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Your abstraction isn't wrong, it's just really bad

26 мая, 2020 - 23:14
Published on May 26, 2020 8:14 PM GMT

For a programming language to qualify as such, the only thing it needs is Turing Completeness. That means, at least in principle, it can be used to solve any (solvable) computational problem.

There are features besides Turing Completeness which makes it desirable, but if you tried hard enough, all of those features could be implemented as part of any Turing complete language.

Let's take a maximally perverse theory of abstraction-making that says:

An abstraction is sufficiently good if, in principle, it can yield answers to all questions that it's designed to solve.

Under this definition, the assembly language (ASM) of virtually any computer is sufficient abstraction for writing code.

The only "problem" with ASM is that writing something simple like quicksort ends up being monstrously large and hard to read (and this implementation is written for clarity, not speed).

Compare that to a quicksort implementation written in, C. It's only 13 lines of code and about as equally efficient as the ASM one above. Even better, it would work on virtually all machines, while the above ASM wouldn't and would have to be rewritten for a bunch of architectures.

C might not be the best abstraction one can use for low-level programming, e.g. writing sorting algorithms, but it's better than ASM.

However the ASM implementation is far from being the maximally perverse version one can get under our naive abstraction-choosing constraints. The prize for that would currently go to something like the brainfuck implementation:

>>+>>>>>,[>+>>,]>+[--[+<<<-]<[<+>-]<[<[->[<<<+>>>>+<-]<<[>>+>[->]<<[<] <-]>]>>>+<[[-]<[>+<-]<]>[[>>>]+<<<-<[<<[<<<]>>+>[>>>]<-]<<[<<<]>[>>[>> >]<+<<[<<<]>-]]+<<<]+[->>>]>>]>>[.>>>]

Now that, that is what I call seriously fucked up.

But brainfuck is Turing complete, so under the above definition, it is no worse than ASM or C.

How we avoid bad languages?

So C, C++, Javascript, Python, Julia, any given ASM, Fortran, Lua, Elm, Clojure, CLISP, Rust, Forth, Elixir, Erlang, Go, Nim, Scala, Ada, Cobolt, Brainfuck, and, sadly enough, even Java, are "good enough to be an abstraction for controlling ANY computing systems" under the perverse requirements above.

In reality, it's fairly easy to agree that some languages are better than others in various situations. I think most criteria for choosing a language are at least somewhat subjective. Things such as:

  • Architectures that the compiler will target
  • Memory safety features.
  • Built-in parallelism and concurrency mechanisms.
  • Functionality of standard library.
  • Available package manager.
  • The companies/projects/communities that already use it.
  • Ease of learning.
  • Ease of reading.
  • Ease of debugging.
  • Speed of compiler[s].
  • Efficiency of memory usage (e.g. via avoiding spurious pointers and having move semantics).
  • Performance on various benchmarks.
  • What I want to use the language for (which is probably the most important).

But even subjective criteria allows me to ask a question like: "What do I need in order to create a language that is better than C and C++ for systems programming?". To which the answer will be something like:

  • Be Turing complete
  • Support all or most targets that C/C++ support
  • Have memory safety feature (e.g. borrow checking, automatic deallocation) that C and C++ don't have.
  • Have the same concurrency abstractions as C and C++ (Threads mapping to kernel threads)) and maybe some extra ones (e.g. futures and async-io semantics for more efficient non-blocking IO).
  • Have a standard library that includes all glibc and std-lib functionality plus some extra things that people want.
  • Have a package manager.
  • Try to make nice companies and welcoming communities that are viewed well by outsiders use your language.
  • Have better, more centralized documentation than C/C++ and community that is nicer and more helpful to newbies.
  • Try to be about as easy to read as C and C++, ideally more so by getting rid of BC syntax and outdated operators.
  • Have a good debugger and nice error logs, or at least nicer than C and C++.
  • {Try} to have a fast compiler.
  • Be about as efficient as C and C++ when working with memory, potentially making useful abstractions (e.g. moving, deallocation) implicit instead of explicit whenever possible. Perhaps by using a better default allocator.
  • Perform equally~ish well to C and C++ on various benchmarks that people trust
  • Be useable in the same situations C and C++ are, maybe more, if possible. This is partially a function of all of the above plus the external library ecosystem, which the language designer can nudge but has no direct control of.

What I describe above is, of course, Rust. A language that, with certain caveats, has managed to become better than C and C++[citation needed] for systems programming (and many other things).

Granted, the world hasn't been rewritten in Rust yet. (Though I hear the R.E.S.F has managed to secure an audience with God and given his buy-in, you'd be only one level removed from converting von Neuman.)

But it does have wide adoption and I would be deeply surprised if in 10-20 years from now most server kernels and common low-level libraries (e.g. BLAS, CUDNN) won't be written in it.

For a more post factum example, you can take something like Python, due to a good package manager and easy to understand syntax, it managed to become the dominant scripting language for... everything.

For another example, take Nodejs, which used people's familiarity with javascript and it's build-in asyncio capabilities to become the language of choice for many low-load http servers.

Or take something simpler, like TypeScript, which just took Javascript, added a feature people really, really wanted (types), and swiftly became a fairly dominant language in the frontend world.

Our model for avoiding bad languages is based on continuous improvement and competition. You look at what existing language are used for and what people want to do with them, you look at their syntax and their performance. Then you build something that fits the basic requirements (is Turing complete, runs on x86) and tries to be better than then them in the pain points previously identified. If your new language is good enough, it will sweep everyone away and 10 years from now we'll all be using it.

A similar but less complex set of criteria can be defined for libraries and open-source software in general. A similar but more complex set of criteria can be defined for processor and computer architectures.

The languages, processors, and libraries of the 80s are all "kinda bad" by modern standards. The ones of the 40s are basically at the brainfuck levels of horribleness. But this is not to say that the people of the 80s or 40s were bad programmers, it's just that they didn't have the knowledge and tools that we have right now.

Indeed, I'd wager that the pioneers of computer science were probably smarter than the people doing it now, successful pioneers tend to be that way, but seeing mistakes in hindsight is much easier than predicting them.

The requirements for avoiding bad abstraction

So, I think the requirements for avoiding bad abstraction, at least in terms of programming languages and programming abstractions in general, can be boiled down to:

  1. Minimal and rather specific core standards (Turing completeness, ability to run on a popular~ish CPU).
  2. Ability to name areas where improvements can be made such that the language could be considered "better" than current ones.
  3. A general willingness of the community to adopt the language. Or at least a mechanism by which the community can be forced to adopt it (see market-driven competition).

Granted, these requirements are far from being fulfilled perfectly. The adage still holds mostly true:

Science progresses one funeral at a time

Programming languages are not quite like scientific paradigms, partially because the field they are used in is far more competitive, partially because they are much easier to design than a scientific theory.

An average programmer might switch between using 2 or 3 significantly different languages in one area over their lifetime. An especially good one might bring that number to 10, 20, or 50 (most of which are going to be failed experiments).

There's no programming God that can just say "X is obviously better than Y so switch all Y code to X and start using X". There is however something close to an efficient market that says something like:

If I can design a piece of software in 1 hour using my language and you can design it in 100 hours using yours, I will soon build a 3 person startup that will eat your mid-sized corporation for lunch.

However, I don't want to downplay the role that community plays here.

Imagine designing a new language and trying to improve and popularize it, you'll need validation and feedback. In general, the reactions you will get will probably be neutral to positive.

Being already popular and respected helps (see Mozzila and Rust), but so does having a language that seems very obvious useful by filling a niche (see frontend devs wanting to work full-stack and Node) or just having an amazing syntax (see Python way back when). However, the worst-case scenario is that you end up getting some "oh that's nice BUT ..." style reactions and are left with a good learning experience as the basis for your next attempt.

I don't think anyone ever tried to build a programming language (or a library or a CPU arch for that matter) and was met with:

Oh, you stupid quack, you think you're better than the thousands of language designers and dozens of millions of developers building and using a programming language. You really think you've managed to out-smart so many people and come up with a better language/library !?

I think that the lack of this reaction is good. However, try to challenge and redesign a core abstraction of any other field (internal medicine, high-energy physics, astronomy, metabolic biology... etc) and that's roughly the reaction you will get. Unless you are a very respected scientist, in which case you will usually get indifference instead and maybe in 60 years, after the death of a few generations, your abstractions will start being adopted. But this is a very subjective impression.

On the other hand, good CS students are routinely challenged to design their language and compiler. Maybe 999/1,000 times it sucks, but that's ok because they get a better understanding of why our current languages are good and of the principles behind them. Even better, maybe 1/1,000 times you get the seeds of something like Scala.

I have it on good authority that no physics students were ever tasked with trying to re-design the mathematical apparatus used to interpret the results of high energy collisions into taxonomical facts.

We make fun of tools, but in general, we never make fun of tool-creation. Creating tools for programming (e.g. language and libraries) is as close as you can get to a sacrament in the programming community. It's something that's subconsciously seen as so desirable that nobody has anything but praise for the attempt (though, again, the results are fair game for harsh critique).

Note: Of course, outliers exist everywhere, including here.

Is your abstraction bad?

So the reasons we know our programming abstractions (e.g. languages and libraries) are kind of good is that we have a market mechanism to promote them, a culture that encourages and helps their creation, and both explicit and implicit criteria that we can judge them by.

We don't know how good they are in an absolute sense, we can be almost certain they are not the best, but we can be 100% sure that they are not the worst, because we know they aren't Brainfuck.

But take away any piece of the puzzle and the whole edifice crumbles, or at least is badly damaged. If you replace the free-market determination with a council of elders or a regulatory board, then Node and Python don't exist. If you replace the community with one that is trying to minimize the amount of new things they have to learn, then Rust doesn't. If the quality criteria were much fuzzier than we might have just stopped at ASM instructions and never designed any language.

This is the fear I have about a lot of other abstractions.

Take for example taxonomies in fields like biology and medicine.

There's a taxonomy that clusters things that can approximately replicate the contents of DNA and RNA molecules encased within them into: Realm > Kingdom > .... > Genus > Specie
.

There are taxonomies that cluster symptoms together into diseases to better target them with drugs, investigate the underlying cause, and predict their progress.

But the way these taxonomies are designed does not seem immediately obvious, nor does it seem like one of the fundamental questions that their respective fields struggle with.

If I inquire "why is life classified the way it is ?", I think the best steelman of a biologist my mind can come up with will answer something like:


Well, because it evolved (no pun intended) all the way from Aristotle to us. There are some good Schelling points such as "things that fly are different from things that walk, are different from things that crawl, are different from things that swim, are different from things which are immutable and green". Rough outlines were built around those and some ideas (e.g. Kingdom, Species) kinda stuck because they seem obvious. Once we understood evolution and the fossil records we realized that things are a bit more complex and we came up with this idea of phylogenetics, but we decided to work around the existing rank in the hierarchy to calcify their meaning a bit better, then added more ranks once they seemed necessary.Yes, maybe even viewing it as a hierarchy is rather stupid, since that whole view works for e.g. multicellular eukaryotes that tend to keep slowly adding to their DNA and leave a fossil record but seems kinda silly for bacteria which swap, add and discard DNA like crazy and leave none but the vaguest trace of their existence a few seconds after their cell wall breaks. But it kinda works even for viruses and bacteria if you make a few adjustments.Yes, there are arbitrary rules we apply, for example, the more foreign a lifeform is to us the more likely we are to use genetics to classify it, and the more often we encounter it the more likely we are to use phenotype.Yes, maybe forcing kids to memorize these things is school is pointless and arbitrary, and yes I see no particular reason to put this taxonomy on a golden pedestal, but it's the one that everyone uses so you might as well get used to it.

This is a fairly good defense of the taxonomy, all things considered, it's a very similar defense to the one I'd give if someone asked me why it's still relevant to know C or C++.

But it's a defense that leverages the idea that the current taxonomy is fit enough for its purpose, thus there's no reason to change it. However, I fail to see why we consider it to be so. The role of a taxonomy is to dictate discussion, indexing, and thought patterns, this is a rather complex subject and can only be explored via querying individual preferences and observing how individuals using different taxonomies fare against each other in a competitive environment. But if my only option is to use this taxonomy and doing away with the whole edifice in favor of something new is off-limits, then I think it's fair to argue that we have exactly 0 datapoints to suggest this is a good taxonomy.

If the whole reason we use the taxonomy is because "It kinda classifies all life, so it's fit for purpose", that's like saying brainfuck is Turing complete, so it's fit for purpose. An abstraction can be fit for purpose under the most perverse possible definition and still be very bad.

In programming, if I think C is garbage, or, even worst, if I think OO and imperative programming as a whole is rubbish, I can go to one of 1001 university departments, meetups, and companies that use functional languages and patterns.

If I think that even those are rubbish and I specifically want a functional language designed under some niche tenants of a sub-branch of set theory designed by a few mathematicians in the 50s... I can go to Glasgow and find a whole university department dedicated to that one language.

The reason I can point to C and say "this is an ok language" is because I can take i3 and xmonad, look at their code+binary and say "Look, this is a C program compared to a Haskell one, they fulfill the same function and have x,y,z advantage, and disadvantages". That times 1000, means I have some reason to believe C is good, otherwise C programmers would be out of a job because everyone would be writing superior software in Haskell.

But I'm not aware of any department of biology that said: "Hmh, you know what, this life-classification system we have here could be improved a lot, let's redesign it and use the new system to communicate and teach our students to use it". If we had one such department or 100 such departments, we would suddenly have some data points to judge the quality of our current taxonomy.

A dominant abstraction still has a huge home-field advantage. But the reason for experimenting with abstraction is not to get minor improvements (e.g. Python3.7 to Python3.8) but to get paradigm-shifting ones (e.g. C vs Rust). The changes we are looking for should still be visible even in those subpar conditions.

If Firefox used to be kinda slow, and in the last 2 years it's become the fastest browser on all platforms, that starts to hint at "Huh, maybe this Rust thing is good". It's not a certainty, but if we have 100 other such examples than that's much better than nothing.

Conversely, if the university working with {counterfactual-taxonomy-of-life} is suddenly producing a bunch of small molecules that help people stay thin without significant side effects, although billions of dollars went into researching them and nobody really found one before, maybe that's a hint that {counterfactual-taxonomy-of-life} is bringing some useful thought patterns to the table. It's not a certainty, but if we have 100 other such examples than that's better than nothing.

Granted, I don't expect fundamental taxonomies to be a good candidate for this approach, they are just an easy and uncontroversial example to give. Everyone agrees they are somewhat arbitrary, everyone agrees that we could create better ones but the coordination problem of getting them adopted is not worth solving.

So fine, let me instead jump ship to the most extreme possible example.

Why do we have to use math?

Generally speaking, I can't remember myself or anyone else as kids protesting against learning numbers and how to add and subtract them. As in, nobody asked "Why do we have to learn this?" or "Is this useful to adults?".

Granted, that might be because children learning numbers have just mustered basic speech and bladder control a few birthdays ago, so maybe there's still a gap to be crossed until they can protests to their teachers with those kinds of questions.

But I doubt it. I think there's something almost intuitive about numbers, additions, and Euclidean geometry. Maybe one doesn't come up with them on their own in the state of nature, but they are very intuitive abstractions, once someone points them out to you they seem obvious, natural, true.

This is a famous argument and I think Plato does better justice to it than I could.

Some context, Socrates (So) is arguing with Menos about the existence of "recollection" (I'd rather think of this as abstractions/ideas that become immediately obvious to anyone once pointed out). He brings out a slave with no knowledge of geometry and draws the following figures:

http://cgal-discuss.949826.n4.nabble.com/file/n2015843/image.jpg

  • So: Tell me, boy, do you know that a square is like this?
  • Slave: I do.
  • So: And so a square has these lines, four of them, all equal?
  • Slave: Of course.
  • So: And these ones going through the center are also equal?

*Slave: Yes.

  • So: And so there would be larger and smaller versions of this area?
  • Slave: Certainly.
  • So: Now, if this side were two feet and this side two feet also, how many feet would the whole be? Look at it like this: if this one were two feet but this one only one foot, wouldn't the area have to be two feet taken once?
  • Slave: Yes.
  • So: When this one is also two feet, there would be twice two?
  • Slave: There would.
  • So: An area of twice two feet?
  • Slave: Yes.
  • So: How much is twice two feet? Calculate and tell me.
  • Slave: Four, Socrates.
  • So: Couldn't there be one different from this, doubled, but of the same kind, with all the lines equal, as in that one?
  • Slave: Yes.
  • So: And how many feet in area?
  • Slave: Eight.
  • So: Come then, try to tell me how long each line of this one will be. In that one, it's two, but what about in that doubled one?
  • Slave: It's clearly double, Socrates.
  • So: You see, Meno, that I am not teaching anything, but put everything as a question. He now believes he knows what sort of line the eight feet area comes from. Or don't you think so?

The point at which children start questioning their math teachers seem to coincide with the point where more complex abstraction is introduced.

There's nothing fundamentally "truer" about euclidian geometry than about analysis. Yes, the idea of breaking down lines into an infinity of infinitely small distances might conflict with the epistemology of a kid, but so might the axioms of Euclidian geometry.

Where the difference between Euclidian geometry and analysis lies is in the obviousness of the abstraction. With something like analysis it seems like the abstractions being thought are kind of arbitrary, not in that they are untrue, but in that they could be differently true.

Maybe it's not even obvious why "infinitely small distance" is a better choice of abstraction than "100,000,000,000 small distances".

It's not obvious why reasoning analytically about the area under a curve is superior to the Greek geometric approach of starting with a few priors and reasoning out equality through similarity. (not to a kid, that is)

It's not obvious why the 'trend' of a certain single-parameter function, represented by another single-parameter function is an important abstraction to have at all.

I think that kids asking their math teachers "Why do we have to learn this?", want, or at least would be best served with an answer to one of two interpretations:

  1. Why is this abstraction more relevant than any other abstraction I could be learning? The abstractions math previously gave me seemed like obvious things about the world. This analysis thing seems counter-intuitive at first, so even if it's true, that in itself doesn't seem like reason enough for me to care.
  2. Why was this abstraction chosen to solve this set of problems? How did people stumble upon those problems and decide they were worth solving? What other abstractions did they try to end up with on that is so complex?

But instead, the answers most teachers give are answers to the question:

Why do we have to learn math?

I think most people have this mental split at some point, between the mathematic that is intuitive and that which isn't. Maybe for some people, it happens right after they learn to count, maybe for others, it happens when they get to 4d geometry.

I think many people blame this split on something like curiosity, or inborn ability. But I blame this split on whichever qualia dictate which mathematical abstractions are "intuitive" and which aren't.

Furthermore, I don't think this split can be easily resolved, I think to truly resolve it you'd need to find a problem impervious to intuitive abstractions, then try out a bunch of abstractions that fail short of solving it, then reason your way to one that does (which is likely going to be the "standard" one).

But it seems that abstraction-finding, whilst most certainly a part of mathematics, is something almost nobody is explicitly taught how to do.

To put it another way, I think that anyone learning to program, if asked "How would you redesign C to make it better?", could give an answer. Maybe a wrong answer, almost certainly an answer far worst than the "best" answers out there. Most people are asked some variant of this question, or at least ask themselves, a significant percentage even try to implement it... maybe not quite at the level of trying to redesign C, but at least at the level of trying to redesign some tiny library.

On the other hand, someone that's learned analysis for a few years, if asked how they would improve it, would fail to answer... even a poor answer, even a wrong answer, the question would seem as intractable to them as it would be to their 6-year-old self.

If I proposed to you that in 20 years schools and colleges would be teaching either Rust or Python instead of C and Java you might argue with that idea, but it would certainly seem like something within the realm of real possibilities.

If I proposed to you that in 20 years schools and colleges would have thrown away their analysis books and started teaching a very different paradigm you would ask me what's the stake and odds I'm willing to bet on that.

Maybe that is because math is perfect, or at least because math is close to perfect. Maybe one can make minute improvements and completions to the old knowledge, but the emphasis in that sentence should be placed on "minute" and "completions".

One thing that strikes me as interesting about mathematics, under this hypothesis, is that it seems to have gotten it impossibly right the first time around. E.g. the way one best abstracts figuring out the equation for the area under a curve in 17th-century with limited ink and paper, is the same way one best abstracts it when sitting at a desk with a computing machine millions of times faster than our brain.

I'm not comfortable making this point about analysis, I've tried thinking about an idea like "What if we assumed continuous variables did not exist and built math from there Pythagora style", every time I ran into an issue, so I'm fairly sure a good mathematician could poke 1001 holes in this approach.

However, in areas that interest me like statistics, I think it's fairly easy to see gralingly bad abstractions that have little reason for existing. From people still writing about trends without using any test data, let alone something like k-fold cross-validation, to people assuming reality has an affinity for straight lines and bell shapes until proven otherwise.

Maybe statistics is alone in using outdated abstractions, or maybe there are many areas of math like it. But because mathematics is (fairly) hierarchical in terms of the abstractions it uses, there's no marketplace for them to fight it out. New abstractions are forced to be born into new niches, or via strangling out a competitor by proving an edge case they couldn't.

When Python was born nobody ever claimed it does more than C, it is, by definition, impossible to do something with Python (as implemented by CPython) that can't be done with C. On the other hand, that doesn't make the Python abstraction inferior, indeed, for the vast majority of jobs, it's much better.

Is there such an abstraction we are missing out on in math? Some way to teach kids analysis that is as obvious as Euclidian geometry. I don't know, but I do know that I'd have no incentive to ever figure it out and I don't think anybody else does either. That fact makes me uncomfortable.

Perhaps mathematics is the worst possible field to reason about abstraction quality, but I feel like there are a lot of other easier picks where even a bit of abstraction competition could greatly improve things.

Alas

I think the way programming handles creating new abstractions is rather unique among any field of intellectual endeavor.

Maybe this is unique to programming for a reason or maybe I'm wrongfully associating the most fluid parts of programming with the most immutable parts of other fields.

But I do think that the idea is worth exploring more, especially light of our knowledge accumulation problem and the severe anti-intellectualism and lack of polymaths in the current world.

Maybe all theory is almost perfect and improving it can only be done incrementally. But maybe, if thousands of people attempted to completely revamp various theories every day, we'd come up with some exponentially better ones. I think the only field that provides evidence regarding this is programming and I think the evidence points towards the latter approach is very promising.



Discuss

Is fake news bullshit or lying?

26 мая, 2020 - 21:05
Published on May 26, 2020 4:55 PM GMT

New strategies for combating misinformation

A layperson-friendly view. Cross-posted from my personal blog, First Principles.

Fake news is on the rise. We know this from Facebook shares, WhatsApp forwards, Twitter trolls, and Potemkin news sites. We see it in elections across the world, novel coronavirus guidance, and nation-state posturing.

We’ve known about the issue for a while, and technology companies — in their role as the primary distributors — have taken action. This action has not stemmed the tide, and meanwhile the techniques of misinformation evolve and proliferate: bot armies and Deep Fakes being only a few recent innovations.

Why is it so difficult to define what fake news is? Why does calling out lies have little impact on those already deceived? And crucially, what can we do to restore trust and reason to public and social media?

What is fake news?

Fake news is deliberate, targeted, misinformation. It’s not necessarily wholly false: perpetrators are as willing to utilize truths that fit their narrative as they are to concoct falsehoods to construct it. They’re necessarily indifferent to the truth, and attached solely to the outcome: the beliefs they wish to plant within the minds of their targets. From Harry Frankfurt’s On Bullshit:

[The bullshitter’s] eye is not on the facts at all, as the eyes of the honest man and of the liar are, except insofar as they may be pertinent to his interest in getting away with what he says. He does not care whether the things he says describe reality correctly. He just picks them out, or makes them up, to suit his purpose.

Fake news, therefore, isn’t lying, but bullshit.

Why is fake news so hard to fight?

Fake news, being an extension of bullshit, inherits much of its traits:

It’s hard to refute

The claims within bullshit are many, nebulous, and often not even wrong. Fact-checking alone isn’t adequate to this task, and neither is reliance upon a trustworthy set of sources.

It’s normalized

As Frankfurt points out:

One of the most salient features of our culture is that there is so much bullshit. Everyone knows this. […] The realms of advertising and of public relations, and the nowadays closely related realm of politics, are replete with instances of bullshit so unmitigated that they can serve among the most indisputable and classic paradigms of the concept.It’s hard to regulate

If bullshit is hard to characterize, it’s harder to legally define. By virtue of either not even being wrong or outlandishly so, fake news can take advantage of freedom of speech protections for parody and satire. In any specific case, the perpetrators may be elusive, not within the same legal jurisdiction as the victims, or, if every content repost is counted, too many in number to sue.

What will it take?

Effectively countering misinformation requires a sea change in how journalistic media engages with fake news’ misleading narratives, and in the metrics by which content distributors value and incentivize activity on their platforms.

Preempt the narrative

Fact-checking is journalists’ prime weapon against fake news, and fact-checking tools have rightfully proliferated and are even surfaced alongside suspect material by content distributors; but fact-checking alone is ineffective at changing minds, and is at best a reactive and arduous activity that can only verify a tiny fraction of publicized claims.

Instead, content creators and journalistic media must track fake news with the aim of anticipating the intended post-truth narratives, and promote countervailing, fact-based narratives instead. This narrative-busting connects with disparate audiences in ways most meaningful to each, but without forgoing journalistic neutrality. From UNESCO’s journalism handbook on fake news:

The core components of professional journalistic practice […] can be fulfilled in a range of journalistic styles and stories, each embodying different narratives that in turn are based on different values and varying perspectives of fairness, contextuality, relevant facts, etc.

Journalists must intimately understand their audience to honestly and confidently convey these narratives. Techniques of causal correction and moral reframing have been shown to be effective in conveying factual information, e.g.

Saying "the senator denies he is resigning because of a bribery investigation" is not that effective, even with good evidence that that's the truth.
More effective would look something like this: "the senator denies he is resigning because of a bribery investigation. Instead, he said he is becoming the president of a university."

Journalistic neutrality has come to mean catering to a single — mostly moderately liberal — audience, but at the expense of the touchpoints of understanding that appealed to large swathes of the population. To counter misleading and polarizing narratives, alternatives grounded in reality must be translated to the value and belief systems of diverse peoples.

Incentivize deliberation

Sharing is easy and uniform across all content, but not all engagement is created equal. Technology companies must recognize that slowing down some kinds of engagement leads to higher-quality content and better shareholder value.

Like journalistic media, content distributors have relied on fact-checking, with Facebook, YouTube, and Twitter tagging suspected misinformation. This has been applied sparingly and with mixed results, and also exacerbated the problem by implying that untagged content is verified to be true. And even on this flagged subset of content, sharing and cross-posting remains frictionless.

Blocking the sharing of any content outright is undesirable, and raises issues of censorship and free expression. However, technology companies can build in features incentivizing users to reflect on problematic content prior to sharing and improving the quality of ensuing discussions.

When a user shares flagged content, platform features can potentially enforce adding accompanying comments of a minimum length and complexity to encourage deliberation, or answering a quick IMVAIN survey to crowdsource its reliability. These need not be mandatory, but disincentives can be applied by indicating to subsequent viewers instances where the user declined to comment on or verify the post.

Such measures can be differentially applied, and distributors have already demonstrated this ability in automatically flagging and prioritizing content for fact-checking. By treating content that is new, unverified, or suspected of being misleading uniformly across the spectra of politics and values, platforms can process larger tracts of content, improve content quality, and sidestep bias.

Summary

Fake news is bullshit: hard to pin down, refute, and regulate. Countering misinformation requires journalists to promote factual narratives by engaging overlooked audiences with causal and moral reframing, and content distributors to incentivize deliberation and crowdsource reliability, discourage uncritical reposting of suspect content, and develop engagement metrics that differentiate for quality activity.



Discuss

Why We Age, Part 2: Non-adaptive theories

26 мая, 2020 - 20:41
Published on May 26, 2020 5:41 PM GMT

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Follows from: Why We Age, Part 1; Evolution is Sampling Error; An addendum on effective population size

Last time, I introduced three puzzles in the evolution of ageing:

This, then, is the threefold puzzle of ageing. Why should a process that appears to be so deleterious to the individuals experiencing it have evolved to be so widespread in nature? Given this ubiquity, which implies there is some compelling evolutionary reason for ageing to exist, why do different animals vary so much in their lifespans? And how, when ageing has either evolved or been retained in so many different lineages, have some animals evolved to escape it?

I divided existing theories of the evolution of ageing into two groups, adaptive and nonadaptive, and discussed why one commonly believed nonadaptive theory – namely, simple wear and tear – could not adequately answer these questions.

In this post I'll discuss other, more sophisticated non-adaptive theories. These theories are characterised by their assertion that ageing provides no fitness benefit to organisms, but rather evolves despite being deleterious to reproductive success. Despite the apparent paradoxicality of this notion, these theories are probably the most widely-believed family of explanations for the evolution of ageing among academics in the field; they're also the group of theories I personally put the most credence in at present.

How can this be? How can something non-adaptive – even deleterious – have evolved and persisted in so many species across the animal kingdom? To answer this question, we need to understand a few important concepts from evolutionary biology, including genetic drift, relaxed purifying selection, and pleiotropy. First, though, we need to clarify some important terminology.

Mortality, survivorship, and fecundity

For the purposes of this post, a cohort is a group of individuals from the same population who were all born at the same time, i.e. they are of the same age. The survivorship of a cohort at a given age is the percentage of individuals surviving to that age, or equivalently the probability of any given individual surviving at least that long. Conversely, the mortality of a cohort at a given age is the probability of an individual from that cohort dying at that age, and not before or after.

Survivorship and mortality are therefore related, but distinct: survivorship is the result of accumulating mortality at all ages from birth to the age of interest[1]. As a result, the mortality and survivorship curves of a cohort will almost always look very different; in particular, while mortality can increase, decrease or stay the same as age increases, survivorship must always decrease. As one important example, constant mortality will give rise to an exponential decline in survivorship[2].

Four hypothetical mortality curves and their corresponding survivorship curves.

In evolutionary terms, survival is only important insofar as it leads to reproduction. The age-specific fecundity of a cohort is the average number of offspring produced by an individual of that cohort at that age. Crucially, though, you need to survive to reproduce, so the actual number of offspring you are expected to produce at a given age needs to be downweighted in proportion to your probability of dying beforehand. This survival-weighted fecundity (let's call it your age-specific reproductive output) can be found by multiplying the age-specific fecundity by the corresponding age-specific survivorship. Since this depends on survivorship, not mortality, it will tend to decline with age: a population with constant mortality and constant fecundity (i.e. no demographic ageing) will show reproductive output that declines exponentially along with survivorship.

Two hypothetical mortality/fecundity curves and their corresponding reproductive outputs.

The fitness of an individual is determined by their lifetime reproductive output (i.e. the total number of offspring they produce over their entire lifespan)[3]. Mutations that significantly decrease lifetime reproductive output will therefore be strongly opposed by natural selection. It seems mutations leading to ageing (i.e. an increase in mortality and decrease in fecundity with time) should be in that category. So why does ageing evolve?

What good is immortality?

Imagine a race of beautiful, immortal, ageless beings -- let's call them elves. Unlike we frail humans, elves don't age: they exhibit constant mortality and constant fecundity. As a result, their age-specific survivorship and reproductive output both fall off exponentially with increasing age -- far more slowly, in other words, than occurs in humans.

Survivorship, cumulative fecundity and cumulative reproductive output curves for a population of elves with 1% fecundity and 0.1% mortality per year.

Under the parameters I've used here (1% fecundity, 0.1% mortality), an elf has about a 50% chance of making it to 700 years old and a 10% chance of living to the spry old age of 2,300. An elf that makes it that far will have an average of 23 children over its life; 7 if it only makes it to the median lifespan of 700.

Since fecundity and mortality are constant, an elf that makes it to 3,000 will be just as fit and healthy then as they were as a mere stripling of 500, and will most likely still have a long and bright future ahead of them. Nevertheless, the chance of any given newborn elf making it that far is small (about 5%). This means that, even though an old elf could in principle have as many children as a much younger individual elf, the actual offspring in the population are mainly produced by younger individuals. Just over 50% of the lifetime expected reproductive output of a newborn elf is concentrated into its first 700 years; even though it could in principle live for millennia, producing children at the same rate all the while, its odds of reproducing are best early in life. You can, after all, only breed when you're living.

This fact -- that reproductive output is concentrated in early life even in the absence of ageing -- has one very important consequence: natural selection cares much more about you when you're young.

Natural selection is ageist

No genome is totally stable -- mutations always occur. Let's imagine that three mutations arise in our elven population. Each is fatal to its bearer, but with a time delay, analogous to Huntington's disease or some other congenital diseases in humans. Each mutation has a different delay, taking effect respectively at 100, 1000, and 10000 years of age. What effect will these mutations have on their bearers' fitness, and how well will they spread in the population?

Three potential fatal mutations in the elven populations, and their effects on lifetime reproductive output.

Although all three mutations have similar impacts on an individual who lives long enough to experience them, from a fitness perspective they are very different. The first mutation is disastrous: almost 90% of wild-type individuals (those without the mutation) live past age 100, and a guaranteed death at that age would eliminate almost 90% of your expected lifetime reproductive output. The second mutation is still pretty bad, but less so: a bit over a third of wild-type individuals live to age 1000, and dying at that age would eliminate a similar proportion of your expected lifetime reproductive output. The third mutation, by contrast, has almost no expected effect: less than 0.005% of individuals make it to that age, and the effect on expected lifetime reproductive output is close to zero. In terms of fitness, the first mutation would be strenuously opposed by natural selection; the second would be at a significant disadvantage; and the third would be virtually neutral.

This extreme example illustrates a general principle:

The impact of a mutation on the fitness of an organism depends on both the magnitude of its effect and the proportion of total reproductive output affected.

— Williams 1957 [4]

Mutations that take effect later in life affect a smaller proportion of total expected reproductive output and so have a smaller selective impact, even if the size of the effect when they do take effect is just as strong. The same principle applies to mutations with less dramatic effects: those that affect early-life survival and reproduction have a big effect on fitness and will be strongly selected for or against, while those that take effect later will have progressively less effect on fitness and will thus be exposed to correspondingly weaker selection pressure. Put in technical language, the selection coefficient of a mutation depends upon the age at which it takes effect, with mutations affecting later life having coefficients closer to zero.

Evolution is sampling error, and selection is sampling bias. When the selection coefficient is close to zero, this bias is weak, and the mutation's behaviour isn't much different from that of a neutral mutation. As such, mutations principally affecting later-life fitness will act more like neutral mutations, and increase and decrease in frequency in the population with little regard for their effects on those individuals that do live long enough to experience them. As a result, while mutations affecting early life will be purged from the population by selection, those affecting late life will be allowed to accumulate through genetic drift. Since the great majority of mutations are negative, this will result in deteriorating functionality at older ages.

So our elves are sadly doomed to lose their immortality, unless something very weird is happening to cause them to keep it. Mutations impairing survival and reproduction early in life will be strenuously removed by natural selection, but those causing impairments later in life will accumulate, leading to a progressive increase in mortality and decline in fecundity. This might seem bad enough, but unfortunately there is more bad news on the horizon -- because this isn't the only way that nonadaptive ageing can evolve.

Perverse trade-offs

Imagine now that instead of a purely negative, Huntingdon-like mutation arising in our ageless elf population, a mutation arose that provided some fitness benefit early in life at the cost of some impairment later; perhaps promoting more investment in rapid growth and less in self-repair, or disposing the bearer more towards risky fights for mates. How would this new mutation behave in the population?

The answer depends on the magnitude of the early-life benefit granted by the mutation, as well as of its later-life cost. However, we already saw that in weighing this trade-off natural selection cares far more about fitness in early life than in later life; as such, even a mutation whose late-life cost far exceeded its early-life benefit in magnitude could be good for overall lifetime fitness, and hence have an increased chance of spreading and becoming fixed in the population. Over time, the accumulation of mutations like this could lead to ever-more-severe ageing in the population, even as the overall fitness of individuals in the population continues to increase.

This second scenario, in which the same mutation provides a benefit at one point in life and a cost at another, is known as antagonistic pleiotropy[5]. It differs from the mutation accumulation theory of ageing outlined above in that, while in the former case ageing arises primarily through genetic drift acting on late-life-affecting deleterious mutations, the latter proposes that ageing arises as a non-adaptive side effect of a fitness-increasing process. Both theories are "non-adaptive" in that the ageing that results is not in itself good for fitness, and both depend on the same basic insight: due to inevitably declining survivorship with age, the fitness effect of a change in survival or reproduction tends to decline as the age at which it takes effect increases.

Mutation accumulation and antagonistic pleiotropy have historically represented the two big camps of ageing theorists, and the theories have traditionally been regarded as being in opposition to each other. I've never really understood why, though: the basic insight required to understand both theories is the same, and conditions that gave rise to ageing via mutation accumulation could easily also give rise to additional ageing via antagonistic pleiotropy[6]. Importantly, both theories give the same kinds of answers to the other two key questions of ageing I discussed last time: why do lifespans differ between species, and why do some animals escape ageing altogether?

It's the mortality, stupid

As explanations of ageing, both mutation accumulation and antagonistic pleiotropy depend on extrinsic mortality; that is, probability of death arising from environmental factors like predation or starvation. As long as extrinsic mortality is nonzero, survivorship will decline monotonically with age, resulting (all else equal) in weaker and weaker selection agains deleterious mutations affecting later ages. The higher the extrinsic mortality, the faster the decline in survivorship with age, and the more rapid the corresponding decline in selection strength.

Age-specific survivorship as a function of different levels of constant extrinsic mortality.

As a result, lower extrinsic mortality will generally result in slower ageing: your chance of surviving to a given age is higher, so greater functionality at that age is more valuable, resulting in a stronger selection pressure to maintain that functionality.

This is the basic explanation for why bats live so much longer than mice despite being so similar: they can fly, which protects them from predators, which reduces their extrinsic mortality.

You can see something similar if you compare all birds and all mammals, controlling for body size (being larger also makes it harder to eat you):

Scatterplots of bird and mammal maximum lifespans vs adult body weight from the AnAge database, with central tendencies fit in R using local polynomial regression (LOESS).

In addition to body size and flight, you are also likely to have a longer lifespan if you are[7]:

  • Arboreal
  • Burrowing
  • Poisonous
  • Armoured
  • Spiky
  • Social

All of these factors share the property of making it harder to predate you, reducing extrinsic mortality. In many species, females live longer than males even in captivity: males are more likely to (a) be brightly coloured or otherwise ostentatious, increasing predation, and (b) engage in fights and other risky behaviour that increases the risk of injury. I'd predict that other factors that reduce extrinsic mortality in the wild (e.g. better immune systems, better wound healing) would similarly correlate with longer lifespans in safe captivity.

This, then, is the primary explanation non-adaptive ageing theories give for differences in rates of ageing between species: differences in extrinsic mortality. Mortality can't explain everything, though: in particular, since mortality is always positive, resulting in strictly decreasing survivorship with increasing age, it can't explain species that don't age at all, or even age in reverse (with lower intrinsic mortality at higher ages).

It's difficult to come up with a general theory for non-ageing species, many of which have quite ideosyncratic biology; one might say that all ageing species are alike, but every non-ageing species is non-ageing in its own way. But one way to get some of the way there is to notice that mortality/survivorship isn't the only thing affecting age-specific reproductive output; age-specific fecundity also plays a crucial role. If fecundity increases in later ages, this can counterbalance, or even occasionally outweigh, the decline in survivorship and maintain the selective value of later life.

Mammals and birds tend to grow, reach maturity, and stop growing. Conversely, many reptile and fish species keep growing throughout their lives. As you get bigger, you can not only defend yourself better (reducing your extrinsic mortality), but also lay more eggs. As a result, fecundity in these species increases over time, resulting – sometimes – in delayed or even nonexistent ageing:

Mortality (red) and fertility (blue) curves from the desert tortoise, showing declining mortality with time. Adapted from Fig. 1 of Jones et al. 2014.

So that's one way a species could achieve minimal/negative senescence under non-adaptive theories of ageing: ramp up your fecundity to counteract the drop in survivorship. Another way would be to be under some independent selection pressure to develop systems (like really good tissue regeneration) that incidentally also counteract the ageing process. Overall, though, it seems to be hard to luck yourself into a situation that avoids the inexorable decline in selective value imposed by falling survivorship, and non-ageing animal species are correspondingly rare.

Next time in this series, we'll talk about the other major group of theories of ageing: adaptive ageing theories. This post will probably be quite a long time coming since I don't know anything about adaptive theories right now and will have to actually do some research. So expect a few other posts on different topics before I get around to talking about the more heterodox side of the theoretical biology of ageing.

  1. In discrete time, the survivorship function of a cohort will be the product of instantaneous survival over all preceding time stages; in continuous time, it is the product integral of instantaneous survival up to the age of interest. Instantaneous survival is the probability of surviving at a given age, and thus is equal to 1 minus the mortality at that age. ↩︎

  2. Exponential in continuous time; geometric in discrete time. ↩︎

  3. Lifetime reproductive output is equal to ∫∞0rada (in continuous time) or ∑∞a=0ra (in discrete time), where ra is the age-specific reproductive output at age a. ↩︎

  4. Williams (1957) Evolution 11(4): 398-411. ↩︎

  5. "Pleiotropy" is the phenomenon whereby a gene or mutation exerts effects of multiple different aspects of biology simultaneously: different genetic pathways, developmental stages, organ systems, et cetera. Antagonistic pleiotropy is pleiotropy that imposes competing fitness effects, increasing fitness in one way while decreasing it in another. ↩︎

  6. Which of the two is likely to predominate depends on factors like the relative strength of selection and drift (which is heavily dependent on effective population size) and the commonness of mutations that cause effects of the kind proposed by antagonistic pleiotropy. ↩︎

  7. My source for this is personal communication with Linda Partridge, one of the directors at my institute and one of the most eminent ageing scientists in the world. I'm happy to see any of these points contested if people think they have better evidence than an argument from authority. ↩︎



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Why aren’t we testing general intelligence distribution?

26 мая, 2020 - 19:07
Published on May 26, 2020 4:07 PM GMT

IQ supposedly measures general intelligence. Assuming the g-factor exist, why are we using IQ to measure it? IQ tests get tweaked so the end result will always form a normal distribution. I checked wikipedia to figure out why, but while it said some people claim that LCT is a good reason to believe the g-factor will have a normal distribution, it didn’t seem terribly confident (and it also didn’t give a source). Even “The Bell Curve” used an intelligence test that didn’t return a bell curve at all. They said they had to turn it into a bell curve to prevent 'skew':



But isn’t this putting the cart before the horse? Surely it would be interesting to see what distributions we would get if we did intelligence testing without a result in mind? I get that it is really difficult to measure, but if a test consistently gave us a different distribution, we could learn valuable insights into how human minds work. So is there a reason why we aren’t testing the hypothesis that general intelligence will follow a normal distribution?



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Spoiler-Free Review: Assassin’s Creed Odyssey

26 мая, 2020 - 17:10
Published on May 26, 2020 2:10 PM GMT

Also see: Plague in Assassin’s Creed Odyssey

Assassin’s Creed Odyssey was my (first?) quarantine game. For about a hundred hours, I’ve wandered around ancient Greece. The game is lengthy, expansive and huge. Perhaps too much!

A lot of that was working through my Odyssey itself. I also spent a bunch of that enjoyably side questing, both the scripted and generic varieties.

There was also a non-trivial amount of it merely admiring the scenery and enjoying the journey itself, including taking photographs. Some of those photographs might end up printed out on my walls. This is a gorgeous, gorgeous game. If you play it, play it on the largest screen available.

My game review format is typically to divide into sections by level of spoiler contained therein. The first section will be spoiler-free. The second will spoil things.

This is the spoiler-free review. It seeks to answer one question.

Should I Play This Game?

Short Answer

If you’re looking for a relaxing experience that lasts a long time, I highly recommend giving this game a shot. I think it mostly does its jobs exceedingly well, and I am very pleased with the time I spent on it. I have not played previous games in the series or many with similar mechanics, so I can’t fully compare to see if this is better or worse than those games. But my guess is this is near the top.

Assassin’s Creed: Odyssey is an open world, action RPG game set (mostly) in ancient Greece. Battles are real time, with success depending on choosing them wisely, fighting them well, and having the necessary levels and gear. Over the course of the game, you’ll do quests, gain experience and gold (technically drachme), improve your gear, choose new abilities and learn better how the game works.

The game can perhaps best be thought of as a sort of one-player MMO. It copies the core open-world structure and quest structure from such games, with a greater emphasis on the overarching plot.

The closest parallel I’ve played would be the excellent Elder Scrolls V: Skyrim. I am not sure which game is better. Skyrim is better at offering an RPG experience, planning in combat and more interesting managing and customization of abilities. Odyssey combat is more viscerally satisfying, but after a while it runs together. Odyssey has stealth and naval elements Skyrim is missing. Odyssey gets to be in Greece, so it has a better world. It also has a better main plot and more interesting side quests. Overall, I think the comparison is close.

Biggest advantages: Gorgeous open world to explore that does its setting justice, strong voice acting, satisfying combat and stealth play, strong central characters, great story and writing including side quests, lots of game to play, game length is very long, some meaningful choices.

Biggest disadvantages: Lack of deep strategy in combat or otherwise, hot spot gaming, some necessary going through side quests, game length is very long and can get repetitive, non-trivial time spent traveling on both land and sea that you may not want, lack of certain types of choices or interactions that would have been welcomed.

Overall rating: Tier 2 of 5. Not a Must Play, but definitely Worth It.

Further Details That Help You Answer

Combat as is now traditional is split in three. You can use a bow, you can try to sneak up on them and assassinate them, or you can fight with melee weapons. Fighting with melee weapons is about dodging and parrying their attacks then attacking back and timing your abilities wisely. Fighting with a bow doesn’t seem great in general, and forces you to divide your ability points, but is useful in spots. Stealth is always welcome where available.

One issue with combat is that you heal your health between battles, so often you’ll be rewarded for running away, healing and then coming back, even if you can’t pick up an additional stealth kill that way. Whereas your adrenaline, which powers special abilities, doesn’t refill outside of battle, so you’ll be tempted to seek extra easy battles to refill it, and/or to not use your cool abilities.

Imposing your own limitations on what you’re willing to do will help balance things out and also keep things fun. Choose your own path.

Odyssey tries to use its world to let you figure out where to go, offering you the choice as to whether to be guided or unguided. They tell you unguided is how the game was meant to be played. I believe them.

If you choose unguided, you’ll be left to listen carefully, look around and figure out where things are, target location icons get turned off.

The problem is that if you fail to locate even one thing, that will block your progress in the quest, which will in turn often will block you in the main quest. You end with a type of ‘hot spot gaming’ where if you don’t figure out what the game wants you to click on or which building the game meant, you wander around endlessly until you get it right.

I chose to therefore leave Guided on. The game still forces you to do enough looking around for my taste. If you remind yourself to look around and stop and smell the roses, rather than charging ahead to the icon, you’ll still get most of the good part of the experience. After a while, the game starts turning off the icon once you get close, so you’re forced to go semi-unguided anyway. It’s probably for the best. Perhaps the right thing to do is to be Unguided, but to flip Guided on whenever you spend more than a few minutes trying to find something and getting frustrated.

The game is long. Shin Megami Tensei levels of long. Completing the main quest lines is not going to happen in under about fifty hours. All the game has to offer is more like one hundred or more. That does not mean you can’t enjoy the game on less than fifty hours, but you will not resolve all the main plot points faster than that.

Some of this being long is that doing main quest tasks does not level you fast enough to keep pace with your enemies. You’ll need to do some number of side quests to keep up, which some players will find boring and repetitive. They did get somewhat repetitive in places, but I enjoyed the setting and flavor a lot, so I didn’t mind.

Reasons of time commitment and repetitiveness are the best reason to not play Odyssey. After a while a lot of what you do gets somewhat repetitive and bleeds together. One can only kill so many bandits, after all, in either Odyssey or Skyrim. Or, there could be another open world game that’s better or excites you more. They take a long time, so one can’t properly check out them all.

The game’s graphics, as noted in the introduction, are the most gorgeous I have ever seen in a video game. It is a delight simply to walk around and see the sights. Even after dozens of hours, this aspect never gets old. I cannot think of a setting that would outdo this one, on this level.

The voice acting is top notch as well, as are the background sounds and combat sound effects. That makes a real difference. It made it quite pleasant to sit back and listen to the stories the quests are telling, even when it’s clear I’m not making meaningful decisions.

The game’s setting is also rich in its cultural feel. They do a great job with the writing. It’s a great place to be. I even feel inspired to go back to the Iliad and Odyssey, and otherwise appreciate the legacy we’ve been given.

You will make choices. Some of them are easy. Others are not so easy, and feel like genuine dilemmas. There’s also the frequent choice of where to make extra effort to use stealth in order to not kill guards, who to make an effort to recruit instead of kill, how much to worry about who your horse runs over or what to do if civilians join the combat or put a bounty on your head. Some of those choices will matter a little. A few will matter a lot. Many of those choices won’t matter in a visible way. That doesn’t mean they didn’t matter. Dead is still dead.

A lot of your journey, and the things you must do, are essentially scripted. There are tasks you are forced to do to get to the end of your path. Not all of them are things I was (in my role as the character) thrilled to be doing. I definitely wished I had more choices and options in some places.

Your first choice is which of two characters to play. Everyone says Kassandra is awesome and you should play her. I chose Alexios instead anyway, and he was good too. Perhaps I missed out, but I think I enjoyed the game more this way.

I was happy to experience the plots of scripted side quests, and ended up doing most of them. Multiple people were often happy to watch (and listen) to those side quests, although they were of course more interested in the central moments and plot of the game, and also came largely for the scenery.

Historical accuracy is a mixed bag. There’s a certain amount of ‘ancient Greek soup’ going on, where as interesting bits and people as possible are thrown together at once. It mostly serves the game well.

By toggling on or off being guided, the difficulty level and a few other options, you get to choose your experience.

I found Normal with Guided to be a good place. I had challenging moments that kept things interesting, but the overall experience felt like a journey rather than a challenge. I self-handicapped in a few ways to keep the experience more fun, such as not optimizing my equipment until things got hard.

That should be enough information to decide. The game is on sale for the next two days on Playstation 4. You can get the complete edition for a $30 digital download, as opposed to $60 for Assassin’s Creed Origins. If this sounds like it appeals, I’d pick it up.

I hope to write a second, spoiler-filled discussion of the game, while it is still fresh.

In the comments to this post, please refrain from spoilers.

 

 

 



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Should I self-variolate to COVID-19

25 мая, 2020 - 23:29
Published on May 25, 2020 8:29 PM GMT

[This is currently in the speculative phase. I am not planning to do anything rash without very careful consideration.

Please do not read this question as an endorsement of the plan described herein.

Please use common sense and don't take dangerous actions without doing due diligence and carefully weighing the costs, benefits, and risks.]

As the coronavirus situation develops, it is becoming more plausible that the pandemic won't meaningfully end for months.

There travel that I would like to do (in the continental US), this year. But, even if I carefully self-isolate, I think it would be socially irresponsible for me to travel from place to place in the country, potentially being a vector for intercity transmission. (Not to mention that I would be visiting committed EAs, who I especially don't want to infect.)

I think the only way that it would be socially responsible for me to travel, is if I knew with very high confidence that I wasn't carrying the virus*. And the only way that I can have that confidence is if I have already caught it, recovered, and now have antibodies against the virus.

I am young (26), healthy, and currently in self isolation. My personal risk of death or disfigurement from catching the virus seems low. (One crux for that is that the risk of developing permanent chronic fatigue is low.)

I'm tempted to intentionally expose myself to (ideally, a controlled dose of) the virus, strictly self-isolate for the contagious period (with careful regular testing of my symptoms), confirm my resistance to the virus with an antibody test, and then travel freely, knowing that I am not a vector for the virus.

I'm wondering if this plane is actually practical.

* - I suppose that another possibility is that, in every city that I visit I could rent an AirBnB and strictly self isolate for 14 days. That seems both expensive and onerous.

The most important question:

Is this a stupid idea?

More specifically,

  • What is the best up to date information on the fatality risk to different age groups?
    • I'm still using the 0.2% fatality rate for 20 to 29 year olds, from China, early in the pandemic. Do we have better numbers now? How much does that figure overestimate, because of asymptomatic cases that are not counted among the "number of infected" denominator?
  • Do we have good information about how much a low initial viral load reduces the probability of developing symptoms or the probability of hospitalization?
    • Do we have anything like the response curve to different initial exposure levels?
  • What is the state of the evidence for long term chronic fatigue resulting from COVID? Did that question get settled?
  • Are there risks that I'm failing to account for?
Logistical questions:
  • Are there any existing variolation projects that I can join?

If not,

  • What is the best way to intentionally catch COVID?
    • Is there a way that a private citizen can get their hands on a sample of the virus?
    • If not, what strategy would result in my catching the virus with high reliability, but low initial viral load? (I would rigorously self-isolate as soon as I was exposed, so it would be really annoying if the virus didn't take hold, but I went into strict isolation needlessly, and then needed to expose myself and isolate again.)
  • What is the ideal dose for a mild case?
  • For how long, counting from the moment I'm exposed to the virus, am I at risk of being contagious? What is the maximum length of time that I could reasonably still be contagious? I would use an antibody test to confirm, but I would probably only have limited tests, and would want to time when to use them.
  • Is there anything else that I'm neglecting here?

I haven't been following this situation closely since entering self isolation, so I would be happy to read answers that merely link to existing articles or LessWrong answers that address my questions.

Again, please don't do anything stupid because I asked this question!



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A Taijitu symbol for Moloch and Slack

25 мая, 2020 - 23:03
Published on May 25, 2020 8:03 PM GMT

There is a balance between Moloch (which I think of as the forces of exploitation) and Slack (which I think of as the forces of exploration).

Scott Alexander writes:

Think of slack as a paradox – the Taoist art of winning competitions by not trying too hard at them. Moloch and Slack are opposites and complements, like yin and yang. Neither is stronger than the other, but their interplay creates the ten thousand things.

LW is mostly not a design website, but Scott's post inspired me to make a symbol for this concept. It's a Taijitu of Moloch and Slack as created by the inadequate equilibria and the hillock:





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Baking is Not a Ritual

25 мая, 2020 - 21:08
Published on May 25, 2020 6:08 PM GMT

I started baking about 2 years ago. Since I became a frequent supplier of baked goods in the office, a lot of people have come to me for baking advice. I’ve noticed a trend of comments about baking that all share a common root cause.

See if you can spot the common failure mode that led to these very-paraphrased comments:

  • “Baking is too precise for me. I want to bake without following recipes exactly, but I feel like I can’t improvise.”
  • “I tried making this. I left out ingredient Y because I didn’t have it and the recipe only needed a little bit of it. Why didn’t it work out?”
  • “I tried doing step X for exactly N minutes this time and that worked well. Oh, you’re saying that duration doesn’t matter? Well, it worked for me so I think I’ll just keep doing it just in case.”
  • “I always have to repeat a new recipe a bunch before I stop ruining it every other time.”

The misconception that leads to these comments is treating a baking recipe like a ritual and blindly following it, without attempting to understand the baking process at a gears-level.

Many people seem to approach baking like it is a ritual, where one follows a recipe exactly and some magic happens to produce the baked goods. Things will go mysteriously wrong if you stirred the cauldron counter-clockwise or added the water too early. In reality, baking is combining and heating up a series of ingredients so they undergo physical and chemical reactions to achieve certain texture and taste. There are underlying principles that govern the process of baking and the results.

Looking at a recipe and following the steps works fine a lot of the time, if a recipe is good and has the right details. However, if one treats the baking process as a black box ritual, without understanding the underlying mechanisms, one can run into troubles, such as:

  • Unable to improvise or change a recipe
  • Not knowing which parts of the recipe matter, i.e. have a strong effect on the end-results. Some people end up compensating for this by just trying to do everything super precisely, even when some of the steps don't make any difference.
  • Unable to react when something goes wrong, like a missing ingredient, or a mistake in an earlier step.

The right way to approach baking is to realize it is not a ritual. Instead, try to understand the principles of how baking works, to understand why an ingredient is in the recipe, and why a particular step is needed. Some examples of gears-level principles are:

  • Acidity interacts with baking soda to create bubbles, so don’t leave out lemon juice in a recipe that calls for baking soda
  • Kneading a wheat dough folds and so strengthens the gluten, which makes the end product more chewy, which is commonly desirable in bread but not in cakes or biscuits
  • Eggs acts as an emulsifier to help combine water and oil ingredients. Don’t skip it in recipes where an emulsifier is needed, but they’re optional in recipes that don’t need an emulsifier.
  • A wetter dough rises more, so add more liquid ingredients if you want a fluffier result, and less if you prefer a denser version.

Understanding these underlying principles can make recipes flexible. One can easily make tweaks if one knows why each ingredient is needed and how it affects the final result. For instance, this apple bread is one of my staple recipes. I once baked it while I was short one egg, but I knew it was not a key ingredient in this recipe (i.e. it did not act as an emulsifier), so I compensated by adding some extra butter and some yogurt to make up for the missing egg’s fat and liquid content - it turned out just fine. I’ve also adapted this recipe to use cherries instead of apples, because I know the fruit part can be fully swapped out.

Cherry bread adapted from an apple bread recipe

Understanding the baking process also means knowing which steps of the process is important, and which are not. This lets one focus on key parts, but be “lazier” with parts that either do not matter or can be easily adjusted later. For instance, the exact amount of vanilla extract doesn’t make a difference in my recipe above, so instead of dirtying yet another spoon to measure exactly ¼ teaspoon of vanilla extract, I just give the bottle a squirt and call it a day. Another example, I know that additional flour can be easily added when kneading a yeast dough, so while many people swear by precisely measuring flour by weight, I can be lazy and approximate when measuring out flour by erring on the side of adding less to start, then sprinkle in more as needed.

Yeast bread, after kneading and the final product

On the other hand, mixing in cold, pea-sized butter is important for achieving the flaky crumbly texture of biscuits, so even though it’s more work, I grate my butter and take care to keep it cold throughout, sometimes even running my hands under freezing water before working with the biscuit dough.

Cold, pea-sized chunks of butter in the dough is crucial to making biscuits flaky and crumbly, so don’t take the easy way out by melting the butter or substituting with canola or olive oil.

Understanding the baking process can help one understand new recipes, because many recipes share the same underlying principles. If it’s a recipe for something similar to baked goods I’m familiar with, I can often evaluate it at a glance and draw conclusions like “oh this step is probably unnecessary” or “I don’t have X but I can substitute with Y”. My friends find it helpful to run a new recipe by me before they begin, as I can often highlight key steps to them and give advice even if I’ve never used that recipe.

Realizing that baking is not a ritual and that there are underlying principles is often sufficient for people to seek out these principles and improve. One additional tip is, when learning to make something completely new, don’t try to perfectly follow one recipe. Instead, look at multiple recipes for the same item. Many recipes on the internet are accompanied by blog posts and comments. These often contain tips and advice at the gears-level and give insights into why a certain amount of an ingredient is needed, and how a certain step would affect the outcome. Paying attention to not only the recipe but also reading through these advice when learning to bake something new allows one to have a much greater success rate, even on the very first attempt.

I was challenged to attempt a souffle. Not perfect, but it did rise on my first try after I researched extensively on how beating and folding in the egg whites makes the souffle airy.

In conclusion, many people I talked to seem to believe baking is a ritual, where you have to follow recipes exactly to be successful. They never open the blackbox and therefore lack the understanding of baking at a gears-level. When one grasps that baking is not a ritual and learns the principles behind the ingredients and the steps in baking, one can easily make adjustments, adapt recipes, and be more successful.



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What is a decision theory as a mathematical object?

25 мая, 2020 - 19:20
Published on May 25, 2020 1:44 PM GMT

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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')}

If we want a space of all decision theories, what mathematical objects does it contain? For example, if a decision theory is a function, what are its domain and codomain?

The only approach I'm familiar with is to view expected utility maximizing decision theories as ways of building counterfactuals (section 5 in the FDT paper). A decision theory could then be described as a function that takes in a state s and an action a and spits out a distribution over world states that result from counterfactually taking action a in state s.

But EDT, CDT and FDT require different amounts and kinds of structure in the description of the state s they take as input (pure probability distributions, causal models and logical models respectively), so this approach only works if there is some kind of structure that is sufficient for all decision theories we might come up with at some point.



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An addendum on effective population size

25 мая, 2020 - 15:35
Published on May 25, 2020 12:35 PM GMT

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Follows from: Evolution is Sampling Error

A lot of people commenting on my previous post (mainly on Facebook) were confused about the difference between effective and census population size. I think the second response is reasonable; for non-experts, the concept of effective population size is legitimately fairly confusing. So I thought I'd follow up with a quick addendum about what effective population size is and why we use it. Since I'm not a population geneticist by training this should also be a useful reminder for me.

The field of biology that deals with the evolutionary dynamics of populations -- how mutations arise and spread, how allele frequencies shift over time through drift and selection, how alleles flow between partially-isolated populations -- is population genetics. "Popgen" differs from most of biology in that its foundations are primarily hypothetico-deductive rather than empirical: one begins by proposing some simple model of how evolution works in a population, then derives the mathematical consequences of those initial assumptions. A very large and often very beautiful edifice of theory has been constructed from these initial axioms, often yielding surprising and interesting results.

Population geneticists can therefore say a great deal about the evolution of a population, provided it meets some simplifying assumptions. Unfortunately, real populations often violate these assumptions, often dramatically so. When this happens, the population becomes dramatically harder to model productively, and the maths becomes dramatically more complicated and messy. It would therefore be very useful if we could find a way to usefully model more complex real populations using the models developed for the simple cases.

Fortunately, just such a hack exists. Many important ways in which real populations deviate from ideal assumptions cause the population to behave roughly like an idealised population of a different (typically smaller) size. This being the case, we can try to estimate the size of the idealised population that would best approximate the behaviour of the real population, then model the behaviour of that (smaller, idealised) population instead. The size of the idealised population that causes it to best approximate the behaviour of the real population is that real population's "effective" size.

There are various ways in which deviations from the ideal assumptions of population genetics can cause a population to act as though it were smaller – i.e. to have an effective size (often denoted Ne) that is smaller than its actual census size – but two of the most important are non-constant population size and, for sexual species, nonrandom mating. According to Gillespie (1998), who I'm using as my introductory source here, fluctuations in population size are often by far the most important factor.

In terms of some of the key equations of population genetics, a population whose size fluctuates between generations will behave like a population whose constant size is the harmonic mean of the fluctuating sizes. Since the harmonic mean is much more sensitive to small values than the arithmetic mean, this means a population that starts large, shrinks to a small size and then grows again will have a much smaller effective size than one that remains large[1]. Transient population bottlenecks can therefore have dramatic effects on the evolutionary behaviour of a population.

Many natural populations fluctuate wildly in size over time, both cyclically and as a result of one-off events, leading to effective population sizes much smaller than would be expected if population size were reasonably constant. In particular, the human population as a whole has been through multiple bottlenecks in its history, as well as many more local bottlenecks and founder effects occurring in particular human populations, and has recently undergone an extremely rapid surge in population size. It should therefore not be too surprising that the human Ne is dramatically smaller than the census size; estimates vary pretty widely, but as I said in the footnotes to the last post, tend to be roughly on the order of 104.

In sexual populations, skewed sex ratios and other forms of nonrandom mating will also tend to reduce the effective size of a population, though less dramatically[2]; I don't want to go into too much detail here since I haven't talked so much about sexual populations yet.

As a result of these and other factors, then, the effective sizes of natural populations is often much smaller than their actual census sizes. Since genetic drift is stronger in populations with smaller effective sizes, that means we should expect populations to be much more "drifty" than you would expect if you just looked at their census sizes. As a result, evolution is typically more dominated by drift, and less by selection, than would be the case for an idealised population of equivalent (census) size.

  1. As a simple example, imagine a population that begins at size 1000 for 4 generations, then is culled to size 10 for 1 generation, then returns to size 1000 for another 5 generations. The resulting effective population size will be: Ne=1091000+110≈91.4. A one-generation bottleneck therefore cuts the effective size of the population by an order of magnitude. ↩︎

  2. According to Gillespie again, In the most basic case of a population with two sexes, the effective population size is given by Ne=(4α(1+α)2)×N, where alpha is the ratio of females to males in the population. A population with twice as many males as females (or vice-versa) will have an Ne about 90% the size of its census population size; a tenfold difference between the sexes will result in an Ne about a third the size of the census size. Humans have a fairly even sex ratio so this particular effect won't be very important in our case, though other kinds of nonrandom mating might well be. ↩︎



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Why Rationalists Shouldn't be Interested in Topos Theory

25 мая, 2020 - 08:35
Published on May 25, 2020 5:35 AM GMT

I spent a lot of the last two years getting really into categorical logic (as in, using category theory to study logic), because I'm really into logic, and category theory seemed to be able to provide cool alternate foundations of mathematics.

Turns out it doesn't really.

Don't get me wrong, I still think it's interesting and useful, and it did provide me with a very cosmopolitan view of logical systems (more on that later). But category theory is not suitable for foundations or even meant to be foundational. Most category theorists use an extended version of set theory as foundations!

In fact, its purpose is best seen as exactly dual to that of foundations: while set theory allows you to build things from the ground up, category theory allows you to organize things from high above. A category by itself is not so interesting; one often studies a category in terms of how it maps from and into other categories (including itself!), with functors, and, most usefully, adjunctions.

Ahem. This wasn't even on topic.

I want to talk about a particular subject in categorical logic, perhaps the most well-studied one, which is topos theory, and why I believe it be to useless for rationality, so that others may avoid retreading my path. The thesis of this post is that probabilities aren't (intuitionistic) truth values.

Topoï and toposes

A topos is perhaps best seen not even as category, but as an alternate mathematical universe. They are, essentially, "weird set theories". 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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')} F:C→Set, for C an arbitrary category.

(Functors assemble into categories if you take natural transformations between them. That basically means that you have maps F(c)→G(c), such that if you compare the images of a path under F and G, all the little squares commute.)

Consider that natural numbers, with their usual ordering like 4≤5, can form a category if you take instead 4→5. 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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')} N→Set, which are really just sequences of sets, like

X1→X2→X3→⋯

where the arrows are regular set theoretic functions. You can do practically any kind of mathematical reasoning using sequences of sets! (as long as it is constructive) For example, you have

  • an "empty set", which is just a sequence of empty sets;
  • a "point" given by a sequence of points;
  • "products" of sequences given by {Xi×Yi};

and so on. Most interestingly, you have truth values given by subobjects of the point; accordingly, in Set those are the empty set and the point itself, since P(∗)={∅,∗}, corresponding to true and false. Notice that ∅⊆∗; in fact the truth values in general will have the structure of a partially ordered set.

What are our truth values here? What is a subobject of a sequence of points? For one, each Xi has to be a subset of ∗. And there are no maps ∗→∅ ; so each "truth value" will look like

∅→∅→⋯→∅→∗→∗→⋯

a bunch of empty sets and, at some position n, all points, meaning that we have as many truth values as natural numbers. This is our first glance into the cosmopolitan nature of topos theory: weird truth values! Notice, however, that if n≤m, their corresponding subobjects will have this ordering reversed (an exercise left for the already knowledgeable reader); so in the end it might have been better to use functors on Nop , natural numbers with their order reversed.

To sum up, we made the category SetN of sequences of sets, and realized that it was a topos with truth values Nop. Isn't it that interesting...

Topoï-logical

Turns out there's a big connection between toposes and topological spaces.

The open sets of a topological space have the structure of a partially ordered set, if you set U≤V whenever U⊆V. Moreover, in that poset, you can describe U∩V as the greatest lower bound of U and V, and U∪V as their least upper bound.

This is in fact (almost) exactly the structure we want of our topos-theoretic poset of truth values: the greatest lower bound corresponds to conjunction p∧q and least upper bound is the disjunction p∨q. So we can use topological spaces as spaces of truth values, and this is in fact the approach used in Heyting semantics of intuitionistic logic.

(so each open set, and not point, corresponds to a truth value; you take the AND of two open sets to be their intersection and the OR to be their union)

Alright, so as with N, the poset of open sets can define a category if you set U→V whenever U≤V. So take X a topological space and O(X) its category of open sets. We'll define a new category Sh(X) of functors O(X)op→Set, except that they won't be all of the functors, only those that preserve the topo/logical structure (sheaves).

Guess what? Not only is Sh(X) a topos, turns out that its truth values are isomorphic to O(X)! And since the truth values are the subobjects of the point, that means that the points of the topos are in fact shaped like X...

Now we have a fuller view of the logically cosmopolitan view that topos theory can bring us. You can create, just like that, a whole parallel mathematical universe of bizarro sets where everything is made up of, say, donuts. Or coffee cups. It is as you wish.

Where's my Bayesian topos?

Since I am at heart a LessWronger, and since I care deeply about problems of logical induction and logical counterfactuals and whatnot, I spent a while trying to design a topos that would behave like manipulating probabilities. Or distributions. Or something. With the objective of making something that would represent beliefs.

Well, I'm sorry, but it doesn't work.

At minimum, we would expect that the truth values of this topos be probabilities, yeah? And with the cosmopolitan principle above, we could then just take the sheaves on this poset of probabilistic truth values.

So these truth-values would be order-isomorphic to [0,1]. But for them to actually represent probabilities, we'd want that p∧q=pq, and yet the order on [0,1] already prescribes that p∧q=min(p,q) , and we are doomed from the start.

Furthermore, even in an intuitionistic logic, the provable statements all have the maximal truth value (which here would be 1); but we all know that 0 and 1 are not probabilities, and so nothing should be provable... which seems like it wouldn't be very useful.

All in all, I'm truly sorry you had to bear through all of the math above just for this conclusion. It's still pretty cool, though, right?

(Geometric) topoï aren't reflective

In order to legitimately use topos theory for rationality, we should have a way for the topos to "think about itself". Analogously to the situation in Peano arithmetic, for a topos E, we'd want some object E∈E (specifically, an internal category) to be isomorphic to E in some sense.

We can define an "element" of an object E∈E as being an arrow ∗→E . So the objects of the internal category are given by the set Hom(∗,E), and in fact the functor Hom(∗,E):E→Set respects the structure of the internal category enough that it becomes a category internal to Set, which is just a small category.

But wait. The toposes generated by sheaves on a topological space are at least as big as Set, but the collection of all sets is too big to be a set, and thus we run into size issues.

It should in principle be possible to do so in small toposes, such as the free (as in syntactic) topos, but I am not sure and will refrain from claiming so. It is however certainly possible to do so in list-arithmetic pretoposes (yes it's a mouthful), as shown by André Joyal in his as of yet unpublished categorical proof of Gödel's incompleteness theorems, which I have studied with him last year.

What now?

It now seems to me that linear logic might be the "right" weakening of classical logic into something probabilistic. I still need to figure out some of the details, but let's say that the work has already been done, and one need only piece it together into something relevant to rationality and agent foundations. Particularly promising is that some claim that linear logic is a good setting for "paraconsistent" logic (logic that deals gracefully with contradictions), which could make it work for logical counterfactuals.

All this and more in my next post, pretentiously monikered "Probabilistic Monads".



Discuss

What are the best tools for recording predictions?

24 мая, 2020 - 22:15
Published on May 24, 2020 7:15 PM GMT

Preferably, follow rules from "Best Textbooks in any Subject." I'm interested in people who have tried 2-3 prediction-recording tools, and can argue why one is better than the others.

I've decided I finally want to get gud at calibration. 

I'm personally just interested in tracking my own predictions, often about private things. I'd prefer something very low-friction where I can record my own predictions, mark them as true/false later on using my own judgment, and automatically see a graph of how calibrated I am.



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Notes about psychopharmacology

24 мая, 2020 - 22:03
Published on May 23, 2020 7:31 PM GMT

I made public my notes about psychoactive compounds, focused on functional/medical use, so that they can be useful for other people.



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[Meta] Finding free energy in karma

24 мая, 2020 - 17:47
Published on May 24, 2020 2:47 PM GMT

As I've been posting here on LessWrong for a few months now, I wanted to share two things I've noticed about the karma system. These are ad-hoc observations, not rigorous empirical results, and while they might feel like ways to "cheat" the system, I hope by making them public and known, it serves to make the "market" for karma more efficient instead.

1. [Fairly confident] Posts appear on the front page unevenly throughout the week. As best I can tell there's a lull on Sunday and a spike on Monday/Tuesday. I'm not sure if this is due to unevenness in when people write, or unevenness in when mods promote posts to the front page. Regardless, since your post's karma depends in part on how long it remains on the front page (which provides more eyeballs), and that depends in part on what other posts it's competing against, there is opportunity here. Consider delaying publishing your post to a less busy time of the week.

1. [Less confident] Automatic cross-posting is a self-fulfilling karma signal. Having automatic cross-posting set up requires both sufficient author interest and sufficient moderator trust, since it's still a fairly manual process. This means that auto-x-posting is a fairly significant indicator of status/karma in the community, and such posts tend to do better in part as a self-fulfilling prophecy (of course they often do better because they're unusually good too - the karma system isn't broken, just slightly unbalanced). There's no way to replicate the automatic-x-post indicator in a manual post, but even just putting "[Cross-posted from MyBlog]" at the top of the text seems to provide a small boost.



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Plague in Assassin’s Creed Odyssey

24 мая, 2020 - 15:00
Published on May 24, 2020 12:00 PM GMT

Spoiler Alert: Contains minor spoilers for Assassin’s Creed Odyssey

Assassin’s Creed Odyssey has become my quaratine game. A gorgeous tour of ancient Greece is a great antidote to never going outside.

One noteworthy thing in the game is how it deals with plague. You encounter it twice.

On the game’s introductory island of Kefalonia, you can run into a side quest called The Blood Fevor. There is a plague, and priests are attempting to contain it… by killing off the families of the infected.

You can either allow this, or you can forcibly prevent it, in which case you have to kill the priests.

If you do not prevent the excutions, you are called a muderer. You try to ‘reassure’ your friend that this was necessary. Later in the game, the Oracle of Delphi tells you that you’re a bad person, because you let yourself be bossed around by an unknown plague.

If you prevent the executions, you are called a hero. Later in the game, you are informed a plauge has spread throughout Kefalonia. No one makes the connection or blames you.

If you also let them keep their money, you are told the plauge has spread throughout Greece. So being superficially generous makes things much, much worse.

This all feels right and seems to explain a lot. It is clear what must be done, but the incentives are all wrong. Even when you are right you still get punished for allowing others to take action. If you prevent others from taking action, even with disasterous consequences, you are considered a hero.

Later, you are given a quest to warn about The Plague of Athens. The plauge is historical, so it can’t be prevented. Warning about it has no effect. Again, this feels right.

When the Plague of Athens does hit, you are given the task to burn some of the bodies to prevent further spread, as Hippokrates (yes, that one, these games are like that) has figured out this will help prevent further infection. The Followers of Ares try to stop you for religious reasons, and you have to kill them. Once again, society primarily moves to violently prevent action.

Then Pericles breaks quarantine and goes outside when everyone tells him not to, and gets himself killed for it. That part is not historical. It felt right in context.

Experiencing the game helped me process why we’ve reacted to today’s plauge the way we did.

 



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Why We Age, Part 1: What ageing is and is not

24 мая, 2020 - 12:06
Published on May 24, 2020 9:06 AM GMT

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At the LessWrong European Community Weekend 2018, I gave a talk explaining the intuition behind non-adaptive theories of the evolution of ageing. This blog post and its followup are adapted from that presentation.

When people find out that I did my PhD in the biology of ageing, they tend to ask one of two questions. First, they ask what they can do to live longer. Second, they ask why people age in the first place. My answer to the first question is unfortunately fairly boring at present -- don't smoke, eat well, get enough exercise, get enough sleep, et cetera -- but when it comes to the second I think I have more of interest to say. So let's dive into the important and fractious question of why we age.

What is ageing?

It is a curious thing that there is no word in the English language that stands for the mere increase of years; that is, for ageing without its connotations of increasing deterioration and decay.

—Peter Medawar, "An Unsolved Problem in Biology"

When people talk about "ageing", there are broadly speaking three different things they might mean[1]. Firstly, there is the simple process of getting older -- of the amount of time since you were born inexorably increasing. Let's call this process "temporal ageing". Ageing in this way has a lot of benefits: more memories, more experience, and with luck more self-knowledge and more wisdom.

Unfortunately, the benefits conferred by temporal ageing are currently inextricably tied to the physical changes denoted by the second meaning of "ageing": a generalised physiological deterioration, characterised by a wide range of unfortunate symptoms affecting almost every system of the body. As a result of this second kind of ageing, we become slower, more fragile, more prone to disease, and generally more likely to experience impaired health and wellbeing as we get older, eventually leading to death. As we as a civilisation have gradually eliminated more and more extrinsic forms of suffering and death, the depredations of ageing have gradually become the primary cause of ill health and death in developed countries by an overwhelming margin. This is the kind of ageing people mean when they worry about getting cancer or dementia, buy "anti-ageing" skin cream, or invest in real anti-ageing research; it's the province of doctors, physiologists, and molecular biologists. Let's call it "physiological ageing".

Finally, the individual changes taking place due to physiological ageing give rise to a distinctive statistical pattern at the level of entire populations of humans or other animals: a progressive increase in mortality (probability of dying) and decrease in fecundity (expected number of offspring) in older age cohorts. This pattern is what gives rise to plots like the ones below, and it's what demographers, actuaries and evolutionary biologists generally mean when they talk about "ageing". From this perspective, the specific functional changes underlying these changes in survival and reproduction are less important than the high-level functional changes that result: changes in the rates of reproduction, illness, disability, and death. From an evolutionary perspective it is the first and last of these, reproduction and death rates, that are the most important. We can call this final meaning of the word ageing "demographic ageing".

Logarithmic mortality curves for British and American populations at different points in the 20th century. The y-axes give the log-probability of dying for individuals in a given age-class in the year and country indicated. Source: US Office of Retirement and Disability Policy

These two phenomena, of physiological and demographic ageing, are tightly interlinked in any given population but are nevertheless conceptually distinct: two different species (an insect and a mammal, say) could undergo very different physiological ageing processes but exhibit very similar patterns of demographic ageing. Physiological and demographic ageing also give us very different perspectives on the question of why we age. From the perspective of physiological ageing, the question is generally asking about the specific genetic, molecular, histological or physiological mechanisms underlying the changes we observe: what particular aspect of our biology is causing our bodies to deteriorate with age in this or that particular way? From the perspective of demographic ageing, the relevant why question is simpler and more fundamental: given that ageing appears to be pretty deleterious to the survival and reproduction chances of any individual experiencing it, how could we have evolved to exhibit declining functionality with age at all?

In these posts, I'll be focusing on the second kind of why question, discussing the evolutionary teleology of the ageing process. As we'll see, from that perspective, ageing is frankly pretty weird.

Three puzzles of ageing

When we look at nature, or at ourselves, we observe something surprising: animals get old. In many, many different species, old age is accompanied by a progressive decline in functionality, leading to higher rates of death[2] and lower rates of reproduction. This pattern is seen almost everywhere you look in the animal kingdom, suggesting that it has either evolved again and again independently or been retained after inheritance from a common ancestor. Yet despite this commonality, there is profound variation in the details of the ageing process: even closely related species can differ dramatically in how quickly they age and how long they tend to live. And here and there, we see species that seem to have escaped the iron grip of ageing, exhibiting mortality that stays constant or even declines over time.

Distribution of lifespans across all mouse and bat species from the AnAge database (accessed 2018-09-01). Despite their relatively close relationship, similar size, and similar metabolic rates, bats live dramatically longer than mice.

This, then, is the threefold puzzle of ageing. Why should a process that appears to be so deleterious to the individuals experiencing it have evolved to be so widespread in nature? Given this ubiquity, which implies there is some compelling evolutionary reason for ageing to exist, why do different animals vary so much in their lifespans? And how, when ageing has either evolved or been retained in so many different lineages, have some animals evolved to escape it?

Any successful theory of the evolution of ageing must be able to convincingly answer all these questions. A number of attempts have been made over the years, none of which has managed to capture the consensus of the academic community. These attempted explanations can be broadly divided into two groups: those that propose with some reason why ageing, which seems so deleterious, is adaptive after all, and those that accept that ageing is deleterious and attempt to explain why it might evolve anyway. I'll discuss the main representatives of each group in separate blog posts, but first I want to tackle one simple non-adaptive theory that doesn't quite manage to do the job.

Why ageing is not (just) wear and tear

The senescence of human organs consists not of their wearing out but of their lack of replacement when worn out.

George C. Williams, "Pleiotropy, Natural Selection, and the Evolution of Senescence"

One common folk theory of ageing is that it is simply wear and tear: like a car, the body is a machine, and like any machine it wears out over time. Exposure to the environment naturally leads to the accumulation of damage, which progressively impairs the function of the machine until it breaks down (i.e. we die). Any imperfection in the machine's components will hasten this process, either by generating more damage or by becoming progressively more dysfunctional over time. This progressive degradation is inevitable: we can keep a car in working order with regular maintenance and repair, but we are not (yet) capable of doing this for the kinds of wear and tear that accumulate in the body. Hence, ageing.

This explanation of ageing is intuitive, and parts of it are true as far as they go. There are certainly various kinds of damage and dysregulation that accumulate in the body with age: genetic mutations, senescent cells, shortened telomeres, cross-linked chemical aggregates, degraded stem-cell niches, and on and on. If we could remove and correct some or all of these issues the way we can replace a dodgy spark plug, we'd go a long way towards addressing the problem of physiological ageing.

But as an explanation of why ageing exists in the first place, "wear and tear is inevitable" just doesn't cut it, because a living body is not like a car. Where a car is dead matter shaped by external tools, a body is a dynamic, self-generating system with incredible powers of self-repair. These self-repair processes are awe-inspiringly good: of the tens of thousands of genetic mutations that occur per cell every day in the human body, virtually all are accurately repaired. Our bodies can repair wounds, fight off infections, kill and replace malfunctioning cells, partially regrow (some) organs, even remodel their bones to best respond to the forces they experience. Many of these regenerative processes decline as we age, but that decline is itself part of the ageing process: young children are amazingly good at healing without scarring, for example.

So while bodily damage is inevitable as part of the daily business of living, our bodies successfully repair almost all of it, especially when we're young. Evolutionarily speaking, the question is not why the damage occurs, but why it is permitted to accumulate. It seems our bodies' repair processes are not quite perfect, and allow damage and dysregulation to progressively accumulate over time. Why aren't they better?

Could our bodies' repair systems be better? They could certainly be worse: there are many, many mammal species with much shorter lifespans than humans', even when kept in very safe conditions. These animals age faster than humans because they accumulate damage and dysregulation faster; for whatever reason, their monitoring and repair systems have evolved to be that much sloppier than ours[3]. Conversely, there are at least a few mammals (such as bowhead whales) that live longer than we do; clearly they have something going for them that we don't, but why? And that's without going into animals like green hydra or naked mole rats that don't seem to age at all: if they can do it, why can't we?

Because it's not an evolutionary theory, wear-and-tear is incapable of addressing these questions. Yes, damage is inevitable, but why does this result in such different rates of ageing in different species? If one species can evolve to remove this damage so efficiently that it doesn't age at all, what is preventing most other species from doing the same? The answers to these questions don't lie in the eternal inevitability of molecular damage, but in the selective pressures each species is exposed to across evolutionary time. In the rest of this series, I'll address theories of ageing that attempt to explain ageing in these terms.

  1. Actually, there's a fourth meaning that gets used in the media quite a lot: "population ageing", by which is meant an increase in the median age of a population and the proportion of old people due to changes in social conditions. This is distinct from my "demographic ageing" in that the former is looking at the age composition of the whole population, while the latter is comparing different age groups within the population. I don't plan on talking about population ageing here. ↩︎

  2. This increase in death rate is both intrinsic and extrinsic: older individuals are more likely to die from heart attack, stroke, cancer and so on, but are also more vulnerable to predation, starvation and disease. ↩︎

  3. I've left out an important consideration here, which is that rather than worse repair systems, these other mammals might be experiencing higher rates of damage, perhaps due to a higher metabolic rate. A repair system with the same stringency that is exposed to a higher level of damage will let more damage events through. Even if this is true, though, the question remains of why these animals haven't evolved better repair mechanisms to cope with this higher rate of damage. ↩︎



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LessWrong v2.0 Anti-Kibitzer (hides comment authors and vote counts)

24 мая, 2020 - 02:44
Published on May 23, 2020 11:44 PM GMT

About 10 years ago, I wrote the original LessWrong 1.0 anti-kibitzer. The original rationale for creating it was as a de-biasing tool to aid in judging ideas by their contents, rather than their author or popularity. The original post goes into the rationale for its creation in more depth so I won't repeat myself here.

As such, I went back and updated it for the modern site. Follow the link to find the UserScript, some screenshots of it in action, and installation instructions. I have also added an additional feature which replaces the usernames with colorful bars of different lengths and colors. This makes threaded conversations easier to follow, as it enables one to tell when two comments are written by the same individual.

Let me know in the comments if you find any bugs or have any feature requests.



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Opposing a hierarchy does not imply egalitarianism

23 мая, 2020 - 23:51
Published on May 23, 2020 8:51 PM GMT

Epistemic status: just thinking aloud...

Trigger warning: bullying (without details)


I have repeatedly seen the claim that people are egalitarian in nature. It goes roughly like this:

Before invention of agriculture, people didn't have hierarchies. Of course, some people were always stronger or more skilled than others. But when we observe the behavior of hunter-gatherer tribes today, we see that they have strong social norms against bragging. For example, a superior hunter always downplays his success, and attributes it to luck rather than his own skills. (And the rest of the tribe often plays along, ritualistically mocking the hunter and his catch.) A hunter violating this social norm would antagonize everyone, and he knows he cannot afford it. This shows that people naturally oppose hierarchies.

I am not an anthropologist, and I could only guess how our ancestors really behaved. This article is not meant as a positive statement about the actual behavior of our ancestors, but rather as a criticism of the argument, which I will shorten to: "I have seen people oppose a hierarchy, which means that they are egalitarians."


Allow me to illustrate what I mean, using a simple model:

Imagine a completely fictional social environment; let's call it "high school". As a simplification, imagine that it contains three kinds of people. First group, let's call them "normies", is average at everything. Second group, let's call them "jocks", is superior at physical skills, and inferior at mental skills. Third group, let's call them "nerds", is superior at mental skills, and inferior at physical skills. The typical behavior observed at the schoolyard is "jocks" bullying "nerds".

An external observer, focusing on a subset of facts, could easily conclude that the high school is an exemplar of egalitarian society, and describe it thusly:

Before becoming adults, children do not have hierarchies. Of course, some children are stronger or smarter than others. But when we observe the behavior at schools today, we see that they have strong social norms against bragging. For example, a student superior at math will try to hide his or her skills in the classroom, to avoid antagonizing classmates. Otherwise, he or she would likely be bullied. This shows that children naturally oppose hierarchies.

And when you read it like this, it sounds kinda convincing.

And when you ask a random student, they would probably tell you there obviously is a hierarchy at the high school, with "jocks" on the top. (No one is bullying them for demonstrating their superior skills in sports, right?)


Again, I have no idea about our ancestors, or about current primitive tribes, -- and I am not even sure how reliably the latter can be used as a model of the former, -- but I can imagine a simple fictional model which would produce the behavior observed as an evidence for egalitarianism, without actually being egalitarian.

Again, as a simplification, let's have three kinds of primitive people. The "normies" are average or below average at everything. The "warriors" are superior at violence against people. The "hunters" are superior at hunting animals.

I would expect the "warriors" to be at the top of status hierarchy, because their skills are directly useful for non-lethal conflicts between individuals. And if that happens, I would assume that "hunters", despite providing most of the meat for the tribe, would engage in some self-deprecating behavior, lest they invoke a punishment from "warriors" for overstepping and trying to reach higher than their status permits.

Is this how you imagine an egalitarian society? Because it can be described so.


A few objections I expect:

1) Yes, I did the cheap trick of taking the "high school" model, and substituting things for things; most questionably, substituting "hunter" for "nerd". Of course, by taking a model with the desired properties, and randomly substituting things for things, you could argue for anything. Is this analogy more substantial than that? Do "hunters" and "nerds" actually have anything in common?

In my model, the common abstraction for "hunter" and "nerd" is "a person specializing at something that is not directly useful for interpersonal violence". By "hunter" I imagine someone who lays traps or shoots animals with a bow; skills that are not useful in a fist fight. (I assume that the tribe has a taboo against murder, so the "hunter" who got beaten by "warriors" cannot reciprocate by shooting them the next day. Just like it is illegal for a bullied child to use a gun against the bullies.) And the common conclusion is that hierachies of competence useless for interpersonal conflict will be suppressed, in favor of hierarchy of competence at interpersonal conflict; and that an observer might mistake this for egalitarianism.

2) Why the dichotomy? A good warrior can also be a good hunter; actually, both should correlate positively with physical skills. And a child good at sport may simultaneously be good at math.

Yes, because I wanted to keep the initial model simple. We could also make a 2×2 model with people good at sport, good at math, good at both, and good at neither. Depending on how high we set the bar for "good", the intersection can be quite small; for example, if we define "good at sport" and "good at math" as being in top 10%, then only about 1% of people would be "good at both". Which means the original model still fits 99% of population. But let's assume generously that we have 25% of people in each quadrant. In that case, my argument becomes that people who are "nerds but not jocks" / "hunters but not warriors" will have to downplay their skills, lest they receive the status-regulating slapdown (and this would be interpreted by the naïve observer as egalitarianism); but the people who are "nerds and jocks" / "hunters and warriors" can be a part of the elite.


This is related to the more general topic of "status" that I am quite confused about, because I believe in two things that both feel true, and also feel contradictory to each other.

First, there is more than one hierarchy of status. People can be feared, if you meet them in a dark alley alone. People can be feared because they have the political or social power to hurt you. People can be admired for... thousands of different things, each of them impressive to some people and unimpressive to others. Each of these can be referred to as "status": a crime lord is high-status, a mayor is high-status, a rock star is high-status, a popular blogger is high-status, etc.

Second, despite the thousand dimensions of status, saying that someone is high-status still feels more meaningful than merely "there exists at least one trait where this person is above average". It feels like if you get a group of people in the same room, there usually will be a unified status ladder. (It seems imaginable, but unlikely, to arrange a meeting of a crime lord, a mayor, a rock star, and a popular blogger, who would see each other as equals. It is possible that each of them would start with the feeling of personal superiority, but further interaction would likely prove some of them wrong.) Now of course this may depend on context: did they meet in a dark alley; surrounded by the police force; in the middle of a concert; or are they having an online debate and the popular blogger is safely pseudonymous?

The way I try to resolve this contradiction is by imagining "status" roughly as "the power to make other people do what you want, whether because they fear you or because they love you". This power can spring from thousand different sources, and yet it makes sense to compare its total amounts wielded by different individuals. (And yes, even the total amount depends on the audience. Or you may have followers that are totally willing to do X for you, but they are incompetent at it. It's complicated. But it's real nonetheless.)

And because in some situations two high-status people may want contradictory things, and their followers then need to make a choice, in that sense status is a zero-sum game. In a sufficiently small group, each of us could be at the top of a different ladder, each of us could excel at a different admirable thing; but in case of an intra-group conflict, some dimensions would become clearly more important than others.


Robin Hanson calls the two basic forms of status "dominance" and "prestige"; the fear-based and the admiration-based sources of social power respectively. He also notes how people high in "dominance" prefer to be perceived as (also) high in "prestige" [1, 2]. Simply said, a brutal dictator often wants to be praised as kind and smart and skilled (e.g. the "coryphaeus of science" Stalin), not merely powerful and dangerous. The same mechanism also applies in less extreme situations.

Which of course means that people who have "prestige" but not "dominance" need to avoid direct competition with the ones who have "dominance" and also desire (the same kind of) "prestige". You don't want to say "actually, I know way more about science than Stalin", no matter how true it is and how many Nobel prices you have to prove it. You probably don't even want to say "I am very impressive at science" when Stalin is in the same room, because there is a risk it might be interpreted as a comparison, and the stakes are just too high. It is safer to stay in the corner of the room, and if someone points at you, say humbly "I am but a shadow compared to our glorious leader". The better at science you actually are, the more careful you need to be.

And... maybe I am imagining things here, but couldn't the self-deprecating behavior of the successful primitive hunters be caused by the same mechanism? If you are not ready to challenge the chieftain for the leadership of the tribe, and if you don't want to risk being perceived as such, the safe behavior is to also downplay your skills as a hunter. (And everyone will pretend to agree with you, because they don't want to be perceived as opposing the chieftain either. Also, it is a nice opportunity to prevent you from having higher status than them.)


Now I don't want to imply that primitive tribes have Stalin-like levels of oppression. There is another, much simpler mechanism reducing the number of hierarchies: fewer people, simpler life (fewer things to compete at). Thinking about the example where the crime boss, the mayor, the rock star, and the popular blogger stop being all high-status when they enter the same room, the number of "rooms" seems like a limiting factor.

What does the "room" mean here, non-metaphorically? Subcultures, I guess, defined as groups of people who regularly meet separately from others. There is not enough place for many "rooms" in a primitive tribe. On the other hand, I would still expect hunters to be one of them. But maybe the whole tribe should be modelled as one "room", because the successful hunter cannot avoid the chieftain indefinitely, in the same way the crime boss, the mayor, the rock star, and the popular blogger can avoid each other and live in their own worlds.


The summary is that it's not just egalitarianism that destroys hierarchies. A very strong "dominance" hierarchy also suppresses "prestige" hierarchies, because it competes with them for the fraction of total status. But a strong "dominance" hierarchy seems like the very opposite of egalitarianism.

How could anyone mistake one for the other? For an external observer, it can be easy to accept the propaganda of the "dominance" hierarchy. (If you sincerely believe that Stalin is a competent scientist, the fact that all scientists agree with him doesn't seem more suspicious than the fact that all mathematicians agree that 2+2=4. No hierarchy here, only great minds thinking alike!) With primitive societies, the observer's exposure to the "noble savage" meme can do the same. And a modern group using egalitarianism as their applause light might even confuse itself, and mistake the submission of other hierarchies to their own "dominance" hierarchy for actual egalitarianism.



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What is your internet search methodology ?

23 мая, 2020 - 23:33
Published on May 23, 2020 8:33 PM GMT

I feel like the heuristics we use to search random tat on the internet are an incredibly larger part of our lives.

I wouldn't make a claim as extended as "googling skills are on the largest determinants of success in life", but I wouldn't look very suspiciously at someone that tried to make it either.

So I'm rather curios what the "search methodology" of people here is.

Let me try to give an example by detailing mine, it's non-ideal since I apply it mainly subconsciously, but that's why I'm looking to improve it, also I'm probably missing some things ,but again, this is just an example not a claim that it's in any way "good.

a) Find a phrasing that is < 5-6 word long and type it into googleb) If a certain key word isn't showing up quote itc) If am looking for a specific type of discussing type the name of a form together with it (e.g. LessWrong: Fobar falacy, SSC: edgy political thing, Reddit: Corporate sponsored product review, StackOverflow: copy paste of a tiny part of the debug log which I hope is unique enough for my problem)d) If what I'm looking for seem high-falutin to be considered "scientific" consider google scholar instead+ ublock origin with strict settings, disable 3rd party cookies, container addons for e.g. google and fb in order to somewhat limit amount of ads+ sci-hub now for quicker paper reading (basically you just click it, it tires to find the doi of the abstract you are looking at and opens it in a scihub tag)

For me, at least, thinking of this requires trying to observe myself and query my subconscious, rather than thinking about it the same way I think about explicit actions/choices.

I'd wager it's the same for many people and thus a lot of critical components might be missing... but, there's not much that can be done about that.



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Mazes Sequence Summary

23 мая, 2020 - 19:40
Published on May 23, 2020 4:40 PM GMT

This post attempts to summarize the key points of the Immoral Mazes Sequence, which begins here, so they can be referenced without asking readers to get through an entire book first. 

Due to the change in format, the posts will be summarized slightly out of order.

Note that this summary, and especially the summary of the summary, represent a not only an abridged and simplified but also sanitized version of the central points. Brains do their best to continuously round down and not fully see these concepts. 

 

Core Ideas (Summary of the Summary)

The book Moral Mazes, by Robert Jackall, is a detailed exploration of middle manager hell. Managers must abandon all other goals and values, in favor of spending all their time and resources on manipulations of the system. They must learn to view such actions as intrinsically good and worthy of reward. Only those who let this process entirely consume them can survive. 

The Immoral Mazes sequence is an exploration of what causes that hell, and how and why it has spread so widely in our society. Its thesis is that this is the result of a vicious cycle arising from competitive pressures among those competing for their own organizational advancement. Over time, those who focus more on and more value such competitions win them, gain power and further spread their values, unless they are actively and continuously opposed.

Once things get bad in an organization they tend to only get worse, but things in general get better because such organizations then decay and are replaced by new ones. Unfortunately, our society now slows or prevents that process, with these same organizations and their values increasingly running the show. 

Investment and flexibility become impossible. Even appearing to care about anything except the competition itself costs you your allies. Thus things inevitably decay and then collapse, flexibility returns, cycle repeats.

Involvement with such patterns is far more destructive to humans than is commonly known. Employment or other involvement with such patterns should be avoided or ended, even at seemingly high cost in money and superficial status. If one wishes to accomplish something other than competition for advancement, one must be vigilant to punish maze-promoting behaviors, and to keep out or cast out those whose values align with mazes.

This is a special case of the principle that sufficiently intense competition along a single axis destroys all. Exit costs to any situation are almost always non-zero, so situations that otherwise look like they are ‘perfectly competitive’ are instead what I dub ‘super-perfectly competitive,’ where profits are negative, and all participants unfortunate enough to have entered continuously lose ground and eat their seed corn. 

Eventually such systems collapse and are replaced. This is why the world has nice things and often greatly improves over time, even when specific things seem to mostly tangibly decay. 

Dangers of Perfect Competition

A long time ago, Scott Alexander wrote Meditations on Moloch. Moloch represents the inevitability of competitive selection pressure, given a long enough time horizon, completely binding all behavior, and thus destroying all value, then all life. 

This hasn’t fully happened yet. But it will. Unless we use this one opportunity offered to us by our technological progress before that happens, and stop it.

A less long time ago, I introduced the (pre-existing) concept of Slack. Slack is the absence of binding constraints on behavior. Metaphorically, slack is life. Literally, slack is also life. Without slack, life is unable to both survive and retain the ability to adapt, and thus loses one, then the other, and dies.

Recently, Scott wrote Studies on Slack, which made a lot of this more explicit and easier to understand, especially the point that slack is life.

A few months ago, I wrote the book-length Immoral Mazes Sequence. This was my attempt to understand the facts described in the important and very hard to get through book (not because it’s badly written, but because the things in it are hard to look at) Moral Mazes. The book describes the lives and experiences of middle managers in major American corporations. 

Why are things so bad for these managers? Why aren’t they as bad for everything and everyone else?

The Molochian nightmare is not how most of the world works. We have nice things. Over time they improve. Systems do not automatically collapse to the elimination of anything that shows the slightest inefficiency. Most places and times and groups get a lot of genuine cooperation towards worthy goals, and avoid or mitigate any race to the bottom. Our lives are full of slack. The question is why.

The reason is because perfectly binding constraints need only arise in the presence of perfect competition. If and only if everyone and everything is identical and standardized, all constraints must bind and behavior must be only that which maximizes short-term measured competitive results. Even worse, there is what I called super-perfect competition. Perfect competition allows everyone to break even (make zero economic profits), but that is because participants are allowed to exit. Super-perfect competition has costly exit (and in reality, all exit has some cost), and thus everyone competes with everything they have and still loses. The “good” news is that such systems, when they arise, over-compete and over-optimize, eat their seed corn, and thus quickly collapse.

The cycle repeats. It repeats with the rise and fall of civilizations, and also the rise and fall of individual groups and corporations. Large organizations are doomed. We need them, but should be cautious about creating things that are inevitably doomed. Over time, things almost always get locally worse in these ways rather than better, until it is disrupted from outside and replaced by the new. We thus should not weep too much when this occurs. 

Our society has chosen, to a large extent that has been laid even more bare by recent events, not to allow these failures and this renewal. Hence the term ‘Too Big to Fail.’ Our institutions of all kinds are becoming increasingly dysfunctional, increasingly concerned with politics in its broad sense, and incapable of useful action. Thus, people increasingly have the instinct that Moloch inevitably wins everywhere. We are disarming the forces that keep Moloch in check. And Moloch wants us to think its victory is inevitable, so we will actively support it rather than oppose it, so we can form an implicit alliance with others doing the same, in the hope the process will kill us last.

Mazes as Super-Perfectly Competitive Battle Between Managers

Despite this trend, when we look around at markets in practice, we instead mostly see highly imperfect competition on lots of different levels. Behaviors are a long way from fully optimized for anything. Market participants enjoy high degrees of differentiation between each other. 

Middle managers at many major corporations, as reported in the book, face a different situation. They are trapped in the Molochian nightmare of super-perfect competition between different managers. The protections against this process that most people have, are gone. Too many managers seek too few promotions, with too level a playing field. Everyone was assumed, past a certain level, to have the same skill at actually managing and getting things done. 

This includes self-modification to seeing this competition as inherently good, and instinctively rewarding and allying with those who do the same, while punishing those who do otherwise. Dedication to the firm and to work and to personal success are the virtues. Valuing other things becomes vice. Morality, like family or religion or a hobby, is one more thing that can distract from your journey to success. If you’re distracted, you’ll lose, so you’d be a bad ally, so you fail. 

These second order effects, combined with the optimization around getting ahead, allow mazes to spread and take over anyone and anywhere they are allowed a foothold.

Big business does not hate your family, but its managers and bottom line see you as composed of things it could profit from, so effectively? It kind of does.

The self-modifications managers do, and the fact that many skills and connections, and much knowledge, and all their local privileges and status that they have become attached to, would expire worthless if they left, heavily discourages exit.

What is their life like? Their whole life is dedicated to “success” within the hierarchy, but awaits them there is another struggle for more “success.” Slack is non-existent, especially in terms of time. Anything that makes you unique, anything else you care about, is stamped out. They choose their activities, their friends, even their family, aiming at this, so all of them rely on the quest for this “success.” Mostly they are failures by their own metrics.

The few who rise through this, and even become CEO, still lose. Their “success” does not bring happiness, or provide reproductive fitness, or improve the world. The fruits are hollow. The game is rigged. Yet even for those who realize this, it is often seen as even worse to stop playing. 

Maze Origins and Damage Mitigation

To avoid or oppose moral mazes, we must identify them. The best known ways to do this are to look for too many levels of hierarchy, for people to not primarily describe their jobs as working for a particular person, and the absence of skin in the game, soul in the game, diversity of acknowledged skill levels, and slack. And of course, to pay attention, and see how things are done. Don’t only check off boxes.

If someone does find themselves trapped in a maze, it is imperative to escape. If at all possible, quit and do something else. That’s easier said than done, but is easier done than those in the position to do it think it is. You’re already doing something hard, and you have the skills to do a different hard thing that won’t make you miserable, even if the pay starts out lower. You’ll adjust. People around you will understand and sympathize more than you’d expect, especially if you tell it to them straight – you were unhappy, the job was toxic. If they don’t sympathize, and demand that you devote yourself to the illusion of security, be sympathetic to that, especially if they love and/or depend on you, but do what you must. If you actually can’t leave, try not caring about “success” or taking bold risks to achieve it.

What has made these mazes so much more powerful than in the past?

Some factors are real and inevitable. We need more large organizations for our civilization to function, than did prior civilizations, and in many ways they get to better leverage big data. machine learning and the internet, giving them an edge. As we grow safer and wealthier, our demand for the illusion of security rises, and mazes are relatively better positioned to provide that illusion.

There are many other reasons that are less real, and less inevitable. We protect organizations from disruption, especially in times of crisis. We see rent seeking of all kinds as increasingly legitimate. Mazes have gotten sufficiently powerful to cause a vicious cycle, as mazes reward and support other mazes and structure things to favor mazes. Our laws and regulations favor mazes over non-mazes, far beyond what is necessary due to civilizational complexity. Our educational system trains people for the maze, so much so that the people have largely forgotten what mazes are and what the alternative to them might be. We have been so atomized, and their ordinary human needs so delegitimized, that we do not see what we are giving up. 

What might we do to change things for the better? Regulatory reform, health care reform, tort reform, ending corporate welfare or even forcibly breaking up large corporations would help. So would educating people on what mazes are and the dangers they pose, especially to their employees. We could work to change consumer behavior, to lower the status and aura of legitimacy of mazes and those who work for them. And we could work to lower demand for the illusion of security.

For a given project, the best defense is to focus on the core elements, and thus do less things and be smaller, while minimizing interaction with other mazes. One should also seek separately to minimize levels of hierarchy, provide skin in the game and soul in the game, and be extremely careful with people. Hire, promote, evaluate and fire them with a keen eye. Anyone making you more like a maze needs to go, no matter how painful that is. You must fight for your institutional culture. 

If someone with several hundred million dollars or more to spend wants to help, my best suggestion is to create a full alternative stack, to allow people to sidestep maze incentives entirely and to actually get things done. 

This only scratches the surface. I encourage you to follow the links to the original posts.



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