus the Advanced Tit For Tat not only defects if the other player defected against itself but also if it could observe the other player defecting against any other player that employs a similar enough algorithm.
Second, there is a middle ground between cooperating and defecting, you could stay neutral. Thus you can harm your opponent, help him or do neither. The question of the best strategy in this real life prisoners dilemma is probably still unanswered. If I see my opponent defecting against some of my peers and cooperating with others, what do I choose?
The reason why there even is a game is because we can deliberate on our action and can take abstract thoughts into account that do not directly pertain to the current situation, which I think is the distinguishing factor of higher animals from lower. This ability is called agency. In order to be an agent a subject must be able to perceive the situation, have a set of possible actions, model the outcomes of these actions, value the outcomes, and then act accordingly.
We could act in such a way that infringes on these abilities in others. If we limit their ability to perceive or model the situation we call this fraud, if we limit their set of possible actions or their ability to choose between them, we call it coercion, if we infringe on their ability to value an outcome, we call it advertising.
I propose that the purpose of our moral or ethical intuitions (I use the two words interchangeably, if there is a distinction please let me know) is to tell us whether some player defected, cooperated or stayed neutral, and to tell us who we should consider as having a close enough decision algorithm to ourselves to 'punish' third players for defecting against them. And I further propose that infringing on someones agency is what we consider as defecting.
Utilitarians tend to see defecting or cooperating as pertaining to creation or destruction of values. (Edit:) Three things bother me about value ethics:
1. Valuations between different people can't really be compared. If we shut up and multiply, we value the lives of everybody exactly the same no matter how they themselves value their own life. If there are chores to be done and one person claims to "not mind too much" while the other claims to "hate it with a passion" we can't tell if the emotional effect on them is really any different or maybe even the other way round.
2. It makes you torture someone to avoid an insanely huge number of dust specs.
3. It makes you push a fat man to his death.
Instead I propose that defecting in the real world game is all about infringing on someone's agency. Thus we intuit bankers who destroy an insane amount of wealth while not as good people still as neutral because they do not infringe on agency. At least that is my moral intuition.
So infringing on agency would make you a bad person, while not infringing on agency doesn't make you a good person. What makes you a good person is increasing value. Maybe agency is more fundamental and you cannot be a good person if you are a bad person, but maybe you can be both. That would create cognitive dissonance in people who consider ethics to be a singular thing and don't see the distinction, and that might be at the root of some ethics discussions.
In my version of ethics it counts as evil to push the fat man or to switch the tracks, as that would mean deliberately causing a death of someone who doesn't want to die. I would let the five die and not feel guilty about it, because I am not the cause of their deaths. I make a fundamental distinction between acting and not acting. If I hadn't been there the five would still die, so how could I be responsible for their deaths? I am aware that this view makes me evil in the eye of utilitarians. But I see less people acting consistent with utilitarianism than I see people arguing that way. Then again, this perception is probably heavily biased.
I don't really have a conclusion except of noticing that there exists a disagreement in fundamental morality and to inform you that there exists at least one person who considers infringing on someone's agency as defecting in a prisoner's dilemma.