Aaro Salosensaari
The picture looks like evidence there is something very weird going on that is not reflected in the numbers or arguments provided. There are homeless encampments in many countries around the world, but very rarely 20 min walk from anyone’s office.
From what I remember form my history of Finland classes, the 19th/early 20th century state project to build a compulsory school system met some not insignificant opposition from parents. They liked having the kids working instead going to school, especially in agrarian households.
Now, I don’t want to get into debate whether schooling is useful or not (and for whom, and for what purpose, and if the usefulness has changed over time), but there is something illustrative in the opposition: children rarely are independent agents to the extent adults are. If the incentives are set in that way, the parents will prefer to make choices about their children labor that result in more resources for the household/family unit (charitable interpretation) or for themselves (not so charitable). Number of children in the family also affects the calculus. (One kid, it makes sense to invest in their career; ten kids, and the investment was in the number.)
“Non-identifiability”, by the way, is the search term that does the trick and finds something useful. Please see: Daly et al. [1], section 3. They study indentifiability characteristics of logistic sigmoid (that has rate r and goes from zero to carrying capacity K at t=0..30) via Fisher information matrix (FIM). Quote:
When measurements are taken at times t ≤ 10, the singular vector (which is also the eigenvector corresponding to the single non-zero eigenvalue of the FIM) is oriented in the direction of the growth rate r in parameter space. For t ≤ 10, the system is therefore sensitive to changes in the growth rate r, but largely insensitive to changes in the carrying capacity K. Conversely, for measurements taken at times t ≥ 20, the singular vector of the sensitivity matrix is oriented in the direction of the growth rate K[sic], and the system is sensitive to changes in the carrying capacity K but largely insensitive to changes in the growth rate r. Both these conclusions are physically intuitive.
Then Daly et al. proceed with MCMC scheme to numerically show that samples at different parts of time domain result in different identifiability of rate and carrying capacity parameters (Figure 3.)
[1] Daly, Aidan C., David Gavaghan, Jonathan Cooper, and Simon Tavener. “Inference-Based Assessment of Parameter Identifiability in Nonlinear Biological Models.” Journal of The Royal Society Interface 15, no. 144 (July 31, 2018): 20180318. https://doi.org/10.1098/rsif.2018.0318
EDIT.
To clarify, because someone might miss it: this is not only a reply to shminux. Daly et al 2018 is (to some extent) the paper Stuart and others are looking for, at least if you are satisfied with their approach by looking what happens to effective Fisher information of logistic dynamics before and after inflection, supported by numerical inference methods showing that identifiability is difficult. (Their reference list also contains a couple of interesting articles about optimal design for logistic, harmonic models etc.)
Only thing missing that one might want AFAIK is a general analytical quantification of the amount of uncertainty, and comparison to specifically exponential (maybe along the lines Adam wrote there), and maybe writing it up in easy to digest format.
I am happy that you mention Gelman’s book (I am studying it right now). I think lots of “naive strong bayesianists” would improve from a thoughtful study of the BDA book (there are lots of worked out demos and exercises available for it) and maybe some practical application of Bayesian modelling to some real-world statistical problems. The practice of “Bayesian way of life” of “updating my priors” sounds always a bit too easy in contrast to doing a genuine statistical inference.
For example, a couple of puzzles I am still myself unsure how to answer properly and with full confidence: Why one would be interested in doing stratified random sampling with your epidemiological study instead of naive “collect every data point that you see and then do a Bayesian update?” Or how multiple comparisons corrections for classical frequentist p-values map into Bayesian statistical framework? Does it matter for LWian Bayesianism if you are doing your practical statistical analyses with frequentist or Bayesian analysis tools (especially if many frequentist methods can be seen as clever approximations to full Bayesian model, see e.g. discussion of Kneser-Ney smoothing as ad hoc Pitman-Yor process inference here: https://cs.stanford.edu/~jsteinhardt/stats-essay.pdf ; similar relationship exists between k-means and EM-algorithm of Gaussian mixture model.) And if there is no difference, is the philosophical Bayesianism then actually that important—or important at all—for rationality?
I have not read Irving either but he is relatively “world-famous” 1970s-1980s author. (In case it helps you to calibrate, his novel The World According To Garp is the kind of book that was published in translation in the prestigious Keltainen Kirjasto series by Finnish publisher Tammi.)
However, I would like make an opposing point about literature and fiction. I was surprised that post author mentioned a work of fiction as a positive example that demonstrates how some commonly argued option is a fabricated one. I’d think literature would at least as often (maybe more often) disseminate belief in fabricated options than correct them, as an author can easily literally fabricate (make things up, it is fiction) easily believable and memorable stories how characters choose one course of action out of many options and it works out (or not, either way, because the narrator decided so) but in reality, all options as portrayed in the story could all turn out be misrepresented, “fabricated options” in real life.
The smartest people tend to be ambitious.
If this is anecdotal, wouldn’t it be easily explained by some sort of selection bias? Smart ambitious people are much visible than smart, definitely-not-ambitious people (and by definition of “smart”, they have probably better chances at succeeding in their ambitions than equally ambitious less smart people).
Anecdotally, I have met some relatively smart people who are not very ambitious, and I can imagine there could be much smarter people one does not meet except by random chance, because they do not have much ambition. Also anecdotally, I would not be surprised if not-so-ambitious smart people would be content with a “default”, probably mildly successful career path and opportunities for a person like them tend to find.
It gets worse. This isn’t a randomly selected example—it’s specifically selected as a case where reason would have a hard time noticing when and how it’s making things worse.
Well, the history of bringing manioc to Africa is not the only example. Scientific understanding of human nutrition (alongside with disease) had several similar hiccups along the way, several which have been covered in SSC (can’t remember the post titles where):
There was a time when Japanese army lost many lives to beriberi during Russo-Japanese war, thinking it was a transmissible disease, several decades [1] after the one of the first prominent Japanese young scholars with Western medical training discovered it was a deficiency related to nutrition with a classical trial setup in Japanese navy (however, he attributed it—wrongly—to deficiency of nitrogen). It took several decades to identify vitamin B1. [2]
Earlier, there was a time when scurvy was a problem in navies, including the British one, but then British navy (or rather, East India Company) realized citrus fruits were useful preventing scurvy, in 1617 [3]. Unfortunately it didn’t catch on. Then they discovered it again with an actual trial and published the results, in 1740-50s [4]. Unfortunately it again didn’t catch on, and the underlying theory was also as wrong as the others anyway. Finally, against the scientific consensus at the time, the usefulness of citrus was proven by a Navy read admiral in 1795 [5]. Unfortunately they still did not have proper theory why the citrus was supposed to work, so when the Navy managed to switch to using lime juice with minimal vitamin C content [6], then managed reason themselves out of use of citrus, and scurvy was determined as a result of food gone bad [7]. Thus Scott’s Arctic expedition was ill-equipped to prevent scurvy, and soldiers in Gallipoli 1915 also suffered from scurvy.
Story of discovering vitamin D does not involve as dramatic failings, but prior to discovery of UV treatment and discovery of vitamin D, John Snow suggested the cause was adulterated food [8]. Of course, even today one can easily find internet debates about what is “correct” amount of vitamin D supplement if one has not sunlight in winter. Solving B12 deficiency induced anemia appears a true triumph of the science, as a Nobel prize was awarded for dietary recommendation for including liver in the diet [9] before B12 (present in liver) was identified [10].
Some may notice that we have now covered many of the significant vitamins in human diet. I have not even started with the story of Semmelweis.
And anyway, I dislike the whole premise of casting the matter about “being for reason” or “against reason”. The issue with manioc, scurvy, beriberi, and hygiene was that people had unfortunate overconfidence in their per-existing model of reality. With sufficient overconfidence, rationalization or mere “rational speculation”, they could explain how seemingly contradictory experimental results actually fitted in their model, and thus claim the nutrition-based explanations as an unscientific hogwash, until the actual workings of vitamins was discovered. (The article [1] is very instructive about rationalizations Japanese army could come up to dismiss Navy’s apparent success with fighting beriberi: ships were easier to keep clean, beriberi was correlated with spending time on contact with damp ground, etc.)
While looking up food-borne diseases while writing this comment, I was reminded about BSE [11], which is hypothesized to cause vCJD in humans because humans thought it was a good idea to feed dead animals to cattle to improve nutrition (which I suppose it does, barring prion disease). I would view this as a failing from not having a full model what side-effects behavior suggested by the partial model would cause.
On the positive side, sometimes the partial model works well enough: It appears that miasma theory of disease like cholera was the principal motivator for building modern sewage systems. While it is today obvious cholera is not caused by miasma, getting rid of smelly sewage in orderly fashion turned out to be a good idea nevertheless [12].
I am uncertain if I have any proper suggested conclusion, except for that, in general, mistakes of reason are possible and possibly fatal, and social dynamics may prevent proper corrective action for a long time. This is important to keep in mind when making decisions, especially novel and unprecedented, and when evaluating the consequences of action. (The consensus does not necessarily budge easily.)
Maybe a more specific conclusion could be: If one has only evidently partial scientific understanding of some issue, it is very possible acting on it can have unintended consequences. It may even not be obvious where the holes in the scientific understanding are. (Paraphrasing the response to Semmelweis: “We don’t exactly know what causes childbed fever, it manifests in many different organs so it could be several different diseases, but the idea of invisible corpse particles that defy water and soap is simply laughable.”)
[1] https://pubmed.ncbi.nlm.nih.gov/16673750/
[2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3725862/
[3] https://en.wikipedia.org/wiki/John_Woodall
[4] https://en.wikipedia.org/wiki/James_Lind
[5] https://en.wikipedia.org/wiki/Alan_Gardner,_1st_Baron_Gardner
[6] https://en.wikipedia.org/wiki/Scurvy#19th_century
[7] https://idlewords.com/2010/03/scott_and_scurvy.htm
[8] https://en.wikipedia.org/wiki/Rickets#History
[9] https://www.nobelprize.org/prizes/medicine/1934/whipple/facts/
[10] https://en.wikipedia.org/wiki/Vitamin_B12#Descriptions_of_deficiency_effects
[11] https://en.wikipedia.org/wiki/Bovine_spongiform_encephalopathy
Howdy. I came across Ole Peters’ “ergodicity economics” some time ago, and was interested to see what LW made of it. Apparently one set of skeptical journal club meetup notes: https://www.lesswrong.com/posts/gptXmhJxFiEwuPN98/meetup-notes-ole-peters-on-ergodicity
I am not sure what to make of criticisms of Seattle meetups (they appear correct, but I am not sure if they are relevant; see my comment there).
Not planning to write a proper post, but here is an example blog post of Peters which I found illustrative and demonstrates why I think the “ergodicity way of thinking” might have something in it: https://ergodicityeconomics.com/2020/02/26/democratic-domestic-product/ . In summary, looking at the aggregate ensemble quantity such GDP per capita does not tell much what happens to individuals in the ensemble: the typical individual experienced growth in population in general is not related to GDP growth per capita (which may be obvious to a numerate person but not necessarily so, given the importance given to GDP in public discussion). And if one takes average of exponential growth rate, one obtains a measure (geometric mean income that they dub “DDP”) known in economics literature, but originally derived otherwise.
But maybe this looks insightful to me because I am not that very well-versed in economics literature, so it would be nice to have some critical discussion about this.
>Glancing back and forth, I keep changing my mind about whether or not I think the messy empirical data is close enough to the prediction from the normal distribution to accept your conclusion, or whether that elbow feature around 1976-80 seems compelling.
I realize you two had a long discussion about this, but my few cents: This kind of situation (eyeballing is not enough to resolve which of two models fit the data better) is exactly the kind of situation for which a concept of statistical inference is very useful.
I’m a bit too busy right now to present a computation, but my first idea would be to gather the data and run a simple “bootstrappy” simulation: 1) Get the original data set. 2) Generate k = 1 … N simulated samples x^k = [x^k_1, … x^k_t] form a normal distribution with linearly increasing mean mu(t) = mu + c * t at time points t= 1960 … 2018, where c and variance are as in “linear increase hypothesis”. 3) How many of simulated replicate time series have an elbow at 1980 that is equally or more extreme than observed in the data? (One could do this not too informal way by fitting a piece-wise regression model with break at t = 2018 to reach replicate time series, and computing if the two slope estimates differ by a predetermined threshold, such as the estimates recovered by fitting the same piece-wise model in the real data).
This is slightly ad hoc, and there are probably fancier statistical methods for this kind of test, or you could fits some kind of Bayesian model, but I’d think such computational exercise would be illustrative.
Sure, but statements like
>ANNs are built out of neurons. BNNs are built out of neurons too.
are imprecise and possibly imprecise enough to be also incorrect if it turns out that biological neurons do something different than perceptrons that is important. Without making the exact arguments and presenting evidence in what respects the perceptron model is useful, it is quite easy to bake in conclusions along the lines of “this algorithm for ANNs is a good model of biology” in the assumptions “both are built out of neurons”.
I was going to suggest that maybe it could be a known and published result in dynamical systems / population dynamics literature, but I am unable to find anything with Google, and textbooks I have at hand, while plenty mentions of logistic growth models, do not discuss prediction from partial data before inflection point.
On the other hand, it is fundamentally a variation on the themes of difficulty in model selection with partial data and dangers of extrapolation, which are common in many numerical textbooks.
If anyone wishes to flesh it out, I believe this behavior is not limited to trying to distinguish exponentials from logistic curves (or different logistics from each other), but also distinguishing different orders of growth from each other in general. With a judicious choice of data range and constants, it is not difficult to create a set of noisy points which could be either from a particular exponential or a particular quadratic curve. Quick example: https://raw.githubusercontent.com/aa-m-sa/exponential_weirdness/master/exp_vs_x2.png (And if you limit data point range you are looking at to 0 to 2, it is quite impossible to say if a linear model wouldn’t also be plausible.)
Hyperbole aside, how many of those experts linked (and/or contributing to the 10% / 2% estimate) have arrived to their conclusion with a thought process that is “downstream” from the thoughtspace the parent commenter thinks suspect? Then it would not qualify as independent evidence or rebuttal, as it is included as the target of criticism.
a backdrop of decades of mistreatment of the Japanese by Western countries.
I find this a bit difficult to take seriously. The WW2 in the Pacific didn’t start with well-treatment of China and other countries by Japan, either. Naturally Japanese didn’t care about that part of the story, but hey had plenty of other options how they could have responded their the UK or the US trade policy instead of invading Manchuria.
making Ukraine a country with a similar international status to Austria or Finland during the Cold War would be one immediate solution.
This is not a simple task, but rather a tall order. Austria was “made neutral” after it was occupied. Finland signed a peace treaty that put it into effectively similar position. Why would any country submit to such a deal voluntarily? The answer is, they often don’t. Finland didn’t receive significant assistance from the Allies in 1939, yet they decided to defend themselves against the USSR anyway when Stalin attacked.
However, if one side in these disputes had refused to play the game of ratcheting up tensions, the eventual wars would simply not have happened. In this context it takes two to dance.
Sure, but the game theoretic implication is that this kind of strategy favors the first party to take the first step and say “I have an army and a map where this neighboring country belongs to us”.
NATO would have refrained from sending lethal arms to Ukraine and stationing thousands of foreign military advisors in Ukrainian territory after Maidan.
What a weird way to present the causality of events. I am quite confident NATO didn’t have time to send any weapons and certainly not thousands of advisors between Maidan and the war starting. Yanukovich fled 22 February. Antimaidan protests started in Donetsk 1 March and shooting war started in April.
My take is that the scientific concept of “heritability” has some problems in its construction: the exact definition (Var(genotype)/Var(phenotype)), while useful in some regard, does not match the intuition of the word.
Maybe the quantity should be called “relative heritability”, “heritability relative to population” or “proportion of population variance explained”, like many other quantities that similarly have form A/B where both A and B are (population) parameters or their estimates.
Addendum 1.
“Heritable variance”? See also Feldman, Lewontin 1975 https://scholar.google.com/scholar?cluster=10462607332604262282
Home delivery is way cheaper than it used to be.
I am going to push back a little on this one, and ask for context and numbers?
As some of my older relatives commented when Wolt became popular here, before people started going to supermarkets, it was common for shops to have a delivery / errand boy (this would have been 1950s, and more prevalent before the WW2). It is one thing that strikes out reading biographies; teenage Harpo Marx dropped out from school and did odd jobs as an errand boy; they are ubiquitous part of the background in Anne Frank’s diaries; and so on.
Maybe it was proportionally more expensive (relative to cost of purchase), but on the other hand, from the descriptions it looks like the deliveries were done by teenage/young men who were paid peanuts.
Open thread is presumably the best place for a low-effort questions, so here goes:
I came across this post from 2012: Thoughts on the Singularity Institute (SI) by Holden Karnofsky (then-Co-Executive Director of GiveWell). Interestingly enough, some of the object-level objections (under subtitle “objections”) Karnofsky raises[1] are similar to some points that were came up in the Yudkowsky/chathamroom.com discussion and Ngo/Yudkowsky dialogue I read the other day (or rather, read parts of, because they were quite long).
What are people’s thought about that post and objections raised today? What the 10 year (-ish, 9.5 year) retrospective looks like?
Some specific questions.
Firstly, how his arguments would be responded today? Any substantial novel contra-objections? (I ask because its more fun to ask than start reading through Alignment forum archives.)
Secondly, predictions. When I look at the bullet points under the subtitle “Is SI the kind of organization we want to bet on?”, I think I can interpolate a prediction Karnofsky could have made: in 2012, SI [2] had not the sufficient capability nor engaged in activities likely to achieve its stated goals (“Friendliness theory” or Friendly AGI before others), as it was not worth a GiveWell funding recommendation in 2012.
A perfect counterfactual experiment this is not, but given what people on LW today know about what SI/MIRI did achieve in the NoGiveWell!2012 timeline, was Karnofsky’s call correct, incorrect or something else? (As in, did his map of the situation in 2012 matched the reality better than some other map, or was it poor compared to other map?) What inferences could be drawn, if any?
Would be curious to hear perspectives from MIRI insiders, too (edit. but not only them). And I noticed Holden Karnofsky looks active here on LW, though I have no idea if how to ping him.
[1] Tool-AI; idea that advances in tech would bring insights into AGI safety.
[2] succeeded by MIRI I suppose
edit2. fixed ordering of endnotes.
Genetic algorithms are an old and classic staple of LW. [1]
Genetic algorithms (as used in optimization problems) traditionally assume “full connectivity”, that is any two candidates can mate. In other words, population network is assumed to be complete and potential mate is randomly sampled from the population.
Aymeric Vié has a paper out showing (numerical experiments) that some less dense but low average shortest path length network structures appear to result in better optimization results: https://doi.org/10.1145/3449726.3463134
Maybe this isn’t news for you, but it is for me! Maybe it is not news to anyone familiar with mathematical evolutionary theory?
This might be relevant for any metaphors or thought experiments where you wish to invoke GAs.
[1] https://www.lesswrong.com/search?terms=genetic%20algorithms
Thanks. I had read it years ago, but didn’t remember that he had many more points than O(n^3.5 log(1/h)) scale and provides useful references (other than Red Plenty).
>And as Duncan is getting at, employment has changed a lot since the term was coined and there’s now a lot more opportunity for jobs and work to be aligned with a person’s personal goals.
I can agree, I am skeptical that this …integratedness(?) is actually a good thing for everyone. From point of view of the old “work vs life” people who valued the life part, it probably looks like them losing if what they get is “your work is supposed to integral part of what you choose to do with your life” but the options of where and what kind of work to do are not that different than they were some decades ago. And even the new^1 options present trade-offs.
Maybe there are some people whose true calling is to found a startup or develop mastery in some particular technology stack or manage projects that create profit for stockholders. However, if the job market environment is shaped by it so that every job expects an applicant whose life goals are integrally aligned to performing the job, it plausibly affects what kind of goals people think are thinkable when they think of their life and careers, because it certainly affects how they present themselves to the hiring committee or people with equivalent power.
--
Another point, concerning integration of work in ones life. I found myself thinking of the movie Tokyo Story (Tokyo Monogatari, 1953), which I saw maybe two years ago. While the story is not exactly about jobs, it explores how the modernity (the contemporary, post-WW2, kind of modernity in particular) intersects with the Japanese society through the lenses of single Japanese family. The many characters in the film work various jobs: there is a son who is a physician (the kind of one who does visits and has a private practice), a daughter who is running a beauty saloon (a family business like setup; if the husband did something not affiliated with business, I forget what), and a daughter-in-law who is a menial clerk at a corporate business.
The part where this musing connects to anything, while writing the first part of the comment, I started to think about, what are the personal goals of the physician and the beauty business owner? If I recall, both of them are the kind of person who wants to strive and get forward and upward in their life in Tokyo (this leads to the one of conflicts in the film) and view their jobs integral to that goal. Their jobs are quite integrated to their life in concrete terms, both practice at their homes. Both kind of professions predate the work-life balance, probably. One could replace the beauty saloon with something a bit more traditional, like a restaurant or inn without much difference to their relevance to story, at the very least. The character with clearest difference between time off and time in work is the office clerk. Which actually connects to another plot point. I recommend the movie.
Maybe the big difference comes implied in the “good for the world in ways I care about” angle There is no crusader or activist, someone who seeks to make change in the world instead of making it well within it. Today the doctor would be likely to emphasize how he wants to help people by being a good doctor, the family business would have a thing (natural beauty products that help the environment, powered by solar!), and the big corp would have mission, too. The owner of the corp, several echelons above, might be even serious about it. Nobody goes to found a start-up.
So, I guess my point is that there always have been people who don’t view their work and non-work lives a fundamentally different kind of thing.
1: The newness might be debatable, though. I don’t think starting a technology business because you have skills and ideas is something truly new in the US, I think both Edison and Tesla tried their hands at it and I have read Tesla’s interviews which indicate he thought it was for the betterment of mankind? It would have been with the spirit of the times.
A reply to comments showing skepticism about how mathematical skills of someone like Tao could be relevant:
Last time I thought I would understood anything of Tao’s blog was around ~2019. Then he was working on curious stuff, like whether he could prove there can be finite-time blow-up singularities in Navier-Stokes fluid equations (coincidentally, solving the famous Millenium prize problem showing non-smooth solution) by constructing a fluid state that both obeys Navier-Stokes and also is Turing complete and … ugh, maybe I quote the man himself:
The relation (if any, to proving stuff about computational agents alignment people are interested in) is probably spurious (I myself don’t follow either Tao’s work or alignment literature), but I am curious if he’d be interested in working on a formal system of self-replicating / self-improving / aligning computational agents, and (then) capable of finding something genuinely interesting.
minor clarifying edits.