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Attach Receipts to Credit Card Transactions

12 ноября, 2019 - 19:30
Published on November 12, 2019 4:30 PM UTC

If you log into your credit card account you'll see a list of charges, each with a date, amount, and merchant. It would be helpful if this also included receipt data:

  • If you didn't recognize a charge, seeing what it was for could remind you.

  • If you needed a receipt for taxes or reimbursement one could be captured automatically.

  • Personal finance tools (or corporate equivalents for company cards) could track spending with higher granularity.

  • Because the credit card company knows what the items are they can better detect fraud.

Receipt data isn't currently part of the protocol used for charges; you'd need to spec out something that let companies communicate everything a receipt can communicate today. This would be a very large change, but everyone who would need to make changes can have incentives in the right direction:

  • The card company likes it because it can market their card as supporting receipts and better detect fraud.

  • The merchant likes it because they see fewer chargebacks and the credit card company probably gives them slightly better rates.

  • Point-of-sale makers like it because they get to sell a lot of upgrades.

The main downside I can think of, aside from it being a lot of work, is that people might not want their credit card company knowing the particular products they bought. The company would probably want to sell this to marketers, though there would be plenty of time to pass regulations limiting that if we wanted to. Personally, I don't mind: the merchant is probably already selling my purchase information or will soon. And the money the credit card company gets from selling the data, since it's a competitive market, probably mostly gets passed on as higher cardholder incentives or higher incentives to merchants to adopt receipt sharing.

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An optimal stopping paradox

12 ноября, 2019 - 11:51
Published on November 12, 2019 8:51 AM UTC

Consider an optimal stopping problem: a company at each time step grows by some constant, and has a certain probability of shutting down. You decide when to sell the company.

Since the math is cleaner in continuous time, we consider the continuous time. Then the company has a linearly increasing value βt, and an exponentially decaying survival curve e^(-αt).

Another framing of the paradox: Schrodinger wants to make a new record for the longest surviving cat, so he put a cat in the box with an atom that might decay and kill the cat, and waits. When should he open the box?

Since at each moment in time, you face the exact same problem (linearly increasing reward, α-exponentially decaying survival rate), if you decide to wait at t=0, you would decide to wait forever, and thus receive no reward.

There are several possible replies to this paradox, none of which is satisfactory to me:

  1. "This looks like St. Petersburg Paradox.". No, because at time t=0, the expectation is β/α^2. In fact, the payoff can grow faster than βt, such as like t^3, and it would still have finite expectation.
  2. Claim that expectation maximization decision theory is flawed. This doesn't stop the procrastination. As long as your decision is purely based on the future, and your rational decision process is constant in time, you either immediately sell the company or never sell the company.
  3. Try some kind of discounting, like exponential discounting. This doesn't stop the procrastination., since at any time, selling the company gives you 0 extra expected reward, and waiting gives you some positive extra expected reward, no matter how much you discount the future.
  4. Claim that there should be a finite lifetime. You can't wait forever. If there is a finite lifetime, then the same decision analysis would tell you to procrastinate until the very end. This effectively is procrastinating forever. It does not converge to a reasonable finite waiting time as your lifetime goes to infinity.
  5. Claim that one should stick to past decisions even when they don't make sense from a purely future-looking decision theory. Such decision theory seems to be just sweeping time-inconsistency under the rug, and I'm sure would suffer from serious paradoxes of their own.
  6. Claim that there is no paradox, and procrastination is really the rational action. I'd not claim a strategy that guarantees 0 reward to be rational.

Option 5 seems at least to have some meaning to it. Sticking to it would mean that, for example, one would at t=0 decide to choose T to maximize βT e^(-αT), then at t=T really sell the company, even though it's irrational, conditional on the company still alive at t=T.



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Can indifference methods redeem person-affecting views?

12 ноября, 2019 - 07:23
Published on November 12, 2019 4:23 AM UTC

My uninformed paraphrase/summary of "the person-affecting view" is: "classical utilitarianism + indifference to creating/destroying people".

These views seem problematic (e.g. see Hillary Greeves interview on 80k), and difficult to support.

Indifference methods (e.g. see Stuart Armstrong's paper) seem like they might be a way to formalize the person-affecting view in a rigorous way.

If we have a policy, we can always reverse engineer a corresponding reward function (see our Reward Modelling agenda, bottom page 6).

So while there might still be highly counter-intuitive bullets that need to be bitten, this might provide a way of cashing out person-affecting views in a way that is mathematically coherent/consistent.

What do you think? Does it work?

And is that even an open problem, or an interesting result to people in ethics?




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Operationalizing Newcomb's Problem

12 ноября, 2019 - 01:52
Published on November 11, 2019 10:52 PM UTC

The standard formulation of Newcomb's problem has always bothered me, because it seemed like a weird hypothetical designed to make people give the wrong answer. When I first saw it, my immediate response was that I would two-box, because really, I just don't believe in this "perfect predictor" Omega. And while it may be true that Newcomblike problems are the norm, most real situations are not so clear cut. It can be quite hard to demonstrate why causal decision theory is inadequate, let alone build up an intuition about it. In fact, the closest I've seen to a real-world example that made intuitive sense is Narrative Breadcrumbs vs Grizzly Bear, which still requires a fair amount of suspension of disbelief.

So, here I'd like to propose a thought experiment that would (more or less*) also work as an actual experiment.

A psychologist contacts you and asks you to sign up for an experiment in exchange for a payment. You agree to participate and sign all the forms. The psychologist tells you: "I am going to administer a polygraph (lie detector) test in which I ask whether you are going to sit in our waiting room for ten minutes after we finish the experiment. I won't tell you whether you passed, but I will give you some money in a sealed envelope, which you may open once you leave the building. If you say yes, and you pass the test, it will be $200. If you say no, or you fail the test, it will be $10. Then we are done, and you may either sit in the waiting room or leave. Please feel no obligation to stay, as the results are equally useful to us either way. The polygraph test is not perfect, but has so far been 90% accurate in predicting whether people stay or leave; 90% of the people who stay for ten minutes get $200, and 90% of those who leave immediately get $10."

You say you'll stay. You get your envelope. Do you leave the building right away, or sit in the waiting room first?

Does the answer change if you are allowed to open the envelope before deciding?

*I don't know if polygraphs are accurate enough to make this test work in the real world or not.



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The randomness/ignorance model solves many anthropic problems

11 ноября, 2019 - 20:02
Published on November 11, 2019 5:02 PM UTC

(Follow-up to Randomness vs Ignorance and Reference Classes for Randomness)

I've argued that all uncertainty can be divided into randomness and ignorance and that this model is free of contradictions. Its purpose is to resolve anthropic puzzles such as the Sleeping Beauty problem.

If the model is applied to these problems, they appear to be underspecified. Details required to categorize the relevant uncertainty are missing, and this underspecification might explain why there is still no consensus on the correct answers. However, if the missing pieces are added in such a way that all uncertainty can be categorized as randomness, the model does give an answer. Doing this doesn't just solve a variant of the problem, it also highlights the parts that make these problems distinct from each other.

I'll go through two examples to demonstrate this. The underlying principles are simple, and the model can be applied to every anthropic problem I know of.

1. Sleeping Beauty

In the original problem, a coin is thrown at the beginning to decide between the one-interview and the two-interview version of the experiment. 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Sleeping Beauty knows this but isn't being told which version she's currently participating in. This leads to 2n instances of Sleeping Beauty waking up on Monday, and n instances of her waking up on Tuesday. All instances fall into the same reference class, because there is no information available to tell them apart. Thus, Sleeping Beauty's uncertainty about the current day is random with probability 23 for Monday.

2. Presumptuous Philosopher

In the original problem, the debate is about the question of how the size of the universe influences the probability that the universe is large, but it is unspecified whether our current universe is the only universe.

Let's fill in the blanks. Suppose there is one universe at the base of reality which runs many simulations, one of them being ours. The simulated universes can't run simulations themselves, so there are only two layers. Exactly half of their simulations are of "small" universes (say with 1015 people), and the other half are of "large" universes (say with 1021 people). All universes look identical from the inside.

Once again, there is only one reference class. Since there is an equal number of small and large universes, exactly 1021 out of 1015+1021 members of the class are located in large universes. If we know all this, then (unlike in the original problem) our uncertainty about which universe we live in is clearly random with probability 10211015+1021 i.e. 10000001000001 for the universe being large.

Bostrom came up with the Presumptuous Philosopher problem as an argument against SIA (which is one of the two main anthropic theories, and the one which answers 23 on Sleeping Beauty). Notice how it is about the size of the universe, i.e. something that might never be repeated, where the answer might always be the same. This is no coincidence. SIA tends to align with the randomness/ignorance model whenever all uncertainty collapses into randomness, and to diverge whenever it doesn't. Naturally, the way to construct a thought experiment where SIA appears to be overconfident is to make it so the relevant uncertainty might plausibly be ignorance. This is an example of how I believe the randomness/ignorance model adds to our understanding of these problems.

So far I haven't talked about how the model computes probability if the relevant uncertainty is ignorance. In fact it behaves like SSA (rather than SIA), but the argument is lengthy. For now, simply assume it's agnostic.



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[Math] Proofs, forgetting, and an eldritch god

11 ноября, 2019 - 18:46
Published on November 11, 2019 3:46 PM UTC

Epistemic status: Wild and flailing.

I envy computers sometimes for their memory.

My basic intuition from learning signals and Fourier transforms in my EE major is that any limited-space memory system that has to adapt to new surroundings, also has to have some factor by which old memories decay over time -- otherwise the system would become saturated with information content. (I am not being precise with these terms. Look where I point.)

But computers don't usually have as ruthless a "use-it-or-lose-it" forgetting mechanism as human brains do built into them. Their decay is mostly due to (i) other conscious agents going in and rearranging their memories; (ii) the slow encroaching wave of entropy on their hard disks. (i) can be dealt with by locking the computer in a room away from human hands, and praying the Poincare recurrence theorem combined with thermodynamic noise doesn't imply it will eventually birth an AGI all on its own. But that's a symptom of (ii), which can be mostly dealt with via RAID-5 and an influx of hard drives. With those two in place, on a human timescale, computers essentially never have to forget something once it's encoded in memory.

Human beings, we aren't like that. We forget. We forget so easily. And we warp the memories we do have to fit our twisted little narratives in the moment. I'm not a terribly existential person, but the fact that we ship of Theseus ourselves according to the ridiculous whims of future us, people just like us now but ever-so-slightly slower, crazier, and more tied down to the local flora for their survival, you have to admit -- it's a little unsettling.

I've been having a bit of a crisis over the last few days. It's about math, as all my crises are.

The problem is simple: I prove $X$ once. I walk away for a few days. When I go back to look at $X$ again, I suddenly realize the steps of the proof don't immediately spring to mind any more. I have to prove it again, don't I; or accept it on faith that $X$ is a fact, that former me proved $X$ to be a fact, that no, there's really nothing much to be gained from re-proving something in roughly the same way that I did before.

The cognition is contrarian: Proofs take effort. I don't like wasting effort. And just because I could prove $X$ once is no guarantee at all that I could prove $X$ again right now -- memories decay, I only have limited vision and limited CPU cycles, maybe I actually can't prove $X$ again, and that means I don't really understand $X$ in some sense, and I won't know unless I try, right? And math is supposed to be this tower of logic, where -- in theory -- we could break everything all the way down to set/category theory and build it back up, right? That's what good mathematicians do, right? Aren't we supposed to be the true Scotsmen of deductive reasoning?

---

I once ran a campaign where there were 4 deities. One of them was called the White Noise; the Noise derived its power from all friction generated in the environment. When you and a stranger are walking down a street and don't want to bump into each other, but you step in the same direction they step, and then you step the other way at the same time that they do -- the White Noise feeds. When you're short 2 cents at the convenience store and the clerk says "Don't worry about it", but then you leave and worry that maybe the clerk will get fired because of the disparity at the end of the day. When lovers quarrel. When soldiers don't fight as hard as they should because they don't fully believe in their cause -- but also (scholars posit) the whole friction generated by war itself, the ultimate (pacifists say) zero-sum waste of resources. Eventually all becomes friction, and time stops.

If I had to choose a deity out of the 4 to follow, it would be the White Noise in a heartbeat. To me, it represents the ultimate decision that pointless human bickering is far preferable to brutal, paperclip-maximizing, mechanized efficiency.

Which is why it's so fucking stressful to me that the one time I do want mechanized efficiency, I can't seem to acquire it. I just want to be able to prove something once, and then store the memory of that proof in my head so that I don't have to keep doing it over and over again, man. Is that too much to ask?

---

Yes, it is. But let's go back to what we said before.

Mathematicians (human ones at least, not Coq or something) don't actually work in a strictly deductive fashion. Even if they wanted to, they realistically couldn't. We all have limits to what we can store in our short term memory, and the limits vary, but they presumably don't vary by orders of magnitude; similarly, we all have limits to how fast we can think through the logical implications of a thing, but again, this doesn't vary by orders of magnitude. And the subset of math we care about here is math-as-group-enterprise; so the fact that mathematicians are all competing for roughly the same rewards suggests that they would find ways to work around those fundamental limits in order to outdo each other. So it's certainly not the case that every other good mathematician on the planet does a proof once, memoizes exactly how to do it, and then just keeps that memory fresh.

What does happen, then? Probably what we would expect from a common sense view of human nature: They forget. They forget almost everything they learn at the undergraduate and graduate level, just like anyone else would. Oh, they probably don't forget as much relevant to their field of interest -- but I highly doubt there's any tenured, non-set theory professor out there who consciously reruns through proving Zorn's lemma from scratch once or twice a year, just to make sure they can still do it. It's a difficult, convoluted proof, and they have more important work to be done. New work.

Now, I don't think any working mathematician will admit this. They'll probably say something along the lines of, "Well of course I can't prove it on sight right now, but give me a couple days and I can probably get back to you with one. Just need to refresh my memory." That might even be true. But they're not just refreshing their memory -- they're turning a highly-tuned neural network, full of a lifetime of proving difficult propositions and engaging in clever logical tricks, back towards a problem they already understood the solution to once when they were less well-attuned.

Maybe, someday, we'll be able to fuck around with human memory enough that nobody will forget how to do a proof after the first time they spend energy on figuring out how to do it. But that day isn't today. Today, the White Noise scores a point for human imperfection. And while that might be locally frustrating -- think how much easier I would have it if that were the case! -- I wouldn't actually want to live in a world where everyone, universally, had those powers of memory. The competition would just adapt, quickly, to the newfound power, and I wouldn't end up with an obvious improvement in the group effort as before. I want it for me, and me alone; and that's a sign that I'm not actually upset for a good reason. I'm just being selfish. Even my OCD channels human bias.




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Ban the London Mulligan

11 ноября, 2019 - 14:10
Published on November 11, 2019 11:10 AM UTC

Previously: On The London Mulligan

Oko, Thief of Crowns is a highly messed up Magic card and needs to be banned in Standard. On that we can all agree. Throne of Eldraine contains many other messed up Magic cards. Some of them, like Once Upon a Time, Wicked Wolf and Gilded Goose, are not getting the appreciation they deserve because Oko, Thief of Crowns is stealing the spotlight. If both of those cards are Standard legal when they rotate out, I will be quite surprised. Then there are Fires of Invention, Caldron Familiar / Witch’s Oven, Embercleave, Emry, Lurker of the Lock. Then there’s Feasting Troll King, Questing Beast, Bonecrusher Giant, Lovestruck Beast, Edgewall Inkeeper and the list goes on. And yes, it has a lot of green in it. There are also messed up Magic cards one can choose from previous sets, although the density of them is far lower.

You are not going to succeed in Standard, for a long time, no matter what is banned, without building around at least one messed up Magic card from Throne of Eldraine. If design does not make large adjustments, and likely even if they do, every good Standard deck for a long time is going to have a key messed up Magic card.

Even more than a single messed up Magic card, these decks have central play patterns. Gilded Goose into Oko, Thief of Crowns. Third turn Nissa, Who Shakes the World. Fires of Invention into Cavalier of Flame. Witch’s Oven sacrificing Caldron Familiar sacrificing food triggering Trail of Crumbs or Mayhem Devil. Embercleave on my Questing Beast or Rotting Regisaur or knight. First turn Pelt Collector or else your deck doesn’t quite work. Explosive or super efficient adventure or Veteran Loxodon starts. Growth Spiral into Wilderness Reclamation. And so on.

If you get to do these things, a seventh card you had to put on the bottom will not be overly missed. If you don’t get these things, an extra card will not much matter.

Thus, the first player is forced to mulligan hands that look perfectly good, but which cannot pull off their key play pattern. Decks get designed with a key consideration being what you can and cannot keep as an opening hand. The deck I would have run at Mythic Championship V, Elk Blade, keeps no configuration of cards that lacks Arboreal Grazer, Gilded Goose or Once Upon a Time against an unknown opponent, even at six cards. Because once you see that such hands are going to be bad no matter what, why build your deck to make them 25% to win rather than 10%, given you’re not going to keep them anyway?

Then the second player has an even more stark choice. If the opponent kept seven, they probably have their central play pattern. What are you going to do about it? You’re already behind because you’re going second. You can ill afford to go second, face a hand of seven, and not have a Gilded Goose. So you mulligan even more. If the opponent goes to six, they still likely have their central pattern, and now that they’re down a card. Answering that becomes what the game will be all about. Or they’ll miss, and there won’t be a game.

Thus, we have lots of mulligans to find key play patterns.

Every game looks the same. Both players do their thing, or else one player fails to do it, is likely also down cards, and never has a chance. Lots of time is spent shuffling, and going through the same motions over and over again.

Once Upon a Time makes all this even worse and made it easier to see, but the problem would persist without it.

Decks that rely on a critical mass of cards rather than a central pattern are at a disadvantage two ways. They don’t get the free wins off their messed up starts, and they suffer far more when forced to send their hands back.

Online on Magic Arena, one tires of playing the same games over and over again, but speeding up the operations, especially shuffling, makes it a lot more palatable. Playing in person, the ratio of dead time to interesting Magic is devastatingly poor. I dropped from the Grand Prix, despite still having a good shot at cashing, because I really, really didn’t want to keep playing Standard.

Food decks are actually less bad than they might otherwise be and relatively good compared to some other decks, because they have multiple such play patterns. Sometimes they’re about Nissa, Who Shakes the World or even Wicked Wolf rather than Oko, Thief of Crowns, and because we have strong hate cards like Noxious Grasp and Aether Gust that allow mirror games to be more dynamic and less lopsided or repetitive than one would otherwise expect. In an important sense, we are currently quite fortunate. The food mirror is a luxurious tapestry compared to a Fires of Invention, adventure or sacrifice mirror.

As I warned in my previous analysis, On the London Mulligan, this is not about one deck getting such a big advantage that it dominates. It is about the pernicious effect on play patterns and deck designs across the spectrum, whether the format is balanced or otherwise. Once enough cards are banned, presumably a variety of decks will focus on a variety of messed up Magic cards. But as long as they are still all using the London Mulligan to find them, the problem will continue.

Magic is great because it continuously presents unique situations to its players. Decks and players are forced to be flexible and roll with the punches, to plan for not having access to their key cards. When instead decks and players are rewarded for relying on their central repetitive play patterns, because fallback sequences would lose anyway, Magic loses much of its appeal.

This effect will only grow as players fully appreciate the new world they live in, and learn how to mulligan effectively, and then how to build decks based on those mulligan decisions by both themselves and others. Again, Gilded Goose aside, traditional Oko, Thief of Crowns decks are in many ways relatively good at having alternative patterns that are competitive with the primary one, and providing life totals that allow time to recover if you can avoid too big a snowball on the board.

I won’t discuss here whether we should be banning other cards along with Oko, Thief of Crowns. I would ban at least one additional green card now rather than later, but I am not super confident that I am correct. There is a reasonable case to be made both for and against additional bans.

What we do need to ban, in addition to Oko, Thief of Crowns, is the London Mulligan. We must return to the Vancouver Mulligan for traditional constructed play. If we are willing to bear the complexity cost of having distinct mulligan rules, I am willing to allow the London Mulligan to remain in limited and even in formats like brawl and commander. In Standard, Pioneer, Modern, Legacy and Vintage, we must act. If we do not, these problems likely only get worse over time, no matter how many cards we choose to ban.



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Pieces of time

11 ноября, 2019 - 10:00
Published on November 11, 2019 7:00 AM UTC

My friend used to have two ‘days’ each day, with a nap between—in the afternoon, he would get up and plan his day with optimism, whatever happened a few hours before washed away. Another friend recently suggested to me thinking of the whole of your life as one long day, with death on the agenda very late this evening. I used to worry, when I was very young, that if I didn’t sleep, I would get stuck in yesterday forever, while everyone else moved on to the new day. Right now, indeed some people have moved on to Monday, but I’m still winding down Sunday because I had a bad headache and couldn’t sleep. Which is all to say, a ‘day’ does not just mean a 24 hour measure of time, in our minds. Among its further significance, we treat it as a modular unit: we expect things within it to be more continuous and intermingled with each other than they are with things outside of it. What happens later today is more of a going concern at present than something that happens after sleeping. The events of this morning are more part of a continuous chapter, expected to flavor the present, than what happened yesterday. The same is true to some extent for weeks, months and years (but not for fortnights or periods of 105 hours).

I think days are well treated as modular like this because sleeping really separates them in relevant ways. I notice two other kinds of natural modular time-chunks that seem worth thinking in terms of, but which I don’t have good names for:

  • Periods during which you are in one context and stream of thought (usually a minute to a few hours long). For instance the period of going for a walk, or the period between getting home and receiving a phone call that throws you into a new context and set of thoughts. During one such chunk, I can remember a lot about the series of thoughts so far, and build upon them. Whereas if I try to go back to them later, they are hard to bring back to life, especially the whole set of thoughts and feelings that I wandered around during a period, rather than just a single insight brought from it. Within chunks like this, my experience seems more continuous and intermingled with other experience within the chunk. Then I get an engaging message or decide to go out, and a new miniature chapter begins, with new feelings and thoughts. (Though I’m not sure how much other people’s thoughts depend on their surroundings, so maybe for others a change of context is less of a reset).
  • Similarly, longer periods of repeatedly being in particular places with particular people. These might be decades of settled marriage or a few days of being on a trip. For me they are often a month to a year. They are punctuated by moving, breaking up, changing jobs. They tend to have their own routines and systems and patterns of thought. For me, starting a new one is often marked by a similar optimism and ambition for a fresh start as mornings. And ending one shares with evenings a risk of sadness at wasted opportunity.

Both of these also end because of something like sleep—changes of context that break the continuity of thoughts or habits within the period, either because those things relied on the previous context as something like memory, or because the new context asks for a new activity that replaces the old one, and the old one needed the continuity to stay alive.



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Epistemic Spot Check: Unconditional Parenting

10 ноября, 2019 - 23:10
Published on November 10, 2019 8:10 PM UTC

Epistemic spot checks started as a process in which I investigate a few of a book’s claims to see if it is trustworthy before continuing to read it. This had a number of problems, such as emphasizing a trust/don’t trust binary over model building, and emphasizing provability over importance. I’m in the middle of revamping ESCs to become something better. This post is both a ~ESC of a particular book and a reflection on the process of doing ESCs and what I have and should improve(d).

As is my new custom, I took my notes in Roam, a workflowy/wiki hybrid. Roam is so magic that my raw notes are better formatted there than I could ever hope to make them in a linear document like this, so I’m just going to share my conclusions here, and if you’re interested in the process, follow the links to Roam. Notes are formatted as follows:

  • The target source gets its own page
  • On this page I list some details about the book and claims it makes. If the claim is citing another source, I may include a link to the source.
  • If I investigate a claim or have an opinion so strong it doesn’t seem worth verifying (“Parenting is hard”), I’ll mark it with a credence slider. The meaning of each credence will eventually be explained here, although I’m still working out the system.
    • Then I’ll hand-type a number for the credence in a bullet point, because sliders are changeable even by people who otherwise have only read privileges.
  • You can see my notes on the source for a claim by clicking on the source in the claim
  • You may see a number to the side of a claim. That means it’s been cited by another page. It is likely a synthesis page, where I have drawn a conclusion from a variety of sources.

This post’s topic is Unconditional Parenting (Alfie Kohn) (affiliate link), which has the thesis that even positive reinforcement is treating your kid like a dog and hinders their emotional and moral development.

Unconditional Parenting failed its spot check pretty hard. Of three citations I actually researched (as opposed to agreed with without investigation, such as “Parenting is hard”), two barely mentioned the thing they were cited for as an evidence-free aside, and one reported exactly what UP claimed but was too small and subdivided to prove anything. 

Nonetheless, I thought UP might have good ideas kept reading it. One of the things Epistemic Spot Checks were designed to detect was “science washing”- the process of taking the thing you already believe and hunting for things to cite that could plausibly support it to make your process look more rigorous. And they do pretty well at that. The problem is that science washing doesn’t prove an idea is wrong, merely that it hasn’t presented a particular form of proof. It could still be true or useful- in fact when I dug into a series of self-help books, rigor didn’t seem to have any correlation with how useful they were. And with something like child-rearing, where I dismiss almost all studies as “too small, too limited”, saying everything needs rigorous peer-reviewed backing is the same as refusing to learn. So I continued with Unconditional Parenting to absorb its models, with the understanding that I would be evaluating its models for myself.

Unconditional Parenting is a principle based book, and its principles are:

  • It is not enough for you to love your children; they must feel loved unconditionally. 
  • Any punishment or conditionality of rewards endangers that feeling of being loved unconditionally.
  • Children should be respected as autonomous beings.
  • Obedience is often a sign of insecurity.
  • The way kids learn to make good decisions is by making decisions, not by following directions.

These seem like plausible principles to me, especially the first and last ones. They are, however, costly principles to implement. And I’m not even talking about things where you absolutely have to override their autonomy like vaccines. I’m talking about when your two children’s autonomies lead them in opposite directions at the beach, or you will lose your job if you don’t keep them on a certain schedule in the morning and their intrinsic desire is to watch the water drip from the faucet for 10 minutes. 

What I would really have liked is for this book to spend less time on its principles and bullshit scientific citations, and more time going through concrete real world examples where multiple principles are competing. Kohn explicitly declines to do this, saying specifics are too hard and scripts embody the rigid, unresponsive parenting he’s railing against, but I think that’s a cop out. Teaching principles in isolation is easy and pointless: the meaningful part is what you do when they’re difficult and in conflict with other things you value.

So overall, Unconditional Parenting:

  • Should be evaluated as one dude’s opinion, not the outcome of a scientific process
  • Is a useful set of opinions that I find plausible and intend to apply with modifications to my potential kids.
  • Failed to do the hard work of demonstrating implementation of its principles.
  • Is a very light read once you ignore all the science-washing.

 

 

As always, tremendous thanks to my Patreon patrons for their support.

 



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Experiments and Consent

10 ноября, 2019 - 17:50
Published on November 10, 2019 2:50 PM UTC

One of the responses to my Uber self-driving car post was objecting to Uber experimenting on public roads: Self-driving research as practiced across the industry is in violation of basic research ethics. They should not be allowed to toss informed consent out the window, no matter how cool or revolutionary they think their research is. I've seen this general sentiment before: if you want to run an experiment involving people you need to get their consent, and get approval from an IRB, right?

While academia and medicine do run on a model of informed consent, it's not required or even customary in most fields. Experimentation is widespread, as organizations want to learn what effect their actions have. Online companies run tons of a/b tests. UPS ran experiments on routing and found it was more efficient if they planned routes to avoid left turns. Companies introduce new products in test markets. This is all very standard and has been happening for decades, though automation has made it easier and cheaper, so there's more now.

When you look at historical cases of experimentation gone wrong, the problem is generally that the intervention was unethical on its own. Leaving syphilis untreated, infecting people with diseases, telling people to shock others, and dropping mosquitoes from planes are all things you normally shouldn't do. The problem in these cases wasn't that they were experimenting on people, but that they were harming people.

Similarly, the problem with Uber's car was that if you have an automatic driving system that can't recognize pedestrians, can't anticipate the movements of jaywalkers, freezes in response to dangerous situations, and won't brake to mitigate collisions, it is absolutely nowhere near ready to guide a car on public roads.

We have a weird situation where the rules for experimentation in academia and medicine are much more restrictive than everywhere else. So restrictive that even a very simple study where you do everything you normally do but also record whether two diagnostics agreed with each other can be bureaucratically impractical to run. We should remove most of these restrictions: you should still have to get approval and informed consent if you want to hurt people or violate a duty you have to them, but "if it's ok to do A or B then it's fine to run an experiment on A vs B" should apply everywhere.

(I wrote something similar earlier, after facebook's sentiment analysis experiment.)

Comment via: facebook



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Indescribable

10 ноября, 2019 - 16:31
Published on November 10, 2019 1:31 PM UTC

Some things can be described only via experience.

  • Direct sensory experience (such as the color red)
  • Foreign untranslatable words and phrases
  • Rasas
  • Certain meditative states (such as kenshō and satori)

Other things cannot be precisely described at all.

  • Any particular noncomputable number

Indescribable things cannot be described in a finite number of words. That's because each one contains an infinite quantity of information. I don't mean they convey this information all at once (except for noncomputable numbers). Rather, they open up a new channel of information.

Opening up a new channel of information is mathematically equivalent to adding an input node to a neural network. This is a totally different process from training a machine learning system. When you train a neural network you adjust the weights of the connections between neurons to solve a problem. Adding hidden nodes amounts to basically the same thing. Adding an input node to a neural network unfolds a new dimension of the problem space thereby adding information for the network to work with. In other words, adding a new input node doesn't improve your solution to a problem at all. It makes the problem easier instead.

For example, the traditional Chinese method of teaching strategy involves memorizing ancient Taoist texts. This pedagogical technique is off the radar of modern MBA programs for reasons independent of its effectiveness. The "memorized passages of concise time-tested wisdom" input node is just missing.

You can't tell someone an indescribable thing but you can tell her where to look. You can tell someone to identify the color of blood, listen to a piece of music or stare silently at a blank wall until you see the essence of reality.

The arts of war…cannot be ignored.

―first line of Sunzi's The Art of War

Where else should I look?



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Ethical experimentation

10 ноября, 2019 - 11:00
Published on November 10, 2019 8:00 AM UTC

I suggested experimenting with different settings on personal characteristics that aren’t obviously good or bad. For instance, trying out being more or less perfectionistic for a day.

A particular variety of this that interests me is experimentation with different ethical principles, where opinion differs on which is correct. Both at levels of action (being a vegan for a week) and of abstract belief (being a virtue ethicist for a week).

I think this is a particularly non-obvious thing to do, because:

  1. You already have views on what is correct, and trading your good ethical principles for bad ethical principles seems unethical.
  2. Ethics seems like an area where experimentation is particularly unhelpful, being mostly about things outside of you that you don’t have direct access to, and also arguably inhabiting a separate realm that doesn’t interact with empirical facts. 

I think it is a good idea anyway. On 1, this is basically the same as the case for placebo controlled medical trials, assuming that the thing can actually help you be more ethical in the long run.

On 2, the main thing you have to go by on ethics is intuitions and arguments that are salient and moving to you. But people are notoriously bad at coming up with an unbiased selection of considerations to make salient on topics where they feel something, and it is easy to hear an argument and not really feel it. Actually trying to inhabit the different positions seems helpful for these.

I haven’t done this, but I have become a vegetarian for no great reason and in spite of my argument that it is not an effective use of effort, and then gone back to eating fish, and I found both things had pretty interesting effects on my intuitions about things and the arguments I thought about (possibly changing ethical positions for no good reason is especially good, because then your brain tries to make up its best reasons).

I was thinking of trying nihilism week soon, but then I got busy and maybe became a nihilist anyway, so we’ll see.



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Self-Keeping Secrets

10 ноября, 2019 - 10:59
Published on November 10, 2019 7:59 AM UTC

A magician never reveals his secrets.

The secret behind nearly every magic trick ever performed is available at your local library. Magicial secrets stay secret because they're inconsequential. Unless you are a magician or aspire to become one, you have better things to learn than magic tricks. If magic tricks did anything that mattered they wouldn't be magic tricks. They'd be technology.

Magicians don't need a conspiracy to keep magic tricks secret. It takes work to learn how to do magic. Friction and inertia are sufficient to keep out the riffraff.

This is true of more important subjects too, like computer security. Though zero-day exploits themselves are precious secrets, "how to find" zero-days is public knowledge. And since zero-day exploits have a limited shelf-life, "how to find" zero-days is what matters.

Three may keep a secret, if two of them are dead.

―Benjamin Franklin

Organizations leak like a sponge. Organizations can keep passwords secret most of the time only because a good password is easy to reset. If you're even the slightest bit concerned that your passwords have been stolen then you can re-randomize them. Similarly, an intelligence agency maintains its stockpile of zero-day exploits by constantly replenishing them. To an organization, maintaining secrecy is about restoring secrecy. Techniques can't be kept secret because they change too infrequently to restore secrecy after they get stolen.

In practice, organizations face the opposite problem. Training people is so hard that the limiting factor of an organization's size is how many skilled employees it can hire. The bigger your organization gets the more it'll suffer a regression to the mean. Scaling a large organization is an exercise in dumbing down your employees' jobs to counteract this.

Large organizations can neither keep knowledge secret nor spread it around. In other words, a dependence on knowledge of any kind inhibits the growth of an organization. An organization can scale to the extent it makes its employees'—and especially its customers'—intelligence unnecessary.

SCP-055 is a "self-keeping secret" or "anti-meme".

―internal document, SCP Foundation

Large organizations are precisely those that make knowledge unnecessary. The public school system is, by headcount, among the largest organizations in modern civilization. It must therefore, by necessity, minimize the need for students to learn anything hard[1].

Most adults are employed by large companies. Most adults buy most of our products from large companies. Small businesses are dying out[2]. Modern civilization is increasingly dominated by large organizations. These organizations don't just shape our society. They are our society. We are our jobs. We are the products we use. We are the media we consume. We are our communities.

Our most popular activities are those that scale the best. Those that scale the best are those that require the least thinking, the least skill, the least specialized knowledge, the least individuality. If you want to measure your individuality, ask yourself this: of all the things you do, how much of it is so hard that your friends and coworkers literally can't do it.

  1. By "hard" I mean "conceptual". Schools can effectively force students to learn by rote. As coercive institutions, schools are incapable of forcing students to productively misbehave or otherwise exercise critical thinking ↩︎

  2. Small companies that concentrate a lot of talent in their small number of employees are doing well. But these companies will continue to constitute a small fraction of total employment. ↩︎



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Goal-thinking vs desire-thinking

10 ноября, 2019 - 03:31
Published on November 10, 2019 12:31 AM UTC

[Adapted from an old post on my personal blog]

There's a lot of long-running arguments on the internet that basically consist of people arguing past each other due to differing basic assumptions that they don't know how to make explicit, preventing them from noticing the fundamental disagreement. I've noticed a few of these and tried to see if I can make both sides more explicit. In this post I'd like to try to explicate one.

Let's start with a concrete example; there are a number of people who would say that wireheading is a good thing, which is obviously not the general thinking on LW. What's the source of this disagreement? One possible explanation would be to say that the former are saying "happiness is our only terminal value, all other values are subsidiary to it", while the latter say hell no it's not, but I think there's more to it than that.

Without yet saying what I think the fundamental distinction is, let me give another example that I think stems from the same disagreement. Consider this essay -- and this isn't the only thing I've seen along these lines -- which takes the point of view that obviously a rational person would kill themselves, while to me this just seems... dumb.

So what's going on here? What's the actual distinction that leads to such arguments? Again, I can't know, but here's my hypothesis. I think there are two sorts of thinking going on here; I'm going to call them "goal-thinking" and "desire-thinking" (these are my own terms, feel free to devise better ones).

So -- goal thinking is thinking in terms of what I'm calling "goals". Goals are to be accomplished. If you're thinking in terms of goals, what you're afraid of is being thwarted, or having your capacity to act, to effect your goals, reduced -- being somehow disabled or restrained; if your capabilities are reduced, you have less ability to make an effect on the future and steer it towards what you want. (This is important; goal-thinking thinks in terms of preferences about the future.) The ultimate example of this is death -- if you're dead, you can't affect anything anymore. While it's possible in some unusual cases that dying could help accomplish your goals, it's pretty unlikely; most of the time, you're better off remaining alive so that you can continue to affect things. So suicide is almost always unhelpful. Goals, remember, about the world, external to oneself.

Wireheading is similarly disastrous, because it's just another means of rendering oneself inactive. We can generalize "wireheading" of course to anything that causes one to think one has accomplished one's goals when one hasn't. Or of course to having one's goals altered. We all know this argument; this is just the old "murder pill" argument. Indeed, you've likely noticed by this point that I'm just recapitulating Omohundro's basic AI drives.

Another way of putting this is, goals themselves are driving forces.

So what's the alternative, "desire-thinking", that I'm claiming is how many people think? One answer would be to say, this alternative way of thinking is that "it's all about happiness vs unhappiness" or "it's all about pleasure vs pain", thinking in terms of internal experience rather than the external state of the world -- so for instance, people thinking this way tend to focus on unhappiness, pain, and suffering as the general bad thing, rather than having one's capacity to act reduced.

But, as I basically already said above, I actually don't think this gets at the root of the distinction, because there are still things this fails to explain. For instance, I think it fails to explain the suicide article above, or, say, Buddhism; since applying the goal-thinking point of view but applied to internal experiences instead would just lead to hedonism instead. And presumably there are a number of people thinking that way! (Which may include a number of the "wireheading is good" people.) But we can basically group this in as a variant of goal-thinking. How do we explain the truly troublesome cases above, that don't fit into this?

I think what's actually going on with these cases involves not thinking in terms of goals in the above sense at all, but rather what I'm calling "desires" instead. The distinction is that whereas goals are to be accomplished, desires are to be extinguished. From a goal-thinking point of view, you can model this as having one single goal, "extinguish all desires", which is the only driving force; and the desires themselves are, just, like, objects in the model, not themselves driving forces.

So under the desire-thinking point of view, having one's desires altered can be a good thing, if the new ones are easier. If you can just make yourself not care, great. Wireheading is excellent from this point of view, and even killing oneself can work. Indeed, desire-thinking doesn't really think in terms of preferences about the future, so much as just an anticipation of having preferences in the future (about the then-present).

Now while I, and LW more generally, may sympathize more with the former point of view, it's worth noting that in reality nobody uses entirely one or the other. Or at least, it seems pretty clear that even here people won't actually endorse pure goal-thinking for humans (although it's another matter for AIs; this is one of those times when it's worth remembering that LW really has two different functions -- refining the art of human rationality, and refining the art of AI rationality, and that these are not always the same thing). While I don't have a particular link on-hand, this issue has often been discussed here before in terms of preference regarding flavors of ice cream, and how it's not clear that one should resist modifications to this; this can be explained if one imagines that desire-thinking should be applied to such cases.

Thus when Eliezer Yudkowsky says "I wouldn't want to take a pill that would cause me to want to kill people, because then maybe I'd kill people, and I don't want that", we recognize it as an important principle of decision theory; but when someone says "I don't like spinach, and I'm glad I don't, because if I liked it I'd eat it, and I just hate it", we correctly recognize this as a joke. (Despite it being isomorphic.) Still, despite people not actually being all one way or the other, I think it's a useful way of understanding some arguments that have resulted in a lot of people talking past each other.



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Neural nets as a model for how humans make and understand visual art

9 ноября, 2019 - 19:53
Published on November 9, 2019 4:53 PM UTC

This is a new paper relating experimental results in deep learning to human psychology and cognitive science. I'm excited to get feedback and comments. I've included some excerpts below.


Abstract

This paper is about the cognitive science of visual art. Artists create physical artifacts (such as sculptures or paintings) which depict people, objects, and events. These depictions are usually stylized rather than photo-realistic. How is it that humans are able to understand and create stylized representations? Does this ability depend on general cognitive capacities or an evolutionary adaptation for art? What role is played by learning and culture?

Machine Learning can shed light on these questions. It’s possible to train convolutional neural networks (CNNs) to recognize objects without training them on any visual art. If such CNNs can generalize to visual art (by creating and understanding stylized representations), then CNNs provide a model for how humans could understand art without innate adaptations or cultural learning. I argue that Deep Dream and Style Transfer show that CNNs can create a basic form of visual art, and that humans could create art by similar processes. This suggests that artists make art by optimizing for effects on the human object-recognition system. Physical artifacts are optimized to evoke real-world objects for this system (e.g. to evoke people or landscapes) and to serve as superstimuli for this system.


From "Introduction"

In a psychology study in the 1960s, two professors kept their son from seeing any pictures or photos until the age of 19 months. On viewing line-drawings for the first time, the child immediately recognized what was depicted. Yet aside from this study, we have limited data on humans with zero exposure to visual representations.

...

For the first time in history, there are algorithms [convolutional neural nets] for object recognition that approach human performance across a wide range of datasets. This enables novel computational experiments akin to depriving a child of visual art. It’s possible to train a network to recognize objects (e.g. people, horses, chairs) without giving it any exposure to visual art and then test whether it can understand and create artistic representations.


From "Part 1: Creating art with networks for object recognition"

Figure 2. Outputs from testing whether a conv-net model can generalize to paintings. Results are fairly impressive overall. However, in Picasso painting on the right, the people are classified as "stop sign, frisbee".



Figure 12. Diagram showing how Deep Dream and Style Transfer could be combined. This generates an image that is a superstimulus for the conv net (due to the Deep Dream loss) and has the style (i.e. low-level textures) of the style image. Black arrows show the forward pass of the conv net. Red arrows show the backward pass, which is used to optimize the image in the center by gradient descent.



Figure 13. Diagram showing how the process in Figure 12 can be extended to humans. This is the "Sensory Optimization" model for creating visual art. For humans, the input is a binocular stream of visual perception (represented here as "content video" frames). The goal is to capture the content of this input in a different physical medium (woodcut print) and with a different style. The optimization is not by gradient descent but by a much slower process of blackbox search that draws on human general intelligence.



Figure 14. Semi-abstract images that are classified as “toilet”, “house tick”, and “pornographic” (“NSFW”) by recognition nets. From Tom White’s “Perception Engines” and “Synthetic Abstractions” (with permission from the artist).








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Notes on Running Objective

9 ноября, 2019 - 18:40
Published on November 9, 2019 3:40 PM UTC

I've been playing Killer Queen lately at work. It's a ten-person arcade game, five-vs-five. The general idea is that you're a "bumblebear" drone that runs and jumps around trying to win by (a) collecting berries or (b) riding the snail. Some bumblebears will bring berries to "wing gates" and become warriors, who can fly around and kill others. Each side also has one queen, who is like a warrior but also has the ability to dive.

You fly by tapping a button, sometimes very fast (7-15hz depending), and it turns out this hurts my wrists. I have bad wrists, and generally need to be pretty careful. Not being able to play any flying roles, I've been getting a lot of practice playing drone, and wanted to write up some notes.

The two main things drones do are collect berries and ride the snail. Since those are the direct paths to victory, the objectives, we call this "running objective." Typically a team will have one member who always runs objective, and another member who plays "flex", either playing warrior or running objective depending on the needs of the situation.

Presenting everything at an introductory level would take way too long, though, so the rest of this post will be terse and jargony. If you'd like me to explain something ask in the comments, or you can ask your teammates.

General

  • The longer you hold the button the higher you jump. Making a minimum-sized jump requires only a very slight tap.

  • Your forward speed is the same whether you're walking, jumping, or falling.

  • If you're not making progress because of offensive guards, call for a hive/snail clear.

  • Even though you can't kill anyone, booping others is still very valuable in the right situation. You can boop drones out of gates, boop enemy warriors into your own warriors, and boop warriors out of the hive. Play around with booping to learn which parts are safe and which will kill you.

  • Coordinate with teammates before the round starts to pick a strategy (typically 2-2 berries or 3-1 snail) and figure out if there are any gates you should plan to deny off the bat.

  • Watch the whole board so you have as much warning as possible about what's coming next. Give other players a heads up about important things: number of berries remaining, progress of the snail, warrior counts, gate possession, queen lives. What's worth tracking and calling takes time and depends on your team's preferences, but as dedicated objective you generally have more spare bandwidth than anyone else.

  • Don't get speed unless you literally have nothing better to do. As a drone you're going to die a lot, and the time spent getting speed is not worth it. Speed can help you deliver berries past an inexperienced hive guard, but you want to be building skills that will still work when you play really good players.

  • You can trap standing warriors by jumping onto their heads, but I haven't been able to figure out a good use for this.

Running Berries

  • Go for the hardest spots first, which means the spots that are easiest for the opponents to guard. Top berries on Dusk and Twilight, back berries on Day and Night. Though possibly on Day and Night you should fill front berries first at the very start, if this lets you get an extra cycle in before the other team gets their hive guard up.

  • To get around the hive guard, coordinate with the other objective runner to run the hive at the same time. A solid d-guard can block a solid drone most of the time, but it's very hard to stop two drones at once. On Day you can often boop a hive guard by sticking; see the end of the post.

  • Make sure you and someone else don't go for the same hole. A good convention is you divide left-right based on your position at the cabinet, but make sure you're using the same convention as your teammates. Some teams prefer to call holes.

  • Practice the hard jumps, especially jumping across the top on dusk. Work on delivering the berry into a specific target hole. On Dusk and Twilight learn which holes can be reached by running off the ledge vs jumps of various sizes, and which can be reached by jumping from the bottom ("bottom berries"; lowest three rows on Dusk, lowest two berries on Twilight) vs which need to be filled from the top ("top berries").

  • Figure out the fastest routes on every map.

  • Keep in mind that you're going to crawl back out of a hole, and while you do that the hole is occupied. This means there are times when you should hold back just a little to give another objective runner time to catch up, so you can deliver together. This matters the most for the end of the game, but can also apply to top berries. Don't hold back if it's going to get you killed though.

  • If you're holding a berry and run into another berry you'll kick it. Flying berries that go into holes register immediately. Play around with kicking berries to learn the physics of how your speed affects their speed and angle, because it's not obvious. While warriors absolutely need to learn how to kick so they can clear berries after killing the drone carrying them, berry soccer is only rarely useful for drones and can be a distraction.

Riding Snail

  • When the snail is eating someone, its rider is trapped and is an easy target for warriors. If the snail rider is killed, however, the drone being eaten is immediately free. These combine to mean that you can feed the snail to trap your opponent, get rescued by a teammate, and then ride. If there's high military pressure and you're not sure if rescue is advisable, your o-guard should make the decision.

  • If you're not going to be able to be rescued, and especially if they're not going to be able to be rescued either, it's generally better to just stay on the snail and wait while it slowly eats them.

  • When to hop off the snail and run away vs stay on it and get a few more pixels of progress is a really hard judgement, and mostly depends on whether they're just going to kill you anyway even if you hop off.

Sticking

Normally, if you jump and hit the ceiling you'll bounce off. But if you hit the ceiling at just the right speed, you'll stick to it for a short time instead. This gets you high enough that you can boop warriors and even the queen, but you have time time it well enough that you're still sticking when you boop them.

Compare bouncing:

To sticking:

Generally you'll want to stick while moving:

Unlike with warriors, tapping while you're sticking has no effect.

To practice sticking, first practice jumping right next to a low ledge, aiming to get your bear's knees even with the underside of the ledge. Once you see what that feels like, try to do the same thing just under a low ledge. Once you're good at sticking you're surprisingly difficult to kill when there's a low ceiling, you can boop hive guards on Day, you can boop warriors out of their own sticks, and you can be one side of a pinch.

With perfect timing it is possible to jump from one ledge and stick on a ledge above that you're not on yet, effectively lipping as a drone, which could be powerful. For example, it should let you boop a hive guard on Night.

Comment via: facebook, r/KillerQueen



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Reference Classes for Randomness

9 ноября, 2019 - 17:41
Published on November 9, 2019 2:41 PM UTC

(Follow-up to Randomness vs. Ignorance)

I've claimed that, if you roll a die, your uncertainty about the result of the roll is random, because, in 1/6th of all situations where one has just rolled a die, it will come up a three. Conversely, if you wonder about the existence of a timeless God, whatever uncertainty you have is ignorance. In this post, I make the case that this distinction isn't just a useful analog to probability inside vs. outside a model, but is actually fundamental (if some more ideas are added).

The randomness in the above example doesn't come from some inherent "true randomness" of the die. In fact, this notion of randomness is compatible with determinism. (You could then argue it is not real randomness but just ignorance in disguise, but please just accept the term randomness, whenever I bold it, as a working definition.) This randomness is simply the result of taking all situations which are identical to the current one from your perspective, and observing that, among those, one in six will have the die come up a three. This is a general principle that can be applied to any situation: a fair die, a biased die, delay in traffic, whatever.

The "identical" in the last paragraph needs unpacking. If you roll a die and we consider only the situations that are exactly identical from your perspective, then the die will come up a three either in a lot more or a lot less than 1/6th of them. Regardless of whether the universe is fully deterministic or not, the current state of the die is sure to at least correlate with the chance for a three to end up on top.

However, you are not actually able to distinguish between the situation where you just rolled a die in such a way that it will come up a three, and the situation where you just rolled a die in such a way that it will come up a five, and thus you need to group both situations together. More precisely, you need to group all situations that, to you, look indistinguishable with respect to the result of the die, into one class. 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This grouping is based both on computational limitations (you see the die but can't compute how it'll land) and on missing information (you don't see the die). If you were replaced by a superintelligent agent, their reference class would be smaller, but some grouping based on hidden information would remain. Formally, think of an equivalence relation on the set of all brain states.

So at this point, I've based the definition of randomness both on a frequentist principle (counting the number of situations where the die comes up a three vs not a three) and on a more Bayesian-like principle of subjective uncertainty (taking your abilities as a basis for the choice of reference class). Maybe this doesn't yet look like a particularly smart way to do it. But with this post, I am only arguing that this model is consistent: all uncertainty can be viewed as made up of randomness and/or ignorance and no contradictions arise. In the next post, I'll argue that it's also quite useful, in that several controversial problems are answered immediately by adopting this view.



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Pricing externalities is not necessarily economically efficient

9 ноября, 2019 - 15:07
Published on November 9, 2019 12:07 PM UTC

[A]s long as externalities exist and are not internalized via Pigouvian taxes, the result is inefficient. The inefficiency is eliminated by charging the polluter an emission fee equal to the damage done by his pollution. In some real world cases it may be difficult to measure the amount of the damage, but, provided that that problem can be solved, using Pigouvian taxes to internalize externalities produces the efficient outcome.

That analysis was accepted by virtually the entire economics profession prior to Coase's work in the field. It is wrong—not in one way but in three. The existence of externalities does not necessarily lead to an inefficient result. Pigouvian taxes, even if they can be correctly calculated, do not in general lead to the efficient result. Third, and most important, the problem is not really externalities at all—it is transaction costs.

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