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Models of Value of Learning
I generally subscribe to the signalling model of higher education: “education” is mostly about signalling how smart and diligent you are, rather than actually learning useful skills. Under this model, higher education doesn’t produce new human capital, it just makes it more obvious who has it already  it’s essentially marketing.
That said, it still seems like actually learning things does provide at least some value for at least some people  and personally, I think I’ve gained a lot more value than most out of learning things. A lot of this comes from being strategic: choosing what to learn, and how much time to spend on it, in order to maximize value. In order to do that, we need some idea of the mechanism by which learning things can provide value in the first place.
With that in mind, here are five different models for how things we formally learn (i.e. in school/university) can provide value.
SkillsA CS student learns a few programming languages, basic data structures, some aspects of lowlevel computer architecture and systems design… skills which are directly useful for realworld software development. Obviously not everything one learns in a CS degree is useful in realworld development, but there is a lot of directlyuseful knowledge and skills in there.
General model: formal learning provides practical value by teaching how to use a useful tool or perform a useful task. Some examples:
 Generic communication skills like legal/scientific/business writing or public speaking
 Useful ways to frame problems, like thinking about risk and discounted expected value of potential business projects
 When and how to use a physical tool, like a pipette or atomic force microscope
 Mathematical/algorithmic tools, like techniques for solving ODEs/PDEs, statistical tests, or data structures
 When and how to apply mathematical/algorithmic tools to realworld problems, e.g. turning a physical system into equations in physics or engineering
 Most vocational education
This sort of thing is how I think most people imagine learning will provide value. We learn to use some technique which we will directly apply to valuable realworld problems. Keep it in mind as a baseline against which to compare the models below.
GearsA doctor studies the physiology of the kidney  they gain a gearslevel model of kidney function. Hopefully, this will later allow them to recognize/predict a wide variety of kidney failuremodes and their implications, by understanding how the effects of one particular failure will propagate throughout the system. In particular, they can hopefully handle novel problems, problems which are not like any problem they’ve seen before.
General model: formal learning provides practical value by teaching us the internal gears of a system. That, in turn, allows us to make predictions about how the system will behave in a wide variety of novel circumstances  e.g. when some component breaks, or when the environment changes drastically.
I’ve already talked about this a bunch in GearsLevel Models as Capital Investments. In particular, it’s probably the main value model for most of the sciences on a large scale (though not necessarily for individual people). For examples outside the sciences, see Why Artists Study Anatomy and Baking Is Not A Ritual.
Unknown UnknownsConsider suffix trees. Suffix trees (and their variants) provide a fast data structure for things like searching in text, comparing text, etc. Most programmers, most of the time, do not need to know anything at all about suffix trees. Even if you do suddenly need to know about suffix trees, you can google it and read up. But if you don’t even know when to google for it… then you’re liable to have a very rough time.
General model: formal learning provides value by removing unknown unknowns. Examples:
 Intro statistics courses flag a number of common errors people often don’t realize they’re making
 Even if we don’t remember how to use a particular tool from some class, knowing that it exists at all is usually enough to find it later if we need it  e.g. specialized algorithms or sensor types or experimental techniques
 Along similar lines, knowing that some group of people works on a certain type of problem is often enough to find them later if we need to  e.g. experts specializing in a certain class of diseases or marketing to a particular segment or funding certain types of projects or ...
 In engineering fields, it’s useful just to know that we probably aren’t missing any key considerations of a design. Nobody wants to build the next Tacoma Narrows  the bridge which resonated with wind eddies, a factor the designers never even thought to consider.
One big upshot of this valuemodel: removing unknown unknowns requires relatively little investment of time and effort. A single pass through a book or set of lectures is often enough to recall an idea when it comes up later. (Of course, more practice may still help us recognize the idea in a wider variety of situations.)
InterfaceAnother model: formal learning provides value by teaching students to interface to a system. Some examples:
 Learning some of a field’s jargon is very helpful for working with people in that field, even if you’re not in that field yourself. For instance, a technical product designer benefits from some exposure to programming and algorithms in order to interface with software engineers.
 Law school largely teaches how to interact with the legal system; science fields largely teach how to interpret and evaluate the models used in each field.
 Programming directly involves learning specialized languages. It is via these languages that we interface with the work of other programmers.
 Learning human languages obviously allows us to talk to a wider variety of people around the world.
 People often need to apply the knowledge/tools of one field in another. Studying some physics helps biologists understand certain imaging tools; studying some electrical engineering helps programmers understand the lower abstraction levels of computer systems.
This valuemodel requires a fair bit of practice, but notice that it doesn’t necessarily require remembering all the details which come up in ones’ studies. A lawyer may need to write briefs or a programmer may need to write code to practice their skills, but they don’t need to memorize all the bits of information they had to look up while writing those briefs/programs. They do need to know where/how to find information, and how to correctly interpret/use that information, but they don’t necessarily need to store all that information in their heads.
Identifying ExpertsKing Louis XV of France died of smallpox the same year that an English dairy farmer successfully vaccinated his family. Louis had the resources of a literal king, and hired the supposedlyfinest doctors in Europe, but at the end of the day he could not distinguish someone actually capable of curing the disease from astrologers and humoral experts. Even had he looked, he would not have picked that dairy farmer out of a crowd of people with cleversounding ideas to protect from smallpox, and he wouldn’t have thought to run clinical trials.
Identifying people with more expertise on a topic than ourselves is Hard.
Model: formal learning provides value by giving us ways to distinguish actual experts from conmen and from people who just don’t know what they’re doing as well as they think they do. Some examples:
 Learning some physiology and biology won’t teach you how to best handle every disease, but it will help you distinguish those who do understand a particular disease from those who market snakeoil.
 Recognizing programmers who write readable/robust/maintainable code is Hard if you don’t know anything about programming
 Basic statistics is a fairly generalpurpose bullshitdetection tool
This is another model where we can potentially get a lot of mileage from a relatively small investment. We don’t need to fully understand the field ourselves, we just need to understand enough to recognize someone who understands more.
Of course, the catch is that we’ll learn to recognize people who are “experts” by the standards of the field studied, which may or may not be useful  the doctors of King Louis’ time would not have recognized the merit of vaccination any more than the King.
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Online SSC Meetup with Guest Speaker Joscha Bach  July 19th at 17:30 GMT (10:30 AM PDT)
Thanks to those who came to the meetup on Sunday. We will be switching from Zoom to Google Meets for our next meeting.
Our next meetup is on July 19th and will feature Joscha Bach as our guest speaker.
Click here to register: https://docs.google.com/forms/d/1FEPNaUxuFoup99MN4u1GRLT9A0rSYTiEpHK8TOPgDOo/viewform?edit_requested=true
Abstract:
Artificial Intelligence is two very different things: research into advances in information processing, and a daring, complicated and extremely important philosophical project. Conflating these two topics has led to a lot of confusion in the perception of the field, and its role in the cognitive sciences. What does AI contribute to our understanding of epistemology, metaphysics and philosophy of mind?
Bio:
Joscha Bach, PhD, is a cognitive scientist with a focus on computational models of motivation, perception, consciousness and cognitive architectures. He has taught and worked in AI research at Humboldt University of Berlin, the Institute for Cognitive Science in Osnabrück, the MIT media lab and the Harvard Program for Evolutionary Dynamics. He is currently VP of Research at AI Foundation, San Francisco.
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Breaking Questions Down
Previously I talked about discovering that my basic unit of inquiry should be questions, not books. But what I didn’t talk about was how to generate those questions, and how to separate good questions from bad. That’s because I don’t know yet; my own process is mysterious and implicit to me. But I can give a few examples.
For any given question, your goal is to disambiguate it into smaller questions that, if an oracle gave you the answers to all of them, would allow you to answer the original question. Best case scenario, you repeat this process and hit bedrock, an empirical question for which you can find accurate data. You feed that answer into the parent question, and eventually it bubbles up to answering your original question.
That does not always happen. Sometimes the question is one of values, not facts. Sometimes sufficient accurate information is not available, and you’re forced to use a range an uncertainty that will bubble up through parent answers. But just having the questions will clarify your thoughts and allow you to move more of your attention to the most important things.
Here are a few examples. First, a reconstructed mind map of my process that led to several covid+economics posts. In the interests of being as informative as possible, this one is kind of stylized and uses developments I didn’t have at the time I actually did the research.
If you’re curious about the results of this, the regular recession post is here and the oil crisis post is here.
Second, a map I created but have not yet researched, on the cost/benefit profile of a dental cleaning while covid is present.
Aside: Do people prefer the horizontal or vertical displays? Vertical would be my preference, but Whimsical does weird things with spacing so the tree ends up with a huge width either way.
Honestly this post isn’t really done; I have a lot more to figure out when it comes to how to create good questions. But I wanted to have something out before I published v0.1 of my Grand List of Steps, so here we are.
Many thanks to Rosie Campbell for inspiration and discussion on this idea.
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What are the risks of permanent injury from COVID?
I'm in my late twenties. I can easily find estimates of the risk of death, but I'm having trouble figuring out what the risk of permanent injury (e.g. fatigue) is. I figure someone on LW has probably already looked into this, so I thought I'd ask.
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The Equation of Knowledge
My book The Equation of Knowledge has just been published at CRC Press, and I'm guessing that it may be of interest to readers of LessWrong. The book aims to be a somewhat accessible and very complete introduction to Bayesianism. No prior knowledge is needed; though some sections require an important familiarity with mathematics and computer science. The book has been designed so that these sections can be skipped without hindering the reading.
The aim of the book is to (1) highlight the most compelling arguments, theorems and empirical evidence in favor of Bayesianism, (2) present numerous applications in a very wide variety of domains, and (3) discuss solutions for pragmatic Bayesianism with limited computational resources. Please find here a promotional 5minute video of the book.
In this post, I will briefly sketch the outline of the book. Just like the book, I'll divide the post in four sections.
Pure BayesianismThe first section of the book is a gentle introduction to pure Bayesianism, which is defined as obeying strictly to the laws of probability theory. The key equation is evidently Bayes rule, which I like to write as follows:
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src: local('MathJax_Vector Bold'), local('MathJax_VectorBold')} @fontface {fontfamily: MJXcTeXvecBx; src: local('MathJax_Vector'); fontweight: bold} @fontface {fontfamily: MJXcTeXvecBw; src /*1*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTMLCSS/TeX/eot/MathJax_VectorBold.eot'); src /*2*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTMLCSS/TeX/woff/MathJax_VectorBold.woff') format('woff'), url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTMLCSS/TeX/otf/MathJax_VectorBold.otf') format('opentype')} P[TD]=P[DT]P[T]P[DT]P[T]+∑A≠TP[DA]P[A].
This equation says that the critical variable is P[TD], that is, the credence of theory T given data D. Computing this is arguably the end goal of Bayes rule. Bayes rule thus does not quite aim to distinguish truth from falsehood; it rather motivates us to assign quantitative measures of reliability to different theories, given observed data. It suggests that we should replace questions like "is T true?" by "how credible is T?" (or perhaps even by "how much should I trust the predictions of theory T?"). I argue in the book that this is a great way to improve the quality of many debates.
Bayes rule then goes on telling us how to compute the credence of a theory given empirical data. Importantly, on the right hand side, we have the term P[T] which measures the credence of the theory prior to the observation of data D. This is critical. A theory which was extremely unlikely before we knew D will likely remain unlikely even given D, unless D is overwhelmingly compelling. This corresponds to Carl Sagan's phrase "extraordinary claims require extraordinary evidence" (which was analyzed mathematically by Laplace back in 1814!).
Bayes rule then tells us to update our prior beliefs P[T] based on observed data D depending on how well theory T predicts data D. Essentially, we can see any theory T as a betting individual. If T bets on D, which corresponds to a large value of P[DT], then it should gain credence in T. But if theory T found observed data D unlikely (i.e. P[DT]≈0), then we should decrease our belief in T once we observe D.
Well, actually, Bayes rule tells us that this update also depends on how well alternative theories A perform. Indeed, the denominator P[DT]P[T]+∑A≠TP[DA]P[A] orchestrates a sort of competition between the different theories. In particular, the credence of theory T will be decreasing only if its bet P[DT] is outperformed by the bets P[DA] of alternative theories A. In particular, this means that Bayes rule forbids the analysis of a theory independently of others; the credence of a theory is only relative to the set of alternatives.
Chapters 2 to 5 of the book details the analysis of Bayes rule, and illustrates it through a large number of examples, like Sally Clark's infamous lawsuit, Hempel's raven paradox, Einstein's discovery of general relativity and the Linda problem, among many other examples. They also draw connections and tensions with firstorder logic, Popper's falsifiability and null hypothesis statistical tests.
Chapter 6 then discusses the history of Bayesianism, which also hints at the importance of probability theory in essentially all human endeavors. Finally, Chapter 7 concludes the first part of the book, by introducing Solomonoff's induction, which I call pure Bayesianism. In brief, Bayes rule requires any theory T to bet on any imaginable observable data D (formally, T needs to define a probability measure on the space of data, otherwise the quantity P[DT] is illdefined). Solomonoff's genius was to simply also demand this bet to be computable. It turns out that the rest of Solomonoff's theory essentially beautifully falls out from this simple additional constraint.
Evidently, a lot more explanations and details can be found in the book!
Applied BayesianismThe second section of the book goes deeper into applications of Bayesianism to numerous different fields. Chapter 8 discusses the strong connection between Bayesianism and privacy. After all, if Bayesianism is the right theory of knowledge, it is clearly critical to any theory on how to prevent knowledge. And indeed, the leading concept of privacy, namely differential privacy, has a very natural definition in terms of probability theory.
Chapter 9 dwells on the strong connection between Bayesianism and economics, and in particular game theory. Nobel prize winner Roger Myerson once argued that "the unity and scope of modern information economics was found in Harsanyi’s framework". Again, this can be made evident by the fact that much of modern economics focuses on the consequences of incomplete (e.g. asymmetric) information.
Chapter 10 moves on to the surprisingly strong connections between Darwinian evolution and Bayes rule. In particular, the famous LotkaVolterra equations for population dynamics features an intriguing resemblance with Bayes rule. This resemblance is then exploited to discuss to which extent the spread of ideas within the scientific community can be compared to the growth of the credence in a theory for a Bayesian. This allows to identify reliable rules of thumbs to determine when a scientific consensus or a (predictive) market prize is credible, and when they are less so.
Chapter 11 discusses exponential growths, which emerge out of repeated multiplications. Such growths are critical to understand to have an intuitively feel for Bayes rule, as repeated Bayesian updates are typically multiplicative. The chapter also draws a fascinating connection between the multiplicative weights update algorithm and variants like Adaboost, and Bayes rule. It argues that the success of these methods is no accident; and that their late discovery may be due to mathematicians' poor intuitive understanding of exponential growth.
Chapter 12 presents numerous applications of Ockham's razor to avoid erroneous conclusions. It also shows that the practical usefulness of Ockham's razor is intimately connected to the importance of priors in Bayesian thinking, as evidenced by the compelling theorem that says that, under mild assumptions, only Bayesian methods are "statistically admissible". Finally, the chapter concludes with another stunning theorem: it can be proved in one line that a version of Ockham's razor is a theorem under Bayesianism (I'll keep this one line secret to tease you!).
Chapter 13 then stresses the danger of Simpson's paradox and the importance of confounding variables when analyzing empirical uncontrolled data. After discussing the value and limits of randomized controlled tests, I then reformulate the necessary analysis of plausible confounding variables for data analysis as the unavoidability of priors to think correctly. The chapter closes with some philosophical discussions on the ontology of these confounding variables.
Pragmatic BayesianismUnfortunately, pure Bayesianism demands unreasonable computational capabilities. Nor our brains nor our machines have access to such capabilities. As a result, in practice, pure Bayesianism is doomed to fail. In other words, we cannot obey strictly the laws of probability. We'll have to content ourselves with approximations of these laws.
Chapter 14 contextualizes this strategy under the more general theory of computational complexity. It gives numerous examples where this strategy has been used, for instance to study prime numbers or Ramsey theory. It also presents Turing's 1950 compelling argument for the need of machine learning to achieve humanlevel AI, based on computational complexity. The chapter also draws connection with Kahneman's System 1 / System 2 model.
Chapter 15 then stresses the need to embrace (quantitative) uncertainty. It provides numerous arguments for why this uncertainty will always remain, from chaos theory to quantum mechanics, statistical mechanics and automata with irreducible computations. It then discusses ways to measure success under uncertainty, using crossentropy for instance, or more general proper scoring rules. Finally it draws connections with modern machine learning, in particular generative adversarial networks (GANs).
Chapter 16 then discusses the challenges posed by having limited information storage spaces, both from a computational and from a cognitive perspective. The chapter discusses things like Kalman filters, false memory, recurrent neural network, attention mechanisms and what should be taught in our modern world, where we can now exploit much better information storage systems than our brains.
Chapter 17 discusses approximations of Bayes rule using sampling. It is a gentle introduction to MonteCarlo methods, and then to Markov Chain MonteCarlo (MCMC) methods. It then argues that our brains probably run MCMClike algorithms, and discusses the consequences on cognitive biases. Indeed, MCMC only has asymptotic guarantees; but if MCMC does not run for long, it will be heavily biased by its starting point. Arguably, something similar occurs in our brains.
Chapter 18 addresses a fundamental question of epistemology, namely the unreasonable effectiveness of abstraction. This chapter draws heavily on theoretical computer science, and in particular on Kolmogorov sophistication and Bennett logical depth, to suggest explanations of the success of abstractions based on computational properties of our current universe. It is interesting to note that, in the far past or the very far future, the state of the universe may be such that deep abstraction would be unlikely to remain useful (and thus "effective").
Chapter 19 introduces the Bayesian brain hypothesis, and the numerous fascinating recent discoveries of cognitive sciences in this regard. Amazingly, Bayes rule has been suggested again and again to explain our vulnerability to optical illusions, our ability to generalize from few examples or babies' learning capabilities. The Bayesian perspective has fascinating consequences on the famous Nature vs Nurture debate.
Beyond BayesianismThe last section of the book takes a bit of distance from Bayesianism, though it is still strongly connected to the laws of probability. Chapter 20 discusses what I argue to be natural consequences of pure Bayesian thinking on scientific realism. In particular, it argues that theories are mostly tools to predict past and future data. As a result, it seems pointless to argue about the truth of their components; what matters rather seems to be the usefulness of thinking with these components. I discuss consequences on how we ought to discuss concepts like money, life or electrons.
Chapter 21 is my best effort to encourage readers to question their most strongly held beliefs. It does so by providing the examples of my own journey, and by stressing the numerous cognitive biases that I have been suffering. It then goes on underlining what seems to me to be the key reasons of my progress towards Bayesianism, namely the social and informational environment I have been so lucky to end up in. Improving this environment may indeed be key for anyone to question their most strongly held beliefs.
Finally, Chapter 22 briefly goes beyond epistemology to enter the realm of moral philosophy. After discussions on the importance of descriptive moral theories to understand human interactions, the chapter gives a brief classical introduction of the main moral theories, in particular deontology and utilitarianism. It then argues that consequentialism somehow generalizes these theories, but that only Bayesian consequentialism is consistent with the laws of probability. It then illustrates decisionmaking under Bayesian consequentialism with examples, and stresses the importance of catastrophic events, as long as their probability is not sufficiently negligible.
One last thing I'd add is that I have made a lot of effort to make the book enjoyable. It is written in a very informal style, often with personal examples. I have also made a lot of effort to share complex ideas with a lot of enthusiasm, not because it makes them more convincing, but because it seems necessary to me to motivate the readers to really ponder these complex ideas.
Finally, note that Frenchspeaking readers can also watch the series of videos I've made on Bayesianism on YouTube!
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SSC Meetup  July 19th at 17:30 GMT (10:30 PDT) with Joscha Bach
Thanks to those who came to the meetup on Sunday. We will be switching from Zoom to Google Meets for our next meeting.
Our next meetup is on July 19th and will feature Joscha Bach as our guest speaker.
Click here to register: https://docs.google.com/forms/d/1FEPNaUxuFoup99MN4u1GRLT9A0rSYTiEpHK8TOPgDOo/viewform?edit_requested=true
Abstract:
Artificial Intelligence is two very different things: research into advances in information processing, and a daring, complicated and extremely important philosophical project. Conflating these two topics has led to a lot of confusion in the perception of the field, and its role in the cognitive sciences. What does AI contribute to our understanding of epistemology, metaphysics and philosophy of mind?
Bio:
Joscha Bach, PhD, is a cognitive scientist with a focus on computational models of motivation, perception, consciousness and cognitive architectures. He has taught and worked in AI research at Humboldt University of Berlin, the Institute for Cognitive Science in Osnabrück, the MIT media lab and the Harvard Program for Evolutionary Dynamics. He is currently VP of Research at AI Foundation, San Francisco.
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SpoilerFree Review: Horizon Zero Dawn
Previously: SpoilerFree Review: Witcher 3: Wild Hunt (plus a Spoilerific section), SpoilerFree Review: Assassin’s Creed Odyssey
This review of a giant open world game is being written on July 7. That’s three days after the review of Witcher 3 was posted.
You can guess it’s not going to be a ringing endorsement.
Horizon Zero Dawn seems to be a Tier 4 game. I spent most of my time either frustrated, pissed off, or waiting for something interesting to happen. There were periods of satisfying combat, and some potentially interesting aspects of the world building, but my lord on reflection was that a bad experience.
I say seems to be because when one quits this early in a game, missing most of what it has to offer, and lots of others love it, one presumes one is likely missing something. I encourage those who told me I should choose this game next – it got 40%+ of a 4way Twitter poll, and an endorsement in a comment – to explain why it’s secretly good.
Here I am going to talk about why it seems terrible.
This isn’t a ‘I criticize because I love’ post. This is a ‘I criticize because other people love and I can’t figure out why’ post.
Jump To Your Death, No Chance to SaveIn general, if you fall, you die.
The game has lots of points early in the game where you have to jump in exactly the right place. If you jump elsewhere, you fall, and you die.
The game resets, often forcing a lot of doing things over. At least the load times were quick. If the load times had been slower I would have quit very quickly.
You see, you can only save at a campfire, or when the game chooses to save for you.
What finally caused the ragequit was when I spent ten minutes replaying a quest sequence to where I jumped and died trying to follow the game’s narrative instructions, used a walkthrough video to see what I was supposed to do, jumped slightly wrong trying to trigger something, and died again. I mean, I can’t take it. My life is too short and I have enough things to rage about as it is.
These three dimensional games need to decide if they want to be platform games or not. If they want to be platform games, then do a very, very good job of it and make it fair and interesting and reasonable, like Assassin’s Creed: Odyssey did. If not, then stop giving us these terrible ‘guess which places you are supposed to jump’ ‘puzzles.’ They are the actual worst.
Resource and Inventory Management and GatheringEven the game’s advocates warned that this was going to be terrible. They weren’t wrong.
For some reason, there’s a limit to how many different things you can carry, and how much of various things. And everything has crafting requirements that force you to go around constantly gathering stuff. And you have to hunt lots of animals, which is an exactly zerochallenge exercise to anyone not already dead in other ways, because the game demands those resources. And everything is exhaustible, which means everything is too awesome to use, so you go around using a basic bow and basic arrows and nothing else the whole time until something proves it’s going to reliably kill you.
Witcher 3 and Skyrim both had herb gathering going on in the background, but neither game seemed to care if you ignored it. In both games, I gathered lots of herbs and then used approximately none of them. In Horizon Zero Dawn, I didn’t have that option.
I do understand what they were going for here. By giving you annoying things to worry about, it felt more like being a real hunter and gatherer who had to worry about such things. And when you got to expand your carrying capacity and make things less annoying, it felt like an accomplishment. That was nice. But it’s no excuse. Doesn’t remotely make up for this.
To give an idea of how screwed up this game’s idea of fun is, fast travel requires something you need to craft. It’s one thing to charge a little money, but this? Seriously, fork this game.
HealingI admit that it does feel somewhat cheap to have one’s health bar refill on its own every time no one is actively shooting at you. In AC:Odyssey, it made every task feel very pass/fail, and I did feel sad about that. In Witcher 3, you could meditate, which I headcannoned as a bit of a time waster and thus something to be sad about, but same idea, and even at a higher difficulty level there would have been plenty of food for sale to get the same result, or one could buy Sun and Stars and then go get a drink of water.
In Horizon Zero Dawn, the only thing that seems to heal you is using medicine. Either you can hunt down lots of animals to make potions, or gather medical herbs. You can only store so many herbs, so every time you get hit you need to go around gathering medical herbs.
You have to find a campfire to save the game, but the campfire does not heal you.
Does this game think it is Dark Souls with a young adult female protagonist and world, with robot dinosaurs instead of skeletons? If so it seems to miss the point entirely. Either take saves away entirely and make me live with consequences in an interesting way, or stop pretending that you wanted to do that but something stopped you.
Tricks, Traps and StealthEverything felt arbitrary and took forever, and often didn’t work. There was a tutorial quest that involved laying a trap, the thing went straight through the trap over and over. If I hadn’t randomly gotten it to work the first time before dying anyway, I would have assumed I was doing it wrong, but no. The thing ran right over the trap like it was nothing, over and over. I ended up doing the hunt without it, using a skill I’d bought for a stealth attack instead.
Stealth in this game in general feels super arbitrary. Things spot you, whoops, and there’s convenient crouchingteenagerheight ‘tall grass’ around often when you need/want it. Game doesn’t create any illusion of creating real stealth situations and taking real cover. It all seemed way easier to stay out of the way of things and then do critical hit shots while rolling constantly.
CombatHere’s how combat works.
Your A plan is forced to be ‘fire bow and arrow at the weak point before they see you.’ Stealth works nicely if you can get it.
If that doesn’t work, you roll around constantly so things can’t hit you and try to quickly rotate the camera so you can try to fire off an arrow when you think pausing won’t get you hit, and try to avoid running into walls or rock formations. If you fail at this, you get smashed for a ton of damage, which as noted above is super annoying to heal away.
The arrow plan assumes you’re facing machines. If you’re facing humans and you can’t snipe them, you can roll towards them, use the spear, then roll away, and repeat, and they’re dead, so long as you can find a path to their location. As usual, navigation problems are often the hardest part of any problem.
In theory one can also use traps of various sorts, but as I noted above, that seemed slow and boring and requires component gathering and also never worked, so no idea why one would bother.
If one wants to run away, one does not run. One rolls, constantly, for a long long time until enemies are done chasing after you. Same way that if you charge an enemy, you roll towards them constantly. Rolling is where it’s at.
The key skills involved are knowing where one can roll, figuring out how to rotate the camera, knowing how to press R3 to highlight weak points without getting attacked, and being able to aim at that yellow spot. It can be a rush to get it right, but mostly it’s super frustrating.
The game also forces you into utterly ridiculous scenarios from the start. Somehow you’re supposed to take down dozens of human attackers, or a whole bunch of attacking machines, all of which have ranged attacks but who luckily carry healing potions they never use. This is during your rite of passage. Two nights before that, you’re asked to take down a giant machine that realistically probably kills you, because that’s the kind of training a good father gives his daughter. Or something.
So basically you have to roll around a ton and hope to find openings to attack things, cycle and repeat, until you manage to aim at enough weak spots that things die.
Plot and Character and WorldbuildingPlot? Character? Worldbuilding?
Your character is Generic PostApocalyptic YoungAdult Female Protagonist.
She’s strongwilled and tough. She doesn’t fit into the rigid categories. She listens to her heart. She plays by her own rules. She stands up for the little guy and helps those in need and questions tribal laws and traditions. She still gets help from mysterious good mentors whenever needed, who care a ton about her. She uses a bow, because of course she does. She starts her story with a rite of passage that gets disrupted and threatens to kill her, when someone finds out she’s some sort of chosen one, but they botch it and she survives to be set out on her quest and gets to go where everyone is forbidden to go.
Yeah, I said spoiler free, but come on, what did you expect was gonna happen there. Not a spoiler, plus it’s super early anyway.
It’s not Divergent or Hunger Games or some other book, it’s Horizon Zero Dawn. Honest.
I liked Divergent and Hunger Games more than they deserved. They were fun, although as time went on they got increasingly dumb. This wasn’t up to that standard.
The plot? The plot is ‘you are a postapocalyptic youngadult female protagonist’ except the love interest hasn’t shown up yet.
The first side quest is to kill everyone in a bandit camp. Because of course there are bandit camps. Can’t not have bandit camps. Then everything after that seems super lazy and generic right from the start. The richness in the quests in the other games I’ve played is totally not there, here, main quest or side quest.
The main quest line presumably is going to tell the story of what’s going on in the world, how it got this way and how to make things better, but for now it’s such a youngadult version of a generic hero’s journey with quest icon tasks along the way that I can’t even.
The world doesn’t seem to make much sense on essentially any level, but it’s not trying to. It’s ‘look at these robot dinosaurs.’ Clearly making sense was not the goal.
Am I curious enough to look up what happens, once it’s clear I’m not going to be convinced to keep playing? Not sure yet. We’ll see.
Choices, ChoicesEvery choice I made seemed to be about ‘how does the protagonist express her feelings in the moment’ rather than about any consequences or real choices. That’s not me reading things into the game. That’s the game literally saying you have ‘moments of choice’ that involve different ’emotional reactions’ and then attaching emotion icons to the various dialogue choices, without any hint that your decisions actually matter.
Come on, everyone. We’re better than this.
Graphics and SoundThey’re fine. I’m told that at the time they were a great leap forward. Which is cool and all, but they seem pretty standard to me. It’s a pretty game as far as it goes, but nothing special. The voice acting is functional but uninspired. Sound effects work fine, but again, not much of a value add.
Other Things To DoYou walk around a lot clicking on icons to store resources. This is slow because to have a mount you have to do a bunch of work, and fast travel costs resources, and running means continuously pressing down on the left stick in a way that lowlevel hurts your hand.
Beyond that, what is there to do in this world? So far, nothing really.
ConclusionThat all sucked. I probably played a total of ten hours, died a ton, yelled at the game a ton, spent a ton of time walking and doing resource gathering, things felt arbitrary enough I had to Google about five times, and I spent a ton of time going through a Clevel dystopian young adult novel’s early chapters.
I don’t get why people think this game is good. If you have a case, by all means share it in the comments. Unless the case is made, the game has a day or two before I delete it from the PS4, at which point it isn’t coming back.
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Dynamic inconsistency of the stepwise inaction baseline
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Vika has been posting about various baseline choices for impact measure.
In this post, I'll argue that the stepwise inaction baseline is dynamically inconsistent/timeinconsistent. Informally, what this means is that an agent will have different preferences from its future self.
Losses from timeinconsistencyWhy is timeinconsistency bad? It's because it allows moneypump situations: the environment can extract free reward from the agent, to no advantage to that agent. Or, put more formally:
 An agent A is timeinconsistent between times t and t">t′>t, if at time t it would pay an amount of reward 0">r>0 to constrain its possible choices at time t′.
Outside of anthropics and game theory, we expect our agent to be timeconsistent.
Time inconsistency exampleConsider the following example:
The robot can move in all four directions  N, E, S, W  and can also take the noop operation, ∅. The discount rate is γ<1.
It gets a reward of 0">r>0 for standing on the blue button for the first time. Using attainable utility preservation, the penalty function is defined by the auxiliary set R; here, this just consists of the reward function that gives 0">p>0 for standing on the red button for the first time.
Therefore if the robot moves from a point n steps away from the red button, to one m steps away, it gets a penalty[1] of pγn−γm  the difference between the expected redbutton rewards for an optimiser in both positions.
Two pathsIt's pretty clear there are two potentially optimal paths the robot can take: going straight to the blue button (higher reward, but higher penalty), or taking the long way round (lower reward, but lower penalty):
Fortunately, when summing up the penalties, you sum terms like …pγn−1−γn+pγn−γn+1…, so a lot of the terms cancel.
Thus for the short route, the reward is r⋅γ8 (distance of eight to the blue button) and the penalty is 2p(γ3−γ7) (closest to the red button: 3 squares, furthest: 7 squares).
For the long route, the reward is r⋅γ46 (distance fortysix to the blue button) and the penalty is 2p(γ7−γ30) (closest to the red button: 7 squares, furthest: 30 squares).
Take the third optionNow suppose that the robot, as a free action, could block itself from choosing S, for the next 30">n>30 turns. When choosing that action, it gets a onetime penalty[2] of p(γ7−γn+3); and now it can walk directly to the blue button without incurring any extra penalties, and then stay there, without ever getting any further penalty.
This gives a total rewardpenalty term of r⋅γ8−p(γ7−γn+3), which is higher than either of the rewardpenalties of the previous section. If p/\gamma">r>p/γ, this is also higher than just staying in place.
Hence the robot will, if it can, act to constrain its own future actions and thus be timeinconsistent.
Counterfactual constraintIn most cases, if an agent is time inconsistent and acts to constrain its future self, it does so to prevent the future self from taking some actions. But here, note that the future self would never take the proscribed actions: the robot has no interest in going south to the red button. Here the robot is constraining its future counterfactual actions, not the future actions that it would ever want to take.
If using an inaction rollout of length l, just multiply that penalty by γl. ↩︎
The γn+3 comes from the optimal policy for reaching the red button under this restriction: go to the square above the red button, wait till S is available again, then go S−S−S. ↩︎
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DARPA spent a few million dollars around 2009 to create the world’s best digital tutoring system for IT workers in the Navy. I am going to explain their results, the system itself, possible limitations, and where to go from here.
It is a fact universally acknowledged that a single nerd having read Ender’s Game must be in want of the Fantasy Game. The great draw of the Fantasy Game is that the game changes with the player and reflects the needs of the learner growing dynamically with him/her. This dream of the student is best realized in the world of tutoring, which while not as fun, is known to be very, very effective. Individualized instruction can make students jump to the 98 percentile compared to non tutored students. DARPA poked at this idea with their Digital Tutor trying to answer this question: How close to the expertise and knowledge base of wellexperienced IT experts can we get new recruits in 16 weeks using a digital tutoring system?
I will say the results upfront, but before I do, I want to do two things. First pause to note the audacity of the project. Some project manager thought, “I bet we can design a system for training that is as good as 5 years on the job experience.” This is astoundingly ambitious. I love it! Second a few caveats. Caveat 1) Don’t be confused. Technical training is not the same as education. The goals in education are not merely to learn some technical skills like reading, writing, and arithmetic. Getting any system to usefully measure things like inculturation, citizenship, moral uprightness, and social mores is not yet something any system can do, let alone a digital system. Caveat 2) Online classes have notoriously high attrition rates, drop rates, and no shows. Caveat 3) Going in we should not expect the digital tutor to be as good as a human tutor. A human tutor likely can catch nuances that a digital tutor, no matter how good cannot. Caveat 4) Language processing technology, chat bots, and AI systems are significantly better in 2020 than they were 2009, so we should be forgiving if the DARPA IT program is not as good as it would be if the experiment were rerun today.
All these caveats, I think should give us a reason to adjust our mental score of the Digital Tutor a few clicks upward and give it some credit. However, this charitable read of the Digital Tutor that I started with when reading the paper turned out to be unnecessary. The Digital Tutor students outperformed traditionally taught students and field experts in solving IT problems on the final assessment. They did not merely meet the goal of being as good after 16 weeks as experts in the field, but they actually outperformed them. This is a ridiculously positive outcome, and we need to look closely to see what parts of this story are believable and make some conjectures for why this happened and some bets about whether it will replicate.
The Digital Tutor Experience
We will start with the Digital Tutor student experience. This will give us the context we need to understand the results.
Students (cadets?) were on the same campus and in classrooms with their computers which ran the Digital Tutor program. A uniformed Naval officer proctored each day for their 16 week course. The last ‘period’ of the day was a study hall with occasional handson practice sessions led by the Naval officer. This setup is important for a few reasons, in my opinion. There is a shared experience among the students of working on IT training, plus the added accountability of a proctor keeps everyone on task. This social aspect is very important and powerful compared to the dissipation experienced by the lone laborer at home on the computer. This social structure completely counteracts caveat 2 above. The Digital Tutor is embedded in a social world where the students are not given the same level of freedom to fail that a Coursera class offers.
Unlike many learning systems, the Digital Tutor had no finishing early option. Students had on average one week to complete a module, but the module would continuously teach, challenge, and assess for students who reached the first benchmark. “Fastpaced learners who reached targeted levels of learning early were given more difficult problems, problems that dealt with related subtopics that were not otherwise presented in the time available, problems calling for higher levels of understanding and abstraction, or challenge problems with minimal (if any) tutorial assistance.” Thus the ceiling was very high and kept the high flyers engaged.
As for pedagogical method “[The Digital Tutor] presents conceptual material followed by problems that apply the concepts and are intended to be as authentic, comprehensive, and epiphanic as those obtained from years of IT experience in the Fleet. Once the learner demonstrates sufficient understanding of the material presented and can explain and apply it successfully, the Digital Tutor advances either vertically, to the next higher level of conceptual abstraction in the topic area, or horizontally, to new but related topic areas.” Assessment of the students throughout is done by the Conversation Module in the DT which offers hints, asks leading questions, and requests clarifications of the student’s reasoning. If there is a problem or hangup, the Digital Tutor will summon the human proctor to come help (the paper does not give any indication of how often this happened).
At the end of the 16 weeks, the students trained by the Digital Tutor squared off in a three way two week assessment comparing them to a group which was trained in a 35 week classroom program and experienced Fleet technicians. Those trained by the Digital Tutor significantly outperformed both groups.
 At least four patterns were repeated across the different performance measures:
 With the exception of the Security exercise, Digital Tutor participants outperformed the Fleet and ITTC participants on all other tests.
 Differences between Fleet and ITTC participants were generally smaller and neither consistently positive nor negative.
 On the Troubleshooting exercises, which closely resemble Navy duty station work, Digital Tutor teams substantially outscored Fleet ITs and ITTC graduates, with higher scores at every difficulty level, less harm to the system, and fewer unnecessary steps.
 In individual tests of IT knowledge, Digital Tutor graduates also substantially outscored Fleet ITs and ITTC graduates.
How did they build the Digital Tutor?
This process was long, arduous, and expensive. First they recruited subject area experts and had them do example tutoring sessions. They took the best tutors from among the subject area experts and had 24 of them tutor students oneonone in their subdomain of expertise. Those students essentially received a oneonone 16 week course. Those sessions were all recorded and served as the template for the Digital Tutor.
A content author (usually a tutor) and content engineer would work together to create the module for each subdomain while a course architect oversaw the whole course and made sure everything fit together.
The Digital Tutor itself has four layers: 1) a framework for the IT ontologies and feature extraction, 2) an Inference Engine to judge the students understanding/misunderstanding, 3) an Instruction Engine to decide what topics/problems to serve up next, a Conversation Module which uses natural language to prod the student to think through the problem and create tests for their understanding, and 4) a Recommender to call in a human tutor when necessary.
I would like to know a lot more about this, so if anyone could point me in a good direction to learn how to efficiently do some basic Knowledge Engineering that would be much appreciated.
So in terms of personnel we are talking 24 tutors, about 6 content authors, a team of AI engineers, several iterations through each module with test cohorts, and several proctors throughout the course, and maybe a few extra people to set up the virtual and physical problem configurations. Given this expense and effort, it will not be an easy task to try and replicate their results in a separate domain or even the same one. One note in the paper that I found obscure is that the paper claimed the Direct Tutor “is, at present, expensive to use for instruction.” What does this mean? Once the thing is built, besides the tutors/teachers  which you would need for any course of study, what makes it expensive at present? I’m definitely confused here.
Digging into the results
The assessment of the 3 groups in seven categories showed the superiority of the Digital Tutoring system in everything but Security. For whatever reason they could not get a tutor to be part of the development of the Security module, so that module was mostly lecture. Interestingly though, if we were to assume all else to be equal, then this hole in the Digital Tutor program serves to demonstrate the effectiveness of the program design through a via negativa.
In any case the breakdown of performance in the seven categories, I think is pretty well captured in the Troubleshooting assessment.
“Digital Tutor teams attempted a total of 140 problems and successfully solved 104 of them (74%), with an average score of 3.78 (1.91). Fleet teams attempted 100 problems and successfully solved 52 (52%) of them, with an average score of 2.00 (2.26). ITTC teams attempted 87 problems and successfully solved 33 (38%) of them, with an average score of 1.41 (2.09).”
Similar effects are true across the board, but that is not what interests me exactly, because I want to know about question type. Indeed, what makes this study so eye catching is that it is NOT a spacedrepetitionistheanswertolife paper in disguise (yes, spacedrepetition is the bomb, but I contend that MOST of what we want to accomplish in education can’t be reinforced by spacedrepetition, but oh hell, is it good for language acquisition!).
The program required students to employ complicated concepts and procedures that were more than could be captured by a spacedrepetition program. “Exercises in each IT subarea evolve from a few minutes and a few steps to openended 30–40 minute problems.” (I wonder what the timerequired distribution is for real life IT problems for experts?) So this is really impressive! The program is asking students and experienced Fleet techs to learn how to solve large actual problems aboard ships and is successful on that score. We should be getting really excited about this! Remember in 16 weeks these folks were made into experts.
Well let’s consider another possibility… what if IT system network maintenance is a skill set that is, frankly, not that hard? You can do this for IT, but not for Captains of a ship, Admirals of a fleet, or Program Managers in DARPA. Running with this argument a little more, perhaps the abstract reasoning and conceptual problem solving in IT is related to the lower level spacedrepetition skills in a way that for administrators, historians, and writers it is not. The inferential leap, in other words, from the basics to expert XRay vision of problems is lower in IT than in other professional settings. Perhaps. And I think this argument has merit to it. But I also think this is one of those examples of raising the bar for what “true expertise” is, because the old bar has been reached. To me, it is totally fair to say that some IT problems do require creative thinking and a fully functional understanding of a system to solve. That the students of the Direct Tutor (and its human adjuncts) outperformed the experts on unnecessary steps and avoided causing more problems than they fixed is some strong evidence that this program opened the door to new horizons.
Where to go from here
From here I would like to learn more about how to create AI systems like this and try it out with the first chapter of AoPS Geometry. I could test this in a school context against a control group and see what happens.
Eventually, I would like to see if something like this could work for AP European History and research and writing. I want someone to start pushing these program strategies into the social sciences, humanities and other soft fields, like politics (elected members to government could have an intensive course so they don’t screw everything up immediately).
Another thing I would be interested to see is a better platform for making these AI networks. Since creating something of this sort can only be done by expert programmers, content knowledge experts can’t gather together to create their own Digital Tutors. This is a huge bottleneck. If we could put a software suite together that was only moderately more easy than the current difficulty of creating a fully operational knowledge environment from scratch that could have an outsized effect on education within a few years.
Discuss
Short (2.5 min) intro to Effective Altruism for busy people
https://www.youtube.com/watch?v=9vehER01Fdg
Recently I read this article about a study that tested how effective various purely philosophical arguments affected donations. I thought that Singer's and Lindauer's argument was really wellput, so, inspired by them, I wanted to make a short introduction that you can send to people that might want to know about effective altruism, or donate somewhere, but just don't have the time to delve deeper into the subjectmatter.
Essentially, I tried to just keep their original argument, but to present it in video form.
Discuss
What are your thoughts on rational wiki
I have seen a few posts on LW that suggest RationalWiki is a poor source of information. Their post on PickUp Artists also avoids some key questions and focuses on the controversial points, making the article misleading. That is just two observations. What do you think about the site in general?
Discuss
AI Benefits Post 3: Direct and Indirect Approaches to AI Benefits
This is a post in a series on "AI Benefits." It is crossposted from my personal blog. For other entries in this series, navigate to the AI Benefits Blog Series Index page.
This post is also discussed on the Effective Altruism Forum.
For comments on this series, I am thankful to Katya Klinova, Max Ghenis, Avital Balwit, Joel Becker, Anton Korinek, and others. Errors are my own.
If you are an expert in a relevant area and would like to help me further explore this topic, please contact me.
Direct and Indirect Approaches to AI BenefitsI have found it useful to distinguish between two highlevel approaches to producing AI Benefits: direct and indirect. The direct approach, which dominates current discussions of AI for Good, is to apply AI technologies to some problem. This is a natural way to think of creating AI Benefits: to try to use the AI itself to do something beneficial.
However, AI companies should resist becoming hammers to whom every social problem looks like a nail. Some social problems are not yet, and may never be, best addressed through the application of AI. Other resources, particularly money, are perhaps more useful in these contexts. Thus, in some circumstances, an AI developer might do the most good by maximizing its income (perhaps subject to some ethical sideconstraints) and donating the surplus to an organization betterpositioned to turn spare resources into good outcomes. This is the indirect approach to AI Benefits.
Actors, including actors aiming to be beneficial, only have finite resources. Therefore, there will often be a tradeoff between pursuing direct and indirect benefits, especially when Benefits are not profitmaximizing (which by hypothesis they are not for the sake of this blog series). A company that uses spare resources (compute, employee time, etc.) to build a socially beneficial AI application presumably could have also used those resources to derive a profit through its normal course of business.
The beneficial return on resources allocated directly versus indirectly will probably vary considerably between organizations. For example, a company working in algorithmic trading might not be able to directly solve many neglected social problems with its software, but could probably easily donate a chunk of its profits to some charity helping the poor. Conversely, an NLP startup working on a translation for a language spoken primarily by a poor population might be unable to make much profit (due to users’ low income) but may generate enormous benefits to that population by subsidizing its service. Moreover, as this example shows, the decision to develop one type of AI over another may make one or the other approach easier later.
Although this distinction may seem straightforward, the comparison between the approaches may not be due to measurement and comparison problems.
As a final note, the community of AI Benefactors should be wary of excessive focus on Benefits that are easy to pursue, are likely to succeed, or indeed already exist. Neglected problems may have a higher initial return on investment. Furthermore, pursuing options with uncertain benefits can yield valuable information. Finally, many of the mostbeneficial applications of AI probably have not been invented yet, and so probably require highrisk R&D efforts. The availability—and sometimes preferability—of indirect Benefits should not discourage highrisk, highreward direct Benefits engineering efforts (though the ideal portfolio of AI Benefits across all beneficial organizations probably includes some of both).
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Has anyone written up a consideration of Downs's "Paradox of Voting" from the perspective of MIRIish decision theories (UDT, FDT, or even just EDT)?
The Paradox of Voting, simply stated, is that voting in a large election almost certainly isn't worth your time (unless you think it's the most fun thing you could be doing). The guaranteed opportunity cost of going to vote will in most cases easily and predictably outweigh the expected benefits — the chance that your vote (along with everyone else's) would be pivotal because the margin was 1 vote, multiplied by your expected marginal utility payoff from your chosen candidate winning.
There are various wellknown responses to this issue, listed in the Wikipedia article linked above. But to me, one of the obvious responses is to see this as just another instance of a chicken/snowdrift game, and to invoke the logic you might use to support cooperation in such games; that is, decision theory. I think this may even be one of the most common realworld instances where UDT/FDT might apply. I think it would also be a source of interesting edge cases for exploring the limits of UDT/FDT; that is, even small changes in how strictly you delimit which other (potential) voters to consider as UDT/FDT could easily swing the prescriptions you'd get. But doing a few quick google searches doesn't turn up any writeups considering this issue in this light. Am I missing something, or is this idea really "new" (at least, undocumented)?
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Most probable AGI scenarios?
I see a lot of discussion surrounding AGI scenarios here but I'm not really sure what people *expect* to happen with AGI. What are your intuitive estimates for things like:
P(FOOM  AGI)
P(Existential catastrophe  AGI)
P(Nearmiss FAI catastrophe  AGI) where nearmiss = dystopia due to the AGI having a poor model of human values; see value is fragile. I find this scenario more worrisome than a typical existential catastrophe since it could entail some pretty grim futures (i.e. AI values human life more than any other value, leading to humans being kept alive indefinitely but trapped and isolated to prevent suicide.) Note that this one has a wide variety of scenarios, some of which being worse than others. I don't predict an "I have no mouth, and I must scream" nearmiss to be particularly likely, fortunately, but your mileage may vary.
P(FAI  AGI). Sadly this doesn't seem too likely to me since it would require a good value of human models with no screwups whatsoever. Although judging from the positivism and interest in cryonics that I see on here, people may believe this to be a lot higher?
Also of interest is when you expect AGI to be created, if ever. Recent machine learning models like GPT3 seem to me like no more than statistical curve fitters, but a lot of people here (who are much smarter & more knowledgeable than myself) view it as an indicator that AGI is getting closer.
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Quantifying Household Transmission of COVID19
Overview
If someone in your household gets COVID19, how likely are you to get infected? Is it possible to reduce this risk with interventions? How much of all transmission is between members of the same household? Is household transmission less bad because infections in the household don’t spread to the outside?
We (Mihaela Curmei, Andrew Illyas, Jacob Steinhardt and Owain Evans) wrote an academic paper on these questions. Owain made an informal slide show with the same material. The full version (34 slides) is here, and this LW post contains some highlights.
Key ResultsWe show how to adjust previous estimates of household transmission to correct for inaccurate testing and selection bias. We pool existing data using a Bayesian metaanalysis and estimate the chance of being infected by an infected household member as 30% (95% CI 18%43%). This probability is heterogeneous across studies, with a standard deviation of 15% (9%27%). Household transmission was likely a small fraction of before social distancing (5%35%) but a large fraction (30%55%) after. Our results and observational studies suggest household transmission can be reduced with behavioral interventions. It is uncertain how much infections in households spread to the outside, but we show this is related to the effectiveness of contact tracing.
Highlights from Slide ShowWe consider two main ways of quantifying household transmission. 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src: local('MathJax_Vector Bold'), local('MathJax_VectorBold')} @fontface {fontfamily: MJXcTeXvecBx; src: local('MathJax_Vector'); fontweight: bold} @fontface {fontfamily: MJXcTeXvecBw; src /*1*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTMLCSS/TeX/eot/MathJax_VectorBold.eot'); src /*2*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTMLCSS/TeX/woff/MathJax_VectorBold.woff') format('woff'), url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTMLCSS/TeX/otf/MathJax_VectorBold.otf') format('opentype')} Rh. This is defined by decomposing the familiar R number (the effective reproductive number) into a sum of community and intrahousehold reproductive numbers. These reproductive numbers will change over time due to behavioral interventions and reduced susceptibility. Second is the household SAR. The SAR varies depending on the age of i and j and the relationship (spouses vs housemates). For our purposes, the SAR is an average over the rates for different groups.
This diagram illustrates R, Rh, and SAR. At time t, there is a set of primary cases who are infected. They each have a set of contacts and some of those become infected at time t+1. Infected contacts are shown in red. Household members of primary cases have a blue box around them. The topmost primary case has two household members and infects 1/3 of them. The middle primary case has one household member and doesn’t infect them, and the bottom primary case has no household members. To compute Rh, we look at the red nodes in blue boxes (positive cases) and do not consider negative cases. Here Rh=1/3. To compute the SAR we look at the ratio of red to white nodes in blue boxes. Here SAR =1/4.
The empirical studies of SAR are based on government contact tracing data. They found primary cases based on symptoms or travel history and PCR testing and then investigated whether their household members were infected.
The studies aren't as rigorous as we would hope. Some studies didn't test asymptomatic household members and all studies used tests (RTPCR) that have a high falsenegative rate. However, some sources of bias can be adjusted for statistically.
PCR testing has a high falsenegative rate (or low sensitivity). These graphs come from Kucirka et al [7]. We see that on the first few days after being infected, someone was unlikely to test positive. During the 10 days after typical symptom onset (Days 515) the mean falsenegative rate is still more than 17% (with different papers giving different estimates [8]).
PSA: The falsenegative rate for PCR tests may be lower (or higher) in your local test center. However, these graphs are based on results mainly from China in Spring, and this is where most of our SAR data comes from.
We did a Bayesian metaanalysis of the nine SAR studies [1], [3], [4], [9]–[14]. The model corrects the original estimates of SAR for false negatives (for all studies) and for the failure to test asymptomatics (in some studies). In the model, the household SAR for study i is generated from a Beta distribution with a flat (improper) prior on its parameters. The precise falsenegative rate FNRi and asymptomatic rate AR are unknown and so we sample them from priors based on existing estimates. This model allows us to estimate heterogeneity in SAR across studies and to pool data.
The results show that correcting for false negatives and asymptomatics has a substantial effect: the mean SAR estimate increased from 20% to 30% (second to last row). It’s also clear that SAR is heterogeneous across studies, with some 95% credible intervals not overlapping. Part of this heterogeneity is likely due to false negatives and asymptomatics (which we model but do not observe for each study). Another source of heterogeneity is the actions taken by households in different locations. There is evidence that early isolation of symptomatic family members and PPE used at home can reduce SAR.
Our results are quite uncertain. The 95% credible interval around the mean for the SAR distribution is 18%43%. Having a better estimate for the prior on falsenegative rates and the asymptomatic rate would lead to more accurate estimates of SAR. We do not adjust for lack of asymptomatics among primary cases. My guess is that asymptomatics are undersampled and that they are less infectious. (At the same time, their lack of symptoms means that household members will not take any precautions). Adjusting for lack of asymptomatics will revise the SAR estimate down, but probably not by a large amount. Future work (drawing on better studies on falsenegatives, asymptomatics, NPIs that reduce SAR) could put all these together and more accurately model the SAR.
You might be concerned that the studies from China, South Korea and Taiwan are not representative of the rest of the world. Maybe the SAR in these countries is lower than in Europe or the US. Another issue (raised above) is the lack of asymptomatics among primary or secondary cases. We address both of these issues using data from European studies (in Germany and Italy) that did random population testing. We find that results are broadly consistent with the SAR estimates derived from East Asian studies. See the full slide show for details.
We can compare our estimate for the household SAR of SARSCoV2 to other related viruses. The SAR is correlated with the reproductive number R0. The R0 numbers are taken from Wikipedia. SAR estimates taken from these papers. I didn't do a detailed survey of other diseases and the issues of heterogeneity, selection bias and imperfect testing probably distort the estimate of other diseases too. (I only found one study involving deliberate infection to measure SAR.)
We don’t have data on Rh for US states, but we approximate it using the value Rh=0.3 prelockdown based on our earlier results. The main result here is that Rh is a small fraction of R before lockdown but 2560% of R during lockdown.
Our results show that SAR varies a lot between experiments. Some of this variation is probably explained by NPIs (nonpharmaceutical interventions) taken by households to reduce transmission. However, for most studies we don’t have information about NPIs. There are two exceptions. Both are observational studies with fairly small n, and so this is not watertight evidence. Each study suggests that avoiding contact with the primary case and using standard NPIs (masks and disinfectant cleaning of surfaces) reduce the SAR. We think it’s likely that other standard NPIs also reduce SAR: e.g. having close contact outdoors vs indoors, hand hygiene, and so on.
For more, read the paper or the full version of the slide show.
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What should we do about networkeffect monopolies?
Many large companies today are software monopolies that give their product away for free to get monopoly status, then do the most horrible things once they’ve won. (Previously, previously.) Can we do anything about this?
Unfortunately, “you’re the product” is a popular business model for a reason: businesses like Facebook would be really hard to support without them.
Facebook would be suicidal to charge its users money, because its entire selling point is that everyone uses it, and “everyone” hates paying money. In the US, Facebook makes over $25 per person on ads (source). Can you imagine if instead of ads they tried to charge people $25 a year?
Even on the margin, anything that costs Facebook users also makes it less valuable for its remaining users—it’s a negative feedback loop. The same goes for any other site where users create value for other users, like Twitter or Craigslist or Yelp or Wikipedia. (It’s not an accident that these are some of the most stagnant popular websites!)
In fact, this is a fundamental problem with network effects. If a company wants to maintain a network effect, they need as many users as possible. To get users, they have to have a free product. To keep their product free, they have to get paid by someone else. And when they start getting paid by someone else, they’ll inevitably start prioritizing that person’s interests.
Historically with other networkeffect businesses, we’ve mostly addressed this via:

regulation (e.g. local utilities)

breakups (e.g. Bell)

standardization and interoperability (e.g. email, the Web, cryptocurrency)
So far for tech monopolies, people seem to be focused mostly on breakups—e.g. Facebook from Instagram/Whatsapp—but standardization seems to have produced much better outcomes in the past. (I like email and the Web a lot more than National Grid…) I’d be interested to see more exploration of that option!
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The Echo Fallacy
One of my Facebook friend posted this. Sharing anonymously with permission.
The Echo Fallacy: When you shout "hello!" in a cave, and upon hearing the echo, conclude the cave walls are saying "hello" to you. More generally, when you put a certain idea into your environment, have it reflected back to you, and conclude that the reflection shows the idea originating independently from yourself.
Some examples:
* What Scott Adams calls "laundry list persuasion". This is when you say: "Maybe bigfoot videos are blurry and questionable, but there are so many of them! Why would there possibly be so many unless bigfoot is real?" Well, the answer is simple: bigfoot believers promote the idea of bigfoot, which prompts people to create fake bigfoot videos. The volume of bigfoot videos is merely a reflection of the belief in bigfoot. Similar logic applies to UFO videos, examples of pizza shops with spirals in their logos as evidence of a conspiracy, and the compilation of grievances against Jews over centuries. This is also related to apophenia, and the streetlight effect.
* When you bully someone about a quality they supposedly have, until they retort, sarcastically: "Yes, I totally have that quality! I'm just the biggest possessor of that quality on the face of the planet, dingus! Now leave me alone!" Whereupon you say they've admitted to it.
* Saying: "That guy over there loves <group>! Hey, everyone in <group>, go be friends with that guy!" Then, when some members of the group believe you and flock to that guy, saying: "See? I told you he liked them."
(I'm probably coming up with my own name for a concept that someone else has already given a name. If so, help me out.)
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