So, the USA seems steadily on trend for between 100-200k deaths. Certainly *feels* like there’s no way the stock market has actually priced this in. Reference classes feel pretty hard to define here.
Why shouldn’t 0.1% of the population be reasonable worth as much as 30% of the value of the companies listed in the stock market and why should it be more then 30%?
There’s the rub. And markets are anti-inductive, so even if we had good examples, we should expect this one to follow a different path.
Remember the impact of the 1957 Asian Flu (116K killed in the US, 1.1M worldwide) or the 1968 Hong Kong Flu (only a bit less)? Neither does anyone else. I do not want to be misinterpreted as “this is only the flu”—this is much more deadly and virulent. And likely more than twice as bad as those examples. But not 10x as bad, as long as we keep taking it seriously.
The changes in spending and productivity are very likely, IMO, to cause both price and monetary inflation. Costs will go up. People will have less stuff and the average lifestyle will likely be worse for a few years. but remember that stocks are priced in NOMINAL dollars, not inflation-adjusted. It’s quite believable that everything can slow down WHILE prices and stock values rise.
Isn’t it also plausible that the impact of the virus is deflationary? (Increased demand for USD as a store of value exceeds the impact of the Fed printing money, etc)
I’d expect not. Overall, productivity is going down mostly because of upheaval and mismatch in supply chains and in efficient ways for labor to use capital. So return to well-situated capital and labor is up, but amount of capital and labor that is well-situated is down. Pure undifferentiated capital has a lower return, plus rising nominal prices means seeking returns is the main motivation, not avoiding risk.
TIPS seem like useful things to have in your portfolio, but rates are lagging quite a bit, so either the market disagrees with me, or the safety value is so high that people are willing to lose value over time. I think stocks will be OK—the last 40 years has seen a lot of financial and government backstops that mean we’re pretty good at protecting the rich on this front, and if you can’t beat ‘em, join ’em. Cash or the like is probably a mistake. I have no good model for Bitcoin or Gold, but my gut says they’ll find a way to lose value against consumer prices. Real Estate (especially where there’s not a large population-density premium) seems pretty sane.
Note: I am not a superforcaster, and have no special knowledge or ability in this area. I’m just pointing out mechanisms that could move things the other direction that the obvious.
Well if we had confidence in any major parameter shifting in either direction it would be tradeable, so I expect reasonable pressures on both sides of such variables.
remember that stocks are priced in NOMINAL dollars, not inflation-adjusted. It’s quite believable that everything can slow down WHILE prices and stock values rise.
In that case, TIPS (Treasury Inflation-Protected Securities) or precious metals like gold might be good investments. Unless the market has already priced it in, of course.
High status feels better when you are near your subordinates (when you can watch them, randomly disrupt them, etc.). High-status people make the decision whether remote work is allowed or not.
Employee focus (having punctuated behaviors separating work from personal time)
Tax advantages for employers to own workspaces and fixtures rather than employees
Not clear that “can be done remotely” is the right metric. We won’t know if “can be done as effectively (or more effectively) remotely” is true for some time.
For short-term, individual cost/benefit calculations around C19, it seems like uncertainty in the number of people currently infected should drop out of the calculation.
For instance: suppose I’m thinking about the risk associated with talking to a random stranger, e.g. a cashier. My estimated chance of catching C19 from this encounter will be roughly proportional to Ninfected. But, assuming we already have reasonably good data on number hospitalized/died, my chances of hospitalization/death given infection will be roughly inversely proportional to Ninfected. So, multiplying those two together, I’ll get a number roughly independent of Ninfected.
How general is this? Does some version of it apply to long-term scenarios too (possibly accounting for herd immunity)? What short-term decisions do depend on Ninfected?
Hot take: The actual resolution to the simulation argument is that most advanced civilizations don’t make loads of simulations.
Two things make this make sense:
Firstly, it only matters if they make unlawful simulations. If they make lawful simulations, then it doesn’t matter whether you’re in a simulation or a base reality, all of your decision theory and incentives are essentially the same, you want to take the same decisions in all of the universes. So you can make lots of lawful simulations, that’s fine.
Secondly, they will strategically choose to not make too many unlawful simulations (to the level where the things inside are actually conscious). This is because to do so would induce anthropic uncertainty over themselves. Like, if the decision-theoretical answer is to not induce anthropic uncertainty over yourself about whether you’re in a simulation, then by TDT everyone will choose not to make unlawful simulations.
I think this is probably wrong in lots of ways but I didn’t stop to figure them out.
Your first point sounds like it is saying we are probably in a simulation, but not the sort that should influence our decisions, because it is lawful. I think this is pretty much exactly what Bostrom’s Simulation Hypothesis is, so I think your first point is not an argument for the second disjunct of the simulation argument but rather for the third.
As for the second point, well, there are many ways for a simulation to be unlawful, and only some of them are undesirable—for example, a civilization might actually want to induce anthropic uncertainty in itself, if it is uncertainty about whether or not it is in a simulation that contains a pleasant afterlife for everyone who dies.
I don’t buy that it makes sense to induce anthropic uncertainty. It makes sense to spend all of your compute to run emulations that are having awesome lives, but it doesn’t make sense to cause yourself to believe false things.
I’m not sure it makes sense either, but I don’t think it is accurately described as “cause yourself to believe false things.” I think whether or not it makes sense comes down to decision theory. If you use evidential decision theory, it makes sense; if you use causal decision theory, it doesn’t. If you use functional decision theory, or updateless decision theory, I’m not sure, I’d have to think more about it. (My guess is that updateless decision theory would do it insofar as you care more about yourself than others, and functional decision theory wouldn’t do it even then.)
I just don’t think it’s a good decision to make, regardless of the math. If I’m nearing the end of the universe, I prefer to spend all my compute instead maximising fun / searching for a way out. Trying to run simulations to make it so I no longer know if I’m about to die seems like a dumb use of compute. I can bear the thought of dying dude, there’s better uses of that compute. You’re not saving yourself, you’re just intentionally making yourself confused because you’re uncomfortable with the thought of death.
Well, that wasn’t the scenario I had in mind. The scenario I had in mind was: People in the year 2030 pass a law requiring future governments to make ancestor simulations with happy afterlives, because that way it’s probable that they themselves will be in such a simulation. (It’s like cryonics, but cheaper!) Then, hundreds or billions of years later, the future government carries out the plan, as required by law.
Not saying this is what we should do, just saying it’s a decision I could sympathize with, and I imagine it’s a decision some fraction of people would make, if they thought it was an option.
Thinking more, I think there are good arguments for taking actions that as a by-product induce anthropic uncertainty; these are the standard hansonian situation where you build lots of ems of yourself to do bits of work then turn them off.
But I still don’t agree with the people in the situation you describe because they’re optimising over their own epistemic state, I think they’re morally wrong to do that. I’m totally fine with a law requiring future governments to rebuild you / an em of you and give you a nice life (perhaps as a trade for working harder today to ensure that the future world exists), but that’s conceptually analogous to extending your life, and doesn’t require causing you to believe false things. You know you’ll be turned off and then later a copy of you will be turned on, there’s no anthropic uncertainty, you’re just going to get lots of valuable stuff.
The relevant intuition to the second point there, is to imagine you somehow found out that there was only one ground truth base reality, only one real world, not a multiverse or a tegmark level 4 verse or whatever. And you’re a civilization that has successfully dealt with x-risks and unilateralist action and information vulnerabilities, to the point where you have the sort of unified control to make a top-down decision about whether to make massive numbers of civilizations. And you’re wondring whether to make a billion simulations.
And suddenly you’re faced with the prospect of building something that will make it so you no longer know whether you’re in the base universe. Someday gravity might get turned off because that’s what your overlords wanted. If you pull the trigger, you’ll never be sure that you weren’t actually one of the simulated ones, because there’s suddenly so many simulations.
And so you don’t pull the trigger, and you remain confident that you’re in the base universe.
This, plus some assumptions about all civilizations that have the capacity to do massive simulations also being wise enough to overcome x-risk and coordination problems so they can actually make a top-down decision here, plus some TDT magic whereby all such civilizations in the various multiverses and Tegmark levels can all coordinate in logical time to pick the same decision… leaves there being no unlawful simulations.
My crux here is that I don’t feel much uncertainty about whether or not our overlords will start interacting with us (they won’t and I really don’t expect that to change), and I’m trying to backchain from that to find reasons why it makes sense.
My basic argument is that all civilizations that have the capability to make simulations that aren’t true histories (but instead have lots of weird stuff happen in them) will all be philosophically sophisticated to collectively not do so, and so you can always expect to be in a true history and not have weird sh*t happen to you like in The Sims. The main counterargument here is to show that there are lots of civilizations that will exist with the powers to do this but lacking the wisdom to not do it. Two key examples that come to mind:
We build an AGI singleton that lacks important kinds of philosophical maturity, so makes lots of simulations that ruins the anthropic uncertainty for everyone else.
Civilizations at somewhere around our level get to a point where they can create massive numbers of simulations but haven’t managed to create existential risks like AGI. Even while you might think our civilization is pretty close to AGI, I could imagine alternative civilizations that aren’t, just like I could imagine alternative civilizations that are really close to making masses of ems but that aren’t close enough to AGI. This feels like a pretty empirical question about whether such civilizations are possible and whether they can have these kinds of resources without causing an existential catastrophe / building singleton AGI.
Why appeal to philosophical sophistication rather than lack of motivation? Humans given the power to make ancestor-simulations would create lots of interventionist sims (as is demonstrated by the populatity games like The Sims), but if the vast hypermajority of ancestor-simulations are run by unaligned AIs doing their analogue of history research, that could “drown out” the tiny minority of interventionist simulations.
That’s interesting. I don’t feel comfortable with that argument, it feels too much like random chance whether or not we should expect ourselves to be in an interventionist universe or not, whereas I feel like I should be able to find strong reasons to not be in an interventionist universe.
Now that’s fun. I need to figure out some more stuff about measure, I don’t quite get why some universes should be weighted more than others. But I think that sort of argument is probably a mistake—even if the lawful universes get more weighting for some reason, unless you also have reason to think that they don’t make simulations, there’s still loads of simulations within each of their lawful universes, setting the balance in favour of simulation again.
One big reason why it makes sense is that the simulation is designed for the purpose of accurately representing reality.
Another big reason why (a version of it) makes sense is that the simulation is designed for the purpose of inducing anthropic uncertainty in someone at some later time in the simulation. e.g. if the point of the simulation is to make our AGI worry that it is in a simulation, and manipulate it via probable environment hacking, then the simulation will be accurate and lawful (i.e. un-tampered-with) until AGI is created.
I think “polluting the lake” by increasing the general likelihood of you (and anyone else) being in a simulation is indeed something that some agents might not want to do, but (a) it’s a collective action problem, and (b) plenty of agents won’t mind it that much, and (c) there are good reasons to do it even if it has costs. I admit I am a bit confused about this though, so thank you for bringing it up, I will think about it more in the coming months.
Another big reason why (a version of it) makes sense is that the simulation is designed for the purpose of inducing anthropic uncertainty in someone at some later time in the simulation. e.g. if the point of the simulation is to make our AGI worry that it is in a simulation, and manipulate it via probable environment hacking, then the simulation will be accurate and lawful (i.e. un-tampered-with) until AGI is created.
Ugh, anthropic warfare, feels so ugly and scary. I hope we never face that sh*t.
[Carl Schmitt is a good philosopher but] One nightmarish way to understand The Discourse is that somehow Carl Schmitt became the obvious, agreed-upon, common-sense interpretation of politics. All sides nod sagely, but each fetishizes a different book.
So the left gets Political Theology, the right gets Nomos of the Earth. Quilette-style centrists are 100% indebted to the Concept of the Political.
The Wealth of Nations by Adam Smith is a great book. My favorite part is Book III, Chapter 4 on the end of feudalism. In particular, I like these two paragraphs:
In a country which has neither foreign commerce, nor any of the finer manufactures, a great proprietor, having nothing for which he can exchange the greater part of the produce of his lands which is over and above the maintenance of the cultivators, consumes the whole in rustic hospitality at home. If this surplus produce is sufficient to maintain a hundred or a thousand men, he can make use of it in no other way than by maintaining a hundred or a thousand men. He is at all times, therefore, surrounded with a multitude of retainers and dependants, who, having no equivalent to give in return for their maintenance, but being fed entirely by his bounty, must obey him, for the same reason that soldiers must obey the prince who pays them. … In a country where there is no foreign commerce, nor any of the finer manufactures, a man of ten thousand a year cannot well employ his revenue in any other way than in maintaining, perhaps, a thousand families, who are all of them necessarily at his command. In the present state of Europe, a man of ten thousand a year can spend his whole revenue, and he generally does so, without directly maintaining twenty people, or being able to command more than ten footmen not worth the commanding. Indirectly, perhaps, he maintains as great or even a greater number of people than he could have done by the ancient method of expense. For though the quantity of precious productions for which he exchanges his whole revenue be very small, the number of workmen employed in collecting and preparing it must necessarily have been very great. Its great price generally arises from the wages of their labour, and the profits of all their immediate employers. By paying that price he indirectly pays all those wages and profits and thus indirectly contributes to the maintenance of all the workmen and their employers. He generally contributes, however, but a very small proportion to that of each, to very few perhaps a tenth, to many not a hundredth, and to some not a thousandth, nor even a ten-thousandth part of their whole annual maintenance. Though he contributes, therefore, to the maintenance of them all, they are all more or less independent of him, because generally they can all be maintained without him.
Singaporean government released their own Trace Together app last week and they’re now working to open-source it.
Pros:
uses Bluetooth to track proximity between the user’s phones and notifies anyone who has been in vicinity of someone who tested positive (there is an interesting story here on how they had to first overcome the issue of varying Bluetooth strength based on phone models)
does not collect personal information (a consent is needed upfront)
does not capture GPS data
installed by 620,000 users (as of 23 March) which is roughly 11% of Singapore’s population in 3 days (if we assume constant growth, it should be around 30% today, March 28th)
Cons:
Depends on universal adoption (this will be true for any consent-based app)
I have anecdotal evidence that in the first 1-2 days after launch, the button on the consent page did not actually work, making it impossible to install. I believe this would have been solved now.
Battery drainage (will depend on the phone & user’s usage of Bluetooth)
Some reviews in the App Store indicate that it might disrupt the working of other connected devices (e.g. Bluetooth headphones) which might discourage usage
a big problem might be that it does not work in the background, but I believe this is already being solved following public requests.
Palintir already helping government track cases—Article notes that government can get location data from telecoms, but that google has even more precise data from maps and android, which the government can also ask for in an emergency
It seems to me that Zeno’s paradoxes leverage incorrect, naïve notions of time and computation. We exist in the world, and we might suppose that that the world is being computed in some way. If time is continuous, then the computer might need to do some pretty weird things to determine our location at an infinite number of intermediate times. However, even if that were the case, we would never notice it – we exist within time and we would not observe the external behavior of the system which is computing us, nor its runtime.
Don’t have much of an opinion—I haven’t rigorously studied infinitesimals yet. I usually just think of infinite / infinitely small quantities as being produced by limiting processes. For example, the intersection of all the ϵ-balls around a real number is just that number (under the standard topology), which set has 0 measure and is, in a sense, “infinitely small”.
Collection of discussions of key cruxes related to AI safety/alignment
These are works that highlight disagreements, cruxes, debates, assumptions, etc. about the importance of AI safety/alignment, about which risks are most likely, about which strategies to prioritise, etc.
I’ve also included some works that attempt to clearly lay out a particular view in a way that could be particularly helpful for others trying to see where the cruxes are, even if the work itself don’t spend much time addressing alternative views. I’m not sure precisely where to draw the boundaries in order to make this collection maximally useful.
These are ordered from most to least recent.
I’ve put in bold those works that very subjectively seem to me especially worth reading.
Psychology: An Imperfect and Improving Science
This is an essay I wrote in 2017 as coursework for the final year of my Psychology undergrad degree. (That was a year before I learned about EA and the rationalist movement.)
I’m posting this as a shortform comment, rather than as a full post, because it’s now a little outdated, it’s just one of many things that people have written on this topic, and I don’t think the topic is of central interest to a massive portion of LessWrong readers. But I do think it holds up well, is pretty clear, and makes some points that generalise decently beyond psychology (e.g., about drawing boundaries between science and pseudoscience, evaluating research fields, and good research practice).
I put the references in a “reply” to this.
Psychology’s scientific status has been denied or questioned by some (e.g., Berezow, 2012; Campbell, 2012). Evaluating such critiques and their rebuttals requires defining “science”, considering what counts as psychology, and exploring how unscientific elements within a field influence the scientific standing of that field as a whole. This essay presents a conception of “science” that consolidates features commonly seen as important into a family resemblance model. Using this model, I argue psychology is indeed a science, despite unscientific individuals, papers, and practices within it. However, these unscientific practices make psychology less scientific than it could be. Thus, I outline their nature and effects, and how psychologists are correcting these issues.
Addressing whether psychology is a science requires specifying what is meant by “science”. This is more difficult than some writers seem to recognise. For example, Berezow (2012) states we can “definitively” say psychology is non-science “[b]ecause psychology often does not meet the five basic requirements for a field to be considered scientifically rigorous: clearly defined terminology, quantifiability, highly controlled experimental conditions, reproducibility and, finally, predictability and testability.” However, there are fields that do not meet those criteria whose scientific status is generally unquestioned. For example, astronomy and earthquake science do not utilise experiments (Irzik & Nola, 2014). Furthermore, Berezow leaves unmentioned other features associated with science, such as data-collection and inference-making (Irzik & Nola, 2011). Many such features have been noted by various writers, though some are contested by others or only present or logical in certain sciences. For example, direct observation of the matters of interest has been rightly noted as helping make fields scientific, as it reduces issues like the gap between self-reported intentions and the behaviours researchers seek to predict (Godin, Conner, & Sheeran, 2005; Rhodes & de Bruijn, 2013; Sheeran, 2002; Skinner, 1987). However, self-reported intentions are still useful predictors of behaviour and levers for manipulating it (Godin et al., 2005; Rhodes & de Bruijn, 2013; Sheeran, 2002), and science often productively investigates constructs such as gravity that are not directly observable (Bringmann & Eronen, 2016; Chomsky, 1971; Fanelli, 2010; Michell, 2013). Thus, definitions of science would benefit from noting the value of direct observation, but cannot exclude indirect measures or unobservable constructs. This highlights the difficulty – or perhaps impossibility – of defining science by way of a list of necessary and sufficient conditions for scientific status (Mahner, 2013).
An attractive solution is instead constructing a family resemblance model of science (Dagher & Erduran, 2016; Irzik & Nola, 2011, 2014; Pigliucci, 2013). Family resemblance models are sets of features shared by many but not all examples of something. To demonstrate, three characteristics common in science are experiments, double-blind trials, and the hypothetico-deductive method (Irzik & Nola, 2014). A definition of science omitting these would be missing something important. However, calling these “necessary” excludes many sciences; for example, particle physics would be rendered unscientific for lack of double-blind trials (Cleland & Brindell, 2013; Irzik & Nola, 2014). Thus, a family resemblance model of science only requires a field to have enough scientific features, rather than requiring the field to have all such features. The full list of features this model should include, the relative importance of each feature, and what number or combination is required for something to be a “science” could all be debated. However, for showing that psychology is a science, it will suffice to provide a rough family resemblance model incorporating some particularly important features, which I shall now outline.
Firstly, Berezow’s (2012) “requirements”, while not actually necessary for scientific status, do belong in a family resemblance model of science. That is, when these features can be achieved, they make a field more scientific. The importance of reproducibility is highlighted also by Kahneman (2014) and Klein et al. (2014a, 2014b), and that of testability or falsifiability is also mentioned by Popper (1957) and Ferguson and Heene (2012). These features are related to the more fundamental idea that science should be empirical; claims should be required to be supported by evidence (Irzik & Nola, 2011; Pigliucci, 2013). Together, these features allow science to be self-correcting, incrementally progressing towards truth by accumulation of evidence and peer-review of ideas and findings (Open Science Collaboration, 2015). This is further supported by scientists’ methods and results being made public and transparent (Anderson, Martinson, & De Vries, 2007, 2010; Nosek et al., 2015; Stricker, 1997). Additionally, findings and predictions should logically cohere with established theories, including those from other sciences (Lilienfeld, 2011; Mahner, 2013). These features all support science’s ultimate aims to benefit humanity by explaining, predicting, and controlling phenomena (Hansson, 2013; Irzik & Nola, 2014; Skinner, cited in Delprato & Midgley, 1992). Each feature may not be necessary for scientific status, and many other features could be added, but the point is that each feature a field possesses makes that field more scientific. Thus, armed with this model, we are nearly ready to productively evaluate the scientific status of psychology.
However, two further questions must first be addressed: What is psychology, and how do unscientific occurrences within psychology affect the scientific status of the field as a whole? For example, it can generally be argued parapsychology is not truly part of psychology, for reasons such as its lack of support from mainstream psychologists. However, there are certain more challenging instances, such as the case of a paper by Bem (2011) claiming to find evidence for precognition. This used accepted methodological and analytical techniques, was published in a leading psychology journal, and was written by a prominent, mainstream psychologist. Thus, one must accept that this paper is, to a substantial extent, part of psychology. It therefore appears important to determine whether Bem’s paper exemplifies science. It certainly has many scientific features, such as use of experiments and evidence. However, it lacks other features, such as logical coherence with the established principle of causation only proceeding forwards in time.
But it is unnecessary here to determine whether the paper is non-science, insufficiently scientific, or bad science, because, regardless, this episode shows psychology as a field being scientific. This is because scientific features such as self-correction and reproducibility are most applicable to a field as a whole, rather than to an individual scientist or article, and these features are visible in psychology’s response to Bem’s (2011) paper. Replication attempts were produced and supported the null hypothesis; namely, that precognition does not occur (Galak, LeBoeuf, Nelson, Simmons, 2012; Ritchie, Wiseman, & French, 2012; Wagenmakers, Wetzels, Borsboom, van der Maas, & Kievit, 2012). Furthermore, publicity, peer-review, and self-correction of findings and ideas were apparent in those failed replications and in commentary on Bem’s paper (Wagenmakers, Wetzels, Borsboom, & van der Maas, 2011; Francis, 2012; LeBel & Peters, 2011). Peers discussed many issues with Bem’s article, such as several variables having been recorded by Bem’s experimental program yet not mentioned in the study (Galak et al., 2012; Ritchie et al., 2012), suggesting that the positive results reported may have been false positives emerging by chance from many, mostly unreported analyses. Wagenmakers et al. (2011) similarly noted other irregularities and unexplained choices in data transformation and analysis, and highlighted that Bem had previously recommended to psychologists: “If you see dim traces of interesting patterns, try to reorganize the data to bring them into bolder relief. […] Go on a fishing expedition for something—anything—interesting” (Bem, cited in Wagenmakers et al., 2011). These responses to Bem’s study by psychologists highlight that, while the scientific status of that study is highly questionable, isolated events such as that need not overly affect the scientific status of the entire field of psychology.
Indeed, psychology’s response to Bem’s (2011) paper exemplifies ways in which the field in general fits the family resemblance model of science outlined earlier. This model captures how different parts of psychology can each be scientific, despite showing different combinations of scientific features. For example, behaviourists may use more direct observation and clearly defined terminology (see Delprato & Midgley, 1992; Skinner, 1987), while evolutionary psychologists better integrate their theories and findings with established theories from other sciences (see Burke, 2014; Confer et al., 2010). These features make subfields that have them more scientific, but lacking one feature does not make a subfield non-science. Similarly, while much of psychology utilises controlled experiments, those parts that do not, like longitudinal studies of the etiology of mental disorders, can still be scientific if they have enough other scientific features, such as accumulation of evidence to increase our capacity for prediction and intervention.
Meanwhile, other scientific features are essentially universal in psychology. For example, all psychological claims and theories are expected to be based on or confirmed by evidence, and are rejected or modified if found not to be. Additionally, psychological methods and findings are made public by publication, with papers being peer-reviewed before this and open to critique afterwards, facilitating self-correction. Such self-correction can be seen in the response to Bem’s (2011) paper, as well as in how most psychological researchers now reject the untestable ideas of early psychoanalysis (see Cioffi, 2013; Pigliucci, 2013). Parts of psychology vary in their emphasis on basic versus applied research; for example, some psychologists investigate the processes underlying sadness while others conduct trials of specific cognitive therapy techniques for depression. However, these various branches can support each other, and all psychological research ultimately pursues benefitting humanity by explaining, predicting, and controlling phenomena. Indeed, while there is much work to be done and precision is rarely achieved, psychology can already make predictions much more accurate than chance or intuition in many areas, and thus provides benefits as diverse as anxiety-reduction via exposure therapy and HIV-prevention via soap operas informed by social-cognitive theories (Bandura, 2002; Lilienfeld, Ritschel, Lynn, Cautin, & Latzman, 2013; Zimbardo, 2004). All considered, most of psychology exemplifies most important scientific features, and thus psychology should certainly be considered a science.
However, psychology is not as scientific as it could be. Earlier I noted that isolated papers reporting inaccurate findings and utilising unscientific practices, as Bem (2011) seems highly likely to have, should not significantly affect psychology’s scientific status, as long as the field self-corrects adequately. However, as several commentators on Bem’s paper noted, more worrying is what that paper reflects regarding psychology more broadly, given that it largely met or exceeded psychology’s methodological, analytical, and reporting standards (Francis, 2012; LeBel & Peters, 2011; Wagenmakers et al., 2011). The fact Bem met these standards, yet still “discovered” and got published results that seem to violate fundamental principles about how causation works, highlights the potential prevalence of spurious findings in psychological literature. These findings could result from various flaws and biases, yet might fail to be recognised or countered in the way Bem’s report was if they are not as clearly false; indeed, they may be entirely plausible, yet inaccurate (LeBel & Peters, 2011). Thus, I will now discuss how critiques regarding Bem’s paper apply to much of mainstream psychology.
Firstly, the kind of “fishing expedition” recommended by Bem (cited in Wagenmakers et al., 2011) is common in psychology. Researchers often record many variables, and have flexibility in which variables, interactions, participants, data transformations, and statistics they use in their analyses (John, Loewenstein, & Prelec, 2012). Wagenmakers et al. (2012) note that such practices are not inherently problematic, and indeed such explorations are useful for suggesting hypotheses to test in a confirmatory manner. The issue is that often these explorations are inadequately reported and are presented as confirmatory themselves, despite the increased risk of false positives when conducting multiple comparisons (Asendorpf et al., 2013; Wagenmakers et al., 2012). Neuropsychological studies can be particularly affected by failures to control for multiple comparisons, even if all analyses are reported, because analysis of brain activity makes huge numbers of comparisons the norm. Thus, without statistical controls, false positives are almost guaranteed (Bennett, Baird, Miller, & Wolford, 2009). The issue of uncontrolled multiple comparisons, whether reported or not, causing false positives can be compounded by hindsight bias making results seem plausible and predictable in retrospect (Wagenmakers et al., 2012). This can cause overconfidence in findings and make researchers feel comfortable writing articles as if these findings were hypothesised beforehand (Kerr, 1998). These practices inflate the number of false discoveries and spurious confirmations of theories in psychological literature.
This is compounded by publication bias. Journals are more likely to publish novel and positive results than replications or negative results (Ferguson & Heene, 2012; Francis, 2012; Ioannidis, Munafò, Fusar-Poli, Nosek, & David, 2014; Kerr, 1998). One reason for this is that, despite the importance of self-correction and incremental progress, replications or negative results are often treated as not show anything substantially interesting (Klein et al., 2014b). Another reason is the idea that null results are hard to interpret or overly likely to be false negatives (Ferguson & Heene, 2012; Kerr, 1998). Psychological studies regularly have insufficient power; their sample sizes mean that, even if an effect of the expected size does exist, the chance of not finding it is substantial (Asendorpf et al., 2013; Bakker, Hartgerink, Wicherts, & van der Maas, 2016). Further, the frequentist statistics typically used by psychologists cannot clearly quantify the support data provides for null hypotheses; these statistics have difficulty distinguishing between powerful evidence for no effect and simply a failure to find evidence for an effect (Dienes, 2011). While concerns about the interpretability of null results are thus often reasonable, they distort the psychological literature’s representation of reality (see Fanelli, 2010; Kerr, 1998). Publication bias also takes the form of researchers being more likely to submit for publication those studies that revealed positive results (John et al., 2012). This can occur because researchers themselves also often find negative results difficult to interpret, and know they are less likely to be published or to lead to incentives like grants or prestige (Kerr, 1998; Open Science Collaboration, 2015). Thus, flexibility in analysis, failure to control for or report multiple comparisons, presentation of exploratory results as confirmatory, publication bias, low power, and difficulty interpreting null results are interrelated issues. These issues in turn make psychology less scientific by reducing the transparency of methods and findings.
These issues also undermine other scientific features. The Open Science Collaboration (2015) conducted replications of 100 studies from leading psychological journals, finding that less than half replicated successfully. This low level of reproducibility in itself makes psychology less scientific, and provides further evidence of the likely high prevalence and impact of the issues noted above (Asendorpf et al., 2013; Open Science Collaboration, 2015). Together, these problems impede self-correction, and make psychology’s use of evidence and testability of theories less meaningful, as replications and negative tests are often unreported (Ferguson & Heene, 2012). This undermines psychology’s ability to benefit humanity by explaining, predicting, and controlling phenomena.
However, while these issues make psychology less scientific, they do not make it non-science. Other sciences, including “hard sciences” like physics and biology, also suffer from issues like publication bias and low reproducibility and transparency (Alatalo, Mappes, & Edgar, 1997; Anderson, Burnham, Gould, & Cherry, 2001; McNutt, 2014; Miguel et al., 2014; Sarewitz, 2012; Service, 2002). Their presence is problematic and demands a response in any case, and may be more pronounced in psychology than in “harder” sciences, but it is not necessarily damning (see Fanelli, 2010). For example, the Open Science Collaboration (2015) did find a large portion of effects replicated, particularly effects whose initial evidence was stronger. Meanwhile, Klein et al. (2014a) found a much higher rate of replication for more established effects, compared to the Open Science Collaboration’s quasi-random sample of recent findings. Both results highlight that, while psychology certainly has work to do to become more reliable, the field also has the capacity to scientifically progress towards truth and is already doing so to a meaningful extent.
Furthermore, psychologists themselves are highlighting these issues and researching and implementing solutions for them. Bakker et al. (2016) discuss the problem of low power and how to overcome it with larger sample sizes, reinforced by researchers habitually running power analyses prior to conducting studies and reviewers checking these analyses have been conducted. Nosek et al. (2015) proposed guidelines for promoting transparency by changing what journals encourage or require, such as replications, better reporting and sharing of materials and data, and pre-registration of studies and analysis plans. Pre-registration side-steps confirmation and hindsight bias and unreported, uncorrected multiple comparisons, as expectations and analysis plans are on record before data is gathered (Wagenmakers et al., 2012). Journals can also conditionally accept studies for publication based on pre-registered plans, minimising bias against null results by both journals and researchers. Such proposals still welcome exploratory analyses, but prevent these analyses being presented as confirmatory (Miguel et al., 2014). Finally, psychologists have argued for, outlined how to use, and adopted Bayesian statistics as an alternative to frequentist statistics (Ecker, Lewandowsky, & Apai, 2011; Wagenmakers et al., 2011). Bayesian statistics provide clear quantification of evidence for null hypotheses, combatting one source of publication bias and making testability of psychological claims more meaningful (Dienes, 2011; Francis, 2012). These proposals are beginning to take effect. For example, many journals and organisations are signatories to Nosek et al.’s guidelines. Additionally, the Centre for Open Science, led by the psychologist Brian Nosek, has set up online tools for researchers to routinely make their data, code, and pre-registered plans public (Miguel et al., 2014). This shows psychology self-correcting its practices, not just individual findings, to become more scientific.
I have argued here that claims that psychology is non-scientific may often reflect unworkable definitions of science and ignorance of what psychology actually involves. A family resemblance model of science overcomes the former issue by outlining features that sciences do not have to possess to be science, but do become more scientific by possessing. This model suggests psychology is a science because it generally exemplifies most scientific features; most importantly, it accumulates evidence publicly, incrementally, and self-critically to benefit humanity by explaining, predicting, and controlling phenomena. However, psychology is not as scientific as it could be. A variety of interrelated issues with researchers’ and journals’ practices and incentive structures impede the effectiveness and meaningfulness of psychology’s scientific features. But failure to be perfectly scientific is not unique to psychology; it is universal among sciences. Science has achieved what it has because of its constant commitment to incremental improvement and self-correction of its own practices. In keeping with this, psychologists are researching and discussing psychology’s issues and their potential solutions, and such solutions are being put into action. More work must be done, and more researchers and journals must act on and push for these discussions and solutions, but already it is clear both that psychology is a science and that it is actively working to become more scientific.
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Something else in the vein of “things EAs and rationalists should be paying attention to in regards to Corona.”
There’s a common failure mode in large human systems where one outlier causes us to create a rule that is a worse equilibrium. In the PersonalMBA, Josh Kaufman talks about someone taking advantage of a “buy any book you want” rule that a company has—so you make it so that you can no longer get any free books.
This same pattern has happened before in the US, after 9-11 - We created a whole bunch of security theater, that caused more suffering for everyone, and gave government way more power and way less oversight than is safe, because we over-reacted to prevent one bad event, not considering the counterfactual invisible things we would be losing.
This will happen again with Corona, things will be put in place that are maybe good at preventing pandemics (or worse, making people think they’re safe from pandemics), but create a million trivial conveniences every day that add up to more strife than they’re worth.
These types of rules are very hard to repeal after the fact because of absence blindness—someone needs to do the work of calculating the cost/benefit ratio BEFORE they get implemented, then build a convincing enough narrative to what seems obvious/common sense measures given the climate/devastation.
Uneducated hypothesis: All hominidae species tend to thrive in huge forests, unless they’ve discovered fire. From the moment a species discovers fire, any individual can unilaterally burn the entire forest (due to negligence/anger/curiosity/whatever), and thus a huge forest is unlikely to serve as a long-term habitat for many individuals of that species.
Counterpoint: people today inhabit forested/jungled areas without burning everything down by accident (as far as I know; it’s the kind of fact I would expect to have heard about if true), and even use fire for controlled burns to manage the forest/jungle.
To prolong my medicine stores by 200%, I’ve mixed in similar-looking iron supplement placebos with my real medication. (To be clear, nothing serious happens to me if I miss days)
I find it interesting what kind of beliefs one needs to question and in which ways in order to get people angry/upset/touchy.
Or, to put it in more popular terms, what kind of arguments make you seem like a smart-ass when arguing with someone.
For example, reading Eliezer yudkowsky’s Rationality from AI to Zombies, I found myself generally speaking liking the writing style and to a karge extent the book was just reinforcing the biases I already had. Other then some of his poorly thought out metaphysics based on which he bases his ethics argument… I honestly can’t think of a single thing from that book I disagree with. Same goes for Inadequate Equilibria.
One seeming pattern for this is something like:
Arguing about a specific belief
Going a level down and challenging a pillar of the opponent’s belief that was not being considered as part of the discussion.
E.g: “Arguing about whether or not climate change is a threat, going one level down and arguing that there’s not enough proof climate change is happening to being with”
You can make this pattern even more annoying by doing something like:
Arguing about a specific belief
Going a level down and challenging a pillar of the opponent’s belief that was not being considered as part of the discussion.
Not entertaining an opposite argument about one of your own pillars being shaky.
E.g.: After the previous climate change argument, not entertaining the idea that “Maybe acting upon climate change as if it were real and as if it were a threat, would actually result in positive consequences even if those two things were unture”
You can make this pattern even more annoying by doing something like:
Arguing about a specific belief
Going a level down and challenging a pillar of the opponent’s belief that was not being considered as part of the discussion.
Doing so with some evidence that the other party is unaware or cannot understand
E.g.: After the previous climate change argument, back up your point about climate change not being real by citing various studies that would take hours to fact check and might be out of reach knowledge-wise for either of you.
***
I think there’s other things that come into account.
For example there’s some specific fields which are considered more sacrosanct then others, trying to argue against a standard position in that field as part of your argument seems to much more easily put you into the “smartass” camp.
For example, arguing against commonly held religious or medical knowledge, seems to be almost impossible, unless you are taking an already-approved side of the debate.
E.g. You can argue ibuprofen against paracetamol as the go to for common cold since there’s authoritative claims for each, you can’t argue for a 3rd lesser backed NSAID or for using corticosteroids or no treatment instead of NSAIDs.
Other fields such as ethics or physics or computer science seem to be fair game and nobody really minds people trying to argue for an unsanctioned viewpoint.
***
There’s obviously the idea of politics being overall bad, and the more politicized a certain subject is the less you can change people’s minds about it.
But to some extent I don’t feel like politics really comes into play.
It seems that people are fairly open to having their minds changed about economic policy but not about identity policy.… no matter which side of the spectrum you are on. Which seem counter intuitive, since the issue of “should countries have open borders and free healthcare” seems like one much more deeply embedded in existing political agendas and of much more import than “What gender should transgender people be counted in when participating in the olympics”.
***
One interesting thing that I observed: I’ve personally been able to annoy a lot of people when talking with them online. However, IRL, in the last 4 years or so (since I actually begun explicitly learning how to communicate), I can’t think of a single person that I’ve offended.
Even though I’m more verbose when I talk. Even though the ideas I talk about over coffee are usually much more niche and questionable in their verity then the ones I write about online.
I wonder if there’s some sort of “magic oratory skill” I’ve come closer to attaining IRL that either can’t be attained on the internet or is very different… granted, it’s more likely it’s the inherent bias of the people I’m usually discussing with.
Was thinking a bit about the how to make it real for people that the quarantine depressing the economy kills people just like Coronavirus does.
Was thinking about finding a simple good enough correlation between economic depression and death, then creating a “flattening the curve” graphic that shows how many deaths we would save from stopping the economic freefall at different points. Combining this was clear narratives about recession could be quite effective.
On the other hand, I think it’s quite plausible that this particular problem will take care of itself. When people begin to experience depression, will the young people who are the economic engine of the country really continue to stay home and quarantine themselves? It seems quite likely that we’ll simply become stratified for a while where young healthy people break quarantine, and the older and immuno-compromised stay home.
But getting the time of this right is everything. Striking the right balance of “deaths from economic freefall” and “deaths from an overloaded medical system” is a balancing act, going too far in either direction results in hundreds of thousands of unnecessary deaths.
Then I got to thinking about the effect of a depressed economy on x-risks from AI. Because the funding for AI safety is
1. Mostly in non-profits
and
2. Orders of magnitude smaller than funding for AI capabilities
It’s quite likely that the funding for AI safety is more inelastic in depressions than than the funding for AI capabilities. This may answer the puzzle of why more EAs and rationalists aren’t speaking cogently about the tradeoffs between depression and lives saved from Corona—they have gone through this same train of thought, and decided that preventing a depression is an information hazard.
Wait, you received evidence that didn’t just refute your hypothesis, it reversed it. If you accept that, shouldn’t you also reverse your proposed remedy? Shouldn’t you now argue _IN FAVOR_ of shutting down more completely—it saves lives both directly by limiting the spread of the virus AND indirectly by slowing the economy.
(note: this is intended to be semi-humorous—my base position is that the economic causes and effects are far too complex and distributed to really predict impact on that level, or to predict what policies might improve what outcomes).
It’s interesting because you would intuitively think this, but there is actually not terrible evidence linking periods of economic growth to increased mortality.
It’s interesting because you would intuitively think this, but there is actually not terrible evidence linking periods of economic growth to increased mortality.
Wow that is fascinating. It does make the case harder to make because you have to start quantifying happiness/depression, etc and trade off against lives. Much much harder to simplify enough to make it viral. Updates towards capitalism being horrible.
Is non-profit funding really that inelastic in depression?
It probably varies quite a bit by sector, and where funding comes from for different non-profits. In the case of AI safety I think it’s likely more inelastic than AI capability.
The most efficient form of practice is generally to address one’s weaknesses. Why, then, don’t chess/Go players train by playing against engines optimized for this? I can imagine three types of engines:
Trained to play more human-like sound moves (soundness as measured by stronger engines like Stockfish, AlphaZero).
Trained to play less human-like sound moves.
Trained to win against (real or simulated) humans while making unsound moves.
The first tool would simply be an opponent when humans are inconvenient or not available. The second and third tools would highlight weaknesses in one’s game more efficiently than playing against humans or computers. I’m confused about why I can’t find any attempts at engines of type 1 that apply modern deep learning techniques, or any attempts whatsoever at engines of type 2 or 3.
I haverepeatedlyargued for a departure from pure Bayesianism that I call “quasi-Bayesianism”. But, coming from a LessWrong-ish background, it might be hard to wrap your head around the fact Bayesianism is somehow deficient. So, here’s another way to understand it, using Bayesianism’s own favorite trick: Dutch booking!
Consider a Bayesian agent Alice. Since Alice is Bayesian, ey never randomize: ey just follow a Bayes-optimal policy for eir prior, and such a policy can always be chosen to be deterministic. Moreover, Alice always accepts a bet if ey can choose which side of the bet to take: indeed, at least one side of any bet has non-negative expected utility. Now, Alice meets Omega. Omega is very smart so ey know more than Alice and moreover ey can predict Alice. Omega offers Alice a series of bets. The bets are specifically chosen by Omega s.t. Alice would pick the wrong side of each one. Alice takes the bets and loses, indefinitely. Alice cannot escape eir predicament: ey might know, in some sense, that Omega is cheating em, but there is no way within the Bayesian paradigm to justify turning down the bets.
A possible counterargument is, we don’t need to depart far from Bayesianism to win here. We only need to somehow justify randomization, perhaps by something like infinitesimal random perturbations of the belief state (like with reflective oracles). But, in a way, this is exactly what quasi-Bayesianism does: a quasi-Bayes-optimal policy is in particular Bayes-optimal when the prior is taken to be in Nash equilibrium of the associated zero-sum game. However, Bayes-optimality underspecifies the policy: not every optimal reply to a Nash equilibrium is a Nash equilibrium.
This argument is not entirely novel: it is just a special case of an environment that the agent cannot simulate, which is the original motivation for quasi-Bayesianism. In some sense, any Bayesian agent is dogmatic: it dogmatically beliefs that the environment is computationally simple, since it cannot consider a hypothesis which is not. Here, Omega exploits this false dogmatic belief.
And here I thought the reason was going to be that Bayesianism doesn’t appear to include the cost of computation. (Thus, the usual dutch book arguments should be adjusted so that “optimal betting” does not leave one worse off for having payed, say, an oracle, too much for computation.)
Bayeseans are allowed to understand that there are agents with better estimates than they have. And that being offered a bet _IS_ evidence that the other agent THINKS they have an advantage.
Randomization (aka “mixed strategy”) is well-understood as the rational move in games where opponents are predicting your choices. I have read nothing that would even hint that it’s unavailable to Bayesean agents. The relevant probability (updated per Bayes’s Rule) would be “is my counterpart trying to minimize my payout based on my choices”.
edit: I realize you may be using a different definition of “bayeseanism” than I am. I’m thinking humans striving for rational choices, which perforce includes the knowledge of incomplete computation and imperfect knowledge. Naive agents can be imagined that don’t have this complexity. Those guys are stuck, and Omega’s gonna pwn them.
I’m thinking humans striving for rational choices,
It feels like there’s better words for this like rationality, whereas bayesianism is a more specific philosophy about how best to represent and update beliefs.
Am I one of the few people here who has looked at the covid-19 data and reached the conclusion that it’s probably only about as severe/fatal as seasonal influenza?
I have a longer blog post outlining the case here.
TLDR: CFR!=IFR, influenza CFR is similar to covid-19 CFR, and we know from influenza data that typically IFR << CFR due to enormous selection sampling bias from mostly testing only those with more severe disease. We can correct for that by comparing the covid-19 confirmed case age structure to the population age structure using uniform or age-dependent attack rate. The resulting IFR is similar to influenza, which is also the best fit for the Diamond Princess data (where selection bias is mostly avoided so CFR~IFR).
Selection bias can help explain why the CFR is higher in Italy, and probably why it’s so much lower in Germany (I’m looking for age structure data on covid-19 cases from Germany, I’m predicting it will be flatter than US or Italy data). South Korea is also another interesting case (which I found some data for but haven’t put into the blog post yet) - we can clearly reject a typical attack rate age structure there, which was surprising at first but then made sense given that the outbreak in SK started in a large tight-knit cult with a young median age and they tested everyone in the cult.
Anyway if anyone here has already encountered these thoughts and still believes covid-19 IFR is much higher than influenza IFR I’m curious what the best arguments/evidence are.
So, the USA seems steadily on trend for between 100-200k deaths. Certainly *feels* like there’s no way the stock market has actually priced this in. Reference classes feel pretty hard to define here.
Why shouldn’t 0.1% of the population be reasonable worth as much as 30% of the value of the companies listed in the stock market and why should it be more then 30%?
There’s the rub. And markets are anti-inductive, so even if we had good examples, we should expect this one to follow a different path.
Remember the impact of the 1957 Asian Flu (116K killed in the US, 1.1M worldwide) or the 1968 Hong Kong Flu (only a bit less)? Neither does anyone else. I do not want to be misinterpreted as “this is only the flu”—this is much more deadly and virulent. And likely more than twice as bad as those examples. But not 10x as bad, as long as we keep taking it seriously.
The changes in spending and productivity are very likely, IMO, to cause both price and monetary inflation. Costs will go up. People will have less stuff and the average lifestyle will likely be worse for a few years. but remember that stocks are priced in NOMINAL dollars, not inflation-adjusted. It’s quite believable that everything can slow down WHILE prices and stock values rise.
Isn’t it also plausible that the impact of the virus is deflationary? (Increased demand for USD as a store of value exceeds the impact of the Fed printing money, etc)
I’d expect not. Overall, productivity is going down mostly because of upheaval and mismatch in supply chains and in efficient ways for labor to use capital. So return to well-situated capital and labor is up, but amount of capital and labor that is well-situated is down. Pure undifferentiated capital has a lower return, plus rising nominal prices means seeking returns is the main motivation, not avoiding risk.
TIPS seem like useful things to have in your portfolio, but rates are lagging quite a bit, so either the market disagrees with me, or the safety value is so high that people are willing to lose value over time. I think stocks will be OK—the last 40 years has seen a lot of financial and government backstops that mean we’re pretty good at protecting the rich on this front, and if you can’t beat ‘em, join ’em. Cash or the like is probably a mistake. I have no good model for Bitcoin or Gold, but my gut says they’ll find a way to lose value against consumer prices. Real Estate (especially where there’s not a large population-density premium) seems pretty sane.
Note: I am not a superforcaster, and have no special knowledge or ability in this area. I’m just pointing out mechanisms that could move things the other direction that the obvious.
Why do you think nominal prices will keep rising?
Real estate likely just became significantly more illiquid at least for the next few months.
Well if we had confidence in any major parameter shifting in either direction it would be tradeable, so I expect reasonable pressures on both sides of such variables.
Economists mostly disagree with present market sentiment, which could be the basis for a trade: http://www.igmchicago.org/surveys/policy-for-the-covid-19-crisis/
In that case, TIPS (Treasury Inflation-Protected Securities) or precious metals like gold might be good investments. Unless the market has already priced it in, of course.
EtA: moved to a question: https://www.lesswrong.com/posts/zAwx3ZTaX7muvfMrL/why-do-we-have-offices
Why do we have offices?
They seem expensive, and not useful for jobs that can apparently be done remotely.
Hypotheses:
Social presence of other people working: https://www.focusmate.com/
Accountability
High bandwidth communication
Meta communication (knowing who’s available to talk to)
Status quo bias
status: to integrate
High status feels better when you are near your subordinates (when you can watch them, randomly disrupt them, etc.). High-status people make the decision whether remote work is allowed or not.
thanks for your comment!
I just realized I should have used the question feature instead; here it is: https://www.lesswrong.com/posts/zAwx3ZTaX7muvfMrL/why-do-we-have-offices
Increased sense of relatedness seems a big one missed here.
thanks for your comment!
I just realized I should have used the question feature instead; here it is: https://www.lesswrong.com/posts/zAwx3ZTaX7muvfMrL/why-do-we-have-offices
Employee focus (having punctuated behaviors separating work from personal time)
Tax advantages for employers to own workspaces and fixtures rather than employees
Not clear that “can be done remotely” is the right metric. We won’t know if “can be done as effectively (or more effectively) remotely” is true for some time.
thanks for your comment!
I just realized I should have used the question feature instead; here it is: https://www.lesswrong.com/posts/zAwx3ZTaX7muvfMrL/why-do-we-have-offices
For short-term, individual cost/benefit calculations around C19, it seems like uncertainty in the number of people currently infected should drop out of the calculation.
For instance: suppose I’m thinking about the risk associated with talking to a random stranger, e.g. a cashier. My estimated chance of catching C19 from this encounter will be roughly proportional to Ninfected. But, assuming we already have reasonably good data on number hospitalized/died, my chances of hospitalization/death given infection will be roughly inversely proportional to Ninfected. So, multiplying those two together, I’ll get a number roughly independent of Ninfected.
How general is this? Does some version of it apply to long-term scenarios too (possibly accounting for herd immunity)? What short-term decisions do depend on Ninfected?
Collection of discussions of epistemic modesty, “rationalist/EA exceptionalism”, and similar
These are currently in reverse-chronological order.
AI, global coordination, and epistemic humility—Jaan Tallinn, 2018
In defence of epistemic modesty—Greg Lewis, 2017
Inadequate Equilibria—Eliezer Yudkowsky, 2017
Common sense as a prior—Nick Beckstead, 2013
From memory, I think a decent amount of Rationality: A-Z by Eliezer Yudkowsky is relevant
Philosophical Majoritarianism—Hal Finney, 2007
Somewhat less relevant/substantial
This comment/question—Michael Aird (i.e., me), 2020
Naming Beliefs—Hal Finney, 2008
I intend to add to this list over time. If you know of other relevant work, please mention it in a comment.
Personal Wiki
might be useful for people to have personal wiki where they take note instead of everyone taking notes in private Gdoc
status: to do / to integrate
Hot take: The actual resolution to the simulation argument is that most advanced civilizations don’t make loads of simulations.
Two things make this make sense:
Firstly, it only matters if they make unlawful simulations. If they make lawful simulations, then it doesn’t matter whether you’re in a simulation or a base reality, all of your decision theory and incentives are essentially the same, you want to take the same decisions in all of the universes. So you can make lots of lawful simulations, that’s fine.
Secondly, they will strategically choose to not make too many unlawful simulations (to the level where the things inside are actually conscious). This is because to do so would induce anthropic uncertainty over themselves. Like, if the decision-theoretical answer is to not induce anthropic uncertainty over yourself about whether you’re in a simulation, then by TDT everyone will choose not to make unlawful simulations.
I think this is probably wrong in lots of ways but I didn’t stop to figure them out.
Your first point sounds like it is saying we are probably in a simulation, but not the sort that should influence our decisions, because it is lawful. I think this is pretty much exactly what Bostrom’s Simulation Hypothesis is, so I think your first point is not an argument for the second disjunct of the simulation argument but rather for the third.
As for the second point, well, there are many ways for a simulation to be unlawful, and only some of them are undesirable—for example, a civilization might actually want to induce anthropic uncertainty in itself, if it is uncertainty about whether or not it is in a simulation that contains a pleasant afterlife for everyone who dies.
I don’t buy that it makes sense to induce anthropic uncertainty. It makes sense to spend all of your compute to run emulations that are having awesome lives, but it doesn’t make sense to cause yourself to believe false things.
I’m not sure it makes sense either, but I don’t think it is accurately described as “cause yourself to believe false things.” I think whether or not it makes sense comes down to decision theory. If you use evidential decision theory, it makes sense; if you use causal decision theory, it doesn’t. If you use functional decision theory, or updateless decision theory, I’m not sure, I’d have to think more about it. (My guess is that updateless decision theory would do it insofar as you care more about yourself than others, and functional decision theory wouldn’t do it even then.)
I just don’t think it’s a good decision to make, regardless of the math. If I’m nearing the end of the universe, I prefer to spend all my compute instead maximising fun / searching for a way out. Trying to run simulations to make it so I no longer know if I’m about to die seems like a dumb use of compute. I can bear the thought of dying dude, there’s better uses of that compute. You’re not saving yourself, you’re just intentionally making yourself confused because you’re uncomfortable with the thought of death.
Well, that wasn’t the scenario I had in mind. The scenario I had in mind was: People in the year 2030 pass a law requiring future governments to make ancestor simulations with happy afterlives, because that way it’s probable that they themselves will be in such a simulation. (It’s like cryonics, but cheaper!) Then, hundreds or billions of years later, the future government carries out the plan, as required by law.
Not saying this is what we should do, just saying it’s a decision I could sympathize with, and I imagine it’s a decision some fraction of people would make, if they thought it was an option.
Thinking more, I think there are good arguments for taking actions that as a by-product induce anthropic uncertainty; these are the standard hansonian situation where you build lots of ems of yourself to do bits of work then turn them off.
But I still don’t agree with the people in the situation you describe because they’re optimising over their own epistemic state, I think they’re morally wrong to do that. I’m totally fine with a law requiring future governments to rebuild you / an em of you and give you a nice life (perhaps as a trade for working harder today to ensure that the future world exists), but that’s conceptually analogous to extending your life, and doesn’t require causing you to believe false things. You know you’ll be turned off and then later a copy of you will be turned on, there’s no anthropic uncertainty, you’re just going to get lots of valuable stuff.
The relevant intuition to the second point there, is to imagine you somehow found out that there was only one ground truth base reality, only one real world, not a multiverse or a tegmark level 4 verse or whatever. And you’re a civilization that has successfully dealt with x-risks and unilateralist action and information vulnerabilities, to the point where you have the sort of unified control to make a top-down decision about whether to make massive numbers of civilizations. And you’re wondring whether to make a billion simulations.
And suddenly you’re faced with the prospect of building something that will make it so you no longer know whether you’re in the base universe. Someday gravity might get turned off because that’s what your overlords wanted. If you pull the trigger, you’ll never be sure that you weren’t actually one of the simulated ones, because there’s suddenly so many simulations.
And so you don’t pull the trigger, and you remain confident that you’re in the base universe.
This, plus some assumptions about all civilizations that have the capacity to do massive simulations also being wise enough to overcome x-risk and coordination problems so they can actually make a top-down decision here, plus some TDT magic whereby all such civilizations in the various multiverses and Tegmark levels can all coordinate in logical time to pick the same decision… leaves there being no unlawful simulations.
My crux here is that I don’t feel much uncertainty about whether or not our overlords will start interacting with us (they won’t and I really don’t expect that to change), and I’m trying to backchain from that to find reasons why it makes sense.
My basic argument is that all civilizations that have the capability to make simulations that aren’t true histories (but instead have lots of weird stuff happen in them) will all be philosophically sophisticated to collectively not do so, and so you can always expect to be in a true history and not have weird sh*t happen to you like in The Sims. The main counterargument here is to show that there are lots of civilizations that will exist with the powers to do this but lacking the wisdom to not do it. Two key examples that come to mind:
We build an AGI singleton that lacks important kinds of philosophical maturity, so makes lots of simulations that ruins the anthropic uncertainty for everyone else.
Civilizations at somewhere around our level get to a point where they can create massive numbers of simulations but haven’t managed to create existential risks like AGI. Even while you might think our civilization is pretty close to AGI, I could imagine alternative civilizations that aren’t, just like I could imagine alternative civilizations that are really close to making masses of ems but that aren’t close enough to AGI. This feels like a pretty empirical question about whether such civilizations are possible and whether they can have these kinds of resources without causing an existential catastrophe / building singleton AGI.
Why appeal to philosophical sophistication rather than lack of motivation? Humans given the power to make ancestor-simulations would create lots of interventionist sims (as is demonstrated by the populatity games like The Sims), but if the vast hypermajority of ancestor-simulations are run by unaligned AIs doing their analogue of history research, that could “drown out” the tiny minority of interventionist simulations.
That’s interesting. I don’t feel comfortable with that argument, it feels too much like random chance whether or not we should expect ourselves to be in an interventionist universe or not, whereas I feel like I should be able to find strong reasons to not be in an interventionist universe.
Alternatively, “lawful universe” has lower Kolmogorov complexity than “lawful universe plus simulator intervention” and thereore gets exponentially more measure under the universal prior?? (See also “Infinite universes and Corbinian otaku” and “The Finale of the Ultimate Meta Mega Crossover”.)
Now that’s fun. I need to figure out some more stuff about measure, I don’t quite get why some universes should be weighted more than others. But I think that sort of argument is probably a mistake—even if the lawful universes get more weighting for some reason, unless you also have reason to think that they don’t make simulations, there’s still loads of simulations within each of their lawful universes, setting the balance in favour of simulation again.
One big reason why it makes sense is that the simulation is designed for the purpose of accurately representing reality.
Another big reason why (a version of it) makes sense is that the simulation is designed for the purpose of inducing anthropic uncertainty in someone at some later time in the simulation. e.g. if the point of the simulation is to make our AGI worry that it is in a simulation, and manipulate it via probable environment hacking, then the simulation will be accurate and lawful (i.e. un-tampered-with) until AGI is created.
I think “polluting the lake” by increasing the general likelihood of you (and anyone else) being in a simulation is indeed something that some agents might not want to do, but (a) it’s a collective action problem, and (b) plenty of agents won’t mind it that much, and (c) there are good reasons to do it even if it has costs. I admit I am a bit confused about this though, so thank you for bringing it up, I will think about it more in the coming months.
Ugh, anthropic warfare, feels so ugly and scary. I hope we never face that sh*t.
two tweets:
The Wealth of Nations by Adam Smith is a great book. My favorite part is Book III, Chapter 4 on the end of feudalism. In particular, I like these two paragraphs:
COVID-19 Contact tracing efforts
US Efforts:
Covid Watch
https://www.covid-watch.org/
Private Kit, Safe Paths
https://www.technologyreview.com/s/615372/coronavirus-infection-tests-app-pandemic-location-privacy/
http://safepaths.mit.edu/
South Korea’s Tracking Effort
Their app
https://www.technologyreview.com/s/615329/coronavirus-south-korea-smartphone-app-quarantine/
SMS messages about cases locations, etc.
https://www.nature.com/articles/d41586-020-00740-y
Other Articles
Wired’s reporting on S Korea and China’s use of apps
https://www.wired.com/story/phones-track-spread-covid19-good-idea/]
Open letter asking tech companies to implement opt-in contact tracing:
https://stop-covid.tech/
Israeli intelligence efforts to track people and use that data for epidemiological purposes
https://www.nytimes.com/2020/03/16/world/middleeast/israel-coronavirus-cellphone-tracking.html
Singaporean government released their own Trace Together app last week and they’re now working to open-source it.
Pros:
uses Bluetooth to track proximity between the user’s phones and notifies anyone who has been in vicinity of someone who tested positive (there is an interesting story here on how they had to first overcome the issue of varying Bluetooth strength based on phone models)
does not collect personal information (a consent is needed upfront)
does not capture GPS data
installed by 620,000 users (as of 23 March) which is roughly 11% of Singapore’s population in 3 days (if we assume constant growth, it should be around 30% today, March 28th)
Cons:
Depends on universal adoption (this will be true for any consent-based app)
I have anecdotal evidence that in the first 1-2 days after launch, the button on the consent page did not actually work, making it impossible to install. I believe this would have been solved now.
Battery drainage (will depend on the phone & user’s usage of Bluetooth)
Some reviews in the App Store indicate that it might disrupt the working of other connected devices (e.g. Bluetooth headphones) which might discourage usage
a big problem might be that it does not work in the background, but I believe this is already being solved following public requests.
Here’s some contact tracing discussion on reddit, you might want to post there/contact individuals there:
https://www.reddit.com/r/Coronavirus/comments/fksnbf/im_bill_gates_cochair_of_the_bill_melinda_gates/fkuoxbi/
Government and tech companies / tracking
https://www.wsj.com/articles/to-track-virus-governments-weigh-surveillance-tools-that-push-privacy-limits-11584479841
Palintir already helping government track cases—Article notes that government can get location data from telecoms, but that google has even more precise data from maps and android, which the government can also ask for in an emergency
It seems to me that Zeno’s paradoxes leverage incorrect, naïve notions of time and computation. We exist in the world, and we might suppose that that the world is being computed in some way. If time is continuous, then the computer might need to do some pretty weird things to determine our location at an infinite number of intermediate times. However, even if that were the case, we would never notice it – we exist within time and we would not observe the external behavior of the system which is computing us, nor its runtime.
What are your thoughts on infinitely small quantities?
Don’t have much of an opinion—I haven’t rigorously studied infinitesimals yet. I usually just think of infinite / infinitely small quantities as being produced by limiting processes. For example, the intersection of all the ϵ-balls around a real number is just that number (under the standard topology), which set has 0 measure and is, in a sense, “infinitely small”.
Collection of discussions of key cruxes related to AI safety/alignment
These are works that highlight disagreements, cruxes, debates, assumptions, etc. about the importance of AI safety/alignment, about which risks are most likely, about which strategies to prioritise, etc.
I’ve also included some works that attempt to clearly lay out a particular view in a way that could be particularly helpful for others trying to see where the cruxes are, even if the work itself don’t spend much time addressing alternative views. I’m not sure precisely where to draw the boundaries in order to make this collection maximally useful.
These are ordered from most to least recent.
I’ve put in bold those works that very subjectively seem to me especially worth reading.
General or focused on technical work
Fireside Chat: AI governance—Ben Garfinkel & Markus Anderljung, 2020
My personal cruxes for working on AI safety—Buck Shlegeris, 2020
What can the principal-agent literature tell us about AI risk? - Alexis Carlier & Tom Davidson, 2020
Beyond Near- and Long-Term: Towards a Clearer Account of Research Priorities in AI Ethics and Society—Carina Prunkl & Jess Whittlestone, 2020 (commentary here)
Brief summary of key disagreements in AI Risk—iarwain, 2019
A list of good heuristics that the case for AI x-risk fails—capybaralet, 2019
Debate on Instrumental Convergence between LeCun, Russell, Bengio, Zador, and More − 2019
Clarifying some key hypotheses in AI alignment—Ben Cottier & Rohin Shah, 2019
A shift in arguments for AI risk—Tom Sittler, 2019 (summary and discussion here)
What failure looks like—Paul Christiano, 2019 (critiques here and here; counter-critiques here; commentary here)
Disentangling arguments for the importance of AI safety—Richard Ngo, 2019
Reframing superintelligence—Eric Drexler, 2019 (I haven’t yet read this; maybe it should be in bold)
Prosaic AI alignment—Paul Christiano, 2018
How sure are we about this AI stuff? - Ben Garfinkel, 2018 (it’s been a while since I watched this; maybe it should be in bold)
AI Governance: A Research Agenda—Allan Dafoe, 2018
Some conceptual highlights from “Disjunctive Scenarios of Catastrophic AI Risk”—Kaj Sotala, 2018 (full paper here)
A model I use when making plans to reduce AI x-risk—Ben Pace, 2018
Focused on governance/strategy work
My Updating Thoughts on AI policy—Ben Pace, 2020
Some cruxes on impactful alternatives to AI policy work—Richard Ngo, 2018
Somewhat less relevant
A small portion of the answers here − 2020
I intend to add to this list over time. If you know of other relevant work, please mention it in a comment.
I think you should add Clarifying some key hypotheses in AI alignment.
Ah yes, meant to add that but apparently missed it. Added now. Thanks!
Psychology: An Imperfect and Improving Science
This is an essay I wrote in 2017 as coursework for the final year of my Psychology undergrad degree. (That was a year before I learned about EA and the rationalist movement.)
I’m posting this as a shortform comment, rather than as a full post, because it’s now a little outdated, it’s just one of many things that people have written on this topic, and I don’t think the topic is of central interest to a massive portion of LessWrong readers. But I do think it holds up well, is pretty clear, and makes some points that generalise decently beyond psychology (e.g., about drawing boundaries between science and pseudoscience, evaluating research fields, and good research practice).
I put the references in a “reply” to this.
Psychology’s scientific status has been denied or questioned by some (e.g., Berezow, 2012; Campbell, 2012). Evaluating such critiques and their rebuttals requires defining “science”, considering what counts as psychology, and exploring how unscientific elements within a field influence the scientific standing of that field as a whole. This essay presents a conception of “science” that consolidates features commonly seen as important into a family resemblance model. Using this model, I argue psychology is indeed a science, despite unscientific individuals, papers, and practices within it. However, these unscientific practices make psychology less scientific than it could be. Thus, I outline their nature and effects, and how psychologists are correcting these issues.
Addressing whether psychology is a science requires specifying what is meant by “science”. This is more difficult than some writers seem to recognise. For example, Berezow (2012) states we can “definitively” say psychology is non-science “[b]ecause psychology often does not meet the five basic requirements for a field to be considered scientifically rigorous: clearly defined terminology, quantifiability, highly controlled experimental conditions, reproducibility and, finally, predictability and testability.” However, there are fields that do not meet those criteria whose scientific status is generally unquestioned. For example, astronomy and earthquake science do not utilise experiments (Irzik & Nola, 2014). Furthermore, Berezow leaves unmentioned other features associated with science, such as data-collection and inference-making (Irzik & Nola, 2011). Many such features have been noted by various writers, though some are contested by others or only present or logical in certain sciences. For example, direct observation of the matters of interest has been rightly noted as helping make fields scientific, as it reduces issues like the gap between self-reported intentions and the behaviours researchers seek to predict (Godin, Conner, & Sheeran, 2005; Rhodes & de Bruijn, 2013; Sheeran, 2002; Skinner, 1987). However, self-reported intentions are still useful predictors of behaviour and levers for manipulating it (Godin et al., 2005; Rhodes & de Bruijn, 2013; Sheeran, 2002), and science often productively investigates constructs such as gravity that are not directly observable (Bringmann & Eronen, 2016; Chomsky, 1971; Fanelli, 2010; Michell, 2013). Thus, definitions of science would benefit from noting the value of direct observation, but cannot exclude indirect measures or unobservable constructs. This highlights the difficulty – or perhaps impossibility – of defining science by way of a list of necessary and sufficient conditions for scientific status (Mahner, 2013).
An attractive solution is instead constructing a family resemblance model of science (Dagher & Erduran, 2016; Irzik & Nola, 2011, 2014; Pigliucci, 2013). Family resemblance models are sets of features shared by many but not all examples of something. To demonstrate, three characteristics common in science are experiments, double-blind trials, and the hypothetico-deductive method (Irzik & Nola, 2014). A definition of science omitting these would be missing something important. However, calling these “necessary” excludes many sciences; for example, particle physics would be rendered unscientific for lack of double-blind trials (Cleland & Brindell, 2013; Irzik & Nola, 2014). Thus, a family resemblance model of science only requires a field to have enough scientific features, rather than requiring the field to have all such features. The full list of features this model should include, the relative importance of each feature, and what number or combination is required for something to be a “science” could all be debated. However, for showing that psychology is a science, it will suffice to provide a rough family resemblance model incorporating some particularly important features, which I shall now outline.
Firstly, Berezow’s (2012) “requirements”, while not actually necessary for scientific status, do belong in a family resemblance model of science. That is, when these features can be achieved, they make a field more scientific. The importance of reproducibility is highlighted also by Kahneman (2014) and Klein et al. (2014a, 2014b), and that of testability or falsifiability is also mentioned by Popper (1957) and Ferguson and Heene (2012). These features are related to the more fundamental idea that science should be empirical; claims should be required to be supported by evidence (Irzik & Nola, 2011; Pigliucci, 2013). Together, these features allow science to be self-correcting, incrementally progressing towards truth by accumulation of evidence and peer-review of ideas and findings (Open Science Collaboration, 2015). This is further supported by scientists’ methods and results being made public and transparent (Anderson, Martinson, & De Vries, 2007, 2010; Nosek et al., 2015; Stricker, 1997). Additionally, findings and predictions should logically cohere with established theories, including those from other sciences (Lilienfeld, 2011; Mahner, 2013). These features all support science’s ultimate aims to benefit humanity by explaining, predicting, and controlling phenomena (Hansson, 2013; Irzik & Nola, 2014; Skinner, cited in Delprato & Midgley, 1992). Each feature may not be necessary for scientific status, and many other features could be added, but the point is that each feature a field possesses makes that field more scientific. Thus, armed with this model, we are nearly ready to productively evaluate the scientific status of psychology.
However, two further questions must first be addressed: What is psychology, and how do unscientific occurrences within psychology affect the scientific status of the field as a whole? For example, it can generally be argued parapsychology is not truly part of psychology, for reasons such as its lack of support from mainstream psychologists. However, there are certain more challenging instances, such as the case of a paper by Bem (2011) claiming to find evidence for precognition. This used accepted methodological and analytical techniques, was published in a leading psychology journal, and was written by a prominent, mainstream psychologist. Thus, one must accept that this paper is, to a substantial extent, part of psychology. It therefore appears important to determine whether Bem’s paper exemplifies science. It certainly has many scientific features, such as use of experiments and evidence. However, it lacks other features, such as logical coherence with the established principle of causation only proceeding forwards in time.
But it is unnecessary here to determine whether the paper is non-science, insufficiently scientific, or bad science, because, regardless, this episode shows psychology as a field being scientific. This is because scientific features such as self-correction and reproducibility are most applicable to a field as a whole, rather than to an individual scientist or article, and these features are visible in psychology’s response to Bem’s (2011) paper. Replication attempts were produced and supported the null hypothesis; namely, that precognition does not occur (Galak, LeBoeuf, Nelson, Simmons, 2012; Ritchie, Wiseman, & French, 2012; Wagenmakers, Wetzels, Borsboom, van der Maas, & Kievit, 2012). Furthermore, publicity, peer-review, and self-correction of findings and ideas were apparent in those failed replications and in commentary on Bem’s paper (Wagenmakers, Wetzels, Borsboom, & van der Maas, 2011; Francis, 2012; LeBel & Peters, 2011). Peers discussed many issues with Bem’s article, such as several variables having been recorded by Bem’s experimental program yet not mentioned in the study (Galak et al., 2012; Ritchie et al., 2012), suggesting that the positive results reported may have been false positives emerging by chance from many, mostly unreported analyses. Wagenmakers et al. (2011) similarly noted other irregularities and unexplained choices in data transformation and analysis, and highlighted that Bem had previously recommended to psychologists: “If you see dim traces of interesting patterns, try to reorganize the data to bring them into bolder relief. […] Go on a fishing expedition for something—anything—interesting” (Bem, cited in Wagenmakers et al., 2011). These responses to Bem’s study by psychologists highlight that, while the scientific status of that study is highly questionable, isolated events such as that need not overly affect the scientific status of the entire field of psychology.
Indeed, psychology’s response to Bem’s (2011) paper exemplifies ways in which the field in general fits the family resemblance model of science outlined earlier. This model captures how different parts of psychology can each be scientific, despite showing different combinations of scientific features. For example, behaviourists may use more direct observation and clearly defined terminology (see Delprato & Midgley, 1992; Skinner, 1987), while evolutionary psychologists better integrate their theories and findings with established theories from other sciences (see Burke, 2014; Confer et al., 2010). These features make subfields that have them more scientific, but lacking one feature does not make a subfield non-science. Similarly, while much of psychology utilises controlled experiments, those parts that do not, like longitudinal studies of the etiology of mental disorders, can still be scientific if they have enough other scientific features, such as accumulation of evidence to increase our capacity for prediction and intervention.
Meanwhile, other scientific features are essentially universal in psychology. For example, all psychological claims and theories are expected to be based on or confirmed by evidence, and are rejected or modified if found not to be. Additionally, psychological methods and findings are made public by publication, with papers being peer-reviewed before this and open to critique afterwards, facilitating self-correction. Such self-correction can be seen in the response to Bem’s (2011) paper, as well as in how most psychological researchers now reject the untestable ideas of early psychoanalysis (see Cioffi, 2013; Pigliucci, 2013). Parts of psychology vary in their emphasis on basic versus applied research; for example, some psychologists investigate the processes underlying sadness while others conduct trials of specific cognitive therapy techniques for depression. However, these various branches can support each other, and all psychological research ultimately pursues benefitting humanity by explaining, predicting, and controlling phenomena. Indeed, while there is much work to be done and precision is rarely achieved, psychology can already make predictions much more accurate than chance or intuition in many areas, and thus provides benefits as diverse as anxiety-reduction via exposure therapy and HIV-prevention via soap operas informed by social-cognitive theories (Bandura, 2002; Lilienfeld, Ritschel, Lynn, Cautin, & Latzman, 2013; Zimbardo, 2004). All considered, most of psychology exemplifies most important scientific features, and thus psychology should certainly be considered a science.
However, psychology is not as scientific as it could be. Earlier I noted that isolated papers reporting inaccurate findings and utilising unscientific practices, as Bem (2011) seems highly likely to have, should not significantly affect psychology’s scientific status, as long as the field self-corrects adequately. However, as several commentators on Bem’s paper noted, more worrying is what that paper reflects regarding psychology more broadly, given that it largely met or exceeded psychology’s methodological, analytical, and reporting standards (Francis, 2012; LeBel & Peters, 2011; Wagenmakers et al., 2011). The fact Bem met these standards, yet still “discovered” and got published results that seem to violate fundamental principles about how causation works, highlights the potential prevalence of spurious findings in psychological literature. These findings could result from various flaws and biases, yet might fail to be recognised or countered in the way Bem’s report was if they are not as clearly false; indeed, they may be entirely plausible, yet inaccurate (LeBel & Peters, 2011). Thus, I will now discuss how critiques regarding Bem’s paper apply to much of mainstream psychology.
Firstly, the kind of “fishing expedition” recommended by Bem (cited in Wagenmakers et al., 2011) is common in psychology. Researchers often record many variables, and have flexibility in which variables, interactions, participants, data transformations, and statistics they use in their analyses (John, Loewenstein, & Prelec, 2012). Wagenmakers et al. (2012) note that such practices are not inherently problematic, and indeed such explorations are useful for suggesting hypotheses to test in a confirmatory manner. The issue is that often these explorations are inadequately reported and are presented as confirmatory themselves, despite the increased risk of false positives when conducting multiple comparisons (Asendorpf et al., 2013; Wagenmakers et al., 2012). Neuropsychological studies can be particularly affected by failures to control for multiple comparisons, even if all analyses are reported, because analysis of brain activity makes huge numbers of comparisons the norm. Thus, without statistical controls, false positives are almost guaranteed (Bennett, Baird, Miller, & Wolford, 2009). The issue of uncontrolled multiple comparisons, whether reported or not, causing false positives can be compounded by hindsight bias making results seem plausible and predictable in retrospect (Wagenmakers et al., 2012). This can cause overconfidence in findings and make researchers feel comfortable writing articles as if these findings were hypothesised beforehand (Kerr, 1998). These practices inflate the number of false discoveries and spurious confirmations of theories in psychological literature.
This is compounded by publication bias. Journals are more likely to publish novel and positive results than replications or negative results (Ferguson & Heene, 2012; Francis, 2012; Ioannidis, Munafò, Fusar-Poli, Nosek, & David, 2014; Kerr, 1998). One reason for this is that, despite the importance of self-correction and incremental progress, replications or negative results are often treated as not show anything substantially interesting (Klein et al., 2014b). Another reason is the idea that null results are hard to interpret or overly likely to be false negatives (Ferguson & Heene, 2012; Kerr, 1998). Psychological studies regularly have insufficient power; their sample sizes mean that, even if an effect of the expected size does exist, the chance of not finding it is substantial (Asendorpf et al., 2013; Bakker, Hartgerink, Wicherts, & van der Maas, 2016). Further, the frequentist statistics typically used by psychologists cannot clearly quantify the support data provides for null hypotheses; these statistics have difficulty distinguishing between powerful evidence for no effect and simply a failure to find evidence for an effect (Dienes, 2011). While concerns about the interpretability of null results are thus often reasonable, they distort the psychological literature’s representation of reality (see Fanelli, 2010; Kerr, 1998). Publication bias also takes the form of researchers being more likely to submit for publication those studies that revealed positive results (John et al., 2012). This can occur because researchers themselves also often find negative results difficult to interpret, and know they are less likely to be published or to lead to incentives like grants or prestige (Kerr, 1998; Open Science Collaboration, 2015). Thus, flexibility in analysis, failure to control for or report multiple comparisons, presentation of exploratory results as confirmatory, publication bias, low power, and difficulty interpreting null results are interrelated issues. These issues in turn make psychology less scientific by reducing the transparency of methods and findings.
These issues also undermine other scientific features. The Open Science Collaboration (2015) conducted replications of 100 studies from leading psychological journals, finding that less than half replicated successfully. This low level of reproducibility in itself makes psychology less scientific, and provides further evidence of the likely high prevalence and impact of the issues noted above (Asendorpf et al., 2013; Open Science Collaboration, 2015). Together, these problems impede self-correction, and make psychology’s use of evidence and testability of theories less meaningful, as replications and negative tests are often unreported (Ferguson & Heene, 2012). This undermines psychology’s ability to benefit humanity by explaining, predicting, and controlling phenomena.
However, while these issues make psychology less scientific, they do not make it non-science. Other sciences, including “hard sciences” like physics and biology, also suffer from issues like publication bias and low reproducibility and transparency (Alatalo, Mappes, & Edgar, 1997; Anderson, Burnham, Gould, & Cherry, 2001; McNutt, 2014; Miguel et al., 2014; Sarewitz, 2012; Service, 2002). Their presence is problematic and demands a response in any case, and may be more pronounced in psychology than in “harder” sciences, but it is not necessarily damning (see Fanelli, 2010). For example, the Open Science Collaboration (2015) did find a large portion of effects replicated, particularly effects whose initial evidence was stronger. Meanwhile, Klein et al. (2014a) found a much higher rate of replication for more established effects, compared to the Open Science Collaboration’s quasi-random sample of recent findings. Both results highlight that, while psychology certainly has work to do to become more reliable, the field also has the capacity to scientifically progress towards truth and is already doing so to a meaningful extent.
Furthermore, psychologists themselves are highlighting these issues and researching and implementing solutions for them. Bakker et al. (2016) discuss the problem of low power and how to overcome it with larger sample sizes, reinforced by researchers habitually running power analyses prior to conducting studies and reviewers checking these analyses have been conducted. Nosek et al. (2015) proposed guidelines for promoting transparency by changing what journals encourage or require, such as replications, better reporting and sharing of materials and data, and pre-registration of studies and analysis plans. Pre-registration side-steps confirmation and hindsight bias and unreported, uncorrected multiple comparisons, as expectations and analysis plans are on record before data is gathered (Wagenmakers et al., 2012). Journals can also conditionally accept studies for publication based on pre-registered plans, minimising bias against null results by both journals and researchers. Such proposals still welcome exploratory analyses, but prevent these analyses being presented as confirmatory (Miguel et al., 2014). Finally, psychologists have argued for, outlined how to use, and adopted Bayesian statistics as an alternative to frequentist statistics (Ecker, Lewandowsky, & Apai, 2011; Wagenmakers et al., 2011). Bayesian statistics provide clear quantification of evidence for null hypotheses, combatting one source of publication bias and making testability of psychological claims more meaningful (Dienes, 2011; Francis, 2012). These proposals are beginning to take effect. For example, many journals and organisations are signatories to Nosek et al.’s guidelines. Additionally, the Centre for Open Science, led by the psychologist Brian Nosek, has set up online tools for researchers to routinely make their data, code, and pre-registered plans public (Miguel et al., 2014). This shows psychology self-correcting its practices, not just individual findings, to become more scientific.
I have argued here that claims that psychology is non-scientific may often reflect unworkable definitions of science and ignorance of what psychology actually involves. A family resemblance model of science overcomes the former issue by outlining features that sciences do not have to possess to be science, but do become more scientific by possessing. This model suggests psychology is a science because it generally exemplifies most scientific features; most importantly, it accumulates evidence publicly, incrementally, and self-critically to benefit humanity by explaining, predicting, and controlling phenomena. However, psychology is not as scientific as it could be. A variety of interrelated issues with researchers’ and journals’ practices and incentive structures impede the effectiveness and meaningfulness of psychology’s scientific features. But failure to be perfectly scientific is not unique to psychology; it is universal among sciences. Science has achieved what it has because of its constant commitment to incremental improvement and self-correction of its own practices. In keeping with this, psychologists are researching and discussing psychology’s issues and their potential solutions, and such solutions are being put into action. More work must be done, and more researchers and journals must act on and push for these discussions and solutions, but already it is clear both that psychology is a science and that it is actively working to become more scientific.
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Something else in the vein of “things EAs and rationalists should be paying attention to in regards to Corona.”
There’s a common failure mode in large human systems where one outlier causes us to create a rule that is a worse equilibrium. In the PersonalMBA, Josh Kaufman talks about someone taking advantage of a “buy any book you want” rule that a company has—so you make it so that you can no longer get any free books.
This same pattern has happened before in the US, after 9-11 - We created a whole bunch of security theater, that caused more suffering for everyone, and gave government way more power and way less oversight than is safe, because we over-reacted to prevent one bad event, not considering the counterfactual invisible things we would be losing.
This will happen again with Corona, things will be put in place that are maybe good at preventing pandemics (or worse, making people think they’re safe from pandemics), but create a million trivial conveniences every day that add up to more strife than they’re worth.
These types of rules are very hard to repeal after the fact because of absence blindness—someone needs to do the work of calculating the cost/benefit ratio BEFORE they get implemented, then build a convincing enough narrative to what seems obvious/common sense measures given the climate/devastation.
Uneducated hypothesis: All hominidae species tend to thrive in huge forests, unless they’ve discovered fire. From the moment a species discovers fire, any individual can unilaterally burn the entire forest (due to negligence/anger/curiosity/whatever), and thus a huge forest is unlikely to serve as a long-term habitat for many individuals of that species.
Counterpoint: people today inhabit forested/jungled areas without burning everything down by accident (as far as I know; it’s the kind of fact I would expect to have heard about if true), and even use fire for controlled burns to manage the forest/jungle.
To prolong my medicine stores by 200%, I’ve mixed in similar-looking iron supplement placebos with my real medication. (To be clear, nothing serious happens to me if I miss days)
I find it interesting what kind of beliefs one needs to question and in which ways in order to get people angry/upset/touchy.
Or, to put it in more popular terms, what kind of arguments make you seem like a smart-ass when arguing with someone.
For example, reading Eliezer yudkowsky’s Rationality from AI to Zombies, I found myself generally speaking liking the writing style and to a karge extent the book was just reinforcing the biases I already had. Other then some of his poorly thought out metaphysics based on which he bases his ethics argument… I honestly can’t think of a single thing from that book I disagree with. Same goes for Inadequate Equilibria.
Yet, I can remember a certain feeling popping up in my head fairly often when reading it, one that can be best described in an image: https://i.kym-cdn.com/entries/icons/facebook/000/021/665/DpQ9YJl.jpg
***
One seeming pattern for this is something like:
Arguing about a specific belief
Going a level down and challenging a pillar of the opponent’s belief that was not being considered as part of the discussion.
E.g: “Arguing about whether or not climate change is a threat, going one level down and arguing that there’s not enough proof climate change is happening to being with”
You can make this pattern even more annoying by doing something like:
Arguing about a specific belief
Going a level down and challenging a pillar of the opponent’s belief that was not being considered as part of the discussion.
Not entertaining an opposite argument about one of your own pillars being shaky.
E.g.: After the previous climate change argument, not entertaining the idea that “Maybe acting upon climate change as if it were real and as if it were a threat, would actually result in positive consequences even if those two things were unture”
You can make this pattern even more annoying by doing something like:
Arguing about a specific belief
Going a level down and challenging a pillar of the opponent’s belief that was not being considered as part of the discussion.
Doing so with some evidence that the other party is unaware or cannot understand
E.g.: After the previous climate change argument, back up your point about climate change not being real by citing various studies that would take hours to fact check and might be out of reach knowledge-wise for either of you.
***
I think there’s other things that come into account.
For example there’s some specific fields which are considered more sacrosanct then others, trying to argue against a standard position in that field as part of your argument seems to much more easily put you into the “smartass” camp.
For example, arguing against commonly held religious or medical knowledge, seems to be almost impossible, unless you are taking an already-approved side of the debate.
E.g. You can argue ibuprofen against paracetamol as the go to for common cold since there’s authoritative claims for each, you can’t argue for a 3rd lesser backed NSAID or for using corticosteroids or no treatment instead of NSAIDs.
Other fields such as ethics or physics or computer science seem to be fair game and nobody really minds people trying to argue for an unsanctioned viewpoint.
***
There’s obviously the idea of politics being overall bad, and the more politicized a certain subject is the less you can change people’s minds about it.
But to some extent I don’t feel like politics really comes into play.
It seems that people are fairly open to having their minds changed about economic policy but not about identity policy.… no matter which side of the spectrum you are on. Which seem counter intuitive, since the issue of “should countries have open borders and free healthcare” seems like one much more deeply embedded in existing political agendas and of much more import than “What gender should transgender people be counted in when participating in the olympics”.
***
One interesting thing that I observed: I’ve personally been able to annoy a lot of people when talking with them online. However, IRL, in the last 4 years or so (since I actually begun explicitly learning how to communicate), I can’t think of a single person that I’ve offended.
Even though I’m more verbose when I talk. Even though the ideas I talk about over coffee are usually much more niche and questionable in their verity then the ones I write about online.
I wonder if there’s some sort of “magic oratory skill” I’ve come closer to attaining IRL that either can’t be attained on the internet or is very different… granted, it’s more likely it’s the inherent bias of the people I’m usually discussing with.
Was thinking a bit about the how to make it real for people that the quarantine depressing the economy kills people just like Coronavirus does.
Was thinking about finding a simple good enough correlation between economic depression and death, then creating a “flattening the curve” graphic that shows how many deaths we would save from stopping the economic freefall at different points. Combining this was clear narratives about recession could be quite effective.
On the other hand, I think it’s quite plausible that this particular problem will take care of itself. When people begin to experience depression, will the young people who are the economic engine of the country really continue to stay home and quarantine themselves? It seems quite likely that we’ll simply become stratified for a while where young healthy people break quarantine, and the older and immuno-compromised stay home.
But getting the time of this right is everything. Striking the right balance of “deaths from economic freefall” and “deaths from an overloaded medical system” is a balancing act, going too far in either direction results in hundreds of thousands of unnecessary deaths.
Then I got to thinking about the effect of a depressed economy on x-risks from AI. Because the funding for AI safety is
1. Mostly in non-profits
and
2. Orders of magnitude smaller than funding for AI capabilities
It’s quite likely that the funding for AI safety is more inelastic in depressions than than the funding for AI capabilities. This may answer the puzzle of why more EAs and rationalists aren’t speaking cogently about the tradeoffs between depression and lives saved from Corona—they have gone through this same train of thought, and decided that preventing a depression is an information hazard.
It was brought to my attention on Lesswrong that depressions actually save lives.
Which would make it much harder to build a simple “two curves to flatten” narrative out of.
Wait, you received evidence that didn’t just refute your hypothesis, it reversed it. If you accept that, shouldn’t you also reverse your proposed remedy? Shouldn’t you now argue _IN FAVOR_ of shutting down more completely—it saves lives both directly by limiting the spread of the virus AND indirectly by slowing the economy.
(note: this is intended to be semi-humorous—my base position is that the economic causes and effects are far too complex and distributed to really predict impact on that level, or to predict what policies might improve what outcomes).
I did update from this quite significantly.
It’s interesting because you would intuitively think this, but there is actually not terrible evidence linking periods of economic growth to increased mortality.
Here is the article in nature.
Is non-profit funding really that inelastic in depression?
Wow that is fascinating. It does make the case harder to make because you have to start quantifying happiness/depression, etc and trade off against lives. Much much harder to simplify enough to make it viral. Updates towards capitalism being horrible.
It probably varies quite a bit by sector, and where funding comes from for different non-profits. In the case of AI safety I think it’s likely more inelastic than AI capability.
The most efficient form of practice is generally to address one’s weaknesses. Why, then, don’t chess/Go players train by playing against engines optimized for this? I can imagine three types of engines:
Trained to play more human-like sound moves (soundness as measured by stronger engines like Stockfish, AlphaZero).
Trained to play less human-like sound moves.
Trained to win against (real or simulated) humans while making unsound moves.
The first tool would simply be an opponent when humans are inconvenient or not available. The second and third tools would highlight weaknesses in one’s game more efficiently than playing against humans or computers. I’m confused about why I can’t find any attempts at engines of type 1 that apply modern deep learning techniques, or any attempts whatsoever at engines of type 2 or 3.
I have repeatedly argued for a departure from pure Bayesianism that I call “quasi-Bayesianism”. But, coming from a LessWrong-ish background, it might be hard to wrap your head around the fact Bayesianism is somehow deficient. So, here’s another way to understand it, using Bayesianism’s own favorite trick: Dutch booking!
Consider a Bayesian agent Alice. Since Alice is Bayesian, ey never randomize: ey just follow a Bayes-optimal policy for eir prior, and such a policy can always be chosen to be deterministic. Moreover, Alice always accepts a bet if ey can choose which side of the bet to take: indeed, at least one side of any bet has non-negative expected utility. Now, Alice meets Omega. Omega is very smart so ey know more than Alice and moreover ey can predict Alice. Omega offers Alice a series of bets. The bets are specifically chosen by Omega s.t. Alice would pick the wrong side of each one. Alice takes the bets and loses, indefinitely. Alice cannot escape eir predicament: ey might know, in some sense, that Omega is cheating em, but there is no way within the Bayesian paradigm to justify turning down the bets.
A possible counterargument is, we don’t need to depart far from Bayesianism to win here. We only need to somehow justify randomization, perhaps by something like infinitesimal random perturbations of the belief state (like with reflective oracles). But, in a way, this is exactly what quasi-Bayesianism does: a quasi-Bayes-optimal policy is in particular Bayes-optimal when the prior is taken to be in Nash equilibrium of the associated zero-sum game. However, Bayes-optimality underspecifies the policy: not every optimal reply to a Nash equilibrium is a Nash equilibrium.
This argument is not entirely novel: it is just a special case of an environment that the agent cannot simulate, which is the original motivation for quasi-Bayesianism. In some sense, any Bayesian agent is dogmatic: it dogmatically beliefs that the environment is computationally simple, since it cannot consider a hypothesis which is not. Here, Omega exploits this false dogmatic belief.
And here I thought the reason was going to be that Bayesianism doesn’t appear to include the cost of computation. (Thus, the usual dutch book arguments should be adjusted so that “optimal betting” does not leave one worse off for having payed, say, an oracle, too much for computation.)
Bayeseans are allowed to understand that there are agents with better estimates than they have. And that being offered a bet _IS_ evidence that the other agent THINKS they have an advantage.
Randomization (aka “mixed strategy”) is well-understood as the rational move in games where opponents are predicting your choices. I have read nothing that would even hint that it’s unavailable to Bayesean agents. The relevant probability (updated per Bayes’s Rule) would be “is my counterpart trying to minimize my payout based on my choices”.
edit: I realize you may be using a different definition of “bayeseanism” than I am. I’m thinking humans striving for rational choices, which perforce includes the knowledge of incomplete computation and imperfect knowledge. Naive agents can be imagined that don’t have this complexity. Those guys are stuck, and Omega’s gonna pwn them.
It feels like there’s better words for this like rationality, whereas bayesianism is a more specific philosophy about how best to represent and update beliefs.
Am I one of the few people here who has looked at the covid-19 data and reached the conclusion that it’s probably only about as severe/fatal as seasonal influenza?
I have a longer blog post outlining the case here.
TLDR: CFR!=IFR, influenza CFR is similar to covid-19 CFR, and we know from influenza data that typically IFR << CFR due to enormous selection sampling bias from mostly testing only those with more severe disease. We can correct for that by comparing the covid-19 confirmed case age structure to the population age structure using uniform or age-dependent attack rate. The resulting IFR is similar to influenza, which is also the best fit for the Diamond Princess data (where selection bias is mostly avoided so CFR~IFR).
Selection bias can help explain why the CFR is higher in Italy, and probably why it’s so much lower in Germany (I’m looking for age structure data on covid-19 cases from Germany, I’m predicting it will be flatter than US or Italy data). South Korea is also another interesting case (which I found some data for but haven’t put into the blog post yet) - we can clearly reject a typical attack rate age structure there, which was surprising at first but then made sense given that the outbreak in SK started in a large tight-knit cult with a young median age and they tested everyone in the cult.
Anyway if anyone here has already encountered these thoughts and still believes covid-19 IFR is much higher than influenza IFR I’m curious what the best arguments/evidence are.