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What Resources on Journal Analysis are Available?

28 декабря, 2019 - 23:00
Published on December 28, 2019 8:00 PM UTC

TL;DR I keep a fancy journal and was wondering what people know about what techniques people use to navel-gaze their writings.

I keep a journal on my computer. I put ideas, writing, and diary entries in it. Basically, anything I write is put in the journal. Every entry is timestamped, has hashtags, points to the entry that generated it, and is separated by a newlines and '---' strings. This allows for a non-linear structure to emerge with time. I can also cite older entries which allows me to reuse older entries. For example, I might start a branch of thinking on 'bananas' and then cite those entries later in writing about 'genetic modification' even though they might inhabit different areas of the journal.

Overall, this is roughly analogous to the Zettelkasten Method that's been discussed here previously. The difference is that I use homebrew scripts together with a markdown processor to automate management of tedious details such as timestamps, parent pointer, and organization.

Once I got over 100k words I started to realize that I needed to partition the journal. This will make a lot more sense when I present a visualization of the journal. The data structure being used is a tree so it was most natural to use a phylogenetic tree (older is cooler in color)

Using this model it became apparent that I could approximate the growth probabilities fairly well by looking at how recent the entry was and how close it was to the leaves of the tree. Using similar logic, I was also able to cut the journal into significant branches. This allowed to me to add entries to a smaller journal with about ~50k words making rendering easier on my laptop. All the changes are synchronized with the main journal still, I can just work on branches directly instead of loading the entire journal every-time I want to make a change.

Up to this point, this analysis has spurred mostly out of necessity. When I experimentally switched from the linear format to the non-linear format I quickly realized that it was too good to ever go back. However, it's not practical to maintain a single journal over long periods of time. Now that this problem has been mostly solved, I've had some wilder ideas about things I could do. I wonder what other techniques people use to analyze their own journaling/diary. Is there a resource on how to do journal analysis or am I stuck copy/pasting techniques from the NLP and citation network people? Thanks!



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Stupidity and Dishonesty Explain Each Other Away

28 декабря, 2019 - 22:21
Published on December 28, 2019 7:21 PM UTC

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The explaining-away effect (or, collider bias; or, Berkson's paradox) is a statistical phenomenon in which statistically independent causes with a common effect become anticorrelated when conditioning on the effect.

In the language of d-separation, if you have a causal graph X → Z ← Y, then conditioning on Z unblocks the path between X and Y.

Daphne Koller and Nir Friedman give an example of reasoning about disease etiology: if you have a sore throat and cough, and aren't sure whether you have the flu or mono, you should be relieved to find out it's "just" a flu, because that decreases the probability that you have mono. You could be inflected with both the influenza and mononucleosis viruses, but if the flu is completely sufficient to explain your symptoms, there's no additional reason to expect mono.[1]

Judea Pearl gives an example of reasoning about a burglar alarm: if your neighbor calls you at your dayjob to tell you that your burglar alarm went off, it could be because of a burglary, or it could have been a false-positive due to a small earthquake. There could have been both an earthquake and a burglary, but if you get news of an earthquake, you'll stop worrying so much that your stuff got stolen, because the earthquake alone was sufficient to explain the alarm.[2]

Here's another example: if someone you're arguing with is wrong, it could be either because they're just too stupid to get the right answer, or it could be because they're being dishonest—or some combintation of the two, but more of one means that less of the other is required to explain the observation of the person being wrong. As a causal graph—[3]

Notably, the decomposition still works whether you count subconscious motivated reasoning as "stupidity" or "dishonesty". (Needless to say, it's also symmetrical across persons—if you're wrong, it could be because you're stupid or are being dishonest.)

  1. Daphne Koller and Nier Friedman, Probabilistic Graphical Models: Principles and Techniques, §3.2.1.2 "Reasoning Patterns" ↩︎

  2. Judea Pearl, Probabilistic Reasoning in Intelligent Systems, §2.2.4 "Multiple Causes and 'Explaining Away'" ↩︎

  3. Thanks to Daniel Kumor for example LATEX code for causal graphs. ↩︎



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Measuring Epistemic Rationality

28 декабря, 2019 - 21:46
Published on December 28, 2019 6:46 PM UTC

Collecting some previous discussions on the matter, trying to reach a conclusion that would benefit me in my path to be more rational. Written with haste to serve as a reference for myself, possibly useful for you, and to be more accountable to be methodological in my rationality self-improvement.

1. The Rationality Checklist from CFAR has many rationality habits listed. I can go through it once in a while to feel progress, or focus on one that I feel I need to improve more and incorporate to my daily routine. (say- " I notice my mind flinching away from a thought; and when I notice, I flag that area as requiring more deliberate exploration. "). I'm not sure that I trust myself to reflect accurately. This is also "Not meant to be a way to get a ‘how rational are you?’ score, but, rather, to help you notice specific habits you might want to develop. "

2. Aliveness in Training - Gives the analogy of a Martial Artist, that improves by working against an opponent. I have three thoughts -

i. Work with a mentor, seriously consider criticism from friends, write down more of my beliefs publicly.

ii. Measure my research success. (Correlated with lots of other things, and unclear, but that's actually what I care about)

iii. Make lot's of predictions

3. In Swimming Upstream, TurnTrout gives his personal experience with instrumental rationality. Parts of this story makes me think that I can measure how seriously I take my beliefs by collecting my own decisions and counting how many of them were due to newly generated beliefs. Not sure about that, because I may be biased in the wrong way here. I think that a fine idea here is to track all decisions made, and their reasons, and afterwards I'll understand how to analyze them.

4. It turns out that there is a Rationality Quotient, by Keith Stanovich. I did not find the test, but I feel aversion toward it as a method to improve rationality. I think it's because I visualize myself Goodharting to it. It might be very useful as a one-time diagnostic tool.

In total, I think that I should

  • be systematic in analyzing my shortcomings
  • work on rationality habits like any other skill/habit
  • use my intuition to guide me to the most important skills I need to develop
  • be more clear about my reasoning, and seek criticism and help when communicating
  • track predictions and world view better


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How was your decade?

28 декабря, 2019 - 20:14
Published on December 28, 2019 5:14 PM UTC

Hi everybody,

With the New Year coming up and next year being 2020, I think it's worth reflecting on the last 10 years. As you probably remember the last Sequences post came out towards the end of the decade. How have things gone for you since then? If you're willing to share, here's some things to consider. You can answer as much or as little of this as you want, don't let it scare you off from posting:

Before & After

How were things for you at the start and end of the decade? Some categories to ponder:

  • Philosophy
  • Skills
  • Career
  • Lifestyle
Between

How did things change over time? What was your progression through the 2010's?

  • What made you say "oops" and "duh"? What did you update on?
  • How have your preferences changed over time? (e.g. Music, Food)
  • Did you break any promises? Did you keep some difficult ones?
  • Do you have any new habits? Did you break some?
Experiences

How did you experience the last 10 years? What proportion of the time was spent on good experiences vs. bad? Any notable highlights?

Worth Noting
  • When did you first read The Sequences, if ever?
  • Are you overall satisfied or dissatisfied with how the decade went?
  • Is there any advice you'd give to your past self? How do you think they'd react to it? (Just so we don't have to read it 30 times, yes, you would buy bitcoin)
  • What do you think the zeitgeist of this decade was? What aspects and events do you think you'll remember the 2010's for? What do you think society at large will remember the 2010's for?

And of course if there's anything else you'd like to share, feel free. :)



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Moloch Hasn’t Won

28 декабря, 2019 - 19:30
Published on December 28, 2019 4:30 PM UTC

This post begins the Immoral Mazes sequence. See introduction for an overview of the plan. Before we get to the mazes, we need some background first.

Meditations on Moloch

Consider Scott Alexander’s Meditations on Moloch. I will summarize here. 

Therein lie fourteen scenarios where participants can be caught in bad equilibria.

  1. In an iterated prisoner’s dilemma, two players keep playing defect. 
  2. In a dollar auction, participants massively overpay.
  3. A group of fisherman fail to coordinate on using filters that efficiently benefit the group, because they can’t punish those who don’t profi by not using the filters.
  4. Rats are caught in a permanent Malthusian trap where only those who do nothing but compete and consume survive. All others are outcompeted.
  5. Capitalists serve a perfectly competitive market, and cannot pay a living wage.
  6. The tying of all good schools to ownership of land causes families to work two jobs whose incomes are then captured by the owners of land.
  7. Farmers outcompeted foragers despite this perhaps making everyone’s life worse for the first few thousand years.
  8. Si Vis Pacem, Para Bellum: If you want peace, prepare for war. So we do.
  9. Cancer cells focus on replication, multiply and kill off the host. 
  10. Local governments compete to become more competitive and offer bigger bribes of money and easy regulation in order to lure businesses. 
  11. Our education system is a giant signaling competition for prestige.
  12. Science doesn’t follow proper statistical and other research procedures, resulting in findings that mostly aren’t real.
  13. Governments hand out massive corporate welfare.
  14. Have you seen Congress? 

Scott differentiates the first ten scenarios, where he says that perfect competition* wipes out all value, to the later four, where imperfect competition only wipes out most of the potential value.

He offers four potential ways out, which I believe to be an incomplete list:

  1. Excess resources allow a temporary respite. We live in the dream time.
  2. Physical limitations where the horrible thing isn’t actually efficient. He gives the example of slavery, where treating your slaves relatively well is the best way to get them to produce, and treating them horribly as in the antebellum South is so much worse that it needs to be enforced via government coordination or it will die out.
  3. The things being maximized for in competitions are often nice things we care about, so at least we get the nice things.
  4. We can coordinate. This may or may not involve government or coercion.  

Scott differentiates this fourth, ‘good’ reason from the previous three ‘bad’ reasons, claiming coordination might be a long term solution, but we can’t expect the ‘bad’ reasons to work if optimization power and technology get sufficiently advanced. 

The forces of the stronger competitors, who sacrifice more of what they value to become powerful and to be fruitful and multiply, eventually win out. We might be in the dream time now, but with time we’ll reach a steady state with static technology, where we’ve consumed all the surplus resources. All differentiation standing in the way of perfect competition will fade away. Horrible things will be the most efficient. 

The optimizing things will keep getting better at optimizing, thus wiping out all value. When we optimize for X but are indifferent to Y, we by default actively optimize against Y, for all Y that would make any claims to resources. Any Y we value is making a claim to resources. See The Hidden Complexity of Wishes. We only don’t optimize against Y if either we compensate by intentionally also optimizing for Y, or if X and Y have a relationship (causal, correlational or otherwise) where we happen to not want to optimize against Y, and we figure this out rather than fall victim to Goodhart’s Law

The greater the optimization power we put behind X, the more pressure we put upon Y. Eventually, under sufficient pressure, any given Y is likely doomed. Since Value is Fragile, some necessary Y is eventually sacrificed, and all value gets destroyed.

Every simple optimization target yet suggested would, if fully implemented, destroy all value in the universe. 

Submitting to this process means getting wiped out by these pressures.

Gotcha! You die anyway.

Even containing them locally won’t work, because that locality will be part of the country, or the Earth, or the universe, and eventually wipe out our little corner.

Gotcha! You die anyway.

Which is why the only ‘good’ solution, in the end, is coordination, whether consensual or otherwise. We must coordinate to kill these ancient forces who rule the universe and lay waste to all of value, before they kill us first. Then replace them with something better. 

Great project! We should keep working on that. 

That’s Not How This Works, That’s Not How Any of This Works

It’s easy to forget that the world we live in does not work this way. Thus, this whole line of thought can result in quite gloomy assessments of how the world inevitably always has and will work, such as this from Scott in Meditations on Moloch:

Suppose the coffee plantations discover a toxic pesticide that will increase their yield but make their customers sick. But their customers don’t know about the pesticide, and the government hasn’t caught up to regulating it yet. Now there’s a tiny uncoupling between “selling to Americans” and “satisfying Americans’ values”, and so of course Americans’ values get thrown under the bus.

Or this from Raymond, taken from a comment to a much later, distinct post, where ‘werewolf’ in context means ‘someone trying to destroy rather than create clarity as the core of their strategy’:

If you’re a king with 5 districts, and you have 20 competent managers who trust each other… one thing you can do is assign 4 competent managers to each fortress, to ensure the fortress has redundancy and resilience and to handle all of its business without any backstabbing or relying on inflexible bureaucracies. But another thing you can do is send 10 (or 15!) of the managers to conquer and reign over *another* 5 (or 15!) districts.

This is bad if you’re one of the millions of people who live in the kingdom, who have to contend with werewolves.

It’s an acceptable price to pay if you’re actually the king. Because if you didn’t pay the price, you’d be outcompeted by an empire who did. And meanwhile it doesn’t actually really affect your plans that much.

The key instinct is that any price that can be paid to be stronger or more competitive, must be paid, therefore despair: If you didn’t pay the price, you’d be out-competed by someone who did. People who despair this way often intuitively are modeling things as effectively perfect competition at least over time, which causes them to think that everything must by default become terrible, likely right away.

So many people increasingly bemoan how horrible anything and everything in the world is, and how we are all doomed.

When predictions of actual physical doom are made, as they increasingly are, often the response is to think things are so bad as to wish for the sweet release of death. 

Moloch’s Army: An As-Yet Unjustified But Important Note

Others quietly, or increasingly loudly and explicitly to those who are listening, embrace Moloch.

They tell us that the good is to sacrifice everything of value, and pass moral judgments on that basis. To take morality and flip its sign. Caring about things of value becomes sin, indifference becomes virtue. They support others who support the favoring of Moloch, elevating them to power, and punish anyone who supports anything else.

They form Moloch’s Army and are the usual way Moloch locally wins, where Moloch locally wins. The real reason people give up slack and everything of value is not that it is ever so slightly more efficient to do so, because it almost always isn’t. It is so that others can notice they have given up slack and everything of value.

I am not claiming the right to assert this yet, as not only citation but entire post or sequence needed that is yet unwritten because it’s hard to get right, so please don’t object that I haven’t justified it! But I find it important to say this here, explicitly, out loud, before we continue. 

I also note that I explicitly support the implied norm of ‘make necessary assertions that you can’t explicitly justify if they seem important, and mark that you are doing this, then go back and justify them later when you know how to do so, or change your mind.’ It also led to this post, which led to many of what I think are my best other posts.

Meditations on Elua

The most vital and important part of Meditations on Moloch is hope. That we are winning. Yes, there are abominations and super-powerful forces out there looking to eat us and destroy everything of value, and yet we still have lots of stuff that has value. 

Even before we escaped the Malthusian trap and entered the dream time, we still had lots of stuff that had value. 

Quoting Scott Alexander:

Somewhere in this darkness is another god. He has also had many names. In the Kushiel books, his name was Elua. He is the god of flowers and free love and all soft and fragile things. Of art and science and philosophy and love. Of niceness, community, and civilization. He is a god of humans.

The other gods sit on their dark thrones and think “Ha ha, a god who doesn’t even control any hell-monsters or command his worshippers to become killing machines. What a weakling! This is going to be so easy!”

But somehow Elua is still here. No one knows exactly how. And the gods who oppose Him tend to find Themselves meeting with a surprising number of unfortunate accidents.

Moloch gets the entire meditation. Elua, who has been soundly kicking Moloch’s ass for all of human existence, gets the above quote and little else. 

Going one by one:

Kingdoms don’t reliably expand to their breaking points.

Poisons don’t keep making their way into the coffee.

Iterated prisoner’s dilemmas often succeed.

Dollar auctions are not all over the internet.

Most communities do get most people to pitch in.

People caught in most Malthusian traps still usually have non-work lives.

Capitalists don’t pay the minimum wage all that frequently.

Many families spend perfectly reasonable amounts on housing.

Foragers never fully died out, also farming worked out in the end.

Most military budgets seem fixed at reasonable percentages of the economy, to the extent that for a long time we’ve been mad at our allies like Europe and Japan that they don’t spend enough.

Most people die of something other than cancer, and almost all cells aren’t cancerous.

Local governments enact rules and regulations that aren’t business friendly all the time.

Occasionally, someone in the educational system learns something.

Science has severe problems, but scientists are cooperating to challenge poor statistical methods, resulting in the replication crisis and improving statistical standards.

Governments are corrupt and hand out corporate welfare, but mostly are only finitely corrupt and hand out relatively small amounts of corporate welfare. States that expropriate the bulk of available wealth are rare. 

If someone has consistently good luck, it ain’t luck.

(Yes, I have seen congress. Can’t win them all. But I’ve also seen, feared and imagined much worse Congresses. For now, your life, liberty and property are mostly safe while they are in session.)

(And yes the education exception is somewhat of a cop out but also things could be so much worse there on almost every axis.)

The world is filled with people whose lives have value and include nice things. Each day we look Moloch in the face, know exactly what the local personal incentives are, see the ancient doom looming over all of us, and say what we say to the God of Death: Not today.

Saying ‘not today’ won’t cut it against an AGI or other super strong optimization process. Gotcha. You die anyway. But people speak and often act as if the ancient ones have already been released, and the end times are happening now.

They haven’t, and they aren’t. 

So in the context of shorter term problems that don’t involve such things, rather than bemoan how eventually Moloch will eat us all and how everything is terrible when actually many things are insanely great, perhaps we should ask a different question. 

How is Elua pulling off all these unfortunate accidents?

*As a technical reminder we will expand upon in part two, perfect competition is a market with large numbers of buyers and sellers, homogeneity of the product, free entry and exit of firms, perfect market knowledge, one market price, perfect mobility of goods and factors of production with zero transportation costs, and no restrictions on trade. This forces the price to become equal to the marginal cost of production.  



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Was there a "memetic collapse"?

28 декабря, 2019 - 08:36
Published on December 28, 2019 5:36 AM UTC

I want to know if there has actually been a "memetic collapse" along the lines described here and here.

Does anyone have evidence or arguments in either direction? Or even ideas for how we would be able to tell?



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Out in the Great Northwest

28 декабря, 2019 - 06:40
Published on December 28, 2019 3:40 AM UTC

One of the songs my family would sing growing up was "Out in the Great Northwest" ( mp3, 2011 recording). For me, the point of the song was the verse: A Scotsman went out there to live, he called his house a "hoos".
They showed him a great big animal, they said it was a moose.
Out in the great Northwest! Way out in the great Northwest!
The Scotsman said, "Now, what the deuce! You say you call that thing a moose!
I'd hate to see a rrrat get loose!" Out in the great Northwest. My cousin Valerie wrote up a score ( pdf), and the full lyrics of our version are: I'm going way out West to where the buffalo used to roam.
I'll buy a big ten-gallon hat and build myself a home.
Out in the great Northwest! Way out in the great Northwest!
For men are men out there I swear, they wrestle with a grizzly bear.
Punch his nose and comb his hair, out in the great Northwest.

They have a brand of climate there, that puts hair on your chest.
You take a great big breath and bust the buttons off your vest.
Out in the great Northwest! Way out in the great Northwest!
A lady by the name of Weeks, was fond of swimming in the creeks
But she forgot the mountain peaks, out in the great Northwest

A Scotsman went out there to live, he called his house a "hoos".
They showed him a great big animal, they said it was a moose.
Out in the great Northwest! Way out in the great Northwest!
The Scotsman said, "Now, what the deuce! You say you call that thing a moose!
I'd hate to see a rrrat get loose!" Out in the great Northwest.

There are so many fishes there, in every mountain brook.
You have to hide behind a tree to get to bait your hook.
Out in the great Northwest! Way out in the great Northwest!
The rabbits there are very sly, they never go to school, but my,
How those things can multiply! Out in the great Northwest! I was trying to figure out where this came from, and I found it in the index of the Intercollegiate Outing Club Association's Song Fest (1948). Here are the lyrics as they appear in the seventeenth printing of the New Song Fest:

My grandfather Phil brought it back from the CPS Camps in Montana where he was smokejumping as a CO in the early 1940s, though, so it wouldn't have been via this book.

Looking for earlier references, I found a short newspaper clip from the Sunday December 6th, 1931, Ardmore Daily Ardmoreite:

I'm not sure why that's the piece they decided to quote; it seems to me to be one of the least interesting verses.

Looking more, I found a 1929 Columbia recording of Vernon Dalhart singing it. It lists J. E. Guernsey as the lyricist, and Floyd Thompson as the composer. It seems to have been reprinted on CD by the British Archive of Country Music (B.A.C.M. 17) in 2002, as track eight of Lindberg, the Eagle of the USA (WorldCat). I'm curious what it sounds like, and the CD seems to have other interesting things on it, so I've ordered a copy. We'll see!

Comment via: facebook



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Cambridge LW/SC Meetup

28 декабря, 2019 - 06:07
Published on December 28, 2019 3:07 AM UTC

This is the monthly Cambridge, MA Less Wrong / Slate Star Codex meetup.

The meetup is in apartment 2 (the website won't let me add the apartment number to the address).



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Meditation Retreat: Immoral Mazes Sequence Introduction

28 декабря, 2019 - 03:50
Published on December 28, 2019 12:50 AM UTC

I just got home from a six day meditation retreat and began writing.

The catch is that I arrived at the retreat yesterday.

I knew going in that it was a high variance operation. All who had experience with such things warned us we would hate the first few days, even if things were going well. I was determined to push through that.

Alas or otherwise, I was not sufficiently determined to make that determination stick. I didn’t have a regular practice at all going in, was entirely unfamiliar with the details of how this group operated, and found the Buddhist philosophy involved highly off putting, in a ‘no that’s not how it works that’s not how any of this works nor would we want it to’ kind of way. I am no negative utilitarian. I couldn’t focus, my meditations were entirely unproductive compared to past experience and were increasingly focusing on how terrible things were.

I am highly, highly confident that none of the people who warned me would be surprised by those developments, and similarly confident that they would all tell me to push through it. And will tell me, when I see them, that if I can do so I should try again. But I also realized that the anticipated reaction from others saying I didn’t give it a proper chance was the only reason I was considering not leaving. So I left.

To my surprise, those there said I was likely making a mature decision and were sympathetic. They spun it a little to try and get me not to give up in the future, but that was it, which was a really good look. It did not go unnoticed.

I took the bulk of the day to get home and relax, play a game, saw the excellent movie Knives Out. What I did realize was that yes, some combination of the Solstice plus the meditation retreat, even if I only did a few hours worth of sessions, did have a clarifying and motivating effect to get me back on track. I’m not unhappy I went, even though I bailed, because I was, in a much more practical and everyday very small sense, enlightened.

I’m also leaning towards being happy I left when I did. I do buy that there are serious effects that can only be gained from being out of feedback loops and in silence for several days, but my instincts (however motivated they may be) are strongly telling me that this is not the way for me to do that.

The other motivating part of this is that, while I will absolutely take the majority of tomorrow to enjoy the College Football Playoff, this is both my chance to be alone for a few days and also a time when I would otherwise be in hardcore meditation. It seems wrong to not accomplish something important that isn’t work or game related, to meditate in another way.

The goal is ideally to finish everything up, at least in draft-ready-to-adjust-for-comments-on-earlier-posts form, by the end of the retreat. That is a stretch, so the commit-to-it goal is to declare the first six posts finished and begin publishing them at a reasonable clip, and have momentum on seven and later.

The drafts that currently exist, that will be finalized and likely expanded upon, are the following:

  1. Moloch Hasn’t Won. Have you noticed that the world is in fact very much not a dystonian hellhole of Moloch-worshiping perfect competition and Elua’s enemies keep on having all those unfortunate accidents?
  2. Perfect Competition. Perfect competition, importantly, isn’t a thing, but you can get close. Let’s flesh this out more.
  3. Imperfect Competition. Some practical examples of imperfect competition. Intuition pumps and detailed examples for why perfect competition isn’t a thing and we don’t usually get that close.
  4. What is an Immoral Maze (note that I make a point to say Immoral rather than Moral)? Mazes need not be corporations or (in my current model in ways I’d have to introduce that aren’t in the draft right now, with a subset of the tech ecosystem as a motivating example) even formal organizations. What creates a maze? A system with multiple effective layers of hierarchy forcing its middle management into effectively super-perfect competition against each other largely on the basis of anticipated future success in such competition.
  5. What is Success in an Immoral Maze? There is no true success. What those inside think is success is anything but. Even if you win, you lose. Stay out, get out.
  6. How to Identify an Immoral Maze. Look at levels of hierarchy, skin in the game, soul in the game, how people describe their jobs, diversity of skill levels and degree of slack. Then pay attention, damn it.
  7. How to Interact with Immoral Mazes. They can’t be fully avoided, and some are stuck with them more than others. Practical discussion of what to do about this on a personal level.
  8. The Road to Mazedom. Well? How did we get here? Draft of this is still ongoing and it is freaking huge so it is probably going to get split up once we get to it. Also we need better answers on what to do about all this than what I have, even if it’s a start. Hard problem is hard!
  9. Moloch’s Army. This isn’t written and needs to go somewhere in the sequence or outside of it, or the whole operation is woefully incomplete. I need to finally write it. The devil’s greatest trick was never proving he didn’t exist, I wrote ten minutes ago, it was proving he’d already won, or would inevitably win. That only those who make deals with him get ahead, so they should implicitly or explicitly coordinate against those who don’t. Moloch has an army, who coordinate implicitly around fighting against anyone fighting for anything except Moloch’s Army, or anyone having any values. And this is how Moloch wins, where it wins. And also by making sure no one ever writes this, which makes this hard to write, etc etc. In that sense, it really is true that the Devil’s greatest trick is convincing people he doesn’t exist, because so far everyone I know who has figured this out has found it impossible to talk or write about this without sounding crazy to those who don’t already get it. Much careful background may be necessary. Darwin sequence was originally supposed to be a gateway to this but it wasn’t good enough on its own.

 



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Critiquing "What failure looks like"

28 декабря, 2019 - 02:59
Published on December 27, 2019 11:59 PM UTC

I find myself somewhat confused as to why I should find Part I of “What failure looks like” (hereafter "WFLL1") likely enough to be worth worrying about. I have 3 basic objections, although I don't claim that any are decisive. First, let me summarize WFLL1 as I understand it:

In general, it's easier to optimize easy-to-measure goals than hard-to-measure ones, but this disparity is much larger with ML models than with humans and human-made institutions. As special-purpose AI becomes more powerful, this will lead to a form of differential progress where easy-to-measure goals become optimized well past the point when they correlated with what we actually want.

(See also: this critique, although I agree with the existing rebuttals to it).

Objection 1: Historical precedent

In the late 1940s, George Dantzig invented the simplex algorithm, a practically efficient method for solving linear optimization problems. At the same time, the first modern computers were coming around, which he had access to as a mathematician in the US military. For Dantzig and his contemporaries, a wide class of previously intractable problems suddenly became solvable, and they did use the new methods to great effect, playing a major part in developing the field of operations research.

With the new tools in hand, Dantzig also decided to use simplex to optimize his diet. After carefully poring over prior work, and putting in considerable effort to obtain accurate data and correctly specify the coefficients, Dantzig was now ready, telling his wife:

whatever the [IBM] 701 says that's what I want you to feed me each day starting with supper tonight.

The result included 500 gallons of vinegar.

After delisting vinegar as a food, the next round came back with 200 boullion cubes/day. There were several more iterations, none of which worked, and after everything Dantzig simply went with a "common-sense" diet.

The point I am making is, whenever we create new methods for solving problems, we end up with a bunch of solutions looking for problems. Typically, we try to apply those solutions as widely as possible, and then quickly notice when some of those solutions don't solve the problems we actually want to solve.

Suppose that around 1950, we were musing about the potential consequences of the coming IT revolution. We might've noticed that we were entering the era of the algorithm, where a potentially very wide class of problems could be solved--if they could be reduced to arithmetic and run on the new machines, with their scarcely fathomable ability to memorize a lot and calculate in mere moments. And we could ask "But what about love, honor or justice? Will we forget about those unquantifiable things in the era of the algorithm?" [excuse me if this phrasing sounds snarky] And yet, in the decades since, we seem to have basically just used computers to solve the problems we actually want to solve, and we don't seem to have stopped valuing the things that aren't under their scope.

If we round off WFLL1 to "when you have a hammer, everything looks like a nail", then this only seems mildly and benignly true in the case of most technologies, i.e. the trend seems to be that if technology A makes us better at doing some class of tasks X, we poke around to see just how big X is, until we've delineated the border well and stop, with the exploratory phase rarely causing large-scale harm.

I don't think the OP is intending WFLL1 to say something this broad, but then I feel it should be clarified why "this time is different", such as why modern D(R)L should be fundamentally different from linear optimization, the IT revolution, or even non-deep ML.

(I think the discontinuity-based arguments largely do make the "this time is different" case, roughly because general intelligence seems clearly game-changing. WFLL2 seems somewhere in between these, and I'm unsure where my beliefs fall on that.)

Objection 2: Absence of evidence

I don't see any particular evidence of this as of WFLL1 unfolding as we conclude the 2010s. As I understand, it should gradually "show up" well before AGI, but given that we already have a lot of ML already deployed, this at least causes one to ask when this should be expected to be noticeable, in terms of the necessary capabilities of the AI systems.

Objection 3: Why privilege this axis (of differential progress)

It seems likely that if ML continues to advance substantially over the coming decades (as much as the rate 2012-2019), then it will cause substantial differential progress. But along what axes? WFLL1 singles out the axis "easy-to-measure vs. hard-to-measure", and it's not clear to me why we should worry about this in particular.

For instance, there's also the axis "have massive datasets vs. don't have massive datasets". And we could point to various examples of this form, e.g. it's easy to measure a country's GDP year over year, but we can get at most a few hundred data points on this, hence it's completely unsuitable for DL. So, for instance, we could see differential progress on microeconomics vs. macroeconomics.

More generally, we could ask what things DL seems weak at:

  • Performance at the task must be easy to measure
  • A massive, labelled, digitized training set must exist (or can be easily made with e.g. self-play)
  • DL seems relatively weak at learning causality
  • (Other things listed by e.g. Gary Marcus)

And from there, we could reasonably extrapolate to what DL will be good/bad at, relative to the baseline of human thinking/heuristics.

WFLL1 seems to basically say: "here's this axis of differential progress (arising from a limitation of DL), and here are some examples of ways things can go wrong as a result". But for any other limitation we list, I'd suspect we can also list examples such as "if DL is really capable in general but really bad at causal modeling, here's a thing that can go wrong."

At least to me, the ease-of-measurement bullet point does not seem to pop out as a very natural category: even if interpreted broadly, it does not capture everything that seems plausibly important, and even if interpreted narrowly, it does not seem narrow enough to focus our attention on any one interesting failure mode.



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Vaccine... Help? Deprogramming? Something?

28 декабря, 2019 - 01:14
Published on December 27, 2019 8:27 PM UTC

I need help. Pretty much the entire scientific community and everyone I trust as an intellectual role model has said that vaccines are an almost entirely good thing, yet a close member of my family has made a somewhat convincing argument they are dangerous, and I’m terribly confused. I’ve been trying to figure this issue out for months now, and I just can’t. I’ve seen some (a lot of) dark side epistemology used by the more… out there antivaxers (i.e. homeopathy and essential oil people), but although I have a creeping sense some of what my family member is saying is bullshit, I’m still really confused overall, and some of the stuff they are saying seems reasonable, like family history screening.

They also say some stuff I’m pretty sure is total bullshit, like thimerosal levels in vaccines being a problem, or aluminum content.

I don’t think vaccines are linked to autism, and I don’t think the relative in question does either.

In addition, a very young nephew of mine (with a medium degree of confidence as to whether this story was overinflated by the family member) fell ill with severe seizures and almost died less than 24 hours after vaccination.

Can anyone help me sort out this problem?

I’m honestly confused and I think I might be being programmed by my relative.



Discuss

Expected utility and repeated choices

28 декабря, 2019 - 00:51
Published on December 27, 2019 8:26 PM UTC

Maybe this is a well known kind of problem but I am a novice and it looks puzzling to me.

Here is a lottery: I have these two choices:

  • (a) get 0.5$ for sure
  • (b) win 1$ with probability .mjx-chtml {display: inline-block; line-height: 0; text-indent: 0; text-align: left; text-transform: none; font-style: normal; font-weight: normal; font-size: 100%; font-size-adjust: none; letter-spacing: normal; word-wrap: normal; word-spacing: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0; min-height: 0; border: 0; margin: 0; padding: 1px 0} .MJXc-display {display: block; text-align: center; margin: 1em 0; padding: 0} .mjx-chtml[tabindex]:focus, body :focus .mjx-chtml[tabindex] {display: inline-table} .mjx-full-width {text-align: center; display: table-cell!important; width: 10000em} .mjx-math {display: inline-block; border-collapse: separate; border-spacing: 0} .mjx-math * {display: inline-block; -webkit-box-sizing: content-box!important; -moz-box-sizing: content-box!important; box-sizing: content-box!important; text-align: left} .mjx-numerator {display: block; text-align: center} .mjx-denominator {display: block; 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text-align: center} .mjx-label {display: table-row} .mjx-box {display: inline-block} .mjx-block {display: block} .mjx-span {display: inline} .mjx-char {display: block; white-space: pre} .mjx-itable {display: inline-table; width: auto} .mjx-row {display: table-row} .mjx-cell {display: table-cell} .mjx-table {display: table; width: 100%} .mjx-line {display: block; height: 0} .mjx-strut {width: 0; padding-top: 1em} .mjx-vsize {width: 0} .MJXc-space1 {margin-left: .167em} .MJXc-space2 {margin-left: .222em} .MJXc-space3 {margin-left: .278em} .mjx-test.mjx-test-display {display: table!important} .mjx-test.mjx-test-inline {display: inline!important; margin-right: -1px} .mjx-test.mjx-test-default {display: block!important; clear: both} .mjx-ex-box {display: inline-block!important; position: absolute; overflow: hidden; min-height: 0; max-height: none; padding: 0; border: 0; margin: 0; width: 1px; height: 60ex} .mjx-test-inline .mjx-left-box {display: inline-block; width: 0; float: left} .mjx-test-inline .mjx-right-box {display: inline-block; 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My utility function is U(x)=√x .

What should I choose?

Let's compute the expected utilities:

  • expected utility for one single game is for (b) 23⋅√1=23≈0.67 while for (a) is √0.5≈0.7 so I have maximized expected utility with choice (a)
  • if I compute expected utility for two games I get a different prescription:
    • utility for chosing (a) two times is U(1)=√1=1
    • the expected utility for chosing (b) two times is
P(2 wins)U(2)+P(1 win)U(1)=(23)2⋅√2+2⋅23⋅13⋅√1

This last computation is equal to 49(√2+1)≈1.07 which is greater than the utility of (a) (i.e. 1) so in order to maximize expected utility I should actually prefer to play (b) two times rather than playing (a) two times.

So we have this apparent inconsistency:

  • for one single game it's better to choose (a)
  • for two games it's better to choose (b) both times

This result is puzzling to me because I would expect that utility maximization for one single game should be enough in order to take the decision regardless of what I am allowed to do in future choices. It seems instead that the mere possibility that I could play this same lottery another time changes the convenience of the choices about what to play in the first game. If this is the case then utility theory seems almost useless: I would be forced to put in my computation the whole list of my possible future choices!

Am I missing something or is this an actual problem?





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More on polio and randomized clinical trials

28 декабря, 2019 - 00:07
Published on December 27, 2019 9:07 PM UTC

My post and Twitter thread about the controversy over the 1954 polio vaccine trials generated many replies on Twitter, so here is a followup.

First, I’m very sympathetic to the dilemma that Salk faced. I think it’s a tough problem, and it’s worth thinking about different ways to approach it. I didn’t mean to cast aspersions on Salk.

One way in general to improve this situation is to make sure that all the controls get the treatment immediately after the trial, if it is proved safe and effective. But in this case, that wouldn’t have changed anything. Polio was a seasonal disease, peaking each summer. Getting the vaccine after the trial meant getting it the next season.

Some people have suggested that trials can be ended early if the data clearly shows a conclusion. This is true, although it’s trickier than it appears—if you do it in a naive way, you are prone to reaching false conclusions. The statistics of doing this properly is sophisticated. This is also difficult in the case of a vaccine, where the outcome is binary (you get the disease or you don’t) and you have to wait for a certain period of exposure. This wasn’t like a blood pressure medication, where you can constantly measure a continuous variable.

One idea occurred to me that I haven’t heard anyone suggest: the trial didn’t have to be 50-50. With a large enough group, you could hold back a smaller subset as the control (80-20?). Again, you need statistics here to tell you how this affects the power of your test.

Returning to the issue of: was an RCT needed at all? Again it’s a tough call, but I still think it was, for two reasons: one scientific/epistemological, and one social/political.

Epistemologically, it’s easy to say in hindsight that an observed-control trial would have been conclusive. Here’s the data (copied from Oshinsky’s book):

Placebo-control areas:

• Vaccinated: 200,745 subjects / 33 cases = 1 per 6,083
• Placebo: 201,229 subjects / 115 cases = 1 per 1,750

Observed-control areas:

• Vaccinated: 221,988 subjects / 38 cases = 1 per 5,842
• Observed: 725,173 subjects / 330 cases = 1 per 2,197

But think of how it might have turned out.

One scenario: You run the trial, and it’s not conclusive. Maybe it shows a slight reduction in incidence, but the p-value is high. Then what do you do? Your trial might have been confounded. Run another trial? You’ve just lost a year.

Another scenario: the vaccine is ineffective, but a confounded trial shows effectiveness. Now you are “vaccinating” the entire country with a worthless non-treatment. How many years does it take for the world to figure this out? How many years have you lost then?

Polio came in epidemics each summer, of unpredictable magnitude. No one understood the full epidemiology or could predict the epidemics—when they would start or end, or how many would be stricken. This makes it hard to know whether the vaccine is working or not just from the incidence rate alone. A rise in epidemics could sow needless doubt in the efficacy of the vaccine. Conversely, a dropoff could give false confidence—until the epidemics resurged.

The tests were also looking for safety. What if the vaccine seems effective, but has a side effect no one anticipated? Again, do you have to re-run your trial? Worse, what if the vaccine is actually causing polio (this is possible with a bad vaccine) and this goes undetected?

Remember, these were huge trials. Over a million subjects, nationwide. An enormous effort, difficult to coordinate; tons of data analysis (with only primitive mainframe computers); very expensive. You don’t want to have to do it twice.

And that brings me to the social/political aspect. Polio wasn’t just a scientific question. It was highly emotional and political—within the scientific community, and in the nation at large. Medical researchers were bitterly divided about the best type of vaccine. Salk used a “killed” virus, whose genetic code was virulent, but which had been chemically inactivated. Others favored an “attenuated” virus, which was genetically modified to be harmless to humans. The infighting was stoked by ego and jealousy. Salk was not the only one who wanted to be first to the vaccine.

So, the tests had to be more than scientifically sound. They had to be politically sound. The trials had to be so conclusive that it would silence even jealous critics using motivated, biased reasoning. They had to prove themselves not only to a reasoning mind, but to a committee. A proper RCT was needed for credibility as much as, or more than, for science.

By the way, all this was being funded by the National Foundation for Infantile Paralysis, a private (non-government) charity funded by voluntary contributions from many donors. They relied on good publicity, and above all on the belief that they were making progress. And the trials were front-page news. Botching them would have been a PR disaster. Would the donor base have supported a second trial? Given all the attention, the first trial had to be conclusive.

So that’s why, at the end of the day, I still think Salk was overconfident, and Bell and Francis were right. But again, I sympathize with the issue and I respect the arguments on Salk’s side.



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We need to revisit AI rewriting its source code

27 декабря, 2019 - 21:27
Published on December 27, 2019 6:27 PM UTC

It feels like community discussion has largely abandoned the topic of AGI having the self-modifying property, which makes sense because there are a lot of more fundamental things to figure out.

But I think we should revisit the question at least in the context of narrow AI, because the tools are now available to accomplish exactly this on several levels. This thought was driven by reading a blog post, Writing BPF Code in Rust.

BPF stands for Berkeley Packet Filter, which was originally for network traffic analysis but has since been used for tracing the Linux kernel. The pitch is that this can now be used to let userspace code run in the kernel, which is to say change the way the kernel is working.

The ability to use code to write code is very old; it is arguably the core insight of LISP. But this is becoming increasingly common as a practice now, including things like writing OCaml or Haskell for generating correct C code, and increasingly powerful code generation tricks in compilers.

It's also possible to change how the compilers work now. The Julia language has a package named Cassette.jl, which allows dynamically injecting behavior into the Just-in-Time compiler. As it happens both this Julia trick and the BPF trick for the Linux kernel rely on LLVM.

All of this means at present we can in fact write code that modifies itself, and that modifies the way it is compiled, and that modifies the way the environment runs it (assuming the environment is Linux). The infrastructure seems to be present for large-scale, multi-layer self modification to take place. This seems like it makes questions about self modification testable in a way they weren't before. The BPF and Cassette.jl tricks don't even require software written for them explicitly, they work on previously existing code whose authors had no idea such capability existed. These methods are independent of ideas like Software 2.0/Differentiable Programming, and combined they make me wonder if the safety problems we are concerned with might actually start appearing at the level of applications first.




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Firming Up Not-Lying Around Its Edge-Cases Is Less Broadly Useful Than One Might Initially Think

27 декабря, 2019 - 08:09
Published on December 27, 2019 5:09 AM UTC

Reply to: Meta-Honesty: Firming Up Honesty Around Its Edge-Cases

Eliezer Yudkowsky, listing advantages of a "wizard's oath" ethical code of "Don't say things that are literally false", writes—

Repeatedly asking yourself of every sentence you say aloud to another person, "Is this statement actually and literally true?", helps you build a skill for navigating out of your internal smog of not-quite-truths.

I mean, that's one hypothesis about the psychological effects of adopting the wizard's code.

A potential problem with this is that human natural language contains a lot of ambiguity. Words can be used in many ways depending on context. Even the specification "literally" in "literally false" is less useful than it initially appears when you consider that the way people ordinarily speak when they're being truthful is actually pretty dense with metaphors that we typically don't notice as metaphors because they're common enough to be recognized legitimate uses that all fluent speakers will understand.

For example, if I want to convey the meaning that our study group has covered a lot of material in today's session, and I say, "Look how far we've come today!" it would be pretty weird if you were to object, "Liar! We've been in this room the whole time and haven't physically moved at all!" because in this case, it really is obvious to all ordinary English speakers that that's not what I meant by "how far we've come."

Other times, the "intended"[1] interpretation of a statement is not only not obvious, but speakers can even mislead by motivatedly equivocating between different definitions of words: the immortal Scott Alexander has written a lot about this phenomenon under the labels "motte-and-bailey doctrine" (as coined by Nicholas Shackel) and "the noncentral fallacy".

For example, Zvi Mowshowitz has written about how the claim that "everybody knows" something[2] is often used to establish fictitious social proof, or silence those attempting to tell the thing to people who really don't know, but it feels weird (to my intuition, at least) to call it a "lie", because the speaker can just say, "Okay, you're right that not literally[3] everyone knows; I meant that most people know but was using a common hyperbolic turn-of-phrase and I reasonably expected you to figure that out."

So the question "Is this statement actually and literally true?" is itself potentially ambiguous. It could mean either—

  • "Is this statement actually and literally true as the audience will interpret it?"; or,
  • "Does this statement permit an interpretation under which it is actually and literally true?"

But while the former is complicated and hard to establish, the latter is ... not necessarily that strict of a constraint in most circumstances?

Think about it. When's the last time you needed to consciously tell a bald-faced, unambiguous lie?—something that could realistically be outright proven false in front of your peers, rather than dismissed with a "reasonable" amount of language-lawyering. (Whether "Fine" is a lie in response to "How are you?" depends on exactly what "Fine" is understood to mean in this context. "Being acceptable, adequate, passable, or satisfactory"—to what standard?)

Maybe I'm unusually honest—or possibly unusually bad at remembering when I've lied!?—but I'm not sure I even remember the last time I told an outright unambiguous lie. The kind of situation where I would need to do that just doesn't come up that often.

Now ask yourself how often your speech has been partially optimized for any function other than providing listeners with information that will help them better anticipate their experiences. The answer is, "Every time you open your mouth"[4]—and if you disagree, then you're lying. (Even if you only say true things, you're more likely to pick true things that make you look good, rather than your most embarrassing secrets. That's optimization.)

In the study of AI alignment, it's a truism that failures of failures of alignment can't be fixed by deontological "patches". If your AI is exhibiting weird and extreme behavior (with respect to what you really wanted, if not what you actually programmed), then adding a penalty term to exclude that specific behavior will just result in the AI executing the "nearest unblocked" strategy, which will probably also be undesirable: if you prevent your happiness-maximizing AI from administering heroin to humans, it'll start administering cocaine; if you hardcode a list of banned happiness-producing drugs, it'll start researching new drugs, or just pay humans to take heroin, &c.

Humans are also intelligent agents. (Um, sort of.) If you don't genuinely have the intent to inform your audience, but consider yourself ethically bound to be honest, but your conception of honesty is simply "not lying", you'll naturally gravitate towards the nearest unblocked cognitive algorithm of deception.[5]

So another hypothesis about the psychological effects of adopting the wizard's code is that—however noble your initial conscious intent was—in the face of sufficiently strong incentives to deceive, you just end up accidentally training yourself to get really good at misleading people with a variety of not-technically-lying rhetorical tactics (motte-and-baileys, false implicatures, stonewalling, selective reporting, clever rationalized arguments, gerrymandered category boundaries, &c.), all the while congratulating yourself on how "honest" you are for never, ever emitting any "literally" "false" individual sentences.

Ayn Rand's novel Atlas Shrugged[6] portrays a world of crony capitalism in which politicians and businessmen claiming to act for the "common good" (and not consciously lying) are actually using force and fraud to temporarily enrich themselves while destroying the credit-assignment mechanisms Society needs to coordinate production.[7]

In one scene, Eddie Willers (right-hand man to our railroad executive heroine Dagny Taggart) expresses horror that the government's official scientific authority, the State Science Institute, has issued a hit piece denouncing the new alloy, Rearden Metal, with which our protagonists have been planning to use to build a critical railroad line. (In actuality, we later find out, the Institute leaders want to spare themselves the embarrassment—and therefore potential loss of legislative funding—of the innovative new alloy having been invented by private industry rather than the Institute's own metallurgy department.)

"The State Science Institute," he said quietly, when they were alone in her office, "has issued a statement warning people against the use of Rearden Metal." He added, "It was on the radio. It's in the afternoon papers."

"What did they say?"

"Dagny, they didn't say it! ... They haven't really said it, yet it's there—and it—isn't. That's what's monstrous about it."

[...] He pointed to the newspaper he had left on her desk. "They haven't said that Rearden Metal is bad. They haven't said it's unsafe. What they've done is ..." His hands spread and dropped in a gesture of futility.

She saw at a glance what they had done. She saw the sentences: "It may be possible that after a period of heavy usage, a sudden fissure may appear, though the length of this period cannot be predicted. ... The possibility of a molecular reaction, at present unknown, cannot be entirely discounted. ... Although the tensile strength of the metal is obviously demonstrable, certain questions in regard to its behavior under unusual stress are not to be ruled out. ... Although there is no evidence to support the contention that the use of the metal should be prohibited, a further study of its properties would be of value."

"We can't fight it. It can't be answered," Eddie was saying slowly. "We can't demand a retraction. We can't show them our tests or prove anything. They've said nothing. They haven't said a thing that could be refuted and embarrass them professionally. It's the job of a coward. You'd expect it from some con-man or blackmailer. But, Dagny! It's the State Science Institute!"

I think Eddie is right to feel horrified and betrayed here. At the same time, it's notable that with respect to wizard's code, no lying has taken place.

I like to imagine the statement having been drafted by an idealistic young scientist in the moral maze of Dr. Floyd Ferris's office at the State Science Institute. Our scientist knows that his boss, Dr. Ferris, expects a statement that will make Rearden Metal look bad; the negative consequences to the scientist's career for failing to produce such a statement will be severe. (Dr. Ferris didn't say that, but he didn't have to.) But the lab results on Rearden Metal came back with flying colors—by every available test, the alloy is superior to steel along every dimension.

Pity the dilemma of our poor scientist! On the one hand, scientific integrity. On the other hand, the incentives.

He decides to follow a rule that he thinks will preserve his "inner agreement with truth which allows ready recognition": after every sentence he types into his report, he will ask himself, "Is this statement actually and literally true?" For that is his mastery.

Thus, his writing process goes like this—

"It may be possible after a period of heavy usage, a sudden fissure may appear." Is this statement actually and literally true? Yes! It may be possible!

"The possibility of a molecular reaction, at present unknown, cannot be entirely discounted." Is this statement actually and literally true? Yes! The possibility of a molecular reaction, at present unknown, cannot be entirely discounted. Okay, so there's not enough evidence to single out that possibility as worth paying attention to. But there's still a chance, right?

"Although the tensile strength of the metal is obviously demonstrable, certain questions in regard to its behavior under unusual stress are not to be ruled out." Is this statement actually and literally true? Yes! The lab tests demonstrated the metal's unprecedented tensile strength. But certain questions in regard to its behavior under unusual stress are not to be ruled out—the probability isn't zero.

And so on. You see the problem. Perhaps a member of the general public who knew about the corruption at the State Science Institute could read the report and infer the existence of hidden evidence: "Wow, even when trying their hardest to trash Rearden Metal, this is the worst they could come up with? Rearden Metal must be pretty great!"

But they won't. An institution that proclaims to be dedicated to "science" is asking for a very high level of trust—and in the absence of a trustworthy auditor, they might get it. Science is complicated enough and natural language is ambiguous enough, that that kind of trust that can be betrayed without lying.

I want to emphasize that I'm not saying the report-drafting scientist in the scenario I've been discussing is a "bad person." (As it is written, almost no one is evil; almost everything is broken.) Under more favorable conditions—in a world where metallurgists had the academic freedom to speak the truth as they see it (even if their voice trembles) without being threatened with ostracism and starvation—the sort of person who finds the wizard's oath appealing, wouldn't even be tempted to engage in these kinds of not-technically-lying shenanigans. But the point of the wizard's oath is to constrain you, to have a simple bright-line rule to force you to be truthful, even when other people are making that genuinely difficult. Yudkowsky's meta-honesty proposal is a clever attempt to strengthen the foundations of this ethic by formulating a more complicated theory that can account for the edge-cases under which even unusually honest people typically agree that lying is okay, usually due to extraordinary coercion by an adversary, as with the proverbial murderer or Gestapo officer at the door.

And yet it's precisely in adversarial situations that the wizard's oath is most constraining (and thus, arguably, most useful). You probably don't need special ethical inhibitions to tell the truth to your friends, because you should expect to benefit from friendly agents having more accurate beliefs.

But an enemy who wants to use information to hurt you is most constrained if the worst they can do is selectively report harmful-to-you true things, rather than just making things up—and therefore, by symmetry, if you want to use information to hurt an enemy, you are most constrained if the worst you can do is selectively report harmful-to-the-enemy true things, rather that just making things up.

Thus, while the study of how to minimize information transfer to an adversary under the constraint of not lying is certainly interesting, I argue that this "firming up" is of limited practical utility given the ubiquity of other kinds of deception. A theory of under what conditions conscious explicit unambiguous outright lies are acceptable doesn't help very much with combating intellectual dishonesty—and I fear that intellectual dishonesty, plus sufficient intelligence, is enough to destroy the world all on its own, without the help of conscious explicit unambiguous outright lies.

Unfortunately, I do not, at present, have a superior alternative ethical theory of honesty to offer. I don't know how to unravel the web of deceit, rationalization, excuses, disinformation, bad faith, fake news, phoniness, gaslighting, and fraud that threatens to consume us all. But one thing I'm pretty sure won't help much is clever logic puzzles about implausibly sophisticated Nazis.

(Thanks to Michael Vassar for feedback on an earlier draft.)

  1. I'm scare-quoting "intended" because this process isn't necessarily conscious, and probably usually isn't. Internal distortions of reality in imperfectly deceptive social organisms can be adaptive for the function of deceiving conspecifics. ↩︎

  2. If I had written this post, I would have titled it "Fake Common Knowledge" (following in the tradition of "Fake Explanations", "Fake Optimization Criteria", "Fake Causality", &c.) ↩︎

  3. But it's worth noting that the "Is this statement actually and literally true?" test, taken literally, should have caught this, even if my intuition still doesn't want to call it a "lie." ↩︎

  4. Actually, that's not literally true! You often open your mouth to breathe or eat without saying anything at all! Is the referent of this footnote then a blatant lie on my part?—or can I expect you to know what I meant? ↩︎

  5. A similar phenomenon may occur with other attempts at ethical bindings: for example, confidentiality promises. Suppose Open Opal tends to wear her heart on her sleeve and more specifically, believes in lies of omission: if she's talking with someone she trusts, and she has information relevant to that conversation, she finds it incredibly psychologically painful to pretend not to know that information. If Paranoid Paris has much stronger privacy intuitions than Opal and wants to message her about a sensitive subject, Paris might demand a promise of secrecy from Opal ("Don't share the content of this conversation")—only to spark conflict later when Opal construes the literal text of the promise more narrowly than Paris might have hoped ("'Don't share the content' means don't share the verbatim text, right? I'm still allowed to paraphrase things Paris said and attribute them to an anonymous correspondent when I think that's relevant to whatever conversation I'm in, even though that hypothetically leaks entropy if Paris has implausibly determined enemies, right?"). ↩︎

  6. I know, fictional evidence, but I claim that the kind of deception illustrated in quoted passage to follow is entirely realistic. ↩︎

  7. Okay, that's probably not exactly how Rand or her acolytes would put it, but that's how I'm interpreting it. ↩︎



Discuss

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