See the section about scoring rules in the Technical Explanation.
Zack_M_Davis(Zack M. Davis)
Karma: 8,291
Have you (Zack) previously noted something somewhere about “that’s coordination”… and… somehow wrapping that around to “but words are just for prediction anyway?”.
(Self-review.)
Argument for significance: earlier comment
Unnatural Categories Are Optimized for Deception
Mods: I’m confused that this isn’t showing up in “Latest” even when I set “Personal Blog” to “Required” in a Private-mode window?! Could the algorithm be using “created by” date rather than “published on” date (an earlier version was sitting as a draft for a few months so I could preview some LaTeX), or …?!
Author’s Meta Note
(I continue to maintain that this is fun and basic hidden-Bayesian-structure-of-language-and-cognition stuff that shouldn’t be “political”, but—if we need it—the “Containment Thread on the Motivation and Political Context for My Philosophy of Language Agenda” is now available for talking about the elephant in the room.)
(I intend to eventually reply to all substantive critical comments on this post and the containment thread, but I might be very slow to respond due to life events and priorities outside of this website. Your patience is deeply appreciated.)
(Self-review.) 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The post doesn’t emphasize this angle, but this is also more-or-less my abstract story for the classic puzzle of why disagreement is so prevalent, which, from a Bayesian-wannabe rather than a human perspective, should be shocking: there’s only one reality, so honest people should get the same answers. How can it simultaneously be the case that disagreement is ubiquitous, but people usually aren’t outright lying? Explanation: the “dishonesty” is mostly in the form of motivatedly asking different questions.
Possible future work: varying the model assumptions might yield some more detailed morals. I never got around to trying the diminishing-marginal-relevance variation suggested in footnote 8. Another variation I didn’t get around to trying would be for the importance of a fact to each coalition’s narrative to vary: maybe there are a few “sacred cows” for which the social cost of challenging is huge (as opposed to just having to keep one’s ratio of off-narrative reports in line).
Prior work: So, I happened to learn about the filtered-evidence problem from the Sequences, but of course, there’s a big statistics literature about learning from missing data that I learned a little bit about in 2020 while perusing Ch. 19 of Probabilistic Graphical Models: Principles and Techniques by Daphne Koller and the other guy.
And You Take Me the Way I Am
(Self-review.) I oppose including this post in a Best-of-2019 collection. I stand by what I wrote, but, as with “Relevance Norms”, this was a “defensive” post; it exists as a reaction to “Meta-Honesty”’s candidacy in the 2018 Review, rather than trying to advance new material on its own terms.
The analogy between patch-resistence in AI alignment and humans finding ways to dodge the spirit of deontological rules, is very important, but not enough to carry the entire post.
A standalone canon-potential explanation of why I think we need a broader conception of honesty than avoiding individually false statements would look more like “Algorithms of Deception” (although that post didn’t do so great karma-wise; I’m not sure whether because people don’t want to read code, it was slow to get Frontpaged (as I recall), or if it’s bad for some other reason).
I intend to reply to Fiddler’s review, but likely not in a timely manner.
(Self-review.) I oppose including this post in a Best-of-2019 collection. I stand by what I wrote, but it’s not potential “canon” material, because this was a “defensive” post for the 2018 Review: if the “contextualizing vs. decoupling” idea hadn’t been as popular and well-received as it was, there would be no reason for this post to exist.
A standalone Less Wrong “house brand” explanation of Gricean implicature (in terms of Bayesian signaling games, probably?) could be a useful reference post, but that’s not what this is.
What specific other thing are you doing besides prediction? If you can give me a specific example, I think I should be able to reply with either (a) “that’s a prediction”, (b) “that’s coordination”, (c) “here’s an explanation of why that’s deception/wireheading in the technical sense I’ve described”, (d) “that’s a self-fulfilling prophecy”, or (e) “whoops, looks like my philosophical thesis isn’t quite right and I need to do some more thinking; thanks TAG!!”.
(I should be able to reply eventually; no promises on turnaround time because I’m coping with the aftermath of a crisis that I’m no longer involved in, but for which I have both a moral responsibility and selfish interest to reflect and repent on my role in.)
The direct approach: “I’m curious [if/why …]” → “Tell me [if/why …]”
Thanks; you are right and the thing I originally wrote was wrong. For posterity, I added a disclaimer (sixth paragraph) that credits you and the other person who pointed this out.
But that isn’t relevant to what you are saying, because you are making a normative point: you are saying some concepts are wrong.
You know, I think I agree that the reliance on normativity intuitions is a weakness of the original post as written in April 2019. I’ve thought a lot more in the intervening 20 months, and have been working on a sequel that I hope to finish very soon (working title “Unnatural Categories Are Optimized for Deception”, current draft sitting at 8,650 words) that I think does a much better job at reducing that black box. (That is, I think the original normative claim is basically “right”, but I now have a deeper understanding of what that’s even supposed to mean.)
In summary: when I say that some concepts are wrong, or more wrong than others, I just mean that some concepts are worse than others at making probabilistic predictions. We can formalize this with specific calculations in simple examples (like the Foos clustered at [1, 2, 3] in ℝ³ in the original post) and be confident that the underlying mathematical principles apply to the real world, even if the real world is usually too complicated for us to do explicit calculations for.
This is most straightforward in cases where the causal interaction between “the map” and “the territory” goes only in the one direction “territory → map”, and where where we only have to consider one agent’s map. As we relax those simplifying assumptions, the theory has to get more complicated.
First complication: if there are multiple agents with aligned perferences but limited ability to communicate, then they potentially face coordination problems: that’s what “Schelling Categories” is about.
Second complication: if there are multiple agents whose preferences aren’t aligned, then they might have an incentive to decieve each other, making the other agent have a worse map in a way that will trick it into making decisions that benefit the first agent. (Or, a poorly-designed agent might have an incentive to deceive itself, “wireheading” on making the map look good, instead of using a map that reflects the territory to formulate plans that make the territory better.) This is what my forthcoming sequel post is about.
Third complication: if the map can affect the territory, you can have self-fulfilling (or partially-self-fulfilling, or self-negating) prophecies. I’m not sure I understand the theory of this yet.
The sense in which I deny that scientifically inaccurate maps can have compensatory kinds of usefulness, is that I think they have to fall into the second case: the apparent usefulness has to derive from deception (or wireheading). Why else would you want a model/map that makes worse predictions rather than better predictions? (Note: self-fulfilling prophecies aren’t inaccurate!)
You’re one of them.
Well, yes. I mean, I think I’m fighting for more accurate maps, but that’s (trivially) still fighting! I don’t doubt that the feeling is mutual.
I’m reminded of discussions where one person argues that a shared interest group (for concreteness, let’s say, a chess club) should remain politically neutral (as opposed to, say, issuing a collective condemnation of puppy-kicking), to which someone responds that everything is political and that therefore neutrality is just supporting the status quo (in which some number of puppies per day will continue to be kicked). There’s a sense in which it’s true that everything is political! (As it is written, refusing to act is like refusing to allow time to pass.)
I think a better counter-counter reply is not to repeat that Chess Club should be “neutral” (because I don’t know what that means, either), but rather to contend that it’s not Chess Club’s job to save the puppies of the world: we can save more puppies with a division of labor in which Chess Club focuses on Society’s chess needs, and an Anti-Puppy-Kicking League focuses on Society’s interest in saving puppies. (And if you think Society should care more about puppies and less about chess, you should want to defund Chess Club rather than having it issue collective statements.)
Similarly, but even more fundamentally, it’s not the map’s job to provide compensatory usefulness; the map’s job is to reflect the territory. In a world where agents are using maps to make decisions, you probably can affect the territory by distorting the map for purposes that aren’t about maximizing predictive accuracy! It’s just really bad AI design, because by the very nature of the operation, you’re sabotaging your ability to tell whether your intervention is actually making things better.
(Datapoint on initial perception: at the time, I had glanced at the post, but didn’t vote or comment, because I thought Steven was in the right in the precipitating discussion and the “a prediction can assign less probability-mass to the actual outcome than another but still be better” position seemed either confused or confusingly phrased to me; I would say that a good model can make a bad prediction about a particular event, but the model still has to take a hit.)
(In September 2018, frustrated by this exact issue (albeit not in these exact terms), I impulse-bought a custom T-shirt with the slogan “BEING SMART IS MORE IMPORTANT THAN BEING GOOD”. I have never worn it in public. Not sure what the moral here is.)
What does Nostalgebraist do? They fucking write. (Engaging and educational.)
This passage from Sarah Blaffer Hrdy’s Mothers and Others: The Evolutionary Origin of Mutual Understanding, on unexpected functionalist explanations of childrearing practices among the Beng, presents an evocative example of the kind of cultural adaptation Heinrich writes about—
In her richly textured account of “the culture of infancy” in a West African Beng village, the cultural anthropologist Alma Gottlieb describes infant care practices that initially seem puzzling. To Gottlieb, the way the Beng treat their babies seemed so nonsensical that she became convinced that their mode of childcare could be understood only within their peculiar symbolic system. Like many cultural anthropologists, she saw little point in considering evolutionary contexts or adaptive functions.
At first glance, her prejudice against adaptive explantions in this instance seems well-founded. For Beng mothers engage in some remarkably counterintuitive, maladaptive-seeming behaviors. They force babies to drink water before allowing them access to the breast. They also adminsiter herbal enemas several times a day, and decorate their babies with protective painted symbols thought to promote health and growth, as well as to advertise tribal status or identity. Such practices, Gottlieb argues, flow from belief systems specific to the Beng, having to do with the sacredness of water and the origin of babies who enter the world reincarnated from ancestors, and can only be understood within the context of a specifically Beng worldview.
At first glance, such practices seem to defy common sense and functional explanation. How could enemas and body paint have anything to do with keeping babies healthier or enhancing their survival? [...] Symbolic decorations are not going to encourage babies to grow faster or make them healthier, and parasite- and bacteria-laden water forced down a baby’s throat is likely to do the reverse, causing diarrea. And what use are excretion-promoting enemas when the big problem in this society is malnutrition? [...]
And yet stand back and consider Beng maternal practice in terms of the universal dilemma confronting primate mothers who find themselves torn between heavy subsistence loads and the need to care for infants in the face of high rates of mortality. These are mothers who cannot possibly rear their infants without assistance from others. Next, consider the Beng in the context of a species that must have evolved as a cooperative breeder. Again and again, Gottlieb mentions the “enormous labor demands” on Beng mothers who farm full-time, chop and haul firewood, provide water, do the laundry, and prepare food using labor-intensive methods. A woman, especially an undernourished woman with several children, could not possibly manage these tasks without enlisting kin and other villagers to help her care for her infant. As it turns out, each of the seemingly useless cultural practice mentioned above also just happens to make babies more attractive to allomothers.
“Every Beng mother,” Gottlieb writes, “makes great efforts to toilet-train her baby from birth so as to attract a possible [caretaker] who can be recruited to hte job without fear of being soiled. The goal is for the infant to defecate only once or twice a day, during bath time, so as never to dirty anyone between baths, especially while being carried.” It is to make a baby more easily comforted by a nonlactating allomother that they are taught early—and forcibly—to be satisfied with a drink of water if no one is available to breastfeed. It is specifically to make her infant more attractive to caretakers that a mother beautifies her baby with painted symbols, for “if a baby is irresistably beautiful, someone will be eager to carry the little one for a few hours, and the mother can get her work done.”
(I think the present pandemic was a “warning shot” highlighting the importance of knowledge about biosecurity, and this post therefore becomes important in retrospect.)
If we can quantify how good a theory is at making accurate predictions (or rather, quantify a combination of accuracy and simplicity), that gives us a sense in which some theories are “better” (less wrong) than others, without needing theories to be “true”.