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Why don't countries, like companies, more often merge?

23 августа, 2020 - 05:17
Published on August 22, 2020 11:56 PM GMT

Whereas companies often undergo a friendly merger or acquisition, why don't countries more often do this? Set aside colonialism because I'd like to focus on the case where the threat of violence is not paramount.

I suppose the right incentives aren't in place but is this an intrinsic fact of international relations or democratic politics?

For example, a merger between the US and Mexico seems, naively, like it could be in both nations' interest. The US gets more land and lower-cost labor and Mexico gets access to better governance and knowledge spillover.


Current guidance for COVID-19 self-care?

23 августа, 2020 - 05:10
Published on August 23, 2020 2:10 AM GMT

What is the current consensus on supplements to take or skip? I thought zinc lozenges and high dose Vitamin C were clear winners, but I am now finding conflicting information on how Vitamin C may impact interferon.

There's also the HCQ issue, where it's been so politicized that it's rather difficult to determine efficacy as an early intervention.

Asking for myself, because, well, y'know. Getting tested tomorrow, and regarding that - come on San Francisco, it's nearly September, DO BETTER. Please DM if you know of a way to get tested with a <24 hour turnaround time.


A trick for Safer GPT-N

23 августа, 2020 - 03:39
Published on August 23, 2020 12:39 AM GMT

To clarify definitions a bit, by GPT-N I mean a future deep learning model trained to minimize the error of next-word prediction on a corpus that includes substantial portions of the internet. This model is assumed to be superhuman at its given task.

The general idea is the following: you can make GPT-N model humanity having access to its outputs by adding its outputs to its dataset and training on them. This allows us to use GPT-N's immense knowledge to predict the consequences of us having its output to a question. A practical process for using this to make safer GPT-N is something like the following:

Setup the API to the model such that for every query you want to make of the AI, you make GPT-N predict a continuation without showing you, then you train it on that continuation (basically making it think that what it produced was already available to humanity). Then you ask it to predict the likelihood of various doom-implying headlines from a major newspaper in the 2040s, if training on the continuation to your query makes the likelihood of doom headlines greater, then the AI doesnt publish the continuation to your question.

The question of ensuring safety is then rephrased  to building a corpus of text that we expect to reliably indicate positive vs negative outcomes of humanity. If we have such a corpus, we can have a system that causes GPT-N to only show answers that cause the good outcomes and avoid the bad ones, crucially without it trying to cause either, no direct optimization pressure is being applied to causing these outcomes, it's still producing the most likely text continuation, but we're using its knowledge to know whether we should look at its answers.

This is an extremely cheap thing to implement, no additional fancy things required, so AI leaders get no measurable capability penalty from using this. The corpus of text indicating good/bad outcomes should be made publicly available and as large as possible.

Of course, this is just a patch, and we should use the immense tool of GPT-N to look for proper safe AGI (by making it predict future papers in AI safety), but this trick might help us navigate the short hazardous period before we have such a thing.


Maybe Lying Can't Exist?!

23 августа, 2020 - 03:36
Published on August 23, 2020 12:36 AM GMT

How is it possible to tell the truth?

I mean, sure, you can use your larynx to make sound waves in the air, or you can draw a sequence of symbols on paper, but sound waves and paper-markings can't be true, any more than a leaf or a rock can be "true". Why do you think you can tell the truth?

This is a pretty easy question. Words don't have intrinsic ontologically-basic meanings, but intelligent systems can learn associations between a symbol and things in the world. If I say "dog" and point to a dog a bunch of times, a child who didn't already know what the word "dog" meant, would soon get the idea and learn that the sound "dog" meant this-and-such kind of furry four-legged animal.

As a formal model of how this AI trick works, we can study sender–receiver games. Two agents, a "sender" and a "receiver", play a simple game: the sender observes one of several possible states of the world, and sends one of several possible signals—something that the sender can vary (like sound waves or paper-markings) in a way that the receiver can detect. The receiver observes the signal, and makes a prediction about the state of the world. If the agents both get rewarded when the receiver's prediction matches the sender's observation, a convention evolves that assigns common-usage meanings to the previously and otherwise arbitrary signals. True information is communicated; the signals become a shared map that reflects the territory.

This works because the sender and receiver have a common interest in getting the same, correct answer—in coordinating for the signals to mean something. If instead the sender got rewarded when the receiver made bad predictions, then if the receiver could use some correlation between the state of the world and the sender's signals in order to make better predictions, then the sender would have an incentive to change its signaling choices to destroy that correlation. No convention evolves, no information gets transferred. This case is not a matter of a map failing to reflect the territory. Rather, there just is no map.

How is it possible to lie?

This is ... a surprisingly less-easy question. The problem is that, in the formal framework of the sender–receiver game, the meaning of a signal is simply how it makes a receiver update its probabilities, which is determined by the conditions under which the signal is sent. If I say "dog" and four-fifths of the time I point to a dog, but one-fifth of the time I point to a tree, what should a child conclude? Does "dog" mean dog-with-probability-0.8-and-tree-with-probability-0.2, or does "dog" mean dog, and I'm just lying one time out of five? (Or does "dog" mean tree, and I'm lying four times out of five?!) Our sender–receiver game model would seem to favor the first interpretation.

Signals convey information. What could make a signal, information, deceptive?

Traditionally, deception has been regarded as intentionally causing someone to have a false belief. As Bayesians and reductionists, however, we endeavor to pry open anthropomorphic black boxes like "intent" and "belief." As a first attempt at making sense of deceptive signaling, let's generalize "causing someone to have a false belief" to "causing the receiver to update its probability distribution to be less accurate (operationalized as the logarithm of the probability it assigns to the true state)", and generalize "intentionally" to "benefiting the sender (operationalized by the rewards in the sender–receiver game)".

One might ask: why require the sender to benefit in order for a signal to count as deceptive? Why isn't "made the receiver update in the wrong direction" enough?

The answer is that we're seeking an account of communication that systematically makes receivers update in the wrong direction—signals that we can think of as having been optimized for making the receiver make wrong predictions, rather than accidentally happening to mislead on this particular occasion. The "rewards" in this model should be interpreted mechanistically, not necessarily mentalistically: it's just that things that get "rewarded" more, happen more often. That's all—and that's enough to shape the evolution of how the system processes information. There need not be any conscious mind that "feels happy" about getting rewarded (although that would do the trick).

Let's test out our proposed definition of deception on a concrete example. Consider a firefly of the fictional species P. rey exploring a new area in the forest. Suppose there are three possibilities for what this area could contain. With probability 1/3, the area contains another P. rey firefly of the opposite sex, available for mating. With probability 1/6, the area contains a firefly of a different species, P. redator, which eats P. rey fireflies. With probability 1/2, the area contains nothing of interest.

A potential mate in the area can flash the P. rey mating signal to let the approaching P. rey know it's there. Fireflies evolved their eponymous ability to emit light specifically for this kind of sexual communication—potential mates have a common interest in making their presence known to each other. 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src: local('MathJax_Vector Bold'), local('MathJax_Vector-Bold')} @font-face {font-family: MJXc-TeX-vec-Bx; src: local('MathJax_Vector'); font-weight: bold} @font-face {font-family: MJXc-TeX-vec-Bw; src /*1*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/eot/MathJax_Vector-Bold.eot'); src /*2*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/woff/MathJax_Vector-Bold.woff') format('woff'), url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/otf/MathJax_Vector-Bold.otf') format('opentype')} mate, 16 predator, 12 nothing} to {1 mate}. True information is communicated.

Until "one day" (in evolutionary time), a mutant P. redator emits flashes that imitate the P. rey mating signal, thereby luring an approaching P. rey, who becomes an easy meal for the P. redator. This meets our criteria for deceptive signaling: the P. rey receiver updates in the wrong direction (revising its probability of a P. redator being present downwards from 16 to 0, even though a P. redator is in fact present), and the P. redator sender benefits (becoming more likely to survive and reproduce, thereby spreading the mutant alleles that predisposed it to emit P. rey-mating-signal-like flashes, thereby ensuring that this scenario will systematically recur in future generations, even if the first time was an accident because fireflies aren't that smart).

Or rather, this meets our criteria for deceptive signaling at first. If the P. rey population counteradapts to make correct Bayesian updates in the new world containing deceptive P. redators, then in the new equilibrium, seeing the mating signal causes a P. rey to update its what's-in-this-area probability distribution from {13 mate, 16 predator, 12 nothing} to {23 mate, 13 predator}. But now the counteradapted P. rey is not updating in the wrong direction. If both mates and predators send the same signal, than the likelihood ratio between them is one; the observation doesn't favor one hypothesis more than the other.

So ... is the P. redator's use of the mating signal no longer deceptive after it's been "priced in" to the new equilibrium? Should we stop calling the flashes the "P. rey mating signal" and start calling it the "P. rey mating and/or P. redator prey-luring signal"? Do we agree with the executive in Moral Mazes who said, "We lie all the time, but if everyone knows that we're lying, is a lie really a lie?"

Some authors are willing to bite this bullet in order to preserve our tidy formal definition of deception. (Don Fallis and Peter J. Lewis write: "Although we agree [...] that it seems deceptive, we contend that the mating signal sent by a [predator] is not actually misleading or deceptive [...] not all sneaky behavior (such as failing to reveal the whole truth) counts as deception".)

Personally, I don't care much about having tidy formal definitions of English words; I want to understand the general laws governing the construction and perversion of shared maps, even if a detailed understanding requires revising or splitting some of our intuitive concepts. (Cailin O'Connor writes: "In the case of deception, though, part of the issue seems to be that we generally ground judgments of what is deceptive in terms of human behavior. It may be that there is no neat, unitary concept underlying these judgments.")

Whether you choose to describe it with the signal/word "deceptive", "sneaky", Täuschung, הונאה, 欺瞞, or something else, something about P. redator's signal usage has the optimizing-for-the-inaccuracy-of-shared-maps property. There is a fundamental asymmetry underlying why we want to talk about a mating signal rather than a 2/3-mating-1/3-prey-luring signal, even if the latter is a better description of the information it conveys.

Brian Skyrms and Jeffrey A. Barrett have an explanation in light of the observation that our sender–receiver framework is a sequential game: first, the sender makes an observation (or equivalently, Nature chooses the type of sender—mate, predator, or null in the story about fireflies), then the sender chooses a signal, then the receiver chooses an action. We can separate out the propositional content of signals from their informational content by taking the propositional meaning to be defined in the subgame where the sender and receiver have a common interest—the branches of the game tree where the players are trying to communicate.

Thus, we see that deception is "ontologically parasitic" in the sense that holes are. You can't have a hole without some material for it to be a hole in; you can't have a lie without some shared map for it to be a lie in. And a sufficiently deceptive map, like a sufficiently holey material, collapses into noise and dust.


I changed the species names in the standard story about fireflies because I can never remember which of Photuris and Photinus is which.

Fallis, Don and Lewis, Peter J., "Toward a Formal Analysis of Deceptive Signaling"

O'Connor, Cailin, Games in the Philosophy of Biology, §5.5, "Deception"

Skyrms, Brian, Signals: Evolution, Learning, and Information, Ch. 6, "Deception"

Skyrms, Brian and Barrett, Jeffrey A., "Propositional Content in Signals"


Efficient Market Frontier

23 августа, 2020 - 02:52
Published on August 22, 2020 11:52 PM GMT

Recently I gave a talk on EMH: https://www.lesswrong.com/posts/3TiEZzw4ikneLGp4J/dissolving-the-is-the-efficient-market-hypothesis-dead

In there I had a bonus slide about what I call "Efficient Market Frontier" (EMF). In this post I want to expand on EMF, since I feel like a lot of disagreements and conversations around EMH and the market in general can be helped with this.

But first I hope you read my previous post: "The Holy Grail" of portfolio management. The importance of uncorrelated strategies will play an important part here.

Also, by request, here's a quick recap of most technical terms I use in this post:

  • Market: the set of things you're trading.
  • Backtest: see how your strategy would have performed historically by simulating it on relevant data.
  • Liquidation: losing all your money (but for certain specific reasons not relevant here)
  • Short(ing): betting on the asset price going down.
  • Limit order: you commit to buying if the price goes sufficiently low. If the price never goes that low, your order is not filled. (The flip side is selling if the price goes sufficiently high.)
The market is a game

I also want to reiterate an important assumption I've made in my talk. It will no doubt color my post and I think it might be somewhat contentious, so better state it upfront.

I think fundamentally the market is a game. You're in it to make money. At the end of the day you can have all the theory and correct pricing and best backtested strategies, but if the market "mistakenly" decides Zoom Technologies stock should go up, and you short it because "haha the market is so wrong" and get liquidated as more "mistaken" investors pile on... Well, then it seems like you lost that game.

In this view the primary reason to predict a "correct" price of anything is because it will help you make profitable trades. It's not because you believe that Tesla is worth $350.09B.

How do you play the game?

Your strategy is how you play the game. I'll define a strategy as an algorithm made of three parts: trigger, positioning and exit.

(You can and should, of course, run multiple strategies. How they interact together was covered my previous post. For now let's focus on just one strategy.)

Trigger: this is what makes you consider a trade in the first place. If you have an automated algorithm that runs every minute, then the end of a minute bar is the trigger. Or if you're occasionally paying attention to interesting news then it's: "I notice an interesting piece of news that makes me consider a trade."

Positioning: this is how you determine what you're actually buying / selling, how much, and how you're adjusting it while in the trade. Do you buy a stock or buy options? Do you enter the trade slowly or all at once? Do you double down when the market moves against you? Do you let it ride or slowly take profit? Or maybe you just play it by ear, in which case that's the strategy: "I use my brain to determine what do in every case."

Exit: how do you finally close this position? Do you have a time horizon (like exiting after two years)? Do you have a stop loss? Do you take profit? Is there an external event you're waiting for? Do you exit when you can't bear the pain of a losing position? Or do you exit when it feels right?

One trade can be described as one full cycle through trigger, positioning and exit. You haven't really made money, i.e. the trade isn't profitable, until you've exited your position.

Strategy types

Obviously I can't cover them all, but the natural categories that stand out to me are: intuitive, algorithmic, formulary and technological. I think these are natural categories because they have specific niches where they excel. (More on that later.)

Intuitive: you just use your intuition. Take in all the information and use your brain to make the best call you can. Your strategy will be as unique as you are.

Example 1: AI development might lead to some companies getting a crazy advantage. Think through what those companies might be and buy their stock.

Example 2: You think you have a pretty good sense for great design, so any time you run across a well designed product you buy the stock of the company that made it.

Example 3: You heard about this cryptocurrency thing. Sounds like a ponzi scheme, but you threw in $100 because why not.

Algorithmic: you code it up. In practice there's no 100% algorithmic strategy. Usually there's still some manual discretion, but you can come close.

Example 1: all technical indicators, e.g. MACD.

Example 2: train a RL model and just let it trade.

Example 3: whenever Elon Musk tweets, buy Tesla stock and Dogecoin.

Formulary: this a mix between intuitive and algorithmic. (I came up with the name for this category. Is there a better one?) You define the strategy as rigorously as you can, like you would for an Algorithmic strategy. But the components are hard / impossible to evaluate using code, so you need to use your brain.

Example 1: when a company goes public, I will evaluate the CEO on a rigidly defined 10 point criteria. Each point is somewhat subjective, but I believe I can evaluate it pretty consistently across different CEOs. If they get a score of at least 8, I buy the stock.

Example 2: when a company's stock goes down because of some PR nightmare, I will evaluate if I think it's an overreaction. If it is, I will buy the dip.

Example 3: when I feel like a market is panicking -- there's a "blood in the streets" feeling -- I wait a day and then start buying into that market a bit every day until it recovers.

Technological: this is based on getting a technological advantage over your opponents. You're doing what they are doing just better, where "better" usually just means faster.

Example 1: most of high-frequency trading.

Example 2: many forms of arbitrage. This is where you make trades that are "guaranteed" to be profitable. For example, you buy BTC on Bitmex for $10,000 while at the same time selling it on Huobi for $10,050. You need the best infrastructure because you're competing with other people to make the exact same trade for the exact same reasons.

Example 3: take satellite pictures of Walmart parking lots to estimate their sales for this quarter.

Finally: EMF

You're at the Efficient Market Frontier when your strategy dominates your opponents' strategies. The "size" of your edge is roughly how far you're advancing that frontier.

By the way, it's not true that if you made a winning trade the person you traded against made a losing trade. Even if your entry was their entry into an opposite position, their positioning and exit could still make it a profitable trade for them (not to speak of how this trade interacts with their other trades).

Now I bet the definition doesn't sound quite helpful yet, so let's have a demonstration... in THE ARENA!

In the left corner, the Intuitive strategy, weighing at 3 pounds of pure gray matter. Facing off against the Algorithmic strategy! Weighing at 5 pounds of pure silicon this newcomer is growing fast and itching to prove its merit. Fight!

Intuitive: (mid 2019) Wow, the crypto market sure has been going up a lot. The bulls are back in town! Buy, buy, buy!

Algorithmic: I have defined 12 types of bull markets. For each one I have calculated the distributions of their durations as well as 100 other factors that predict its end. This model has been backtested all the way to the beginning of the crypto market. The expected sharpe ratio is 2.3 and the probability of any given trade being successful is 52%. My current output is to go short.

Point for Algorithmic! Intuitive 0 : 1 Algorithmic

Intuitive: (2016) Hmm, this new cryptocurrency thing seems like a pretty different beast. I think over the long run there's a good chance it'll find its niche in the current economy. I think that niche is likely to be at least as important as gold. This means the market is just beginning to grow. I'm going to buy and hold for 5 years.

Algorithmic: I have nothing to say on this. Nobody programmed me to evaluate never-before-seen asset classes. Also there's no way I'm holding the same position for 5 years, that's not what I was built to do.

Point for Intuitive! Intuitive 1 : 1 Algorithmic

Intuitive: I decided to buy some bitcoin, but I placed some limit orders so I can get filled when the price goes down a bit. It goes up and down all the time, so I'm sure I can buy it at a discount.

Algorithmic: I have 25 TB of orderbook and trades data for this exchange. I have 35 different algorithms providing liquidity on both sides. I readjust my orders every 10 ms and can sense a mile away when a whale tries to move the market and adjust my limit orders accordingly. If I don't, I know my orders will get filled as the most inconvenient time: I'll be buying BTC right as the market is going down.

Point for Algorithmic! Intuitive 1 : 2 Algorithmic

Intuitive: (2020-03-12) The bitcoin price just crashed 50% in less than a day. This is an extremely unusual event the market hasn't seen in a long time. By my estimate most of the leveraged long positions have been liquidated. Long term I'm still bullish on bitcoin, so I'll start buying right now until the market recovers.

Algorithmic: The market crash was predictable and I made a lot of money. Now the volatility has piqued and we've entered a regime I haven't seen before. I'm doing my best, but I haven't been fed the liquidation data, so I'm not sure what's going on there. However, I have noticed the volume in the market increase and historically that has led to short term momentum moves. I'm going to trade accordingly.

Tie! Intuitive 1 : 2 Algorithmic

Intuitive: Hmm, I just read about this virus that killed a few people in China. Seems pretty serious if true.

Algorithmic: There's very little historic data on how pandemics affect the market, but it's probably a good idea to bet on increased volatility. I'm programmed to scan for news about pandemics but so far I haven't noticed a spike in the news article count.

Point for Intuitive! Intuitive 2 : 2 Algorithmic

I hope you get the idea!

Intuitive strategies excel in novel situations which are hard to evaluate algorithmically. Topical examples are: cryptocurrency, pandemics, and other rare events.

Algorithmic strategies excel in doing trades that could have been performed 1000+ times historically. That way you can backtest these strategies and have a pretty high confidence that they'll continue working. For most markets this means most of the time there's little advantage for the Intuitive strategy.

Any time an Intuitive strategy is trying to do something Algorithmic it is at a disadvantage. By the way this includes 99% of trading videos on YouTube. Any time you're trading using indicators or bar patterns or chart lines, you're likely going up against an algorithm that's doing the same thing, except it has the benefit of having been trained on the most optimal parameters over 1000+ such trades.

The difficulty with Intuitive strategies is that they are basically impossible to backtest. You just don't really know what you would have done back then using the information you would have had at the time. And a lot of the time there's no clear back then either, because it's a novel situation.

But it's even worse than that! You don't know your trigger. If it's "anytime I feel like trading" then this will often coincide with other people trading for the same reason: they heard the same news, their portfolio was affected in the same way, or something else.

This also means that one successful intuitive trade is just not that impressive. (Same as winning one roulette roll.) If that's the strategy you're going to use, you don't know in what other situations it'll fire off and pull you into the market.

This is where you can take your Intuitive strategy and move it towards Formulary. This might give you some ability to backtest, but it'll still have to be done manually. But even if you check your trade across 10-100 similar cases, that'll give you some evidence of your strategy's efficacy. It will also help prevent random events triggering your trades.

With an Intuitive strategy you have no idea if you're at EMH because you can't backtest. You can only start measuring it from the time you decide to measure. And your confidence in your strategy will grow over time as you make consistently profitable bets.

With Algorithmic strategies it's much easier to test if you're at EMH. You just backtest them. Unless you overfit on the historic data your strategies should have a similar performance going forward. (Until one day they don't and then you hope that not all of them broke at the same time.)

More edges

To a large extent your strategy type defines the strengths and weaknesses of your strategy. But there are additional things you can do to help or hinder it.

  • Acting on easily accessible information
    • If this information can be easily scraped and analyzed (all price data, news sentiment, etc..) then the Algorithmic strategies will likely have incorporated it already.
    • If it takes some intelligence, creative thinking and rationality to fully understand the implications of this information, then it will take a while for the market to react to it, especially if this kind of information is rare.
    • Relatedly, if the information is not easily accessible it's more likely that you're running a strategy that other people aren't running.
  • Using known algorithms
    • If you're doing what other people are doing you need the Technological advantage.
  • Backtest-able
    • If you strategy is backtest-able but you didn't backtest it, you're at a disadvantage to those who did.
  • Code-able
    • If your strategy (or a close approximation) can be coded up, but you're executing it manually, you might be at a speed disadvantage. You're also likely at a disadvantage because parameter tuning is much easier done with coding assistance.
  • Time scale
    • If your strategy is trading at very short timescale you need to go more Algorithmic. (You need the speed plus shorter time scales mean there's more data for backtesting.)
    • If your strategy is very long term, it'll benefit more from the Intuitive approach, since there's essentially less data for Algorithmic approaches to use.
  • Quick feedback
    • If you can get quick feedback on a strategy, you're at a disadvantage as soon as you start using this strategy to those who have used it before you and used the feedback to adjust.
    • With slow feedback strategies it's a lot more important to get it right the first time. This is where your brain can help you.
  • Palatable risk
    • Some strategies have very hard to stomach risk profiles. For example, betting on mean reversion or volatility often results in months if not years of slowly bleeding money until you make it all back plus some in a few days. Most investors can't stick to this strategy even if they are using an Algorithmic strategy.
  • Understandable
    • If your strategy makes no sense, it's less likely that other people have discovered it. That means there's less competition in trading it.
  • Explainable
    • If you strategy is hard to explain, you might not be able to run it in the environment where you have to explain yourself to higher-ups / investors. This goes double for being able to "explain" the drawdowns. During those critical times you have to make the hard decision of cutting the strategy or letting it ride. If you don't know why it's supposed to work, you'll probably cut it.
  • Complexity of decision
    • Even if your strategy is code-able in principle, if there are a lot of moving parts it's unlikely someone found it without the corresponding intuitive insight.
  • Strategy capacity / market size
    • This is how much money you can trade using a given strategy while it still remains profitable. Almost all strategies have a cap. For example, if you're operating in a low volume market, most strategies will have a low capacity. This is great if you're not trading a lot of money, since you can analyze and fight in this domain. Most big competitors will stay out (it's not worth it for them to participate) or will treat this market using their standard tools, ignoring potential domain-specific quirks.
  • Speed of decision
    • If you're trading by yourself, then you can put on a trade as soon as you decide to. If you're reporting to someone else, it might take more time, especially if your suggestion is unusual. For example, I think this was probably a big factor in the case of COVID. Even if some trading institutions realized that they should react to the virus, its aberrant nature delayed their response time enough so that others who didn't have to explain their decisions could act first.
Wrap up

Now given many common strategies you can go through the list and see if you can tweak it to give it a bigger edge. If it dominates other strategies that people are running in the market, you'll make money over time and you can be more and more certain you're near EMF. In fact, if you're consistently "beating" the market, you're the EMF. You're the reason the market is "efficient". But as the regimes, strategies and players change, so will the EMF frontier, so you better adjust accordingly.


Woop! Woop! Tagging Progress Bar is at 100% (Celebration on Sun, Aug 30th, 12pm PDT)

22 августа, 2020 - 19:45
Published on August 22, 2020 4:45 PM GMT

Tagging Open Call / Discussion

There will be an online Tagging Progress Bar Completion Party on Sunday, August 30th at 12:00PM PDT. We are planning a public conversation about intellectual progress followed by a party/meetup. Details forthcoming.


Probabilities can't be 100%, but progress bars can be. 

I am delighted to announce the Tagging Progress Bar which has graced the LessWrong front page for three weeks is now gloriously full. Every post with over 25 karma now has at least one tag applied to it, most having several. 

Top among of the goals of tagging was to explicitly shape LessWrong more towards long-term intellectual progress: with a major pass at tagging the archives completed, it is now vastly easier to see what LessWrong has discussed over the years, find things you didn't know to search for, locate posts based on interest rather than recency or karma, catch-up on everything said on a topic to date, and generally continue the conversation on topics not currently on the frontpage.

Authors and commenters will hopefully now post to LessWrong secure in the knowledge that their posts and comments will not be forgotten after a week or two. Instead, the best content on each topic will be discoverable for years to come.

Thank you, taggers!!

An enormous shout-out goes out the tagging contributors. Everyone who tagged posts, created tags, wrote tag descriptions, voted tags, or just offered feedback. Tagging is not something the LessWrong team could have rolled out on our own, we needed your help and all users of the tagging system are indebted to you.

The Tag Numbers

To give you a sense of what was accomplished, here are some basic numbers (as of publishing):

  • 14,028 tags applied
  • 7,427 posts have been tagged
  • 419 active tags created
Top Taggers

The tagging system doesn't yet have good recognition and reward systems in place, so unfortunately these taggers haven't gotten the karma they deserve. Still, everyone should know these are the folks whose efforts brought us success.

Top Tag Appliers

Top Tag Creators & Description Writers

As thanks, these top taggers will each receive the professionally-edited, physical five-book set of posts from the 2018 Review. A fitting prize, since that project was also about long-term intellectual progress.

Party time!

We're overdue for a party regardless, this occasion definitely deserves an event.

Please join us Sunday 30th Augusts at 12:00 PM PDT. We're planning to a public conversation on Intellectual Progress with eminent speakers followed by a Gather.town meetup. Stay tuned for details/links.

This isn't even my final forum

Giving all historical posts above 25 at least one tag is a huge milestone but, like a credence, Tagging isn't something that reaches 100%. There's more to be done.

  • Each day, users are finding new concepts or clusters of posts worthy of having their own tags. There are 400 tags right now, and my personal guess there are many, many more to still be created.
  • Some tags should be merged, others split into two or more. Especially some of the large tags harbor several sub-clusters within them that could be identified and separated.
  • Many tagged post don't have every appropriate tag. The most appropriate tag might not have been created.
  • Most tags haven't had their posts list carefully sorted to the best and most relevant posts at the top.
  • Most tags do not have even good basic descriptions. Either there is no description, the description is a poor explanation, or the description fails to link to other relevant tags.
  • Keeping the tagging system high quality means doing "maintenance" by removing bad tags and tags that don't really apply to posts.

If tagging is to succeed long-term, it'll depend on the ongoing effort of those generous enough to make it good even when it's no longer in the spotlight.

I think we'll have to give trusted users Tagging Moderator privileges that lets them merge/split/delete tags as part of doing the needed work.

Tag Discussion/Talk Pages

I've promised them in several places, and they're nearly here. Soon, tags will have discussion pages on which users can discuss that tag: What should it be called? what's the actual definition? Should this post be included? Those kinds of questions.

I hope this will make it much easier for taggers to coordinate with each other, and for others to see the tagging efforts and offer feedback on them.

Tagging Activity in Recent Discussion Feed

We've also done some work to have tagging activity such as major edits to tag descriptions appear in Recent discussion as something that commented and voted upon. Hopefully, that works out, giving more ongoing visibility into tagging work.

Final thanks

Hard to say it too many times, so thanks yet again to everyone who's made tagging awesome. You're awesome.