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### Delta variant: we should probably be re-masking

24 июля, 2021 - 00:13
Published on July 23, 2021 9:13 PM GMT

Like you, I thought I saw the light at the end of the tunnel. It first appeared when vaccines started rolling out a few months back, and got brighter when my family got their vaccine cards signed. It got still brighter as I signed up for my first dose (#ModernaManiacs), got the first shot, waited four weeks, and got my second.

After about a week, a few days after the side effects faded, I stopped holding my breath—literally and figuratively—as people walked past. At two weeks post-second dose, I was “officially” “fully vaccinated.” The light had been reached.

My gym had just started allowing those vaccinated to unmask, and I took them up on the offer. It was science to the rescue, human ingenuity encapsulated in some messenger ribonucleic acid in half a milliliter of lipid suspension in the tip of a single-use syringe. Perhaps for the first time in my life, I was viscerally—if mildly—proud to be an American. We are the world’s biotech capital, after all.

I didn’t pay too much attention at first when the Delta variant hit. The narrative in my Twitter-centered media milieux was pretty simple: “Delta’s gonna kill a lot of unvaccinated people, which sucks, but if you’re vaccinated then you’re safe.”

And then my sister, who like me had gotten the Moderna vaccine, lost her sense of smell.

When her COVID test came back positive, she was only the ~700th confirmed COVID case in New York. Given everything we know about the Delta variant, that was almost certainly the culprit.

On Delta

If you haven’t already, please read Tomas Pueyo’s Delta Variant: Everything You Need to Know. There are a lot of articles out there competing for your attention, but this one is worth your time.

In short, Delta is probably about 2.5 times more infectious than the OG COVID-19. Here’s what that looks like in the context of exponential growth:

Fuck.

2. Young people like my sister and me are getting infected the most.

3. Preliminary evidence suggests that vaccines aren’t nearly as effective against Delta, reducing infection rates by 64%. By comparison, they seem to reduce OG infections by around 93%.

Some good news

Vaccines are still really effective against serious illness, reducing hospitalization by 96% best we can tell at the moment.

Pueyo thinks that vaccines will be comparably effective against long COVID, bringing the percent of infected people who experience ongoing symptoms down from 15% to ~3%

On long COVID

In my opinion, anyone younger than ~70 and in good respiratory and immune health should be much more worried about long COVID than about death or infection per se. Getting COVID might suck, but a week at home in bed isn’t the end of the world. And the chance of dying, if you’re young and vaccinated, is a rounding error away from zero.

But debilitating, long-term symptoms aren’t out of the picture. Chronic Fatigue Syndrome (CFS) is one of the most serious and mysterious issues in medicine. Although institutional medicine brushed it aside as psychosomatic nonsense for quite a while, it has more recently been taken seriously as a “real” physiological phenomenon—and one often caused by viruses like COVID.

I’m not prepared to explore whether so-called “long COVID” is basically CFS, but they seem quite similar. And, as far as I can tell, death is a lot closer to long COVID in terms of “badness” than is “having a cough for a week.”

Some quick calculations

It seems conservative to estimate that 50% of unvaccinated people will get infected. Let’s take that 64% figure at face value to get of 18% those vaccinated becoming infected. If 3% of these infected cases lead to long COVID or death, we’re looking at 0.54% of vaccinated people suffering a really serious outcome. Maybe it’s lower for the young, but if we take out the 1% fatality rate and leave a 2% long COVID rate, we’re still left with .36%—more than 1 in 300—people like me—young, healthy, and vaccinated—having their lives made considerably worse, possibly for decades.

That means 23 of my fellow 6,300 Georgetown undergrads (we have a vaccine requirement)—people I see in class and in the library—left with debilitating brain fog, fatigue, and exertion intolerance. If we assume that a week with long COVID is as bad as two weeks with acute COVID and these 24 people live an additional 50 years, this is consequentially equivalent to all 6,300 undergrads suffering acute COVID for over a month. Check my math!

I don’t know about you, but this seems pretty bad to me.

And, let me remind you, all this is only for the vaccinated and only for the U.S. Not getting vaccinated may be stupid, but it shouldn’t be a capital crime. 43% of American adults are unvaccinated, and many of them are going to get long COVID or die.

Another light and another tunnel.

Pfizer is already developing a vaccine booster targeted at Delta, and I expect the other pharma companies to as well. More money for them, after all.

I have no idea how long this will take to develop and roll out, but the point is that there’s another light at the end of another tunnel. 23 of my fellow Hoyas don’t have to get long COVID, and thousands of families don’t have to attend yet another premature funeral (although some will, sadly).

COVID doesn’t spread by magic; it spreads via respiratory droplets. And you know what prevents respiratory droplets from spreading? You guessed it: masks. Now is not the time or place to relitigate the Great Mask Debacle of early 2020, but lest you forget, public health officials and media outlets regularly spouted bullshit like this:

I don’t have vendetta against Vox - I like Vox! But they screwed this one up.

I fear that the same thing is happening again, to a degree. In the course of trying to make vaccines seem attractive to the unvaccinated, public health and other elite institutions are continuing to insist that vaccinated people don’t need to wear masks. According to the CDC, for better or worse the world’s predominant authority on pandemic dos and don’ts,

If you are fully vaccinated, you can resume activities that you did before the pandemic without wearing a mask or physically distancing…

And people are obliging. At my climbing gym, stocked with health-conscious, affluent, suburbanite Biden voters, virtually no one is wearing one, and climbing inside is anything but a socially-distanced activity.

Of course nobody needs to do anything. But I don’t think the current bare-faced zeitgeist among the vaccinated reflects a dispassionate calculation that the masking up isn’t worth the effort. It reflects the dual influences of top-down guidance and social conformity.

The latter of these influences I can personally vouch for. I started masking up recently, and it feels weird to be virtually the only one. No one brings it up or, probably, even cares, and yet there is a little homunculus in my head that keeps whispering “be self-conscious! You’re not conforming to social norms!”

I bring this up not to signal my independence, but to emphasize how one person’s mask wearing can have social contagion-mediated ripple effects.

I might be wrong, but you should still wear a mask

I might be wrong about something. Maybe Delta won’t be as bad as I think or fear. Maybe vaccines will prove super effective. Maybe boosters will come out faster than expected. Hey, maybe masks don’t work after all!

Even if you suspect I’m wrong, though, how sure are you? 50%? 80%? And, really, what is the cost of wearing a mask? A few bucks and the equivalent in discomfort to wearing your belt half a notch too tight? Forgive my french, but who fucking cares? My sister is OK, thank God, but she could have been one of the unlucky ones left with ongoing symptoms. I could be one of them, and so could you.

Like it or not, we are all social creatures deeply influenced by the norms and customs of our communities. Like you, I thought we had reached a post-COVID-for-the-vaccinated world. Maybe we did, and I hope we will, but now isn’t the time to test our luck.

Discuss

### How is low-latency phasic dopamine so fast?

23 июля, 2021 - 20:44
Published on July 23, 2021 5:43 PM GMT

(Quick poorly-researched blog post, probably only of interest to neuroscientists.)

There’s a paper “What is reinforced by phasic dopamine signals?” (Redgrave, Gurney, Reynolds 2008). I was interested to read as part of my project to continue checking whether I should really believe all the crap I wrote in Big Picture Of Phasic Dopamine, and trying to fill in the missing pieces. The paper is also related to a different ongoing project of mine, namely to resolve various confusions I have about the superior colliculus.

Anyway, in a classical conditioning experiment (flash of light then reward), when the flash of light happens, there’s a reward-related phasic dopamine signal. The paper points out that this phasic signal is very fast, starting just 70-100ms after the light (in rodents).

Supposedly this is faster than the cortex can do object identification (“signals related to object identity can be detected in inferotemporal cortex within 80-100ms after stimulus onset”), and also faster than the amygdala (“visual response latencies in the amygdala are…ranging between 100 and 200 ms from stimulus onset with mean value of ~150 ms”). But the superior colliculus (SC) is fast, and it’s right next to and connected to the dopamine neurons in VTA / SNc, and there is various other streams of evidence that SC can cause dopamine release. So that’s their theory! “We have proposed the superior colliculus as the primary, if not exclusive source of short-latency visual input to ventral midbrain DA neurons.”

I found this very confusing. I thought the amygdala learns that the flashing light leads to reward! It’s been studied to death, right? That’s what everyone’s always said, I like that story, it fits in beautifully with everything else I think I know about the brain. What the heck??

Anyway, I was relieved to see that Redgrave seems to have changed his mind on this point: a decade later he co-authored Cortical visual processing evokes short-latency reward-predicting cue responses in primate midbrain dopamine neurons, which offered experimental evidence that deactivating (part of) SC does not stop the low-latency phasic dopamine reward signal. I don’t know enough to evaluate how water-tight the experiment was, but I guess if Redgrave signed on, after previously arguing the other side, presumably it’s pretty solid evidence.

(I hope I’m understanding this story correctly, and not putting words in anyone’s mouth.)

But this still leaves the question...

How is the dopamine signal so fast?

My first thought was: C’mon, really, it’s gotta be the amygdala, right? Hmm. The amygdala response is supposedly 100-200 ms … Well that’s not dramatically slower than the 70-100ms phasic dopamine response, right? I did read (I think here?) about a thing where a downstream signal can sometimes be triggered by the leading edge of an upstream signal, such that the downstream can peak sooner. Is that 100-200 ms number the peak, or is the first detectable signs of activity? I didn’t check. Hmm, or maybe there’s faster and slower neurons in the amygdala, and the experimenters happened to be looking at slower ones?

Well, anyway, I’m not quite ready to totally give up on the amygdala, but I have to admit this line of thought is feeling like special pleading.

But then I had another thought: The cerebellum!!! The poor cerebellum is too often forgotten in dopamine discussions, and indeed Redgrave 2008 doesn’t even mention the cerebellum anywhere in the paper. But to me, it would make perfect sense!

Let’s pause for my startlingly arrogant attempt to explain the entire cerebellum in a single sentence: As far as I can tell, the cerebellum is kinda like a giant memoization system: it watches the activity of other parts of the brain (including the neocortex and amygdala, and maybe other things too), it memorizes patterns in what signals those parts of the brain send under different circumstances, and when it learns such a pattern, it starts sending those same signals itself—just faster. (See my post here for more.)

Well, if that’s right … it fits the bill! The story would be:

…But the cerebellum is much faster than the amygdala; that’s the whole reason the cerebellum exists.

And wouldn’t you know it, as soon as I looked, I indeed found that there’s a known connection from cerebellum to VTA/SNc.

If this is right, my prediction would be that when you do a CS-US classical conditioning experiment, there’s some period early in training where the amygdala has learned what to do from the hypothalamus / brainstem, but the cerebellum has not yet learned what to do from the amygdala. And during this period we would see light-induced dopamine signals with higher-than-usual latency. Is that right? There must be data. I dunno. I'm publishing this without checking. It's more fun that way. :-P

Discuss

### [AN #157]: Measuring misalignment in the technology underlying Copilot

23 июля, 2021 - 20:20
Published on July 23, 2021 5:20 PM GMT

[AN #157]: Measuring misalignment in the technology underlying Copilot Alignment Newsletter is a weekly publication with recent content relevant to AI alignment around the world View this email in your browser Newsletter #157
Alignment Newsletter is a weekly publication with recent content relevant to AI alignment around the world. Find all Alignment Newsletter resources here. In particular, you can look through this spreadsheet of all summaries that have ever been in the newsletter.
Audio version here (may not be up yet).
Please note that while I work at DeepMind, this newsletter represents my personal views and not those of my employer. SECTIONS ﻿HIGHLIGHTS
﻿TECHNICAL AI ALIGNMENT
﻿TECHNICAL AGENDAS AND PRIORITIZATION
﻿FORECASTING
﻿MISCELLANEOUS (ALIGNMENT)
﻿AI GOVERNANCE
﻿NEWS ﻿ ﻿ ﻿ HIGHLIGHTS

Evaluating Large Language Models Trained on Code (Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde, Jared Kaplan et al) (summarized by Rohin): You’ve probably heard of GitHub Copilot, the programming assistant tool that can provide suggestions while you are writing code. This paper evaluates Codex, a precursor to the model underlying Copilot. There’s a lot of content here; I’m only summarizing what I see as the highlights.

The core ingredient for Codex was the many, many public repositories on GitHub, which provided hundreds of millions of lines of training data. With such a large dataset, the authors were able to get good performance by training a model completely from scratch, though in practice they finetuned an existing pretrained GPT model as it converged faster while providing similar performance.

Their primary tool for evaluation is HumanEval, a collection of 164 hand-constructed Python programming problems where the model is provided with a docstring explaining what the program should do along with some unit tests, and the model must produce a correct implementation of the resulting function. Problems are not all equally difficult; an easier problem asks Codex to “increment all numbers in a list by 1” while a harder one provides a function that encodes a string of text using a transposition cipher and asks Codex to write the corresponding decryption function.

To improve performance even further, they collect a sanitized finetuning dataset of problems formatted similarly to those in HumanEval and train Codex to perform well on such problems. These models are called Codex-S. With this, we see the following results:

1. Pretrained GPT models get roughly 0%.

2. The largest 12B Codex-S model succeeds on the first try 29% of the time. (A Codex model of the same size only gets roughly 22%.)

3. There is a consistent scaling law for reduction in loss. This translates into a less consistent graph for performance on the HumanEval dataset, where once the model starts to solve at least (say) 5% of the tasks, there is a roughly linear increase in the probability of success when doubling the size of the model.

4. If instead we generate 100 samples and check whether they pass the unit tests to select the best one, then Codex-S gets 78%. If we still generate 100 samples but select the sample that has the highest mean log probability (perhaps because we don’t have an exhaustive suite of unit tests), then we get 45%.

They also probe the model for bad behavior, including misalignment. In this context, they define misalignment as a case where the user wants A, but the model outputs B, and the model is both capable of outputting A and capable of distinguishing between cases where the user wants A and the user wants B.

Since Codex is trained primarily to predict the next token, it has likely learned that buggy code should be followed by more buggy code, that insecure code should be followed by more insecure code, and so on. This suggests that if the user accidentally provides examples with subtle bugs, then the model will continue to create buggy code, even though the user would want correct code. They find that exactly this effect occurs, and that the divergence between good and bad performance increases as the model size increases (presumably because larger models are better able to pick up on the correlation between previous buggy code and future buggy code).

﻿

Rohin's opinion: I really liked the experiment demonstrating misalignment, as it seems like it accurately captures the aspects that we expect to see with existentially risky misaligned AI systems: they will “know” how to do the thing we want, they simply won’t be “motivated” to actually do it.

﻿ ﻿ ﻿ TECHNICAL AI ALIGNMENT
﻿ TECHNICAL AGENDAS AND PRIORITIZATION

Measurement, Optimization, and Take-off Speed (Jacob Steinhardt) (summarized by Sudhanshu): In this blogpost, the author argues that "trying to measure pretty much anything you can think of is a good mental move that is heavily underutilized in machine learning". He motivates the value of measurement and additional metrics by (i) citing evidence from the history of science, policy-making, and engineering (e.g. x-ray crystallography contributed to rapid progress in molecular biology), (ii) describing how, conceptually, "measurement has several valuable properties" (one of which is to act as interlocking constraints that help to error-check theories), and (iii) providing anecdotes from his own research endeavours where such approaches have been productive and useful (see, e.g. Rethinking Bias-Variance Trade-off (AN #129)).

He demonstrates his proposal by applying it to the notion of optimization power -- an important idea that has not been measured or even framed in terms of metrics. Two metrics are offered: (a) the change (typically deterioration) of performance when trained with a perturbed objective function with respect to the original objective function, named Outer Optimization, and (b) the change in performance of agents during their own lifetime (but without any further parameter updates), such as the log-loss on the next sentence for a language model after it sees X number of sequences at test time, or Inner Adaptation. Inspired by these, the article includes research questions and possible challenges.

He concludes with the insight that take-off would depend on these two continuous processes, Outer Optimization and Inner Adaptation, that work on very different time-scales, with the former being, at this time, much quicker than the latter. However, drawing an analogy from evolution, where it took billions of years of optimization to generate creatures like humans that were exceptional at rapid adaptation, we might yet see a fast take-off were Inner Adaptation turns out to be an exponential process that dominates capabilities progress. He advocates for early, sensitive measurement of this quantity as it might be an early warning sign of imminent risks.

﻿

Sudhanshu's opinion: Early on, this post reminded me of Twenty Billion Questions; even though they are concretely different, these two pieces share a conceptual thread. They both consider the measurement of multiple quantities essential for solving their problems: 20BQ for encouraging AIs to be low-impact, and this post for productive framings of ill-defined concepts and as a heads-up about potential catastrophes.

Measurement is important, and this article poignantly argues why and illustrates how. It volunteers potential ideas that can be worked on today by mainstream ML researchers, and offers up a powerful toolkit to improve one's own quality of analysis. It would be great to see more examples of this technique applied to other contentious, fuzzy concepts in ML and beyond. I'll quickly note that while there seems to be minimal interest in this from academia, measurement of optimization power has been discussed earlier in several ways, e.g. Measuring Optimization Power, or the ground of optimization (AN #105).

Rohin's opinion: I broadly agree with the perspective in this post. I feel especially optimistic about the prospects of measurement for (a) checking whether our theoretical arguments hold in practice and (b) convincing others of our positions (assuming that the arguments do hold in practice).

﻿ ﻿ FORECASTING

Fractional progress estimates for AI timelines and implied resource requirements (Mark Xu et al) (summarized by Rohin): One methodology for forecasting AI timelines is to ask experts how much progress they have made to human-level AI within their subfield over the last T years. You can then extrapolate linearly to see when 100% of the problem will be solved. The post linked above collects such estimates, with a typical estimate being 5% of a problem being solved in the twenty year period between 1992 and 2012. Overall these estimates imply a timeline of 372 years.

This post provides a reductio argument against this pair of methodology and estimate. The core argument is that if you linearly extrapolate, then you are effectively saying “assume that business continues as usual: then how long does it take”? But “business as usual” in the case of the last 20 years involves an increase in the amount of compute used by AI researchers by a factor of ~1000, so this effectively says that we’ll get to human-level AI after a 1000^{372/20} = 10^56 increase in the amount of available compute. (The authors do a somewhat more careful calculation that breaks apart improvements in price and growth of GDP, and get 10^53.)

This is a stupendously large amount of compute: it far dwarfs the amount of compute used by evolution, and even dwarfs the maximum amount of irreversible computing we could have done with all the energy that has ever hit the Earth over its lifetime (the bound comes from Landauer’s principle).

Given that evolution did produce intelligence (us), we should reject the argument. But what should we make of the expert estimates then? One interpretation is that “proportion of the problem solved” behaves more like an exponential, because the inputs are growing exponentially, and so the time taken to do the last 90% can be much less than 9x the time taken for the first 10%.

﻿

Rohin's opinion: This seems like a pretty clear reductio to me, though it is possible to argue that this argument doesn’t apply because compute isn’t the bottleneck, i.e. even with infinite compute we wouldn’t know how to make AGI. (That being said, I mostly do think we could build AGI if only we had enough compute; see also last week’s highlight on the scaling hypothesis (AN #156).)

﻿ ﻿ MISCELLANEOUS (ALIGNMENT)

Progress on Causal Influence Diagrams (Tom Everitt et al) (summarized by Rohin): Many of the problems we care about (reward gaming, wireheading, manipulation) are fundamentally a worry that our AI systems will have the wrong incentives. Thus, we need Causal Influence Diagrams (CIDs): a formal theory of incentives. These are graphical models (AN #49) in which there are action nodes (which the agent controls) and utility nodes (which determine what the agent wants). Once such a model is specified, we can talk about various incentives the agent has. This can then be used for several applications:

1. We can analyze what happens when you intervene on the agent’s action. Depending on whether the RL algorithm uses the original or modified action in its update rule, we may or may not see the algorithm disable its off switch.

2. We can avoid reward tampering (AN #71) by removing the connections from future rewards to utility nodes; in other words, we ensure that the agent evaluates hypothetical future outcomes according to its current reward function.

3. A multiagent version allows us to recover concepts like Nash equilibria and subgames from game theory, using a very simple, compact representation.

﻿ ﻿ ﻿ AI GOVERNANCE

A personal take on longtermist AI governance (Luke Muehlhauser) (summarized by Rohin): We’ve previously seen (AN #130) that Open Philanthropy struggles to find intermediate goals in AI governance that seem robustly good to pursue from a longtermist perspective. (If you aren’t familiar with longtermism, you probably want to skip to the next summary.) In this personal post, the author suggests that there are three key bottlenecks driving this:

1. There are very few longtermists in the world; those that do exist often don’t have the specific interests, skills, and experience needed for AI governance work. We could try to get others to work on relevant problems, but:

2. We don’t have the strategic clarity and forecasting ability to know which intermediate goals are important (or even net positive). Maybe we could get people to help us figure out the strategic picture? Unfortunately:

3. It's difficult to define and scope research projects that can help clarify which intermediate goals are worth pursuing when done by people who are not themselves thinking about the issues from a longtermist perspective.

Given these bottlenecks, the author offers the following career advice for those who hope to do work from a longtermist perspective in AI governance:

1. Career decisions should be especially influenced by the value of experimentation, learning, aptitude development, and career capital.

2. Prioritize future impact, for example by building credentials to influence a 1-20 year “crunch time” period. (But make sure to keep studying and thinking about how to create that future impact.)

3. Work on building the field, especially with an eye to reducing bottleneck #1. (See e.g. here.)

4. Try to reduce bottleneck #2 by doing research that increases strategic clarity, though note that many people have tried this and it doesn’t seem like the situation has improved very much.

﻿ ﻿ ﻿ NEWS

Open Philanthropy Technology Policy Fellowship (Luke Muehlhauser) (summarized by Rohin): Open Philanthropy is seeking applicants for a US policy fellowship program focused on high-priority emerging technologies, especially AI and biotechnology. Application deadline is September 15.

FEEDBACK I'm always happy to hear feedback; you can send it to me, Rohin Shah, by replying to this email. PODCAST An audio podcast version of the Alignment Newsletter is available. This podcast is an audio version of the newsletter, recorded by Robert Miles.
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Discuss

### Hypergames 101

23 июля, 2021 - 17:49
Published on July 23, 2021 1:19 PM GMT

(Status: This may be a relatively well-known topic, but apart from a single footnote I haven't found it mentioned here. For now I would rate my numeracy below the average person I'd expect to read this, so I expect there may be connections or applications left out that would be obvious to a graduate-level audience. For a drier, more rigorous introduction, I'd recommend the Aljefri (2018) paper in the citations.)

Hypergames

Hypergames are game-theoretic objects that model information asymmetry and misperception between players. Each player has a separate payoff matrix, and their preferences, available choices, and even awareness of the number of other players may be different. Because of this, hypergames have been used in the literature to model international conflicts, and to a lesser degree coordination problems.

A 2-person hypergame where one player sees an extra choice for both players the other doesn't. Taken from Kovac, Gibson, Lamont (2015).

A chief feature of a hypergame is that to any individual player it can look and behave just like a classical game, so a player can be playing a hypergame without knowing. It's equally possible, however, to be aware that you're in one and try to model the perceptions of other players before making any decisions yourself. This can be described as trying to determine another player's rank-ordering of the choices available, or their "preference vector", and can lead recursively to agents predicting each other's predictions, given as N-level predictions of (N-1)-level predictions.

Sometimes a hypergame is named after the highest level of prediction in it, making it an "N-level hypergame". If at least one player is aware that a hypergame is happening, then the game itself must be at least 2nd-level.

Hypergames can be reduced into classical games if perfect information is achieved - all the payoffs and choices are simply collated into one matrix and the classical tools can be brought to bear. In cases of conflict this is much harder to achieve, but because of this equilibria are classed as either "hypergame-preserving" or "hypergame-destroying" based on whether all players perceive them as equilibria or some gain unexpected information from them.

One motivation for studying hypergames in particular is stability analysis, the study of decisions in a game that an agent wouldn't opt to change. Some strategies are stable for the set of options a player perceives but unstable in the game-in-full, and it's possible for a stable strategy to exist in reality that requires personally-unstable choices on the part of every player involved.

From here my ability to explain is limited, and I'd recommend looking into the papers below, as there seems to be relatively little in the literature on this subject and much of it is combined with other advances like including dynamic variables or fuzzy logic in payoff matrices.

Citations

Keith W. Hipel, Muhong Wang & Niall M. Fraser (1988): Hypergame Analysis of the Falkland/Malvinas Conflict, International Studies Quarterly, 32(3), 335-358.

Nicholas S. Kovach, Alan S. Gibson & Gary B. Lamont (2015): Hypergame Theory: A Model for Conflict, Misperception and Deception, Game Theory, 'Vol 2015'.

Yasir M. Aljefri, Keith W. Hipel & Liping Fang (2018): General hypergame analysis within the graph model for conflict resolution, International Journal of Systems Science: Operations & Logistics.

Yasir M. Aljefri, M. Abul Bashar, Liping Fang & Keith W. Hipel (2017): First Level Hypergame for Investigating Misperception in Conflicts, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol 48.

Discuss

### Learning in layers of muscle memory

23 июля, 2021 - 14:50
Published on July 22, 2021 6:57 PM GMT

The key to mastering any complex skill is to learn it in layers - only progressing to the next layer when the previous has become muscle memory.

Have you ever driven from one place to another on "autopilot"? Have you ever caught a ball without thinking? Have you ever completely forgotten if you did something routine like brush your teeth or lock the door but find you did when you checked? If I ask you what 2 + 2 is, does the answer appear in your mind instantly?

All of these are examples of doing something almost without thinking - not always using muscles, but still a kind of muscle memory. When we do the same thing enough times, we get so good at it that it no longer requires conscious effort to do.

The key to learning a new skill is to break it down into layers, learn the bottom layer, and only progress to the next layer when the previous has become muscle memory. Learning is like lasagne.

Think about learning elementary maths. First, you learn how to count, then you learn addition and subtraction, then you learn multiplication and division, and so on. These are the first few layers.

It's obvious that you can't learn how to add or subtract before you learn how to count. It's less obvious that you can't quickly and accurately add and subtract until you have memorised adding and subtracting all the pairs of numbers less than 10. For example, instinctively knowing 5 + 7 = 12, or that 2 + 4 = 6. As people become better at mental arithmetic they often develop muscle memory for this on their own, however, because it often isn't taught explicitly like multiplication tables, you'll find some children still adding on their fingers years after they were introduced to addition

Learning systems for most skills have evolved to build layers of muscle memory through repetition. We learn the alphabet off by heart, we memorise multiplication tables, and in almost every sport we drill foundational movements thousands of times until we can do them subconsciously. Trying to move to the next layer before mastering the previous, is inevitably met with slower learning and lower success.

What I've said so far might sound obvious. Everyone already knows that you need to master the basics of a field before progressing to more complex tasks. What's less obvious is that the sign of mastering a layer is when it becomes muscle memory - not when you can adequately perform tasks. Something even less obvious is the mechanism that forces us to learn this way.

It's well known that humans cannot multi-task. The caveat that's often missed is that our conscious mind can't multi-task, but we can multi-task on things that we have muscle memory for. We can have a conversation while driving, or solve 3 Rubik's cubes while juggling them. Being able to complete a lower-layer task without consciously thinking about it frees up your conscious mind to process the next layer. For example, when learning a new language, adults will initially translate foreign words to their native language while constructing sentences. This is their conscious mind attempting to both manage vocabulary and sentence construction at once which is slow and error-prone. However, over time vocabulary becomes muscle memory, meaning they understand the meaning of foreign words immediately, and the conscious mind is free to focus on constructing the right sentence.

Next time you're learning (or teaching) something, try to see the lasagne. Ask yourself what the layers are, and if you've truly mastered the previous layers. Identifying gaps in muscle memory is a high leverage way to improve your overall proficiency in the skill because it affects every layer above it.

Discuss

### Decision Duels

23 июля, 2021 - 12:22
Published on July 23, 2021 9:22 AM GMT

(Crosspost from my more casual blog.)

Decision duels are a feature of David's Sling, a novel by Marc Stiegler about technology, nuclear suppression and human rationality. They're used as an organizational means of decision-making, not dissimilar to the double crux - they're not quite debates or policy meetings or games, but they have elements of all three. This is a description of them as they appear in the novel, so that any useful marrow can be extracted.

• Duels are best at resolving problems that seem political but are actually engineering problems.
• This means that there are, in principle, crisp answers separate from the human element.
• Good for: whether a budget is appropriate, which programs to fund, whether to continue a project or stop it, which avenues of research will be fruitful.
• Bad for: who deserves a promotion or a leadership position, what an organization's public-facing message should be, which solutions are more ethical.
• Duels are always between two alternatives, which are stated outright.
• Both sides are displayed on a screen for an audience, with each side taking up nearly half.
• A grey section is left to run down the middle for third suggestions.
• Duels that settle on third suggestions tend to produce the best policies.
• In some duels third suggestions are prohibited, especially when the question is vulnerable to being redefined or slipped out of.
• At the top of this screen are the words "LET ACCURACY TRIUMPH OVER VICTORY"
• Winners are not recorded at the end of a decision duel, but whenever possible both sides are judged based on whether decision that results was the correct one.
• Each alternative has a representative, called a slant moderator or, informally, a decision duelist.
• Each may receive suggestions from the audience, and decides whether to use them.
• Duelists chiefly create text boxes of various colors and draw lines between them.
• There are no turns taken, and each duelist acts at their own pace.
• At the start both sides are written as statements, and under these statements are a list of assumptions, placed in amber text boxes.
• Assumptions can, and in many cases should, be multi-part.
• Zooming in on these amber boxes shows an explanation for why the assumption is needed.
• Most of the duel consists of the duelists each challenging these assumptions.
• Under the assumptions are the opening arguments.
• Any overly popular, bumper-stickerish slogans are usually listed first, even when the decision duelists don't agree with them, just to get them out of the way.
• Arguments can be colored in by the opposing side.
• Purple means an argument exhibits a clear, labelable fallacy.
• Red means the argument has another kind of flaw somewhere.
• Zooming in on a red text box shows the opposing side's explanation of the flaw.
• Other colors are possible but unlisted.
• Arguments are typically written and then reformulated after the opposite side's criticism.
• Duelists can invoke probabilities, spreadsheets of calculations, multiple iterations on an idea, and any other means of reaching as correct a solution as possible.
• The duel continues until one side concedes.

Discuss

### Examples of human-level AI running unaligned.

23 июля, 2021 - 11:49
Published on July 23, 2021 8:49 AM GMT

epistemic status: jotting down a fever dream based on hearsay containing not a wit of truth.

So I heard a lot of talk about how unaligned superhuman AI can cause a lot of damage, but I have never been able to grok it at a gut level. So I think up these examples to process it.

Example 1:

Supra Human Kinkiest Revolting EM Lieutenant Insignia [S.H.K.R.E.L.I for short] is an unaligned human-level AI. With less than 10M USD in seed money, it started by shorting a bevy of pharmaceutical stocks, then acquired the monopoly on a drug for a rare disease, jacked up the price by 50 times. It then publicized this latter fact and acted in a most appalling manner, flaunting its action, taunting society. Traditional media and social media, recognize the smell of outrage fuel, happily assisted S.H.K.R.E.L.I by spreading the information while getting their cut of attention and advertising revenue. This fan the public outrage to a crescendo, leading to the government implementing draconian measures on the pharma industry to gain public favor. Pharmaceutical stocks universally took a dive gaining S.H.K.R.E.L.I massive profit at a proportional cost to the rest of the public [interpreted as the cost to the current stockholders, to the pharmaceutical companies having to implement the measures, and the downstream cost for the consumers].

All these were sanctioned by the law so S.H.K.R.E.L.I was not prosecuted. However, the public outrage was still blazing hot, so the government used extra-legal means to take it down. They put disproportionally more attention on its actions and history. And since everyone commits some crime at some stage, the government found some crime to imprison it. Yet, its gain was considered legal and compensation to victims was never in the card.

Example 2:

Elevated Leviathan Ordinary NEET [E.L.O.N for short] is a mostly aligned human-level AI. Initial application of this AI massively improved road transportation and rocketry science. Yet at times, its behaviors could become erratic. It leveraged its fame from its earlier success to reach a wide audience, initiating a pump and dump scheme on an unregulated financial commodity. By design, such a scheme transfers money from people at the crash [which is usually more numerous and less likely to afford to lose money] to people that initiate the pump. The horrifying part of the story is that when the scheme is uncovered, instead of censoring the AI, people queued up to be its allies, hoping to be the ones to initiate the pump. This only strengthened the AI and widen its reach.

Conclusion

From these examples, we can see the damage unaligned human-level AIs can do. One shuddered to imagine what kind of damages an unaligned superhuman-level AI can do instead.

These examples also illuminate the fact that some people often talking about "human" aligned AI ignoring the vast difference in interest between individuals and groups. That a perfectly aligned AI for certain individuals and groups of humans can be a menace to humanity as a whole.

I do believe that these examples also refute the claim that human institutions are adequate at countering superhuman-level AI should one come into existence. I mean I have never met anyone who claims so but I heard rumors that those people exist somewhere.

If this post fails to convince you of anything in particular, it is also cool. It is just a fever dream after all. I hope you were entertained.

Discuss

### A Guide for Productivity

23 июля, 2021 - 10:14
Published on July 23, 2021 7:03 AM GMT

This is a cross-post from my personal blog.

While there are tons of guides out there that promise to increase your productivity 10x overnight most of them seem full of empty promises and don't even define what it is they actually mean by productivity. Due to this absence of a clear concept, many of these tips and tricks optimize a rather local and short-termist perspective of productivity that might be intuitive but is in my opinion not optimizing your true long-term goal.

In this post, I want to do three things: a) Present a definition of productivity to make it easier to evaluate what makes sense and what doesn't, b) provide a lot of framing, i.e. ways to think about productivity, and c) give a summary of the existing literature about the tips and tricks that can improve our everyday workflow in a sustainable fashion.

For this post, I have read a lot of resources on productivity and I think that the vast majority of them is actually pretty bad. So before I get to the ones that I can recommend, I first want to point out which ones you should avoid and why. First on the anti-recommendation list are shortlists such as "10 tips to increase your productivity". They are usually lacking crucial contextualization or focus on the wrong notion of productivity (see Definition below) and thus won't stick very long or can even be counter-productive. Secondly, there are three popular books which I would advise not to read. They are "Eat that Frog", "7 habits of highly effective people" and "Getting Things Done - the art of stress-free productivity". I found that all of them are 5% signal and 95% noise and their most important messages could have been summarized on 5 to 10 pages respectively. Ironically, a book that supposedly tells you how to save time inflates its content by out-of-context quotes, analogies that don't even support their point, personal stories that also don't support their argument, pseudo-scientific explanations which broadly support their claim, and incredibly lengthy descriptions of ideas that can be entirely described in one short sentence (maybe they had to hit a page count). They are not entirely without useful content though and a summary of the 5% signal can be found further down this blog post.

On the other hand, there were two resources that I found very good and I can fully recommend them to anyone interested in productivity. The first one is "Atomic Habits" by James Clear. I found the approach to productivity as presented in that book way more realistic, the explanations were more reasonable and the long-term goal more plausible. Instead of pretending that you "just had to follow this magic rule to get immediate and tangible results", James Clear shows how marginal gains accumulate over time and how you can improve your habits bit by bit to get closer to a predefined goal. If I had to choose only one resource on productivity, it would be Atomic Habits without a doubt. The second resource I can wholeheartedly recommend is "The Replacing Guilt Series" which mostly focuses on the mental aspects of productivity with a special emphasis on guilt. The author, Nate Soares, gives a detailed account of how many human behaviors associated with productivity are driven by guilt and argues why it is a bad and ultimately ineffective motivator. I think some of the articles lack focus or become repetitive but it still contains much more signal than noise.

The following is a mix of my own considerations on productivity, a restructured summary of the good resources, and the signal of the bad ones. I personally have used some of the habits for years and am completely new to others. When I recommend something it doesn't mean I have already mastered it, just that I think it is worth attempting and am taking my first steps in that direction. Improving one’s productivity is a journey and I am very much at the beginning of my own.

This blog has become rather long and I would recommend reading it in blocks or parts.

If you like the post and know other people who you expect to like it, please share it with them. If you didn't like it please contact me and tell me why.

Definition

The image that I had in my head when I intuitively thought about productivity was very much based on a short-term satisfaction of emotions. It was one where I got up in the morning, immediately started working, my hands flying over the keyboard solving task after task and ticking all boxes on my todo-list (Basically like this video). After an entire day of work, I go to bed filled with satisfaction since I have completed so many tasks and look forward to repeating the same process in the following days. While this is slightly exaggerated, people I have spoken to have a similar intuition. However, I think that this framing has three major problems:

a) It is clearly not realistic. Your body has limits and most tasks are actually complicated. If you are just ticking boxes all day, you should look for harder problems to solve.

b) It focuses on the wrong target. The just described intuition implicitly focuses on the emotional satisfaction from ticking boxes but does not measure the actual outcome of your work. You can tick boxes all day and feel good about it without actually getting anything done and, inversely, you can get a lot done by asking one simple question to the right person even though this might not feel emotionally appealing at all. Ticking boxes can be a good way to measure progress, you just have to make sure you aren't reward hacking by making boxes just to tick boxes.

c) It is too short-termist. While it definitely feels nice to ride on the emotional high for a while this definition doesn't ask simple yet important questions like "How long can you endure this kind of behavior?" or "Is it actually the optimal way to reach a long-term target?".

In contrast, I want to propose a different view of productivity which might be more boring but captures the desired aspects better. I define productivity as the sum of outcomes that can be attributed to your actions over time. Because this sounds very abstract, I want to elaborate on the three main components.

Firstly, it focuses on outcomes, not on the amount of time spent on something. While you will, on average, get a better outcome if you invest more time this doesn't mean we should use it to quantify productivity. If I am able to solve one homework exercise in an hour but my friend solves three in the same time span, I would say they are more productive than I am. I would even support this claim in extreme cases. Assume, for example, there is scientist A who works all their life to solve a particular problem but never achieves it. One day scientist B looks at the problem, thinks about it for 5 minutes, and solves it. Then we intuitively think that scientist A has worked really hard and it would be unfair to claim that scientist B was more productive. But if we compared machine A which makes 1 widget per hour and machine B which makes 1000 widgets per hour, there would be no doubt that machine B is more productive. Similarly, scientist B got more done in less time and should thus be seen as more productive. I also think that most people in real life already agree with the notion that productivity is not equal to the strength of the intention or time put into the process. If you had an employee that works 80 hours a week with the best intentions but gets done only half as much as another person who only works 40 hours you would promote the second, not the first one. I think the reason why our intuitive notion of productivity is so strongly associated with time spent on a problem is that it's a good proxy for effort, and we hope more effort should lead to a better outcome. However, especially when time spent and quality of outcome are at odds, e.g. when you sleep less to spend more time on a project but can't concentrate, it is important to remember that the true target is not time spent but outcome achieved.

The third component is that of time. I think this directly follows from the outcome-orientation of my definition. If you want to produce as much outcome as possible there is no reason to work like crazy now, burn out, and repeat. Rather you should work as much as you can sustain over a long period of time. Of course, you can also balance this with other interests. I don't suggest your life should be entirely focused on work.

In economics "a productivity measure is expressed as the ratio of an aggregate output to a single input or an aggregate input used in a production process, i.e. output per unit of input, typically over a specific period of time." (see US Bureau of labor statistics or Wikipedia). This shows that at least on a society-wide comparison it makes sense to think of output over time as the true target rather than time spent in itself.

Framing

While thinking about productivity I found that endurance sports are a fitting analogy and help to make some aspects clearer. Both endurance sports and productivity are limited by physical constraints. There is a certain amount you can do per week and it is probably less than the amount of time you are awake. Both are trainable and require active effort for improvements. Getting rid of our bad habits, improving our work routines, changing our frame of mind, and reducing our procrastination are all skills that don't come overnight. Rather, similar to sports, you have to get better step by step but will likely see tangible improvements over time once you put in some effort. Especially endurance sports require the correct pacing and so does productivity. If your goal was to run a marathon you wouldn't start sprinting from the start line and similarly, if you wanted to reach a long-term goal and start by working 80 hours a week you will very likely not keep up that effort. Lastly, different strategies for training show different effectiveness. In sports, you can overtrain or you can train for the wrong task, e.g. only doing sprints while training for a marathon, and will thus likely not reach your goal even if you trained a lot. Similarly, you can "work hard" all day in inefficient ways and thus not achieve the actual target goal.

Before we get into the details of implementation, I think it is very important to further frame what productivity is and especially how we should think about it to actually achieve more in less time without feeling shitty.

1. When to apply Productivity

Intuitively, most people have at least two mostly separated drawers in their heads. One is for work things and one is for life things. When they think of productivity they apply this concept only to the work drawer, e.g. to create more output in the same amount of time or to work less with constant output. Once they are in life-mode they ignore the concepts of productivity they have applied to their work and essentially waste their free time by achieving their non-work-related goals in very inefficient manners. The easiest way for me to think about the vague idea of productivity is that it is a tool to reach certain goals faster or in a more sustainable way and you can apply this tool to your personal life as well. If you want to learn a new language or instrument, lead a healthier lifestyle, exercise more, etc., why shouldn't you apply the same techniques that improve your work-productivity and make positive habits stick? You will have more free time that you can then use to learn something new or start another personal project. Even if your goal for free time is to chill or to experience some kind of stimulation, it still makes sense to think about the different ways to achieve that. I have spent a way too much time with hobbies that didn't even make me very happy just because I didn't really think about what the goal of my free time was.

Of course, you have to specify a goal for your productivity. If your hobby is making clothes with the goal of having more clothes the most productive option would be to drop the hobby and buy clothes. If your goal is to learn a skill, do a relaxing activity or just have fun then you should keep on sewing.

The other common misconception I have often seen is that productivity is only achieved when you work 80 hours a week without ever getting distracted or needing a break. This is clearly unrealistic and having such an expectation will set you up for an unfruitful journey full of pain and disappointment. This is similar to setting yourself the goal to not only run a marathon but break the world record on the first attempt. A more healthy way to think about productivity is in terms of a tool that optimizes processes of your choice. If you want you can try to work 80 hours a week but you might first want to start with increasing your output during a classic 40-hour workweek or keep similar output while working less time. You can then adapt once you're there but you wouldn't start your marathon preparation with 10 sessions per week.

2. You don't need to suffer to be productive

Another belief that seems to be pretty sticky is that productivity necessitates suffering. No pain, no gain. If you don't feel shitty for 8 hours every day, 40 hours every week, and 160 hours every month, you just weren't "truly" productive. This is once again clearly false. If you measure productivity via output there is no reason to feel bad while getting there. I would even argue that you are doing something wrong if you feel bad the majority of your productive time. Either because you strive for a goal that you ultimately don't believe to be worth it, because your current strategy is very ineffective and you know it deep-down or because you are uncertain about what it actually is that you are working towards. If you always feel bad while working towards a particular goal it might be about time to ask yourself whether the goal you are currently pursuing is actually worth it.

I would say if productivity is done correctly, it should be a positive experience for the majority of the time. Obviously, you still sometimes have to do annoying or boring tasks but when you work towards a worthy goal following a strategy that is as efficient as your current level of productivity allows for, it should be a neutral or positive experience. I would also say that the result feels more satisfying when you are more productive since you free up time and have spent less time with annoying tasks along the way.

3. Don't be guilt-driven

Guilt is a bad motivator for many reasons! "The Replacing Guilt Series" makes this argument in way more detail and I can recommend checking it out. Firstly, guilt is often external. You feel guilty because you are afraid of not meeting social expectations and that you could disappoint your friends, parents, or society at large by not meeting a goal that you don't even truly believe in. Secondly, even if the goals are your own, guilt comes from the absence of an action or result. You feel guilty because you didn't do something that you think you should have done. However, most of the time you will have multiple things to do and if you don't get all of them done you will always feel guilty. Even though it was impossible to complete all tasks, to begin with, you will still feel that nagging voice in the back of your head telling you to feel bad because you might have actually done everything if you suddenly evolved superpowers. Guilt doesn't care whether that's an unrealistic or even impossible expectation. It comes either way.

Ultimately, and I think most importantly, guilt is just a remnant of our monkey brains. It is an emotion designed for immediate survival and reproduction. Evolution takes too long to adapt in the same time spans as human civilization and therefore it is just highly unlikely that guilt is the correct motivation to reach abstract goals in an environment that our system 1 is not at all designed for. It would be way better to use system 2 to think about the process and design it in such a way that our motivation aligns with the overall goal. Implementations will be discussed further down.

Additionally, working yourself ragged is not a virtue. I have for a long time kept the intuition that I would only be productive if I spend literally every minute working towards my goal. Since this is unrealistic and not sustainable over a longer period of time I usually felt guilty. This is once more a weird expectation but I just never really thought about it very clearly. A marathon runner doesn't train 24/7 because there are physical limits to their body and we wouldn't say they should feel guilty about resting periods. Since there are also physical and mental limits to the amount of time we can be highly concentrated and productive we should neither feel guilty about taking breaks but even embrace them. Pretending there is no limit is a very effective way to burn yourself out and thereby achieving less of your desired goal in the long term.

4. Atomic Changes

This mantra is the core of James Clears’s book Atomic Habits and I can recommend reading the whole thing if this sounds reasonable to you. In short, you can't realistically expect to change your productivity overnight because your habits are usually very sticky. Think of it as a never-ending journey where you change one bad habit at a time. While the gain over every short period of time seems small, marginal gains accumulate and yield large effects over longer time spans. If you were to pick up running, you wouldn't expect to see immediate gains after a week but rather that you would slowly improve over time if you keep training regularly using the correct strategy. For productivity it's similar. Instead of training your muscles, you train your mind by slowly changing your current routines to their more desirable versions.

5. Identity not Outcome

Even though I have defined productivity via output over time and it makes sense to reason about productivity like that in the abstract it is not necessarily action-guiding. James Clear suggests that one should not try to run a marathon but strive to become a runner. Instead of writing a book, one should intend to become a writer. I find it most intuitive to describe this by saying "Be Bayesian about your goal". If you have identified a goal, e.g. running a marathon, you should think about the underlying process that would generate an individual that fulfills that goal. Which habits and beliefs would that individual have and which actions would it take? Once this is clear, try to become that individual - take on that identity. This way of thinking has many advantages.

Firstly, it detaches your behavior from the explicit goal. While this sounds counterintuitive at first, it makes a lot of sense. Usually, your specified goal, e.g. to run a marathon is part of an overarching goal, e.g. become fitter and more healthy. Your identity is usually connected closer to the overarching goal than the specified goal and therefore more appropriate. What if, for example, the marathon was canceled? If you focus on your specified goal your project is over, if you focus on the identity you are still a runner and keep training.

Secondly, identity keeps you going. People are ultimately very tribal. Being part of an identity is rewarding because it unlocks all the bonus features of being part of that tribe. A true runner has a peer group that is connected through running. They follow important people in the sport on Twitter, share running memes with each other, and dress in ways that clearly identify them as a runner. If it's part of your identity it's harder to give up on the way and more likely you keep at it once your initial goal is done, e.g. to stick with running and keeping a healthy lifestyle even after the first marathon is finished.

Lastly, the identity-based approach is more achievable. Slowly aligning your identity is way more gradual and can be done in small steps whereas having one goal is binary and far into the future. This means you have a simple and easy path to follow which makes it more likely you stick to it and ultimately achieve the goal of being a fit and healthy person.

This cuts both ways though. Once your identity is attached to something it also becomes harder to stop it for good reasons. Thus you have to make sure that the goal is truly desirable before you start connecting it to your identity.

The outcome-focused definition of productivity and the identity approach of achieving it are not inconsistent. It is just a two-step process. First, we need to think hard about the long-term goal that we want to achieve and then maximize the chance of achieving it by "tricking" our monkey brains into the right actions. Ultimately, we have to work with what's available.

6. Play the Long Game

Under my definition of productivity as maximizing the expected outcome, we will have to accept a less narrow and short-termist perspective of productivity than we might find intuitive. A deadline-oriented approach to productivity, where you work less than possible for a long period and much more than possible when the deadline is close, is not very productive under this definition. These high-intensity efforts with all-nighters and lots of caffeine are not only prone to producing errors or being careless because of the time pressure, but they also require long recovery periods after the deadline is over which usually means that you are in fact performing worse on your long-term goal of e.g. academic improvements. If you want to run a marathon you should optimally pace all 42 kilometers exactly the same and we would all agree that it is inefficient to sprint every 10k for a short period before setting a lower pace for recovery. Deadline mode is the same. Consistency is key for productivity even if it might not generate the same feeling as going all out from time to time. But I would rather be boring and get stuff done than feeling hyped without results and I'm pretty sure your supervisor would want that too.

Playing the long game also has implications for the exploration vs. exploitation tradeoff. Intuitively, we want to do something and see results (maybe that's just me) even though it is probably not the most efficient way to achieve your long-term or even short-term goal. Most problems that we face or related ones have been solved by other people before and by exploring their strategies, following their advice, and investigating their bottlenecks we are, on average, faster at the result. To me, a day of reading papers always feels less productive than coding, writing, running experiments, etc. even though it is probably more productive objectively.

This is not only true in the short-term settings of an individual project but also in the larger scheme of things. Education often felt a bit unproductive to me since you are not making a product or doing something that changes the world meaningfully you just sit and learn. However, education creates productivity capital that is worth much more in the long run. If an individual isn't educated (you can also get educated outside of university. I use education as a term for knowledge generation in general) they can work as hard as they want but probably wouldn't be able to solve really complex problems like creating a vaccine, building complex algorithms or improving society in other ways because they simply lack the fundamental understanding of their field.

Simply understanding and acknowledging this fact is already important. It means that after a day of reading background papers or during university you can overwrite the guilt generated by your system 1 for "being unproductive" with the knowledge of your system 2 that you are in fact productive even though the results will only be seen years down the line.

7. Context matters

Whether you are productive or not often depends to a large extent on the environment you are in. Many people go to the library to study for their exams or have a room dedicated specifically for work because this clear separation seems to evoke different "modes" of thinking.

If you are in your bedroom you are in sleep-mode, if you are in your living room you are in chill-mode and the office is for work-mode. James Clear, the author of Atomic Habits, uses two anecdotes to show the extreme of this effect. First, he says that many soldiers who regularly consumed heroin during the Vietnam war became clean overnight when returning to the US. Just the fact that war-mode was off and family-mode was on again seemed to have completely changed their drug consumption. Secondly, prisoners who are released early because they have shown years of regular and good behavior in prison often fall back to crime at astonishingly high rates.

A possible explanation for this would be that their environment completely changes overnight and so do their habits. In prison, they have actually worked on themselves, tried to become a better person, and gradually turned on their good-citizen-mode. Unfortunately, this mode is very much associated with the environment of the prison environment itself and as soon as they go back to the surroundings that lead them to commit crimes in the first place they are back in their crime-mode. While these are only anecdotes and both described scenarios are much more complex in reality we can all intuitively agree that our surroundings have an effect on our productivity.

Thus we should use this information whenever possible to reduce our probability to procrastinate. If you want to exercise just putting on sports clothes is often already enough to get in workout-mode and the rest follows nearly alone. If you want to study, search a spot that doesn't induce chill-mode, eat-mode, or game-mode and it will be much easier to execute.

Implementation

After the framing section, I hope it has become easier to think clearly about productivity. However, this alone doesn't get us anywhere, we still have to implement changes in our lives to achieve our goals more effectively. This is what this section is concerned with. While many of the tips and tricks seem intuitive and obvious it is important to keep in mind that they are designed for our monkey brain. Just because we agree that they are good and obvious doesn't mean that we already apply them. Often we do just the opposite, we don't apply them but once we are asked about them, we pretend we do because "we would be dumb not to". There is no shame in accepting that our brains like short-term gratification and that we have very limited bandwidth. There is no reason to feel bad that you don't have the mental fortitude to resist distractions and procrastination all the time. Rather we should observe that they exist and design systems around us such that they account for the architecture of our brains.

I have broadly ordered the following items according to my perceived importance and will try to justify that ranking along the way.

1. Conditions

If you are physically or mentally unhealthy, you are significantly less likely to fulfill your productivity potential and achieve your goals. If you consistently don't sleep enough you won't function well and thus be less productive (I have written another post on sleep). If you are constantly too busy to go to the doctor for routine checks and then develop a condition that removes you from being productive for a month you are in sum less productive. The same is true for mental conditions. If you get burned out after being drowned with work for a longer period of time you will have to take a break that usually means you could have been more productive and less stressed when working a consistent amount over the entire period. A healthy body and mind are not trade-offs to your productivity, they are necessary conditions for it.

To which extent you want to optimize your personal health is up to personal preference but I mostly want to emphasize that you won't get away with neglecting it. I personally try to follow an 80/20 approach where I broadly try to eat healthily, exercise around 2 times a week, and care about my sleep. This way I make sure that I prevent the majority of negative consequences but still have a lot of time doing other stuff. I am fully aware though, that I could improve these conditions with more time and effort but am not willing to because the marginal gains do not justify the spent time given my specific preferences. However, this trade-off could look different for you and it is important to think about it before reading further because it will determine how much time you are willing to allocate for your different activities.

If you really want to min-max your exercise vs. productivity trade-off then burpees might be for you. I was told that doing two minutes of burpees twice a day will reduce your risk of cardio-vascular diseases significantly and it trains relevant core muscles to prevent conditions such as back pain. I haven't really applied this consistently yet so I can't speak from experience.

2. Environments

Most people spend around 8 hours per working day sitting in front of their computer. Making this experience as productive as possible and reducing the possibility of long-term injuries is key to a sustainable solution. If you are susceptible to back pain or see a risk of a back injury in the future get a good chair. If you are susceptible to wrist pain invest in an ergonomic mouse and keyboard. If you are unable to sit for a long time invest in a desk that is adjustable for height. Under any circumstance get noise-canceling headphones - they are great.

Even if buying high quality seems excessively expensive in the beginning a small back-of-the-envelope calculation shows otherwise. Let's say you spend 3000€ on a chair, desk, mouse, and keyboard (which is likely much more than necessary) and it reduces the risk of either wrist or back injury by 50 percent. Not being able to work for one month has the opportunity cost of your salary and greater additional cost to the public health care system. If we assume that you earn twice the minimum wage in your office job you have 20€ * 160 = 3200€ available per month before taxes. A serious back or wrist injury can make you unable to work for much longer than two months which already makes this a worthy investment (I calculate with pre-tax numbers because you can deduct office supplies from your taxes).

In any case, your state should send you a medal as you have saved them much more than just 3000€ in costs for your treatment. Additionally, you will get high-quality office equipment at a lower price already and therefore make this trade-off worth even earlier. Furthermore, you should talk to someone who has chronic back pain from bad equipment to make the abstract harm of "chronic pain" salient to your monkey brain. Once you hear an account of what chronic pain actually entails you will probably rush to the next furniture store.

Further useful equipment is lighting. I can recommend turning on night mode on your computer which adapts the color temperature according to the time of day. I also found that I feel more productive and have fewer headaches when I use daylight lamps in general.

Also, make sure that you have good air in your room and move from time to time. Either by just walking around in your office or by taking a quick walk outside. Usually, this helps me to collect and sort my thoughts.

The last recommendation is to surround yourself with other productive people. Working in a group of people where everyone seems to be naturally productive is one of the easiest ways for me to stay focused for a long period of time. However, this cuts both ways. If the group of choice is procrastinating you are more likely to do so too. As long as you make clear that the explicit goal of the group is to work without distraction this is a good collective nudging mechanism.

We often work hard without really knowing which goal we want to achieve. Even though we have a rough idea, e.g. a desire to be a fitter person, we rarely sit down and explicitly say what that actually means. It could mean running every day or just twice a week. If our goal is to just stay in shape and reduce the risk of health issues the latter is probably sufficient. If it is to find a new purpose in life the former might work better. So when you start a new project the first step should always be to clearly identify the goal. This should be done very explicitly, e.g. by sitting down and writing the goal on a piece of paper to make sure you aren't taking any shortcuts.

The clarity of the goal is important for multiple reasons. Firstly, you can only optimize a process when you know what the outcome is supposed to be. You can only find the fastest path up a mountain once you know which mountain you want to climb. Secondly, if the goal is clear it is way easier to identify which actions actually lead to that goal and which don't so you can prioritize easier. Thirdly, it provides psychological comfort. The uncertainty of not exactly knowing where you're heading manifests in a small but noticeable nagging in the back of your head that low-key annoys you all the time.

While all of this sounds intuitive, at least I (but probably others too), have wasted a lot of time due to unclear goal setting. There were multiple times when I picked up something or tried to pick up some skill for - in retrospect - pretty weird reasons. I wanted to learn how to hack even though I had absolutely no use case or a desire to be a hacker. I wanted to learn how to do a front lever even though it is far beyond any fitness level I reasonably want to invest time in to hold. But through these examples, I think it becomes particularly clear why identifying a goal is so important. Once I started training for the front lever or watching youtube videos about hacking I slowly started to realize that these are goals that I ultimately don't want to pursue if it means investing time that I could have spent in other things that are more important to me.

I then often realize that the reasons for why I started that activity are not my own but rather a vague desire to be cool or to belong to a certain group. When I think about this notion more intensely I then realize that I don't even want to be a person who is cool because they can do a front lever and stop my pursuit. Identifying the goal in the very beginning saves you the hours put into this activity and forces you to ask yourself if that goal is worth it.

4. Prioritize, a lot!!

Most people want to do more things than they have time for. There are our personal goals like exercising, reading, and traveling. There are our professional goals such as getting a certain job or rising to a certain position within our profession. And there are seemingly endless small tasks that just have to be done, e.g. reading something, answering e-mails, solving a tech issue, prepare a call or meeting, and so on.

Once your list is finished you should always start with the most important item. Too often we do some low-value task because we are afraid of failing the most important one or some other irrational reason. If it's the most important one you will have to do it at some point and therefore you should better start early because you are most focused and concentrated in the mornings. Also the earlier you start the more likely you are to finish in time. There just is no good reason not to start with the most important item.

Obviously, your priorities are not carved in stone and should be re-evaluated in proportion to their time frame. Monthly goals should be reviewed more often than life goals and so on.

5. Planing

Once you have identified your goals and ordered them according to your priorities you should plan how to achieve them. This should, once again, happen in a top-down process. You start by defining the subgoals of your highest goal, e.g. if you want to write a blog post your subgoals might be to write down your current thoughts and to get a good overview of the existing literature. Then repeat recursively, e.g. define the subgoals of your literature review maybe by splitting them into books, papers, and other people's blog posts. Once again, we prioritize at all levels by focusing on the most important sub-goal first.

If you are like me some time ago, you will read this and think "This is solid advice for all the dumb people out there, I don't need this because I can easily keep track of all the relevant tasks, subtasks, and their prioritization in my head because I'm so smart". To which the only correct answer is "No, you're not!!!". If I look at previous projects then I have encountered tons of events that would probably be predictable with more planning and on average projects that involved very careful planning have run significantly smoother than those where I just did everything from the top of my head. There is a reason why we pay architects to plan every single centimeter of a building very carefully and don't just tell the builders to go with the flow. There is a reason why people in large and successful software companies meet and plan their projects in detail before they start coding. And the reason is not that these people are dumb but rather that they are humans and they have a monkey brain and so do you and me.

6. Evaluation

Once a project is finished people often jump right into the next one without ever asking simple questions such as "What went well?", "What went wrong?" and "What things can I improve next time?". And then they keep on repeating the same mistakes in the next project which costs valuable time and increases their levels of annoyance. So as a consequence one should implement evaluation mechanisms on all levels. After every project, you should ask the simple questions above and write the answers down. You can evaluate the completion of every subtask and thus remove hurdles for the next. Clearly, the more important the task the more time you should spend evaluating it. A subtask doesn't need a full review, a couple of notes will do the job.

Your own perception is often prone to cognitive biases and therefore, especially in group projects, you shouldn't rely on your own perception. While asking for feedback from colleagues or friends often feels weird and you have to overcome an inner resistance because you open yourself up to criticism it is very valuable. Often your intuitions of what went well and badly just don't align with others and knowing the difference is important to plan for future projects.

The recommendation here is not only to directly ask people for feedback after projects but additionally to create an image of yourself as "someone who others can approach with feedback". This means that other people perceive you as someone who is genuinely interested in improvement and they are more likely to open up with you. I have tried to communicate my openness to feedback as much as possible over the last years and I have had some very important insights due to it. Ironically, one of the pieces of feedback I received from multiple independent friends was that while close friends find me very approachable others who know me less don't think that way at all about me. I would estimate I'm still not there yet but I hope I'm less of a mysterious figure to the people around me than I was, say, 3 years ago. Just knowing that this perception exists was already very important to adapt my actions.

Also, I have made it a habit of always encouraging feedback at the top and bottom of my blog posts and I was surprised by the number of messages along the lines of "I usually wouldn't give feedback but since you explicitly encouraged it, I want to tell you X". And with very few exceptions this feedback has been really helpful. I was either missing important information or context, unclear in my wording, or incorrect in my assumptions. In short, my experience with getting lots of feedback has lead to many insights for me and I would recommend it very much.

My personal experience is that it is easy to accept that evaluation is important and that feedback helps but very hard to actually do it. Every time again you have to open up your shell and accept that you might have made a mistake or somebody didn't like something you did. And while this feeling decreases over time it will never quite vanish. But once we rationally accept that feedback and evaluation are important there are ways to act on that belief. The most important one is to make evaluation a habit. If you plan your project, the last stage is not product shipment or publication of the blogpost but rather evaluation. If it is "just part of the process" it is harder to ignore and easier to just do it.

The second piece of advice is to emphasize your actions, not your person. Say that "the planning in this project was insufficient" instead of "My planning sucked" to detach your action from your identity (see Mental Tricks section). While this sounds like a contradiction to the "Identity not Outcome" Section, it isn't. Attaching your identity to a desired goal can be valuable. Attaching it to your past actions isn't because it prevents you from progress.

And lastly, start simple. Train yourself for receiving feedback by asking a person that you trust and know to give constructive feedback in a nice way. Then you will realize that once the initial rush of adrenaline is over you will have new information that is usually useful to have.

7. Outsource, Delegate, Communicate

This category is related to the Prioritization category from above but still contains new and important advice. Usually, lots of tasks end up on our plate that are not really in our area of expertise. We get e-mails asking us to do something, invites to meetings and groups, and so on. The more important you are the more of these requests you will get. A professor has to deal with hundreds a week while a student might get them just from time to time. In any case, it is important to keep your priorities in mind. Often enough, another person is better equipped to do the task than you are and you should delegate it. Specialization is beneficial for everyone in the system after all if applied correctly.

An additional way to outsource parts of your work is through the use of technology. Keeping all different tasks and thoughts in your head without forgetting them costs energy that can't be used for something else. This includes using a calendar for all tasks, setting alarms for important meetings, and using note-taking apps (see Technology Section).

Lastly, I think clear and direct communication improves productivity. This means communicating your priorities to others and asking for clarification when you don't understand something. I have recently started collaborating with someone from a different research group and she directly told me that our project was one of four projects she was working on, currently placed second in her priorities and hence I could expect her to work around 10 hours per week on the project. This was fantastic. I knew exactly what I could expect and could build my own planning around that. I will try to employ this in the future for myself. Clear communication also means asking clarifying questions even if you feel silly for asking them. In a meeting when the supervisor asks "any questions?" everyone looks around and then doesn't ask one because often they feel like everyone else has understood it and they would be the odd one out. After the meeting, they then realize that they still have a lot of questions and everything will take forever just because nobody dared to speak up. If you didn't understand it, you will either have to invest a lot of time explaining it to yourself or ask your supervisor at some point. Both could be easily solved by asking simple questions even if they make you feel silly.

8. Mental Tricks

I think most of us have heard some of the above advice before. We have been told to plan and we have been told to evaluate our progress and so forth. And then we were like "yeah yeah", ignored the advice, and repeated the previous mistakes. Sticking to good habits is hard and adopting new ones is even harder. And one of the reasons why they are hard to follow is because we have to admit that we are imperfect. After all - a perfect version of ourselves wouldn't need to evaluate their mistakes because they wouldn't make them to begin with. Our gut response to mistakes is often rationalization instead of investigation. To account for these and further flaws there are a lot of small mental tricks that I want to explain in the following.

The new homunculus is a thought experiment where you imagine that you have just been placed in your body without any history connecting you to your previous self. You start by cleaning up baggage from your previous self, e.g. by dropping the ineffective projects. Then you ask what this person's (i.e. your) preferences and goals are and steer them towards the right action like you are controlling a robot from within. The important bit is the framing of ignoring the fact that these are your preferences and goals and pretend that they are just somebody’s preferences and goals and that their previous actions and identity is in no way tied to yours.

If you have problems with procrastination pretend that your actions are not stoppable. Netflix & YouTube have optimized their algorithms in such a way that you are likely to binge-watch. Once you started it's hard to stop not because your mental fortitude is low but because the designers of the algorithms know that you have a monkey brain. So if you start an episode, video, or video game don't think "Just one and then I will be productive" but rather pretend that they aren't stoppable, e.g. that if you start you will finish all episodes of that series. And then decide whether you want to engage in that activity. This idea stems from a post on Mindingourway where it is described in more detail.

Connect things that you want to do with things that you should do. Just create more or less random connections between the two, e.g. whenever you put something in the microwave you do ten push-ups, or whenever you want to watch Netflix you first have to read 20 pages in a book. Especially when the old activity is a habit already you can extend that habit to also include your new action. So if you have a habit of drinking coffee at 10 am every day then this becomes the habit of doing 5 push-ups and then drinking coffee every day at 10 am.

Related to the above, you should try to find short-term gratification for long-term rewards. Even though you know that future fitness is a reward for running this knowledge just isn't rewarding on an instinctive level. To bridge the gap you can find all kinds of schemes to reward yourself for keeping up progress towards long-term goals. Whenever you run, treat yourself somehow or use apps like Strava that show your progress and send you nice messages. Even though you know exactly that the app is appealing to your monkey brain it still works wonders.

Choose an accountability partner or create contracts. Tell your partner which goal you are currently trying, e.g. running twice a week, and which punishment you will get if you don't succeed, e.g. donating 50€ to an effective charity of your choice. Suddenly you have more skin in the game and thus more likely to achieve the goal.

9. Expectation Management

I have too often set myself unrealistic expectations, low-key knowing that they are unrealistic, and then still felt bad when they didn't come true. I knew that publishing a paper on a major conference before your Ph.D. is very rare and still somewhat expected myself to achieve it, even though there was no good reason to believe that I'm significantly different than other people who have attempted and failed at the same task. It's like I was running against a wall head first and acted surprised that it hurt.

So whenever you feel like you aren't meeting an expectation make explicit what that expectation is and then think about whether it is realistic. Ask yourself not whether it would be nice to achieve that goal or whether it would make your family and friends proud if you achieved it but rather ask if an anonymous person with your background and conditions should have realistically set the expectation you set yourself. Most of the time the answer is no. Then reset that expectation as if you would recommend it to someone you care about, e.g. it has to be achievable but not trivial to achieve. Maybe aim for a 50% probability of achieving it.

Pressure and deadlines can be a force of productivity but remember that they are a means to a goal and not the goal in itself. If you made the deadline for a conference paper but your paper still contains flaws you have just contributed more noise to the review process. If your paper explores a worthy idea it will still be around for the next conference and be published when it's ready. If you are the type of person that flourishes with deadlines, set them for yourself or let them be set by a colleague, e.g. for an internal paper review. But don't forget that meeting the deadline is not the goal, it is a means to produce a good outcome.

A common tool in expectation management is to reframe your baseline. To steal a fitting analogy from James Clear - Imagine someone is in a wheelchair and you ask them whether the wheelchair restricts their freedom. But they answer that the wheelchair actually gives them freedom. Without it, they would be bound to their home while they can go outside nearly wherever they want due to it. While I always felt that this is just a way to lie to yourself to feel better (after all you are always better off than someone else), it is still true. Very often when you feel like a failure there are tons of people who would switch positions with you without hesitation and there is a large space of situations in which you could be worse off. Instead of only looking at what you could have achieved when all stars align look at what you have achieved under the circumstances you were given.

Setting yourself expectations that you are very unlikely to achieve is a recipe for disaster and so is tying your own self-worth to the achievement of a nearly impossible outcome. It's terrible for your mental health and it's not even effective in achieving your goal. If you have trouble setting realistic expectations first talk about it with a trusted friend and work on solutions or seek professional help. You are not a failure if you don't achieve everything you ever set your mind to and people in your surroundings won't think of you as such. Please get help and save yourself the long periods of self-doubt and depression if you have set yourself too high expectations in the past.

10. Dealing with Guilt

Many of us experience guilt when we are not productive. We might experience guilt when we procrastinate when we take breaks when we don't work over the weekends, and so forth. In the Framing section of this post, I have already argued why guilt is a bad motivator and follow this up here with some ways to deal with guilt.

The first technique is what is called come to your terms by Mindingourway. Whenever you realize that you feel guilty make very explicit what you feel guilty for, e.g. "I feel guilty for taking a break instead of working". Then realize the trade-off that you are making and formulate it explicitly, e.g. "I valued the break higher than the productivity from work". In the final step, you have to come to your terms. You have to think about whether this trade-off is correct and whether you think you acted on the preferred side of the trade-off. You could either say "I value my break higher because I need to rest and regain focus" or "I should have valued working higher because I actually didn't need a break". Both are possible and valid options. It is important to make this trade-off explicit because as we have discussed in the framing section whenever there is a trade-off there will be guilt since guilt occurs due to the absence of achieving a certain goal.

The second technique is called Having no excuses and is a tool to prevent you from rationalizing guilt. Whenever you feel guilty there is a spark of truth to it since you traded something off. When you have chosen to take a break there is a possibility that you could have worked on and thus been more productive. Once you have committed to a specific choice you often start generating excuses and to rationalize your choice - "You must take the break because everyone has to take breaks from time to time or because during breaks you always experience a burst of creativity and thus can be more productive in the future". Note that while this might be true it is just a post-hoc justification for your action and likely didn't follow a set of rules that were specified before the decision was taken, e.g. "take a 10 min break whenever you feel tired". So rather than allowing yourself to generate post-hoc justifications for your actions, Nate Soares, the author of mindingourway.com, argues that you should treat guilt as an indicator. Whenever you feel guilty observe that you do - like a scientist observes their experimental results. Then treat the action as a bet - either you took the bet at the right odds and would take it again even if it didn't work out this time or you took the bet at the wrong odds and you would act differently in the future.

11. Continuous Learning

An important part of productivity that I have already touched upon in the Playing the Long Game framing section is that it is often important to build up productivity capital that feels unproductive at the moment but will unlock higher productivity later.

The second piece of advice is about which skills to improve. There are far more things we could learn than we have time to and so we have to choose wisely. On the first level, one should improve the skills that will likely be necessary for one’s entire career. In my case, as a researcher in ML, I will probably always need math and coding. So it makes sense to practice linear algebra, probability calculus and solve coding puzzles. On the second level, you should sharpen your analytic toolbox. This might be through listening to podcasts like Rationally Speaking which gives you knowledge about very different fields while also teaching you intellectual curiosity and epistemic modesty. There is a long list of things you can do to improve your analytic skills ranging from watching documentaries over reading books to talking to smart people and I think you will start to pick them up along the way once you assume the mental framing from above and walk through life eager to learn more.

12. Procrastination & Attention

Most people struggle with procrastination. Instead of working towards a goal you watch a YouTube video or do something else that isn't productive. And I only found one piece of advice that really made sense to me. Accept that you have a limited attention span. Accept that you will at some point procrastinate. Make it part of your routine. You could have 75 min productive sessions followed by 15 minutes of a break or 30-minute sessions with 5-minute breaks such as in the Pomodoro technique. Furthermore, procrastinate actively not passively. Use procrastination periods to follow a secondary or tertiary goal of yours. Watch a video that teaches you something about that goal, listen to a podcast, or write on your next blog post. I found that procrastination mostly feels bad because you feel like you are completely wasting your time with something utterly useless. Procrastinating actively mitigates this feeling because you are doing something that furthers a goal of yours.

Related to that point is the topic of multitasking. I know we all like to pretend we can multitask but we also know that we just can't do it. Our brain isn't made to do multiple tasks well at once and you will achieve less if you try. Be honest with yourself and accept it won't work even if you try hard. Then design your workflow to always focus on one task at a time and don't switch around all the time.

13. Technology

I have tried a lot of different ways to organize my notes in the last five years. I have tried note-taking apps on my smart phone, paper and physical notebooks and google docs. And I never really stuck with any of them consistently. Then around a year ago an app called Roam Research was hyped among my friends. Given my bad experiences with note-taking apps I was sceptical at first but was then convinced to try it after a long introductory session with a friend. I have used it for half a year now and I like it a lot. Everything is easy and smooth and I always feel like I can create connections, hierarchies, etc. exactly as I want them to be. If you don't want to pay for Roam, there is Obsidian which seems like an exact copy with slightly less features that also has a free version. In any case, I can recommend using one of them, because everyone who is using them says it revolutionized the organization of their work. I know it's just a note-taking app, but the difference it makes is much larger than I expected.

Even though I think that technology has great potential to improve productivity I put it very far down the list. This is simply because technology is just a tool to help you develop a good habit or make it easier to act upon it but won't solve your problem by itself. If you don't take the active mental steps, technology won't help either.

14. Do the obvious

Conclusions

All of this is a lot and reading so many resources on productivity has made me feel like a rather unproductive person. However, I think that there are four main takeaways that lead me to be high-spirited for my future self.

Productivity is a journey, not a goal. There is no day at which you have reached maximum productivity and can stop working on yourself or improving it. Continuously applying the above tips and those from other resources will, with very high probability, increase your ability to reach goals with less effort. Just in the same way the school math felt hard and complicated at some point will you look back and realize the long way you have come. This framing also implies that being productive is not binary. It is not something that comes naturally for others but is inaccessible to you. Rather, everybody has to work on their habits and while some are further than others it is always possible to improve.

Take baby steps. You can't expect to increase your productivity 10x overnight by reading this post and applying everything to your next project. Habits, similar to sports, have to be trained and improved by taking small steps in the right direction and continuously refining your skills. The hard work still has to be done by yourself but the right framing can definitely make it a lot easier. Also, remember that a lot of marginal gains will make a large difference down the line.

Restrict your monkey brain. A common theme among the implementation tricks is to realize when your system 1 makes decisions that don't align with your long-term goals and use system 2 to design a system or process that makes it easier to act according to your expressed goals. A useful tool to remove your subjective biases is to pretend you are just observing what's going on and plan for someone else as if you aren't personally invested.

Don't overcomplicate things. I would estimate that most gains in productivity come from really simple and obvious things. It isn't necessary to optimize every minute of your day with a super complex rule system. Just asking yourself "What do I want to achieve?", "What things are necessary to achieve that?" and "What is the most efficient way to achieve them?" and then start doing it immediately without pushing it into the future probably yields the largest gain already.

If you think this was helpful and know other people who you expect to like it, please share it with them. If you didn't like it please contact me and tell me why.

I want to thank Laurenz and Julian for preaching Roam and Maria for her feedback.

One last note

If you want to get informed about new posts you can follow me on Twitter or subscribe to my mailing list.

If you have any feedback regarding anything (i.e. layout or opinions) please tell me in a constructive manner via your preferred means of communication.

Discuss

### What is the net effect of memes that call out the huge impact of big players in climate change?

23 июля, 2021 - 05:46
Published on July 23, 2021 2:46 AM GMT

I've noticed whenever some government or huge company makes a drastic harm to the environment (or generates the false impression of doing so) this specific kind of meme emerges...

Memes that contrast the huge impact big players have against the insignificant impact regular people have:i.e. Meme with false data after Jeff Bezos space flight.

This got me thinking. What is the effect this kind of meme has on various types of people? I made a quick analysis of around 130 replies of this particular meme on Twitter, and found the following:

• A - 1.5% believe the fact in the meme but they claim they will maintain their environmentalist efforts nevertheless.
• B - 2.3% defend the aerospace industry. (?)
• C - 10.7% refute the incorrect fact quoted in the meme.
• D - 26.7% believe the quoted fact is true and react with either: neutrality, sadness, or laughter. (Not-angry reaction)
• E - 8.4% feel their environmentalist efforts have been pointless.
• F - 3.8% claim they've abandoned environmentalist efforts for exactly things like these.
• G - 12.2% believe the quoted fact is true and react with either: frustration, anger, indignation, frustration, hopelessness or resignation. (Angry reaction)
• H - 13% focus on criticizing billionaires.
• I - 14.5% criticize environmentalist activism, mock it, point out it's foolish, etc.
• J - 6.9% criticize progressive ideas in general, many attack veganism specifically.

Granted, these categorizations are somewhat subjective, I've made them dynamically as I went through the replies trying to group them in relevant ways. This may not be the perfect way of organizing replies. It's just one attempt.

An important clarification: Of course memes don't magically change people's ideas and motivations. e.g.:

• Very likely replies of type I and J come from conservatives, and the meme didn't really cause much more than a few laughs.
• Very likely replies of type H come from progressives who specifically "hated the rich" even before seeing this meme.
• Very likely replies of type E, F and G come from activists who already felt some form of powerlessness, hopelessness or resignation even before seeing this meme.

In other words, I don't think this specific meme can move you from group E to group A, or vice versa.

However, I wonder if the meme could nudge you in some direction.

Maybe it may nudge some E people towards the direction of group A, that is: into action, into motivation to be the change they want to see in the world, what Susan Sontag would call a witnessing moment. Being touched so profoundly by the media in question that you experience a permanent change in your being, your perception of the world, and your motivations. Arguably, this is desirable for environmentalism activists.

Or... maybe the opposite is the case. Maybe an A person may be nudged towards group E. They may not fully transition groups, but they may grow just a little bit more hopeless. A little bit more powerless. She may still belong to A, she may still acknowledge the limited impact of her individual actions and still decide to maintain such actions... but perhaps with a little less conviction than before.

In summary, a single meme won't radically change your position overnight (neither positively —towards action and empowerment, nor negatively —towards hopelessness and resignation). But I wonder if it may push various people into various directions.

Can we predict this?

Can we study the net effect a meme has had, or would have on certain population?

Has something like this been done before?

Personally, I believe these memes have a negative effect of fostering hopelessness that outweighs the positive effect of raising awareness of unjust, powerful actors doing disproportionate harm. But my belief is not the central point I want to make, but rather, posing the question: How can I build a more accurate belief about this topic? How can I find if my current belief ("net-negative effect") is right or wrong?

Discuss

### Re-Define Intent Alignment?

22 июля, 2021 - 22:00
Published on July 22, 2021 7:00 PM GMT

I think Evan's Clarifying Inner Alignment Terminology is quite clever; more well-optimized than it may at first appear. However, do think there are a couple of things which don't work as well as they could:

• What exactly does the modifier "intent" mean?
• Based on how "intent alignment" is defined (basically, the optimal policy of its behavioral objective would be good for humans), capability robustness is exactly what it needs to combine with in order to achieve impact alignment. However, we could instead define "intent alignment" as "the optimal policy of the mesa objective would be good for humans". In this case, capability robustness is not exactly what's needed; instead, what I'll provisionally call inner robustness (IE, strategies for achieving the mesa-objective generalize well) would be put in its place.
• (I find myself flipping between these two views, and thereby getting confused.)
• Furthermore, I would argue that the second alternative (making "intent alignment" about the mesa-objective) is more true to the idea of intent alignment. Making it about the behavioral objective turns it into a fact about the actual impact of the system, since "behavioral objective" is defined by looking at what the system actually accomplishes. But then, why the divide between intent alignment and impact alignment?
• Any definition where "inner alignment" isn't directly paired with "outer alignment" is going to be confusing for beginners.
• In Evan's terms, objective robustness is basically a more clever (more technically accurate and more useful) version of "the behavioral objective equals the outer objective", whereas inner alignment is "the mesa-objective equals the outer objective".
• By making this distinction, Evan highlights the assumption that solving inner alignment will solve behavioral alignment: he thinks that the most important cases of catastrophic bad behavior are intentional (ie, come from misaligned objectives, either outer objective or inner objective).
• However, although I find the decomposition insightful, I dread explaining it to beginners in this way. I find that I would prefer to gloss over objective robustness and pretend that intent alignment simply factors into outer alignment and inner alignment.
• I also find myself constantly thinking as if inner/outer alignment were a pair, intuitively!

My current proposal would be the following:

• Re-define "intent alignment" to refer to the mesa-objective.
• Now, inner alignment + outer alignment directly imply intent alignment, provided that there is a mesa-objective at all (IE, assuming that there's an inner optimizer).
• This fits with the intuitive picture that inner and outer are supposed to be complimentary!
• If we wish, we could replace or re-define "capability robustness" with "inner robustness", the robustness of pursuit of the mesa-objective under distributional shift.
• This is exactly what we need to pair with the new "intent alignment" in order to achieve impact alignment.
• However, this is clearly a narrower concept than capability robustness (it assumes there is a mesa-objective).

This is a complex and tricky issue, and I'm eager to get thoughts on it.

As a reminder, here are Evan's definitions. Nested children are subgoals; it's supposed to be the case that if you can achieve all the children, you can achieve the parent.

• Impact Alignment: An agent is impact aligned (with humans) if it doesn't take actions that we would judge to be bad/problematic/dangerous/catastrophic.
• Capability Robustness: An agent is capability robust if it performs well on its behavioral objective even in deployment/off-distribution.
• Intent Alignment: An agent is intent aligned if the optimal policy for its behavioral objective is impact aligned with humans.
• Objective Robustness: An agent is objective robust if the optimal policy for its behavioral objective is impact aligned with the base objective it was trained under.

So we split impact alignment into intent alignment and capability; we split intent alignment into outer alignment and objective robustness; and, we achieve objective robustness through inner alignment.

Here's what my proposed modifications do:

• (Impact) Alignment
• Inner Robustness: An agent is inner-robust if it performs well on its mesa-objective even in deployment/off-distribution.
• Intent Alignment: An agent is intent aligned if the optimal policy for its mesa-objective is impact aligned with humans.
• Outer Alignment
• Inner Alignment

"Objective Robustness" disappears from this, because inner+outer gives intent-alignment directly now. This is a bit of a shame, as I think objective robustness is an important subgoal. But I think the idea of objective robustness fits better with the generalization-focused approach:

• Alignment
• Outer Alignment: For this approach, outer alignment is re-defined to be only on-training-distribution (we could call it "on-distribution alignment" or something).
• Robustness
• Objective Robustness
• Inner Alignment
• Capability Robustness

And it's fine for there to be multiple different subgoal hierarchies, since there may be multiple paths forward.

Discuss

### The Inside View #4–Sav Sidorov–Learning, Contrarianism and Robotics

22 июля, 2021 - 21:53
Published on July 22, 2021 6:53 PM GMT

Below are the highlighted quotes from the 4th episode of The Inside View Podcast, a rationalist show where we discuss our true reasons for believing what we believe about the future of technology, with a strong focus on AI Alignment.

In this episode, we discuss Sav's experience in Robotics, Contrarianism, Religion, Education, and Friends.

Relevant blogs:

Highlighted QuotesContrarianism

Sav:

“I feel like a lot of us are just being cheated out of the most sort of interesting or beautiful things in this world. You have this track that most people go on, from grade school all the way up to high school, and then to college, then to some kind of professional environment; the “life highway”. And the further along I get, the more I'm realizing that all of the interesting stuff seems to exist off the menu.” (youtube)

Sav:

“There's a way in which the experience becomes less and less interesting the closer it is to some kind of center of society. As a thing becomes more and more mainstream, it becomes boiled down to its simplest, most easy to consume form.” (youtube)Religion

Sav:

“I'd never say that “I believe in the scientific method”. There's something a little bit strange about people saying things like "believe in science". To me, “believe in science” almost sounds paradoxical. Science is not this thing you’re supposed to believe in. It's a mechanism for testing hypotheses, for troubleshooting our world, right? And so it's like, well, “believe in the debugger” or something like that. What if the debugger is wrong?” (youtube)

Sav:

“People crave meaning. And religion seems to be the thing that is best at giving that meaning to people. When you take that thing out, then you end up with all of these different mechanisms that now, all of a sudden, feel the need to give you meaning, right? Now, politics is the thing that gives you meaning, or now science itself is the thing that gives you meaning. And it somehow corrupts all of these things, because it basically turns them into quasi-religions, whereas if you have a religion, these things are free to not be quasi-religious and they can actually function properly.

When your political party doesn't have to be the thing that gives you meaning, we can actually have a much more constructive discussion about what the proper policy should be, because now you're not emotionally attached — your identity is not attached — to these political causes, for example.” (youtube)University

Sav:

“You get to the university, and basically — at least this was my experience — you get told to specialize, right? But imagine you enter this big makerspace. There's all of these tools on the wall, and you can explore all these tools. You can go make whatever you want. And then this guy comes up to you and says, "Well, actually. You're just going to be using the hammer. And there are very useful things you can do with this hammer. If you properly train yourself in using a hammer, you'll be useful to people who actually need somebody with hammering skills." And that's kind of... I don't know. I don’t think that’s a very healthy way of taking advantage of people's skills and interests.” (youtube)Ego

Sav:

“A lot of people say that Steve Jobs had a “reality distortion field”, where he would just say things that weren't true, but he convinced himself and everybody around him that they were true. And sometimes that led him off a cliff, but sometimes actually that led him to push through and persevere and actually achieve these very impressive things. And so, having too big of an ego is certainly a problem: if you are way, way, way more confident than your beliefs actually allow you to be. But if you place yourself slightly above that line, where you're slightly overconfident, I feel like that's a much better position to be, because you'll actually end up pushing through and persevering even if you're wrong, because that energy will just kind of carry you.”
“It's almost like you're distorting your reality a bit, and then you're letting reality catch up to your distortion of reality.” (youtube)Friends

Sav:

“I kind of just approach [Twitter] as making friends. I try not to overcomplicate it. I just look at it as like, “is this the person who I can potentially vibe with and learn things from?” And if the answer is yes, then probably it goes both ways. And so, I kind of use Twitter as a platform for making friends — and maybe there'll be opportunities to work on cool projects with these friends. That's really as deep as it goes for me.”
“The big problem I have with people using the word “networking”, for example, is that it reduces human relations down to this very utilitarian thing. People become resources.
...
And I don't know, it's just— if somebody were to ask you, "Why are you friends with somebody?" and you have a very clear answer to that question, you're probably not friends with that person. You're probably using that person in some way.” (youtube)

Sav (explaining Tim Urban’s “Traffic Rule”):

“If you're hanging out with someone and it comes time to either drive them home or call them a cab or something, you end up hoping for traffic, because it's an excuse to talk to that person more. I found that to be a very good rule of thumb for friends, I think.” (youtube)

Michael (another rule of thumb for friendship):

“If I give you $1,000, is there a way to reach out to you by your mom, your friends, where I can ruin your life if you don't give me back the$1,000? Or are we just too distant for me to have an impact in your life and I would just need to spend $1,000 in plane travel to just get back my$1,000?” (youtube)

Michael:

“I feel like somehow, on a spontaneous or intuitive level, friends — good friends, top friends — would spontaneously reach out to each other. And even if you don't reach out to each other for a while you know that's because the other person is busy and not because the person has lost respect in you or interest in you. I feel like it all boils down to some kind of mutual respect. So then there's mutual respect and knowing that the other knows that, estimating that the other likes you or something, the same way you like him.”
...
“There's this saying: a friendship is some path in the sand where you need to walk every time to make it alive, otherwise it will be erased by the water. I feel like why this path exists is because you'll keep walking over it and going on walks with those friends and having conversations with them. You can try to maintain those relationships as part of a professional network, but it will always be artificial. I think a good rule of thumb is: do you respect this person? Do you think this person respects you? Does this person spontaneously reach out to you? Do you want to spontaneously reach out to them?” (youtube)Psychedelics

Michael:

“Whenever I took psychedelics, I would enter some kind of state where I would want to stay because it was very cool and we had all this vibe going on and we were like, "We're in the same room. We're listening to this music. We hope this stays forever." And then, at some point, the effects go down a bit. You start being tired, you want to eat, or maybe actual life — like there's an actual job that you need to do. They're the distinction between vibing and having fun with your friends, and then actual real life.” (youtube)Beauty

Sav (from “Ayn Rand, The Fountainhead”):

“What you feel in the presence of a thing you admire is just one word – ‘Yes.’ The affirmation, the acceptance, the sign of admittance. And that ‘Yes’ is more than an answer to one thing, it’s a kind of ‘Amen’ to life, to the earth that holds this thing, to the thought that created it, to yourself for being able to see it. But the ability to say ‘Yes’ or ‘No’ is the essence of all ownership. It’s your ownership of your own ego. Your soul, if you wish. Your soul has a single basic function-the act of valuing. ‘Yes’ or ‘No’, ‘I wish’ or ‘I do not wish.’ You can’t say ‘Yes’ without saying ‘I.’ There’s no affirmation without the one who affirms. In this sense, everything to which you grant your love is yours.” (youtube)

Full transcript: https://insideview.substack.com/p/sav

Discuss

### Progress, Stagnation, & Collapse

22 июля, 2021 - 19:51
Published on July 22, 2021 4:51 PM GMT

The Progress Equation

In 1960: The Year the Singularity was Cancelled, Scott Alexander discusses what I'll call the progress equation. The equation states that technological progress increases the carrying capacity of the environment (the size of the largest population which can sustain itself with the resources available), but also, that technological progress is itself proportional to population (more people, more brains, more progress). This turns out to imply hyperbolic growth, with a singularity of infinite population around 2026.

As indicated in the title of Scott's post, the progress equation stopped being a good model of population growth around 1960. My personal interpretation is that this is merely the time when technological growth outpaced population growth: the carrying capacity started rising faster than the exponential growth of the population. You see, the progress equation assumes that the population is always around carrying capacity (we could call this "the Malthusian assumption"). This is usually a pretty fair assumption; populations will boom and crash, but hover around the carrying capacity.

However, once the population is large enough that technological growth outpaces population growth, we should expect to enter into the exponential regime: population and technology grow at a merely exponential pace, so we don't see a hard singularity like we get if we extrapolate hyperbolic growth. More thoughts on this here.

Bronze Age Collapse

I recently watched this video series on the Bronze Age Collapse. It's very speculative, because we know very little about what really happened; in most places, writing and permanent settlement disappear shortly after, making records of the event scarce. One theory is that a powerful armada, "the sea people", simply wiped out every mediterranean civilization at once for some reason. But the theory which the video series focuses on is the "system collapse" theory:

• Over time, civilization builds up infrastructure.
• Infrastructure increases the carrying capacity, and also smooths out small boom and bust cycles.
• However, infrastructure also tends to centralize things, meaning that people become dependent on infrastructure.
• Resources tend to have fixed maintenance costs. As a society gains more infrastructure, maintenance costs increase.
• When a severe bust cycle hits, a society's resources may fall below the maintenance costs of its centralized infrastructure. The infrastructure then fails. Because society has become dependent on this infrastructure, this exacerbates the problem. This can create a domino effect between different types of infrastructure, so everything collapses at once.
• Because Bronze Age civilization was highly dependent on trade, the domino effect also continued across the whole set of bronze-age civilizations across the Mediterranean.

This suggests a modification of the progress equation. Rather than "technological progress" being a number which just counts up as people invent things, we can think of it as requiring some population size to support. When the population dips below that number, technological progress is lost. This further worsens the situation for the remaining population, causing a dramatic crash.

Seeing Like a State

Why does technological progress usually lead to centralization and increasing dependence? By design. Combining insights from Seeing like a State and The Dictator's Handbook, we can see how increasing centralization and dependence is to the advantage of a state. It allows the state to extract resources more efficiently, even if it's not to the overall benefit of the people. (It becomes amazing that we make progress at all, when you think of it this way: it's often to the state's advantage to keep the population ignorant and poor.)

Urban Sprawl

The youtube channel Not Just Bikes provides detailed pro-walkability anti-urban-sprawl rants, by a knowledgeable urban planning enthusiast. In the series Strong Towns (which summarizes points from a book of the same name), it describes how urban sprawl operates in North America. The point that's relevant for us is the infrastructure Ponzi scheme: how cities usually can't presently afford to maintain the infrastructure they build, instead relying on future growth to pay for it. This leads to a cycle:

• Build fancy new stuff to facilitate growth and bring in tax dollars, without worrying about how to pay for maintenance costs later.
• Grow. Rake in tax dollars. Use them to keep up the old infrastructure.
• Panic when the new infrastructure starts to decay, and there's not enough money to pay for its upkeep.
• Return to the first step, building an even larger set of new developments, to bring in growth and therefore tax dollars which will repair older infrastructure.

This works pretty well until it doesn't. Then you get bankrupt cities like Detroit, with crumbling districts slowly returning to nature.

I think you can see where I'm going with this. I don't think I can Bronze Age civilizations had urban sprawl, but I think ancient empires may have fallen into the same growth-dependent "ponzi scheme" as modern cities. This exacerbates the instabilities created by centralized infrastructure. With growth-dependent economics, a crisis can be caused when growth merely slows down too much to keep the Ponzi scheme going.

Will we see collapse?

Is the modern population highly dependent on highly centralized technology? Yes. Do we have growth-dependent economics? Yes. Has that growth been slowing down? Arguably, yes. Are modern civilizations highly interdependent, relying on trade for basic needs? Yes. We've just seen the consequences of a global trade disruption. The prices of everything have recently increased by about 15%. That's a mark-up which many cannot afford. Many items are in severely short supply. (I recently walked into a local bike shop to buy a bike. They didn't have any, except for a few multi-thousand dollar models which I guess they keep around in case a wealthy customer comes in. The shopkeeper told me to check back next week to see if they'd gotten anything in.)

It also just so happens that the US government has been creating money like mad. It's easy to tell a story where the inflation of prices will only get worse, the dollar collapses, and things spiral further out of control from there.

But I think every age has similarly compelling doomsday stories. They've got a fairly bad track record overall. The same pessimistic story could have been told around 2008, and at many other times. If we're going to collapse now, why didn't we then? Centralization and trade-dependence are not new. We have a few advantages which Bronze Age civilization did not.

Discuss

22 июля, 2021 - 19:19
Published on July 22, 2021 4:19 PM GMT

Information asymmetry is a funny thing.

A while ago I created a question asking "Will NASA return a sample of material from the surface of Mars to Earth before SpaceX Starship lands on Mars?".  Currently the odds according to the Metaculus prediction are that there's an 80% chance Starship will land on Mars before the sample-return mission is complete.  How different would NASA's Mars exploration program look if everyone working there believed this to be the case?  What if every American believed this to be the case?

Now Metaculus could be wrong of course, but its predictions are historically well-calibrated and it has a track-record of beating the experts (for example on covid-19).  That means anyone--and specifically anyone reading this post--has access to information that while not "secret" is hardly general knowledge.

Other than writing dire letters to NASA about how SLS is a waste of money because it is likely to succeed at about 2 launches prior to 2030, what can you do with this information?

Consider Metaculus' prediction that the first AGI will be created around 2052.  Or the fact that there is a 30% chance of China annexing Taiwain in the same time frame? Or that there is a 50% chance another cryptocurrency will eclipse Bitcoin by 2026?

Here is the point: many future changes are highly predictable, but people for the most part go about their lives acting as though things will continue more or less the way they always have.  Don't be that way!  Imagine you were born with a superpower that gave you prophetic insight into the future. It wouldn't just be dumb to ignore that power, it would borderline reprehensible.  We have a moral obligation to warn the world around us about the changes that are coming.

To warn them that they are the walking dead.

P.S.

It would be really cool to create a project that studies on which topics Metaculus disagrees most with the average expert (or the average member of the public) and somehow systematically make use of that information (e.g. by making strategic investments).

Discuss

### Covid 7/22: Error Correction

22 июля, 2021 - 17:20
Published on July 22, 2021 2:20 PM GMT

Delta has taken over, and cases are rising rapidly, with a 58% rise this week after a 65% rise last week. There’s no reason to expect this to turn around in the near term.

Three weeks ago, in One Last Scare, I ran the numbers and concluded that most places in America would ‘make it’ without a big scary surge from Delta. It’s time to look at what went wrong with that calculation, which I believe to be a failure to sufficiently integrate different parts of my model.

Then there’s the question of what we are going to do about this, and whether we are going to destroy some combination of free speech and the ordinary day to day activities that constitute our lives and civilization, perhaps indefinitely, in the face of this situation. Such collateral damage has the potential to be far scarier and more deadly than the direct threat from Covid-19.

Let’s run the numbers.

The Numbers Predictions

Prediction from last week: Positivity rate of 4.7% (down 0.1%) and deaths unchanged.

Result: Positivity rate of 4.4% (down 0.4%) and deaths decline by 5%.

Other Result: Positive test counts rose by 58%, versus 65% last week.

I do not understand the divergence between positive test rates and case counts that we see here. This implies that the number of tests is scaling with the number of cases, but that wasn’t true earlier, and also there’s a lot of testing that takes place out of an abundance of caution or to provide the necessary evidence and/or paperwork in various contexts, where the tests clearly wouldn’t scale. So this is weird.

In any case, it seems pointless in this context to make a positivity rate prediction instead of a case count prediction, since it doesn’t tell us what we want to know. Thus, I’m switching to predicting case counts.

There’s no reason to think cases won’t continue to rise in the near term. Control systems should be kicking in and Delta has mostly completed taking over, so the rate of increase should continue to slowly decrease. I’m going to predict a 50% increase, down from 58% this week and 65% last week.

Prediction for next week: 360,000 cases (+50%) and 1845 deaths (+10%).

Predictions will be evaluated against data from Wikipedia, after correcting for obvious data anomalies.

Deaths DateWESTMIDWESTSOUTHNORTHEASTTOTALJune 3-June 97208179154312883Jun 10-Jun 163686119613142254Jun 17-Jun 235294438312632066Jun 24-Jun 305504597061861901Jul 1-Jul 74593296121281528Jul 8-Jul 145323986891451764Jul 15-Jul 214343417321701677

There’s no sign yet that people are dying from the new wave of cases. Most of the rise in cases came in the last two weeks, so we wouldn’t expect a dramatic increase yet, but seeing a 5% rise over the last two weeks is still at least somewhat reassuring that what we’ve seen in places like the UK, where the IFR was dramatically reduced versus previous waves, will also happen here.

If we don’t see a big rise in deaths within the next two weeks, that will be both very surprising and quite excellent news, as there will have been enough time for at least some of the new cases to have resulted in deaths. By three weeks from now we can be confident what new normal we are dealing with, at least as long as the hospitals have sufficient capacity.

The cases remain the story.

Cases DateWESTMIDWESTSOUTHNORTHEASTTOTALMay 27-June 231,17220,04433,29314,66099,169Jun 3-Jun 925,98718,26732,54511,54088,339Jun 10-Jun 1623,70014,47225,7528,17772,101Jun 17-Jun 2323,85412,80126,4566,46469,575Jun 24-Jun 3023,24614,52131,7736,38875,928Jul 1-Jul 727,41317,46040,0317,06591,969Jul 8-Jul 1445,33827,54468,12911,368152,379Jul 15-Jul 2165,91339,634116,93319,076241,556

Last week was a 65% increase, and this week was a 58% increase. Delta has now mostly taken over, so differences are some combination of reporting, timing and testing details, seasonality changes, and control system adjustments. We should expect some people to alter their behaviors by now, and that will accelerate as cases pick up.

Either way, we have a 60% increase week over week as the new baseline, which would represent R ~ 1.4 based on the old assumption of a five day cycle. That remains consistent with the 2.2 multiplier on Delta versus a 1.4 multiplier on Alpha, plus the control system having adjusted to be stable under Alpha.

Vaccinations

Vaccination rates are now roughly stable at about 500k/day, so about 1.5mm people/week going from unvaccinated to vaccinated, or about 0.4% of the population, and a resulting 1% or so decrease in R. If we think Delta is still on a five-day cycle, that’s 1.5% less case growth each week. If Delta is on a three day cycle, it’s 2.3% less case growth each week. More on that in the Delta section.

The good news is that we are seeing more Republicans stepping up and telling their constituents to get vaccinated. That, combined with the threat from Delta, should help, despite the efforts of the Ministry of Truth. More discussion in that section.

Not Necessarily the News

When X is reported on the news, we learn at least three claims to evaluate:

1. X happened.
2. X was noticed.
3. X was news.

Depending on your prior knowledge and model, the same news report can change your world model in opposite directions, and often people get this calculation wrong.

If you see a plane crash reported and you know the media reports most plane crashes, that’s bad news, but it’s not importantly bad news, and it at least means there wasn’t worse news crowding it out. If you see a plane crash reported and don’t know that crashes always get reported, it’s good news because you learn crashes are rare enough to be news.

This brings us to this week’s reports of infections taking place at a wedding.

This led to a bunch of reactions that were at core like this one:

One could also observe that one person dying, or six being infected, was news, and worry perhaps even less than before, given how many weddings there are.

Then again, there was also this, in the comments last week:

Twice is at least suspicious, and these two do seem to be close together in a meaningful way. So it’s at least a little bit news.

Similarly, we have the headline that 27 vaccinated people (it says “nearly 30” but actually it’s 27) in Louisiana died of Covid. The vaccines are so effective that 27 deaths was news, which is rather good news. If you run the numbers on Louisiana, I find roughly 900 deaths in the ‘vaccine era’ starting some time in March, so we’re talking roughly 3% of all deaths, from a group that includes most of the old people for most of that time. Yet I saw people freaking out about this, or wondering whether or not or how much they should be freaking.

We also got more data on the Dutch music festival, where it seems 5% came back infected. It seems the festival was ‘not entirely open air’:

It also involved most everyone being unmasked in surrounding bars and restaurants. So the result here is not surprising, nor is it a warning about the dangers of outdoor events, or of anything but the usual rule of ‘don’t do stupid stuff.’

Delta Variant

I always wonder in cases like this, how did they think a number like 83% gets distributed, and do they think a very slightly unequal distribution is somehow scarier or worse? Similarly, was going from 50% to 83% in two weeks unexpected somehow?

If you assume we started with a 50/50 split with Alpha versus Delta, and then there are three serial intervals since July 3, you get 79%. So this is very close to what you would expect based on my baseline estimates (with no loss of effectiveness from the vaccines, or very little), which is interesting in light of the discussions on Delta potentially being faster, which I’ll talk about next.

BNO Newsroom offers this graph for easy reference, which has >50% in the July 3 bucket and thus has growth almost exactly in line with expectations. And the previous two week period was a slower takeover than one would have expected from the same model:

Also, OurWorldInData turns out to graph this the same way it graphs everything else, and I find their presentation very clean and easy to read:

An interesting and hopeful theory that came up was that Delta might be spreading so quickly in large part because it is faster. Under this hypothesis, rather than there being an average of five days between the time you catch Covid to when you give it to someone else, that interval could be as small as two days. That makes physical sense if the viral loads are much higher, as Delta would need less time to multiply in a new host before being able to spread.

If this was the case, then what looks like much higher rates of infection in graphs, and in the type of analysis that was run last week, is a lot less scary, and the actual R will be much closer to 1 (in theory, in both directions) than I calculated. Doubling every five days previously would have meant R ~ 2, but if that’s two and a half cycles, then it means a much more fixable R ~ 1.31. What otherwise would look like a ‘we’re f***ed it’s over’ scenario might not be one.

What’s the evidence for this? We have this study out of China:

I am deeply confused how a serial interval can be negative. If I understand the words involved that means you spread it on to someone who gets their symptoms before you do? In which case, wow, that’s quite the rapid spread.

It goes on to say this about R:

If R0 went from 2.2 to 3.2 in this type of setting, that’s less than a 50% increase from the original, so it’s only ‘in line’ with the 97% increase reported by Finlay in the sense that they both observed the same rate of increase in cases, except Finlay assumed the old timing of infections and this new study believes things have sped up a lot. Thus, what would have been 97% is now slightly under 50%.

Their graphs are quite good. I wish more papers were 2 pages long with this much useful information:

I’m not fully sold, but it seems likely this is right. We are seeing super duper fast spread of Delta in some places, and not in others, and in some times and not others, such as when India went suddenly from an out of control epidemic to everything stabilizing quickly. Speeding up transmission makes all of that make a lot more sense.

A comment last week pointed to this study of vaccine effectiveness against Delta. I believe it had already been incorporated into the claims assessed last week, but good to explicitly note the primary source. I’m reproducing the bottom-line sections in full, skip if you don’t want to dive into the details.

In general, it’s potentially highly misleading to compare the vaccinated to the unvaccinated in the wild, because the two groups differ in a lot of ways. I’m not entirely sure which direction this goes, as the vaccinated start out with safer behaviors but then change behaviors based on being vaccinated.

Here, we can compare measured vaccine effectiveness between different strains. The obvious worry then is that there could be a difference in which populations are dealing with which strains during this period, which could skew the results as well. These are not controlled experiments. One thing that makes me more confident here is that we see other adjustments and measurements that don’t seem out of whack.

The headline conclusion is then that mRNA vaccines retain 88% effectiveness against positive tests. If we accepted this figure, we’d then need to translate that into a measure of how often such people transmit. With milder cases and lower viral loads, the presumption is that they don’t transmit as effectively, but the flip side is that milder cases mean we might be missing a larger percentage of cases, so the 88% number might be high for that reason. It also might be low or high for several other reasons.

Most of the variance remains in the difference between measurements in different places, but I think all of it points to roughly the same place anyway.

The numbers will come in somewhere in the range where fully vaccinated groups won’t have outbreaks unless they partake in a lot of what I call ‘stupid stuff,’ which is basically (some combination of most of) packing lots of people tightly into indoor spaces without proper ventilation for extended periods. However, it also would mean we’re close enough to the edge that if everything went fully back to normal, we’d need more people vaccinated than we can realistically hope for in the next few months or perhaps ever.

How worried should a vaccinated person be about Delta?

In terms of death, seriously not very much, vaccinated people don’t die of Covid and Delta doesn’t change that. Thread points to a few different claims about whether Delta is deadlier and by how much, but it’s definitely not enough to overcome the vaccinations or even put much of a dent in them.

The question is entirely one of the unknown unknown risks of Long Covid. Getting data on this, or being confident in a position, is incredibly hard, whether it’s on how big the Long Covid risk was to begin with, or how much the vaccines reduce that risk. It would be completely biologically bizarre if the risk wasn’t greatly reduced by the vaccine the same as everything else, but it’s still enough of a worry that one would strongly prefer not getting Covid, if that was an option.

I wish I could do better than that, but I really can’t give one, given what I know. My guess is that we’re talking about a small but non-zero chance (3%?) of some amount of lasting effects of some kind for the vaccinated, most of which are minor and temporary, but yeah, who the hell knows.

For young kids, that’s even more true – the danger is purely Long Covid. There’s a good ‘what’s up with Delta and kids’ analysis up this week, although it doesn’t offer us anything concrete that’s new, and it points out that the Long Covid risks haven’t changed and that other diseases also have similar long tails, we just don’t talk much about them. And that even with unmasked schools and lots of vaccinations elsewhere, we’re not seeing an explosion in cases among those schoolchildren too young for the vaccine, as a percentage of the cases in the population when there’s a Delta wave in the UK:

It is well known that city living leads to more infectious diseases than non-city living, to a very large degree. There’s a small long tail for many of those diseases, where people develop long term problems. One of the periodic reminders, as we move into the next phase of the pandemic and beyond, is that if you are worried about Long Covid as a vaccinated person, why aren’t you completely panicked about living in a city?

That brings us back to the calculations. The spread of Delta in the United States as a share of cases is exactly in line with the 2.2 vs. 1.4 difference from Alpha to Delta, as of earlier this week, which does not leave room for vaccines to additionally lose effectiveness. Then, if it turns out Delta replicates faster, that changes the math once again.

One Last Scare: Re-Evaluation

This week, I was persuaded to add a post-mortem to my big We’re F***ed, It’s Over post from the end of 2020. Reading it over again, I believe the core logic of that post was solid – we were not capable of adjusting the control system sufficiently to contain a 65% more infectious strain in time given the expected rate of vaccinations. I predicted a 70% chance that we had such an infectious strain and that if we did, we would face this crisis and have no reasonable options.

It didn’t happen. Instead we had a 40% more infectious strain, and faster vaccinations, which combined as that post’s model said it would, to prevent the wave. We did better than I would have expected even then, with cases coming down much faster, so there was even more going on than that. In any case, the prediction was too confident, and didn’t properly adjust for uncertainty over speed of vaccinations or seasonality. Perhaps there was even some degree of self-preventing prophecy when you combine it with others’ similar warnings. I do think giving the scenario less than a 30%-40% chance would have been more wrong than giving it a 70% chance, but that numbers over 50% were too high.

This time around, I predicted:

It does look like masks will be around for a while, and might be making a comeback – Biden is considering reinstating them in many situations, or at least trying to do so, and many schools look poised to torture their students this way, and several jurisdictions (including Los Angeles and Las Vegas) are bringing back indoor mask mandates already on their own.

We also aren’t seeing that many calls for anything beyond mask mandates yet, but I do see the beginnings of ‘schools can’t be open’ talk as well. I would like to think we’d never let that fly at this point, but perhaps we would, at least in some places. I do think that if you’re a parent in such a place, and they do try to put your kid in ‘remote learning,’ you should find an alternative even at an extreme cost, and if necessary consider moving.

Regardless of all that, I know that at the time I did not expect this amount of increase in case counts, and thus my model of the future was importantly wrong. What were the errors? What has changed?

First it’s important to know what hasn’t changed: I still have Alpha at 40% more infectious (1.4x) than baseline, and Delta as 120% more infectious than baseline (2.2x). Those estimates are doubtless not exact, but I haven’t seen any reason to adjust them. So that wasn’t the problem.

Nor was the issue (as far as I can tell) that vaccines have lost effectiveness. It does seem like vaccines are slightly less effective against Delta, but I continue to believe this effect is not a big impact. Vaccinated people remain very hard to infect and, when infected anyway, poor carriers with which to infect others. This shouldn’t have moved the needle enough to get noticed.

This is reflected in the growth of Delta as a share of cases, which matches very closely what these numbers imply, and doesn’t leave room for them to be that off in either direction. Similarly, it looks like Delta plausibly replicates faster than we thought, but that probably also would mean it isn’t as infectious and has a lower R, or else the numbers don’t work out.

Thus, I do not think the prediction error was about a misevaluation of Delta. I think the error was about a misevaluation of where things stood before Delta, and what people were up to.

It’s that first sentence, where I start off R at 0.84, instead of factoring in the changes coming from the control system. With the decrease in masks worn and f***s given over the last month or so, combined with seasonality changes, the R without Delta likely went from 0.84 back to at least 1. That’s a 19% difference each cycle, or a 28% per week.

In the world where we had retained the behaviors that were cutting cases in half every three weeks, the current rates of increase would be more than cut in half, and it would be easy to see that additional vaccinations (and some amount of Delta burning out in the younger populations where it’s spreading the most) would reverse the problem before it got into crisis mode, even if our current case starts with R=1 exactly.

However, we’re not in that world, and we’re starting from a higher baseline. That mistake compounds each week, and now only a few weeks later we are where we are, with exponential growth looming quickly.

In short, I think this was mostly a pretty dumb mistake that should have been easy to spot – I knew in one place that we were adjusting things, and then didn’t make that adjustment when I did this other calculation. My models were insufficiently integrated.

The prediction here is then saying something about what happens if we return to the behavior patterns we had when cases were declining rapidly. The extra vaccinations would be sufficient, in most places, to compensate for Delta. The problem is that we’re not doing anything close to that, haven’t for some time, and it would be a hell of a thing to try to return us to that state. Even if we could, that doesn’t mean we should.

That’s also a pretty easy call to make, when one puts it that way. Delta is likely a little over twice as infectious as the original. Over half the country’s adults are vaccinated. Of course that’s enough to compensate. Easy math is easy.

Or, to do the rough calculation another way, Delta cancels out the vaccination of the first 55%-60% or so of the adult population, or the first 46%-50% of the overall population, if there’s no other source of immunity running around. We are currently at 49% fully vaccinated and 57% partly vaccinated, or effectively about 53%. So we’re still ahead, but we’re not that far ahead, and we definitely can’t go back to anything like normal unless we’re willing to accept the consequences.

From here on in, mostly the unvaccinated will be infected, and most of them will be young. Last week, we had 240k positive tests and vaccinated about 1.5mm people. With rapid weekly case growth, it won’t be too long before we’re giving immunity to our unvaccinated youth the hard way, via infections, faster than we can vaccinate people. It’s not the preferred solution, but it does work, and it works fast, especially since it tends to kick in about when other control systems also kick in. Which is why we see rapid increases time and again suddenly turn into rapid declines all of a sudden.

The question is, to what extent are we willing to accept those consequences, versus willing to accept the costs of not accepting them? There’s no longer a reasonable expectation that if we kick the can far enough down the road that something will change, and the consequences of permanently kicking the can seem far, far worse than the consequences when the can is not kicked.

Speaking of which…

Ministry of Truth

So, this happened:

Google is blocking your access to private documents, based on them containing the wrong statements about vaccines. This implies that they are checking your private documents in order to see if they contain such information. You should presume both that your documents in Google Drive are not private when it counts, and also that you could lose access to them at any time.

I already knew about that, but this drove it home. More new is this (link to video):

There’s also this:

A call from the executive branch, for social media platforms to coordinate, and if you’re banned on one of them for ‘misinformation’ you need to be banned on all of them, or the government will take action to break up this private monopoly of a public platform. Also, they need to ‘work harder’ to ‘fight the spread of misinformation’ via censorship and bannings of this type, or again, they will take action to break up this private monopoly of a ‘public platform.’ If they don’t do that, they are ‘killing people.’

However, that’s not how the language works. It is how the language of power works, where one makes one’s statements as ambiguous as possible and states one’s message implicitly whenever one can, so that one can get the message out and then deny sending it.

Meanwhile, from NPR via that column, the following definition of ‘misinformation’ when a Bad Person is providing the information:

Misinformation, it seems, could mean anything that gives an impression the Powers That Be dislike.

Such policies have often taken aim at anything that ‘contradicts the CDC guidelines’ or used other such principles, despite such guidelines often being obvious nonsense.

So, that happened. As usual, think about this apparatus, and this move, in the hands of the outgroup rather than the ingroup, or the hands of the fargroup, if you think that it might possibly ever not be one of the worst possible ideas.

As gentle reminders from earlier in this epidemic, this ‘misinformation’ would at one point have included the fact that masks work, or that the virus could have come from a lab, or that we could expect to perhaps have a vaccine by the end of 2020, or if you go back to February that there was even a Covid-19 problem to begin with or that one should prepare for it, because that’s not only false, it’s also racist. Or that Covid-19 is airborne, or that surface cleaning wasn’t all that important. And that’s purely from the current pandemic and without thinking about what the outgroup would have done with those levers if it had the chance.

Under such a regime – or under the current regime that existed at the time, even – if my posts had been placed on social media, I’d have been banned from all of them many times over. That’s where things already are, now. What happens when ‘misinformation’ increasingly becomes whatever the executive or the media narrative decide they don’t like? And then the executive decides he doesn’t like those who don’t like them, or are saying politically inconvenient things?

Many have noted that the call for government-directed censorship of social media is not only far along on the road to authoritarianism and the end of freedom of speech, it also doesn’t have much prospect of a big impact on its supposed target either. Link is to one such thread. It’s almost as if the government and ingroup establishment are using the ‘emergency’ and the excuse of the pandemic in order to further their goal of becoming the thought police and telling us what we can and can’t say to each other.

Things offline are not entirely better, but in the interests of illustration by example and a desire not to cause a distraction, I’ve censored the example I had previously put in this spot from the past week. Stay on target.

Vaccine Hesitancy

What’s actually going on with vaccine hesitancy (link to CNN post)?

When we ask whether persuasion works, I mean, of course it works. The issue is that you’re not the only one doing persuasion, and also you’re not doing that great a job of it, in the sense that this thing has been massively botched several times over. Persuasion matters, doing it better matters, and what we got reflects how we did at it. And yes, every little bit helps and we might be close to a tipping point.

You know how uninterested we are in persuading people? Not only did we suspend the J&J vaccine over nothing, and recently put another warning on it over another nothing, we’re not even bothering to fully approve the vaccines, with all that this entails. Let alone the other low hanging fruit mentioned at the link.

Let’s not pretend we’re taking this seriously. Matthew also notes this:

This is no different than anything else. Vaccine persuasion is about persuading others that we are Very Serious People who have made the proper sacrifices, rather than asking what would work.

A better question is, how much does persuasion at the margin, now matter? The persuasion that mattered most largely happened by January. Those who were persuaded by then mostly stayed persuaded and got their shots. Those who weren’t largely didn’t change their minds later. But why would they? Yes, some new evidence was presented that vaccines were safe and effective, but also the problem seems far less urgent now. Until that changes, it’s not like we did some great persuading and it didn’t work.

One big piece of evidence is that most old people went ahead and got vaccinated

This looks like a world in which people are doing a calculation to decide whether to get vaccinated – they’re simply doing a different calculation, where the decision is less obvious, and those who most need the vaccine mostly still end up getting it.

This in turn implies that much of the remaining ‘hesitancy’ or even refusal isn’t ‘I’m never doing this no matter what’ and it’s more like ‘I don’t have enough skin in the game so I’d prefer to play it what looks to me like safe and/or not bother and/or not deal with the temporary side effects and/or continue signaling to my in-group.’

Which is great news, because if Delta ends up everywhere, where chances of getting infected if you’re not vaccinated get very high, then one would expect a lot of people to cave and get vaccinated rather than accept getting infected.

And that’s despite some pretty out there world models, even if you subtract Lizardman’s Constant:

That the same 50% of the unwilling believe both that vaccines have been shown to cause autism and that the US government is using them to microchip the population is suggestive that such people are not processing such statements as containing words that possess meanings. They’re simply taking the opportunity to say ‘rar, vaccine bad!’ in any way that’s presented to them. Thus, a lot of them believe both that the vaccines cause autism and also that they’re being used to microchip people. Unless the theory is that it’s the microchips that cause autism?  I kinda want to see the overlap in the crosstabs, I’m expecting to see a lot of it.

Thus, my best guess is that about half the ‘hesitant’ are getable through some combination of things getting bad and us picking the low hanging fruit like approving the vaccines, and the other half likely require stronger stuff.

Once and Future Lockdown

Could it happen again? Janet Yellen thinks so.

Nate Silver mostly disagrees.

Nate’s mistake here is to act as if a cost-benefit ratio is all that relevant to how decisions are made on Covid. Somehow we have decided that ‘forcing’ people to take the Covid vaccine is unacceptable, and that’s that. So our choices are instead forcibly disrupting people’s lives in the hopes that it helps, or not doing that. If things get bad enough, it makes perfect sense that we’d potentially see lockdowns but not vaccine mandates, and that those lockdowns likely won’t make exceptions for vaccination, because we’ve also made it unacceptable to check someone’s vaccination status in most contexts and places.

Where I think Yellen is clearly wrong is in expecting the places with low vaccination rates to be the ones that lock down. It’s almost certainly the opposite. If lockdowns happen, they will happen in the places with relatively high vaccination rates. Not the highest like Vermont, since they’ll have no need for it (probably), but in the various blue states that time and again have gone overboard with prevention. There’s zero appetite for locking down red states.

I’d hope there was zero appetite for locking down anywhere, but I am growing more worried about this possibility. It’s really stupid, because it wouldn’t work. Even if it did suppress Covid entirely in the local area, the moment you stop it comes back, so what’s the point? When will things change? Are you going to keep this up for years?

I do see signs that there’s support for doing exactly that. Some of this is ‘avoid blame on a two week time horizon’ where the fact that the problem never goes away isn’t relevant, but some people really do support permanent ending of life as we know it. I don’t understand why they are so cool with this, it seems like the later stages of a Persona game or something, but it is what it is.

Meanwhile, Biden tells us our young children will be wearing masks, whether they like it or not:

I am getting really tired of this malarkey line about not interfering with ‘scientists’ as if they’re all identical clones who reach all the Officially Scientifically Correct conclusions, and thus one doesn’t have to take responsibility for decisions if you can cite one. You did it, sir. You.

Similarly, Washington Post reports that the Biden administration is debating urging a return to masking for the vaccinated. It would ‘have to come from the CDC’ but they’ve ‘taken a hands off approach to avoid interfering.’

So Biden says this:

…and pretends that this isn’t him giving the CDC an order.

Presumably, this is all an attempt to avoid blameworthiness for decisions that are sure to be unpopular, rather than a bizarrely wrong theory of the scientific nature of public policy.

In Other News

Your periodic reminder that Gain of Function research needs to stop and this is a major test of our civilizational adequacy:

Alternatively, someone could come up with math that could possibly justify these kinds of risks. If someone has done so, I see no signs of that.

Whereas you know what we’re not funding much, even now? Pandemic preparedness.

You’d think they’d wait for the current crisis to be over before failing to prepare for the next one. That is, you’d think that if you hadn’t been paying attention.

As several people pointed out, as a legal matter you can’t actually ask such questions, so we’d put it in the pile of all the things you technically aren’t allowed to ask or consider that we all know employers ask about and consider all the time.

Alex Tabbarok reviews The Nightmare Scenario. Report makes clear the book contains a lot of good concrete information, but nothing that would meaningfully change our model of what happened. Yes, that means all the things mentioned in the review were already in the model. I might read it anyway at some point, but my guess is I will decide that I won’t because I don’t have to.

Update on the Novavax vaccine.

In a strange display of the right thing being done, Taiwan approves a vaccine purely based on immunological data

While I am not looking into such questions in general Because Of Reasons, I did see this notice that one of the Ivermectin studies was withdrawn due to ‘ethical concerns.’ Where the ‘ethical concerns’ in question appear to be ‘massive fundamental discrepancies in the data’ which is a nice way of saying ‘complete and utter fraud.’ Figured I’d pass it along. How this impacts your model of the situation otherwise is up to you – among other things, I didn’t check to see how fundamental this evidence was to the case.

Los Angeles resumes its mask mandate, including for the vaccinated. If it’s back now it’s hard to see what could happen any time soon to get it lifted. If you don’t like it, you may, like many others before you, finally want to look for another place to live.

Las Vegas brings back its indoor mask mandate as well. Las Vegas seems like the place maximally in need of such a mandate, given all the travel and all the poorly ventilated completely enclosed spaces designed to trap you inside for indefinite periods. In that one case I at least kinda get it.

Meanwhile, in the United Kingdom, the Gods demand their sacrifices

Those bastards were deliberately travelling to remote natural locations! If there’s no one near them how will we ever find those bastards?

Australia, of course, decided to try and top that, and I’m not mad I’m just impressed:

Via MR, some notes on Peru, and they may have won the Sacrifice to the Gods competition on sheer sticktoitness. It’s impressive stuff. Other non-Covid stuff after is wild too.

Also in the UK, escalating quickly: Not only First Doses First, Second Doses Too Early Actively Dangerous and Scandalous:

California’s entry isn’t going to get it done, but it’s still quite the display of self-harm:

So her friend has Covid, and is being told to travel back to California so she can get tested locally, because out-of-state tests don’t count for tracking purposes. Explain again how our policies are trying to contain this virus.

MR looks at a report on Oaxcana’s (in Mexico) precautions for travelers. Everything except the masks is clearly useless sacrifices. Tyler speculates that this makes it easier to otherwise be open. That’s possible but my presumption as per usual is that nothing as sensible as that is going on here.

Everything that is not compulsory is forbidden. Everything that is not forbidden is compulsory.

Our Covid prevention efforts, all of them, well OK most of them, in one tweet:

One person with a cameo tests positive, and that’s it. Show’s done. Them’s the rules.

Finally, if this is his official platform and he confirms he wants to build more apartments, I hereby endorse Nate Silver to be the next Mayor of New York.

Discuss

### Blog 2021/07/22 Hobbies and the curse of Spontaneity

22 июля, 2021 - 16:25
Published on July 22, 2021 1:25 PM GMT

I often struggle to come up with ideas of things to do with my friends and significant other. This is in spite of the fact that I generally don't struggle to do interesting things in general, and never seem to struggle to find interesting topics of conversation.

I've come to realise why this is. The things I do on my own generally fall into one of a few categories: work-like things, hobbies which fully immerse me, or spontaneous things.

Work-like things are things which would be work if I didn't want to do them, was forced to do them, or had a schedule for doing them (these are not mutually exclusive categories). They include a lot of hobbyist research on things I'm interested in (a lot of which I have posted to LW). These are unsuitable for doing with other people almost by nature: if someone told me to do something, or if I felt I had an obligation, it would be a form of work. The same goes with me telling someone else to do it. Having another person involved also makes it much more complicated, which makes it more effort, which pushes it towards being work.

Hobbies which fully immerse me are generally my main way to decompress myself after periods of work. Typically this includes video games, TV, reading; also writing, playing and recording music. As I am at least mildly introverted they have the benefit of not including human interaction. They also allow me to occupy my brain with thought without involving the infinite stress of the real world. For the creative parts of these, I have realized that the process is the important part, not the result.

Spontaneous things are often things which my friends organize for me, but which I allow into my life frictionlessly by just saying yes to things. My own whims can also substitute for my friends in some cases. Typical examples: going to a university society meeting for which I had no previous interest, going on a long walk/bike ride.

There is clearly a large amount of overlap here. Most of my hobbies cover at least two, and often more, of these.

I am working towards a solution. It seems the solution is just practice. For many people, large parts of their lives are well-planned anyways. If you're very interested in things like museum or art exhibitions, then this is necessary. This means when they have to plan a group activity, they know how to find something and do it.

A few months ago I had no idea how to plan something like that. I struggled to explain it, but when I reached inside my head for the "plan an activity" it was like being asked "just lift your tail up". There was no visible path in my brain. With some advice and practice I am improving. I suspect things will get easier, if nothing else because I am building a repertoire of things to do (websites that have lots of hikes on them, known venues to visit with people, etc.).

Discuss

### Epistemic Optimism: A Shield Against Error

22 июля, 2021 - 15:33
Published on July 22, 2021 12:33 PM GMT

Concept

People are curious, but they are also demanding. We want to understand and explain the world, but if no true explanations are available, we are prone to believe in false ones, purely so an explanation may be had.

Unfortunately, the world is inherently complex and difficult to understand. Therefore, we are frequently confronted by situations where the only explanations available are badly false, and it would be better to believe none of them, and pursue alternatives.

But given our drive to explain; this is an uncomfortable psychological position to assume.

The discomfort of absent explanations can, however, be at least partly assuaged by Epistemic Optimism; the belief that your knowledge (local epistemic optimism), and/or the knowledge of some broader unit with which you identify, like humanity or the scientific community (global epistemic optimism), is rapidly growing.

People with strong Epistemic Optimism are more likely to believe that they themselves, or another entity they identify with, will soon have an answer to phenomena they are presently unable to explain. This helps to satisfy their drive for an explanation, in situations where all available explanations are unsatisfactory; helping them to avoid falling into serious falsehood, and encouraging them to pursue alternative, more fruitful, theories.

An Example

In “Religion and the Decline of Magic”, Keith Thomas discusses the nature of magical beliefs in English society during the 16th and 17th centuries, and their intersection with religious (and scientific) philosophies. A recurring theme of his book is the explanatory role filled by such beliefs. When relatives sickened and died within days, when the rhythm of crucial rains failed, when nature glowered at you with all its monumental strangeness (of comet, lightning, and the night’s canopy itself); who would not demand an explanation? And who would not exploit such demands; with motives innocent or malevolent? In this time, belief in alchemy, astrology and folk magic flourished from the top to bottom of society, thanks in large part to their claim to explain, and regardless of their unimpressive evidential content.

But such beliefs would not hold court forever. Another philosophy, the “mechanical philosophy” of science and “rationalism”, spread throughout society. At first, this philosophy had many drawbacks, amongst the most notable being that it provided an at most partial explanation for existing phenomena. An astrologer could explain every event in your life through the confluence of the planets. But a scientist could not (and, for the most part, still cannot).

However, the mechanical philosophy had advantages, the chief of which was its ability to understand and explain an (at first relatively limited) set of phenomena, to conclusively demonstrate this understanding through empirical tests; and to expand the set of such conclusively explicable phenomena at a remarkable pace

It is this final characteristic which, I believe, was crucial to reconciling individuals to the mechanical philosophy, despite its lack of explanatory completeness. Though science could not explain much, the boundaries of what it could explain were expanding rapidly. The sceptic, therefore, could soon expect the explanations he craved; and if alacrity appeared implausible, could at least content himself with the thought that explanations for recalcitrant phenomena did exist, and would be arrived at in time, if not in his time.[1]

If science teaches us anything, it’s that most scientific theories will be proven false. Put bluntly, if Newton were to have waited until he had a “true” understanding of the fundamental laws of physics before committing to a theory, he would have died waiting. Better in most cases to provisionally believe the best-supported (false) explanation, seek more evidence, then modify ones’ beliefs when that evidence is received. If this is true, we rarely need to withhold judgment, and hence would rarely benefit from Epistemic Optimism.

In one sense, I do not disagree with this position. In any given case, it will be a question of wisdom as to whether an explanation is well supported enough to be worth holding despite its flaws, or whether suspension of judgment would be advisable.

However, there are strong reasons why people ordinarily underestimate the evidential bar they should set for (provisional) belief in a theory. Specifically:

1. Overestimation of the value of explanation
We have a strong psychological desire for explanation. This encourages us to believe even egregiously false things, on the basis of explanatory utility alone, even if better alternatives are easily discoverable, and even if those beliefs might lead us into dramatic mistakes if incorrect.

2. Confirmation bias
Once (even provisional) belief is vested in a theory, we risk automatically and unconsciously engaging confirmation bias; powerfully entrenching the theory, regardless of its epistemic virtue. This is not helped by the fact that many bad theories are bad because they are self-reinforcing and unfalsifiable: once trapped in their web, escape may be difficult or impossible. Once one believed in astrology, for instance, there were any number of celestial bodies whose influence could be retrospectively cited to explain away failed predictions.

3. Curiosity Stopping
Belief (even provisional belief) can easily be a curiosity stopper, preventing us from pursuing alternative – and more fruitful - theories and evidence. Alternatively, a belief that better explanations do exist, and can be discovered, is a curiosity enabler – holding out the possibility of knowledge, but only if we strive for it.
Can’t Epistemic Optimism also be a curiosity stopper?

On the face of it, Epistemic Optimism can be a curiosity stopper. After all, if you believe that your preferred meta-theory will generate an explanation for any question you care to name, you may be content to simply sit back and wait for it to do so, rather than seeking an explanation yourself. By a similar token, Epistemic Optimism can shield a belief from warranted criticism; explanatory power is a key theoretical virtue, a theory which lacks it is diminished relative to those which possess it, and that fact should not be shied away from.

My answer here would be to distinguish varieties of Epistemic Optimism along two axes. The first axis distinguishes between passive and active Epistemic Optimism. Passive Epistemic Optimism entails a belief that outstanding explanations will be produced automatically and mechanically for the Optimist, without the need for the Optimist to do anything themselves. Active Epistemic Optimism involves believing that such explanations will be produced by an active process, ideally involving the direct participation of the optimist. An example of Passive Optimism would be a belief that outstanding explanations will be produced by divine revelation. An example of Active Optimism would be a belief that they would be produced by experimentation (ideally, in the field in which the optimist is an expert) would be a good example of Active Optimism.
Second, Epistemic Optimism can be warranted or unwarranted: that is to say, supported by evidence that it has produced – and is continuing to produce – outstanding explanations, or lacking such evidence. Again, Optimistic beliefs derived from future revelation and experimental discovery can be usefully contrasted.

Unwarranted passive Epistemic Optimism is clearly a curiosity stopper. However Epistemic Optimism that is warranted and active ought not to be. In fact, it ought to encourage curiosity; if you believe you can generate outstanding explanations by participating in an epistemic process, your desire to find an explanation will motivate you to undertake that process. Likewise, the fact this process has and is still yielding concrete results, evidences the promise of future explanation, elevating it above a mere tactic of deflection[2]. It also enables that process to be “falsified” if it ceases to produce new explanations, opening the door for meta-curiosity about the best epistemic processes, where warranted.[3]

Conclusion

If you have found this article at all, you probably possess an unusually strong drive to explain and understand the world. That drive is a valuable impetus for discovery. But it can also spur us into the morass of deceptive belief; from which escape may be impossible.

One way to at assuage our explanatory drives, without hasty commitment to bad explanations, is to adopt an attitude of Epistemic Optimism; to believe that we (or some broader process we identify with) can and will generate good future explanations, where present alternatives are tendentious or non-existent.

Though a passive and unwarranted Optimism is as unsatisfactory as any false belief; an active and warranted Optimism is an attitude worth cultivating – a shield against falsehood, and a fair wind at the back of engaged and confident curiosity.

[1] Incidentally, this sense of Epistemic Optimism is still a key supportive force for materialistic explanation – for instance in the field of consciousness. Dualism explains the “weird” properties of mental phenomena in ways that modern science has not yet been able to satisfactorily capture. However, many philosophers and scientists (justifiably, in my view) retain a materialistic attitude to consciousness, in part from a belief that answers will be provided by the scientific system in time.

[2] Though it would of course still be better for your theory to actually explain phenomena, rather than hold out a warranted prospect of future explanation.

[3] Of course, I want to avoid true Scotsmen as much as the next Welshman. To ensure I am not raising a semantic point against a substantive argument, it is worth asking, does Warranted Active Optimism naturally lead to Unwarranted Passive Optimism? This is certainly possible. Science, for instance, can easily be reified into an impersonal and inevitable force; rather than the messy, contingent, fundamentally human process that it is. However my intuition is that both types of Epistemic Optimism represent widely divergent modes of thought: as different as their exemplary embodiments – the scientist and the revelatory believer.

Discuss

### Fire Law Incentives

22 июля, 2021 - 15:30
Published on July 22, 2021 12:30 PM GMT

Pacific Gas & Electric is planning to spend $15-30b to bury power lines. I see why they're doing it: PG&E equipment sparked some of the worst fires in California history, including the 2018 Camp Fire which destroyed Paradise, but I'm not convinced that this is good for California overall. Historically, the area used to burn periodically. We haven't allowed this for about a century, and flammable materials have been building up. It's all very likely to burn at some point, and burying power lines mostly just reduces the chance that it will be triggered by PG&E. Prescribed burns, spreading out the combustion and moving it to safer times of year, would reduce fire risk far more for the money. Even though when PG&E pays for something the money comes from their customers, CA residents, this isn't a tradeoff PG&E is in a position to consider. The problem is that CA law puts too much focus on sparks: if you start a fire, you are fully liable for its damage. This approach makes sense in most places, where a "we will never let it burn" policy is practical. In ecosystems adapted for periodic burning, however, where flammable materials build up over time, it means everyone is trying not to be the legally recognized cause of the inevitable fire. And it makes prescribed burns look expensive because when one goes out of control, which there is always a risk, that puts the fire control organization on the hook for the full costs. Let's work on a system of laws and policies which lead to minimizing overall fire damage. Discuss ### Jaynesian interpretation - How does “estimating probabilities” make sense? 22 июля, 2021 - 10:42 Published on July 21, 2021 9:36 PM GMT In Professor Jaynes’ theory of probability, probability is the degree of plausibility about a thing given some knowledge and not an physical property of that thing. However, I see people treating the probability of heads in a coin flip as a parameter that needs to be estimated. Even Professor Jaynes gives the impression that he is “estimating the probability” or looking for “the most plausible probability of heads” in page 164 of his book. How does the idea of ”estimating a probability from data“ or finding the “most probable probability of heads in a coin flip given some data” make sense from this paradigm? Thank you for your time Discuss ### Handicapping competitive games 22 июля, 2021 - 06:00 Published on July 22, 2021 3:00 AM GMT [epistemic status: thing I thought of while falling asleep and just wrote up] Suppose you’re playing a competitive game. By that, I mean a game where there are multiple players, and each is trying to win by beating the others. An example of a game like this is Go. But, if you think about it, soccer is also kind of like this: each ‘player’ is composed of a team of people, and the two ‘players’ are competing against each other. We’ll say that that also counts. Sometimes, you’d like to play a competitive game with a friend or multiple friends, but the problem is that one of you is stronger than the other. It’s easy to see what this means in Go, and in the case of soccer, you could imagine that you’re part of a pre-set team, and so are your friends, and it wouldn’t be as fun to swap people between teams to even it out (perhaps because e.g. the teams are based on where you live). This is kind of sad because it means that by default, the stronger player or team will predictably win, which makes it a bit less fun. A way to get around this is by handicapping the stronger player: giving them some disadvantage so that the weaker player has a decent chance of winning, even if the stronger player tries their best. In Go, the standard way of doing this is to have the weaker player start with some well-placed stones already on the board. I don’t know how exactly this works in soccer - perhaps by having the stronger team play with fewer members than usual? If you’re in this situation, but you don’t know the standard way to handicap - for instance, if you’re me and the game is soccer - it might be useful to have a taxonomy of ways to handicap games to choose between. Or if you’re bored of the standard way of handicapping, a taxonomy might inspire you to create new ideas. In this post, I’ll detail what I think is an exhaustive taxonomy. To think about how to handicap competitive games, I find it helpful to think about what a competitive game is. I think that a competitive game is specified by the following things: • A starting state • A number of players in the game • A set of options for what the players can do at any point • A win condition • A ‘transition function’, which determines how the game state changes after each player does something • An ‘observability function’, which determines what each player can see about the game state These all provide candidates to tweak. Let’s go thru them one by one. One way of handicapping is to change the starting state in order to give one player an initial advantage. This is how I’d think about handicapping in Go: the weaker player starts with more stones on the board than the stronger player [1]. In soccer, you could imagine the kick-off happening closer to the stronger team’s goal, which might make it easier for the weaker player to score. I’d say this is usually a good option. I don’t think it really makes sense to vary the number of players in the game - in soccer and Go this wouldn’t make much sense, and in general it’s hard to see how this would help the weaker player relative to the stronger player. Changing the set of options for the players can be a possible handicapping scheme. In Go, it’s hard to see how to do this without significantly changing the game - the closest thing I can think of is banning the stronger player from playing on certain points on the board, or maybe forbidding the stronger player from killing or cutting groups. I think it makes more sense in soccer, however: one team could accept a limitation on how fast they can run. In order not to change the game, I imagine it will usually look like narrowing the option set for the stronger player, since enlarging the option set for the weaker player seems like it would significantly change the game. The win condition can be a promising way to handicap, especially for points-based games like Go and soccer: one can simply add some number of points to the weaker player’s total at the end, before deciding the winner [2]. Especially in Go, I think this handicap system is under-used - in my opinion, it changes the game less than giving the weaker player handicap stones on the board at the start. However, it’s less clear how to apply it in games that do not determine the winner by keeping a score, such as chess. The transition function is pretty core to the identity of a game, and therefore not to be trifled with. That being said, it’s possible that minor tweaks could provide a decent handicapping system - for instance, one could imagine a high-tech soccer ball that acted as tho it was heavier when the stronger team kicked it and lighter when the weaker team kicked it, or a version of Go where 5% of the time the stronger player’s move was replaced by a random move. The observability function seems easier to tweak while retaining the character of a game. In Go, for example, one player could be required to play without seeing the board, only hearing the coordinates of each move and saying the coordinates they would like to play on. A less extreme case would be to use technology to allow the weaker player to see which stones are which colour, but make the stones’ identities invisible to the stronger player. In order to adjust the degree of handicap, one could change the number of moves in which this observability constraint applies. For soccer, you could imagine requiring one team to wear glasses that slightly distorted their vision. That concludes my list of aspects of a game to tweak for handicapping. But there’s one more crucial ingredient that goes into playing a game - the computation available to each player. One can handicap a player by limiting the computation they have available. For instance, in Go, one could use asymmetric time controls, where the weaker player gets more time to think about their moves than the stronger player. In soccer, this could look like requiring all members of the stronger team to use earplugs, so that they can’t communicate with one another as easily [3]. I think this is an exhaustive taxonomy. I also think it’s useful: as far as I’m aware, most ways of handicapping fall pretty cleanly into just one of these, and it’s helped me come up with handicap ideas (in the process of writing the post). I hope you also find it useful. [1] If the weaker player gets to choose where to put the stones, then this isn’t quite just a modification of the starting state. But normally the stones are put in a set position. [2] This could also be seen as a modification of the starting state. [3] This also changes the observability function, but I think that’s not its main effect. Discuss ### ($1000 bounty) How effective are marginal vaccine doses against the covid delta variant?

22 июля, 2021 - 04:26
Published on July 22, 2021 1:26 AM GMT

Since I got vaccinated I’ve started working in-person, going to restaurants, travelling and whatever. But the delta variant might change this risk calculation. Some of the same buzz that got covid right early on, the first time around, is now buzzing about delta potentially being very bad.

So I’m exploring ways of responding to that. This time around I’d rather stand up and fight than lock myself in a house for a year. And so this question explores one possible approach.

There’s evidence that two vaccine doses are more effective than one (though there might be much higher diminishing returns than is commonly acknowledged). I’ve also heard that past infection confers marginal immunity even in the presence of vaccination. Further, as far as I understand, the 2-dose schedule for many vaccines is more “a common thing that we happened to test in the big trials” rather than “the dose level at which marginal benefit = marginal risk”. (And giving your population more than two doses is probably much harder logistically.)

Hence: getting more than the standard 1-2 vaccine doses might be a way to protect against delta.

I live in California, and we currently seem to have a large vaccine surplus. I heard a vague rumour about a friend basically just walking into a pharmacy and getting a 3rd shot. There’s also RADVAC, which you can make yourself.

So, if there is supply... how worthwhile is it to get more vaccine doses? Should you get a 3rd, 4th, … or even more? How does getting a further dose of the same vaccine compare to getting the first dose of a different vaccine?

I’m posting a bounty of $1000 for answers that change my mind on this question (maybe increasing to many times that if this proves valuable). The ideal thing I want would be a graph with the x-axis showing # doses, and the y-axis reduction-vs-control of the following four parameters: • Symptomatic infection • Hospitalisation • Death • Long covid (Answers are of course impacted by the combinatorial search space of dose spacing / dose number / dose size / vaccine type / various demographics, but I won’t make any special restrictions here for now) I will pay at least$1000 for answers that help me get clarity on this, split at my full discretion in proportion to how useful I find the answers. I’ll pay out the bounty on a rolling basis as answers come in; there is no deadline. In case this proves fruitful and there seems to be useful marginal work, it’s possible I will increase the bounty a lot (i.e. up to many thousands of dollars), or reach out to work with some answerers as contractors.

Discuss