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In Defense of Politics

41 минута 16 секунд назад
Published on April 10, 2020 7:26 PM GMT

I wrote most of the following post in 2013 and cleaned up the draft to publish it now. If the post feels a bit dated, that's why.

Politics is seen as the mindkiller. Alyssa Vance from http://rationalconspiracy.com argues:

Politics is suspicious as the best means to ends for other reasons. [...] It’s also zero-sum, and extremely competitive. So much human and social capital is going there already, yours probably won’t make a dent.

Is that true? Is it impossible for the very smart person without much resources to make a dent?

Years ago I was talking with someone from an East European country about how they demonstrate to get rid of their government. When asked for a detailed background on the reasons of why the particular government should go they said that the reasons aren't well described by any English source.

I suggested that instead of spending their time being the 10,001 person attending a demonstration they should write up the reasons why the government is bad in a detailed manner and publish it in the Guardian's Comment is free section.

In that case, knowledge of the politics of the country and good writing skills would have been enough to make a dent.

When it comes to whether their government steps down, obviously it matters how other countries see the situation in the country. For someone who thinks that the government should step down, it should be obvious that it's important that the best reasons why the government should step down should be available in English.

If you look at how Mubarak lost political power in Egypt, people in the West who thought their media informed them didn't really understand the situation.

They were unaware of the relevant politics. They didn't know that according to US embassy cables released by wikileaks:

Academics and civilian analysts painted a portrait of an Egyptian military in intellectual and social decline, whose officers have largely fallen out of society's elite ranks. They describe a disgruntled mid-level officer corps harshly critical of a defense minister they perceive as incompetent and valuing loyalty above skill in his subordinate

The Egyptian military runs part of the Egytian economy. The IMF pushed for typical Washington Consensus policies and as a result a lot of private business came into Egypt and made money. What happened to the military?

Lee Sustar wrote in February 2011 The roots of Egypt's uprising:

The top military brass, however, hasn't cashed in on Egypt's economic growth to the same extent as private businesses. Indeed, military officials see the privatization of state-owned enterprises as a threat to their economic standing and political clout.

If you look at that situations it much easier to understand why the Military didn't do something against the protesters that brought down Mubarak but now takes strong measures against the Muslim brotherhood. The West wanted to see the fall of Mubarak as a sign that democracy won instead of seeing it as a sign that the military got what they wanted.

Why does that matter to someone who wants to create political change? It shows that the general public is horribly informed about what goes on in the political sphere.

Before Bradley Manning submitted files to Wikileaks, Wikileaks dealt with information about corruption in Kenya. By leaking the Kroll report they contributed significantly to a different party winning the 2007 election.

Why do those examples from Egypt, Kenya and East Europe matter? You might say, of course people who spread information can change things in those countries but what about the USA?

If that sentiment resonates with you it should make you think about whether the USA is democratic in a meaningful way. Democracy is about citizens being able to affect the political process through free speech.

Why might you think such a thing? To be a politician in the US congress you need to gather a lot of money from donors. The Democratic party recommends that new US congressmen spend 4 hours of their day raising money.

Fortunately in practice the amount of time seems to be not that extreme. After all, fundraising isn't fun. Congressman have reelection rates of 90% so why spend so much time in an activity that isn't fun.

The problem is that it's not easy to reach politicians directly. A lot of people try it and it's going to be hard to get sufficient attention from a US congressman to explain to him in detail why a certain policy is wrong.

What options do we actually have to create political change? Julian Assange had arguably a large political impact given that he's a single person who's not that rich.

What's his self professed goal? Justice. Defining justice itself isn't an easy task. We can however observe that sometimes there are groups that do something that isn't in the interest of the broad public.

If one member of a group of 100 people thinks that the group is engaging in injustice that is a valuable data point. Groups where every member thinks the group is doing good should be able to outcompete groups where some members think the group engages in injustice.

That's where Assange comes into play. He wants to empower that single individual that thinks the group is injust. Assange also made the observation that if a group spends a large amount of resources on keeping certain information secret that corresponds to the harm that the group will suffer should the information become public.

He makes the further observation that if it's possible for a single individual who thinks that a group is unjust to bring down the whole group, members of the group won't share all information with each other anymore. When it becomes harder to share information inside the group the group has to effectively pay a secrecy tax. That means that the group is less effective in its battle against other groups in society that don't have to pay that tax because their members believe in their course.

Assange articulated that theory with using graph theory in a paper titled Conspiracy as Governance in 2006. From there he goes to accept every whistleblower that wants to contribute an internal document. He founded Wikileaks for that purpose. In some sense you could say that he took the idea that "everything that can be destroyed by truth, shall be" very seriously.

What is the problem with that political theory of change? It's not that it is ineffectual. It's that it is powerful enough that you get into problems. Assange made some powerful people really angry and as a result he sits now in the Ecuadorian embassy.

I personally don't want to do that kind of politics. I don't want to attack other groups in a manner which endangers myself. That's my conscious choice. The founding fathers of the US payed the price of their victory in blood. But how do you do effective politics without harming yourself? To answer that question it's useful to look at the foundation of our current political orthodoxy.

Milton Friedman wrote in the 1982 preface of Capitalism and Freedom

Only a crisis—actual or perceived—produces real change. When that crisis occurs, the actions that are taken depend on the ideas that are lying around. That, I believe, is our basic function: to develop alternatives to existing policies, to keep them alive and available until the politically impossible becomes politically inevitable.

Milton Friedman developed a lot of political policy ideas that had a big impact. He knew that you can't just go and demand the government to change. Usually the government changes when there some form of crisis. When in crisis mode politicians want to be seen as doing something about the crisis.

As I wrote above, politicians don't really want to engage in hard work. That means that someone else often needs to have done the intellectual legwork beforehand. Politicans seek experts and then take the policy ideas from those experts.

Friedman and a lot of think tanks specialised themselves into developing policy ideas in details. There days there are think tanks that have ready made bills. A congressman can just take the bill and copy paste it without having to do any work.

Thinks tanks have the advantage that they can spend money on feasible developing model bills.

We however have seen that Wikipedia managed to outcompete the Encyclopedia Britanica. There in principle nothing that stops a smart programmer from building a platform that provides for model bills that change society for the better.

The hard part will be around getting contributors to focus on practical effects of policies and move beyond idealism.

Some demands just aren't realistic and have no chance of being implemented. On the other hand I think there a lot of room for smart policies that are simply superior to existing policies.

Once you talk about whether different policy tools work, you also have a discussion that's a lot less mind-killed.

In building such a tool you have to make certain choices. You can build it as a hosted website. You can also build it as a distributed platform that runs on home computers.

Given that you don't want outside interference from NSA, you want a distributed architecture.

Why do these thoughts matter, even if you don't want to invest a significant amount of time into politics? Many readers of LessWrong are software engineers or move in the startup space. As such they are in a position to make important architecture choices. When deciding whether or not a software that you design relies on a central US server that stores all user data you are making a political choice.

I don't want to say that cloud architecture is inherently politically wrong but if the only reason you decide for a cloud architecture is because it's a cool buzzword, you are doing political evil. If you then complain that congress doesn't implement your pet political idea, you should look at yourself and judge the extent to which you are part of the problem.

Whenever you make big architecture choices in software that affect society you should think in detail about how that will change the power balance in society. Commenting on random reddit posts about the evil of software patents isn't being politically active.

To the extent that LessWrong is full of intelligent 20-somethings I would predict that in 10 years people of this group will be in positions to make influential architecture choices. The only way to make those responsible is to understand the political effects.

But what about those things that aren't in your direct sphere of effect? Think about policies. Even if you don't have a ready-made bill that a congressman can copy and paste, ideas about policy are important.

Zeitgeist Addendum is for example a political movie that opposes the status quo. It has some popularity according to some people who hate the status quo. Even if you disagree that the status quo is bad the policy ideas that the movie pushes are dangerous.

One of them is that all economic decisions should be made by a "scientifically" designed central computer who knows what's best for everyone. What about the problem of people not wanting to do what the central computer tells them? People only would do that if they are selfish, and people who get the right education won't be selfish and thus work for the common good and recognize that it's the common good to do what the "scientifically" designed central computer tells them to do.

Here we know about the problems of FAIs, however many people on the internet don't. Because of lack of well argued alternatives to the status quo they might follow insane ideologies like the one articulated in Zeitgeist Addendum. Unfortunately I have seen smart people advocate Zeitgeist Addendum as a means of being contrarian.

Mencius Moldbug effective at changing political opinions despite not writing in a way that optimized for attracting a large audience. Moldbug is right when he argues that most people don't understand politics. Moldbug solution of rolling back all social progress is still bad. You can meaningfully say that ending slavery was good. The case goes for legalising homosexuality.

I don't have a good answer of what our political system should look like. But I do think that's an important question. Discussing it is worthwhile. If we find a design that nicely fits together there are a lot of people who hate the status quo and who gladly take your political philosophy if they don't have to do the leg work of thinking up the fundamentals themselves.



Discuss

App-Based Disease Surveillance After COVID-19

1 час 15 минут назад
Published on April 10, 2020 6:52 PM GMT

With the smartphone apps for location and contact tracing being normalized for fighting coronavirus, it opens up space for using the same techniques to fight other diseases.

HIV, Syphilis, and drug resistant gonorrhea are obvious candidates because they do not have non-human hosts.

Instrumentation would be easy. Generate a database entry when an individual is diagnosed with one of these pathogens, acquire phone data via disease surveillance authorities, use machine learning directed at that data to identify plausible intimate contacts, and send notifications with testing requirements, thrown against a database of most recent tests.

Mandate response (in the usa this would be analogous to a subpoena) to a notification (appearing at a clinic for testing and treatment) and enforce it as aggressively as corona isolation.

From an ethics standpoint, if app based corona tracing is ethical, this is ethical too. From an effectiveness standpoint, this would likely aid in the eradication of two scourges (Syph, GC) and the close control of a third (HIV).

Please let me know why I'm wrong, or alternatively, if this could be expanded even further to trace other social ills.



Discuss

How to evaluate (50%) predictions

2 часа 56 минут назад
Published on April 10, 2020 5:12 PM GMT

I commonly hear (sometimes from very smart people) that 50% predictions are meaningless. I think that this is wrong, and also that saying it hints at the lack of a coherent principle by which to evaluate whether or not a set of predictions is meaningful or impressive. Here is my attempt at describing such a principle.

What are predictions?

Consider the space of all possible futures:

If you make a prediction, you do this:

You carve out a region of the future space and declare that it occurs with some given percentage. When it comes to evaluating the prediction, the future has arrived at a particular point within the space, and it should be possible to assess whether that point lies inside or outside of the region. If it lies inside, the prediction came true; if it lies outside, the prediction came false. If it's difficult to see whether it's inside or outside, the prediction was ambiguous.

Now consider the following two predictions:

  • A coin I flip comes up heads (50%)
  • Tesla's stock price at the end of the year 2020 is between 512$ and 514$ (50%)

Both predictions have 50% confidence, and both divide the future space into two parts (as all predictions do). Suppose both predictions come true. No sane person would look at them and be equally impressed. This demonstrates that confidence and truth value are not sufficient to evaluate how impressive a prediction is. Instead, we need a different property that somehow measures 'impressiveness'. Suppose for simplicity that there is some kind of baseline probability that reflects the common knowledge about the problem. If we represent this baseline probability by the size of the areas, then the coin flip prediction can be visualized like so:

And the Tesla prediction like so:

The coin flip prediction is unimpressive because it assigns 50% to a subset of feature space whose baseline probability is also 50%. Conversely, the Tesla prediction is impressive because it assigns 50% to a subset of future space with a tiny baseline probability. Thus, the missing property is the "boldness" of the prediction, i.e., the (relative) difference between the stated confidence and the baseline probability.

Importantly, note that we can play the same game at every percentage point, e.g.:

  • A number I randomize on random.org falls between 15 and 94 – 80%

Even though this is an 80% prediction, it is still unimpressive because there is no difference between the stated confidence and the baseline probability.

What's special about 50%?

In January, Kelsey Piper predicted that Joe Biden would be the Democratic Nominee with 60% confidence. If this prediction seems impressive now, we can probably agree that this is not so because it's 60% rather than 50%. Instead, it's because most of us would have put it much lower than even 50%. For example, Scott Alexander gave him just 20%, and BetFair only ~15% back in March.

So we have one example where a 50% prediction would have been impressive and another (the random.org one) where an 80% prediction is thoroughly unimpressive. This shows that the percentage being 50% is neither necessary nor sufficient for a prediction being unimpressive. Why, then, do people say stuff like "50% predictions aren't meaningful?"

Well, another thing they say is, "you could have phrased the predictions the other way." But there are reasons to object to that. Consider the Tesla prediction:

  • Tesla's stock price at the end of the year 2020 is between 512$ and 514$ (50%)

As-is, this is very impressive (if it comes true). But now suppose that, instead of phrasing it in this way, we first flip a coin. If the coin comes up heads, we flip the prediction, i.e.:

  • Tesla's stock price at the end of the year 2020 is below 512$ or above 514$ (50%)

Whereas, if it comes up tails, we leave the prediction unchanged.

What is the probability that we are correct from the point of view we have before the flip? Well, at some point it will be possible to evaluate the prediction, and then we will either at a point outside of the small blob or at a point inside of the small blob. In the first case, we are correct if we flipped the prediction (left picture). In the latter case, we are correct if we didn't flip the prediction (right picture). In other words, in the first case, we have a 50% chance of being correct, and in the latter case, we also have a 50% chance of being correct. 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src: local('MathJax_Size4'), local('MathJax_Size4-Regular')} @font-face {font-family: MJXc-TeX-size4-Rw; src /*1*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/eot/MathJax_Size4-Regular.eot'); src /*2*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/woff/MathJax_Size4-Regular.woff') format('woff'), url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/otf/MathJax_Size4-Regular.otf') format('opentype')} @font-face {font-family: MJXc-TeX-vec-R; src: local('MathJax_Vector'), local('MathJax_Vector-Regular')} @font-face {font-family: MJXc-TeX-vec-Rw; src /*1*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/eot/MathJax_Vector-Regular.eot'); src /*2*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/woff/MathJax_Vector-Regular.woff') format('woff'), url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/otf/MathJax_Vector-Regular.otf') format('opentype')} @font-face {font-family: MJXc-TeX-vec-B; src: local('MathJax_Vector Bold'), local('MathJax_Vector-Bold')} @font-face {font-family: MJXc-TeX-vec-Bx; src: local('MathJax_Vector'); font-weight: bold} @font-face {font-family: MJXc-TeX-vec-Bw; src /*1*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/eot/MathJax_Vector-Bold.eot'); src /*2*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/woff/MathJax_Vector-Bold.woff') format('woff'), url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/otf/MathJax_Vector-Bold.otf') format('opentype')} p that the future lands in the small blob, the chance for our prediction to be correct is exactly

(1−p)⋅12+p⋅12=12

Importantly, notice that this remains true regardless of how the original prediction divides the future space. The division just changes p, but the above yields 12 for every value of p.

Thus, given an arbitrary prediction, if we flip a coin, flip the prediction iff the coin came up heads and leave it otherwise, we have successfully constructed a perfect 50% prediction.

Note: if the coin flip thing seems fishy (you might object that, in the Tesla example, we either end up with an overconfident prediction or an underconfident prediction, and they can't somehow add up to a 50% prediction), you can alternatively think of a set of predictions where we randomly flip half of them. In this case, there's no coin involved, and the effect is the same: half of all predictions will come true (in expectation) regardless of their original probabilities. Feel free to re-frame every future mention of coin flips in this way.

This trick is not restricted to 50% predictions, though. To illustrate how it works for other percentage points, suppose we are given a prediction which we know has an 80% probability of coming true. First off, there are three simple things we can do, namely

  • leave it unchanged for a perfect 80% prediction
  • flip it for a perfect 20% prediction
  • do the coin flip thing from above to turn it into a perfect 50% prediction. Importantly, note that we would only flip the prediction statement, not the stated confidence.

(Again, if you object to the coin flip thing, think of two 80% predictions where we randomly choose one and flip it.)

In the third case, the formula

(1−p)⋅12+p⋅12=12

from above becomes

0.2⋅12+0.8⋅12=12

This is possible no matter what the original probability is; it doesn't have to be 80%.

Getting slightly more mathy now, we can also throw a biased coin that comes up heads with probability q≠12 and, again, flip the prediction iff that biased coin came up heads. (You can still replace the coin flip; if q=13, think of flipping every third prediction in a set.) In that case, the probability of our prediction coming true is

0.2⋅q+0.8⋅(1−q)

This term takes values in the interval [0.2,0.8]. Here's the graph:

Thus, by flipping our prediction with some probability other than 12, we can obtain every probability within [0.2,0.8]. In particular, we can transform an 80% probability into a 20% probability, a 30% probability, a 60% probability, a 78.3% probability, but we cannot make it an 83% or a 13% probability.

Finally, the formula with a variable prior probability and a variable flip chance is (1−p)q+p(1−q), and its graph looks like this:

If you fix p, you'll notice that, by changing q, you get the y-value fluctuating between p and 1−p. For q=12, the y-value is a constant at 12. (When I say y-value, I mean the result of the formula which corresponds to the height in the above picture.)

So it is always possible to invert a given probability or to push it toward 50% by arbitrarily introducing uncertainty (this is sort of like throwing information away). On the other hand, it is never possible to pull it further away from 50% (you cannot create new information). If the current probability is known, we can obtain any probability we want (within [p,1−p]); if not, we don't know how the graph looks/where we are on the 3d graph. In that case, the only probability we can safely target is 50% because flipping with 12 probability (aka flipping every other prediction in a set) turns every prior probability into 50%.

And this, I would argue, is the only thing that is special about 50%. And it doesn't mean 50% predictions are inherently meaningless; it just means that cheating is easier – or, to be more precise, cheating is possible without knowing the prior probability. (Another thing that makes 50% seem special is that it's sometimes considered a universal baseline, but this is misguided.)

As an example, suppose we are given 120 predictions, each one with a correct probability of 80%. If we choose 20 of them at random and flip those, 70% of all predictions will come true in expectation. This number is obtained by solving 0.2q+0.8(1−q)=0.7 for q; this yields q=16, so we need to flip one out of every six predictions.

What's the proper way to phrase predictions?

Here is a simple rule that shuts the door to this kind of "cheating":

Always phrase predictions such that the confidence is above the baseline probability.

Thus, you should predict

  • Joe Biden will be the Democratic nominee (60%)

rather than

  • Joe Biden will be not the Democratic nominee (40%)

because 60% is surprisingly high for this prediction, and similarly

  • The price of a barrel of oil at the end of 2020 will be between $50.95 and $51.02 (20%)

rather than

  • The price of a barrel of oil at the end of 2020 will not be between $50.95 and $51.02 (80%)

because 20% is surprisingly high for this prediction. The 50% mark isn't important; what matters is the confidence of the prediction relative to the baseline/common wisdom.

This rule prevents you from cheating because it doesn't allow flipping predictions. In reality, there is a universally accessible baseline, so there is no formal way to detect this. But that doesn't mean you won't notice. The list:

  • The price of a barrel of oil at the end of 2020 will be between $50.95 and $51.02 (50%)
  • Tesla's stock price at the end of the year 2020 is between 512$ and 514$ (50%)
  • ⋯ (more extremely narrow 50% predictions)

which follows the rule looks very different from this list (where half of all predictions are flipped):

  • The price of a barrel of oil at the end of 2020 will be between $50.95 and $51.02 (50%)
  • Tesla's stock price at the end of the year 2020 is below 512$ or above 514$ (50%)
  • ⋯ (more extremely narrow 50% predictions where every other one is flipped)

and I would be much more impressed if the first list has about half of its predictions come true than if the second list manages the same.

Other than preventing cheating, there is also a more fundamental reason to follow this rule. Consider what happens when you make and evaluate a swath of predictions. The common way to do this is to group them into a couple of specific percentage points (such as 50%, 60%, 70%, 80%, 95%, 99%) and then evaluate each group separately. To do this, we would look at all predictions in the 70% group, count how many have come true, and compare that number to the optimum, which is 0.7⋅# predictions in that group.

Now think of such a prediction like this:

Namely, there is a baseline probability (blue pie, ~60%) and a stated confidence (green pie, 70%). When we add such a prediction to our 70% group, we can think of that like so:

We accumulate a confidence pile (green) that measures how many predictions we claim will come true, and a common wisdom pile (blue) that measures how many predictions ought to come true according to common wisdom. After the first prediction, the confidence pile says, "0.7 predictions will come true," whereas the common wisdom pile says, "0.6 predictions will come true."

Now we add the second (70% confidence, ~45% common wisdom):

At this point, the confidence pile says, "1.4 predictions will come true," whereas the common wisdom pile says, "1.05 predictions will come true."

If we keep doing this for all 70% predictions, we eventually end up with two large piles:

The confidence pile may say, "70 predictions will come true," whereas the common wisdom pile may say, "48.7 predictions will come true."

Then (once predictions can be evaluated) comes a third pile, the reality pile:

The reality pile is counting how many predictions did, in fact, come true. Now consider what this result means. We've made lots of predictions at 70% confidence for which common wisdom consistently assigns lower probabilities. In the end, (slightly more than) 70% of them came true. This means we have systematically beaten common wisdom. This ought to be impressive.

One way to think about this is that the difference between the confidence and common wisdom piles is a measure for the boldness of the entire set of predictions. Then, the rule that [each prediction be phrased in such a way that the confidence is above the baseline probability] is equivalent to choosing one of two ways that maximize this boldness. (The other way would be to invert the rule.)

If the rule is violated, the group of 70% predictions might yield a confidence pile of a height similar to that of the common wisdom pile. Then, seeing that the reality pile matches them is much less impressive. To illustrate this, let's return to the example from above. In both cases, assume exactly one of the two predictions comes true.

Following the rule:

  • The price of a barrel of oil at the end of 2020 will be between $50.95 and $51.02 (50%)
  • Tesla's stock price at the end of the year 2020 will be between 512$ and 514$ (50%)


Bold, therefore impressive.

Violating the rule:

  • The price of a barrel of oil at the end of 2020 will be between $50.95 and $51.02 (50%)
  • Tesla's stock price at the end of the year 2020 will be below 512$ or above 514$ (50%)

Not bold at all, therefore unimpressive.

... and that is why you should not be able to phrase 50% predictions in the opposite way arbitrarily.

Note that the 50% group is special insofar as predictions don't change groups when you flip them, but the principle nonetheless applies to other percentage points.

Summary/Musings

According to this model, when you make predictions, you should follow the confidence > baseline rule; and when you evaluate predictions, you should

  • estimate their boldness (separately for each group at a particular percentage point)
  • be impressed according to the product of accuracy ⋅ boldness

Boldness is not formal because we don't have universally accessible baseline probabilities for all statements lying around (50% is a non-starter), and I think that's the primary reason why this topic is confusing. However, baselines are essential for evaluation, so it's much better to make up your own baselines and use those than to use a model that ignores baselines (that can give absurd results). It does mean that the impressiveness of predictions has an inherent subjective component, but this strikes me as a fairly intuitive conclusion.

In practice, I think people naturally follow the rule to some extent – they tend to predict things they're interested in and then overestimate their probability – but certainly not perfectly. The rule also implies that one should have separate groups for 70% and 30% predictions, which is currently not common practice.



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The Value Is In the Tails

3 часа 14 минут назад
Published on April 10, 2020 4:53 PM GMT

I have a friend who makes a (great) living as a professional gambler. He's originally from outside the US, but was living there during 2016 and, like everyone else in our friend group, started following the election. Since he loves to gamble, he also decided to bet on the election.

I'm not sure what his political leanings are exactly, but he's a sensible guy and made fun of Trump like the rest of us. But he also ended up betting on Trump to win, and won enough money to pay for a trip to Houston to watch the Super Bowl live in January (flights+hotel+tickets...I told you he does well).

When I asked him why he bet on Trump, he said he didn't really understand American politics, but that it seemed extremely complicated, with random things like whether it rains in certain parts of Pennsylvania potentially being important. If it's extremely complicated and arbitrary, then it's probably also hard to model. The projections he saw had the most likely outcome being a moderate win by Hilary, but since he didn't trust the models, he figured the value was in the tails: the models are likely off, which likely affects the tails more than the mean. So a landslide win by Hilary or a Trump victory seemed undervalued by gambling markets. He wished he could hedge his bets by betting on both outcomes (as opposed to a moderate Hilary win), but the only option he had was to bet on Trump to win.

This logic has stuck with me ever since: in the face of large uncertainty, we're probably getting the tails wrong more than the mean. The value is in the tails.

For a fast-moving, highly uncertain crisis -- like COVID-19 --the value being in the tails means that we're probably underestimating the risk. We're probably getting the worst-case scenarios, and the odds of them happening, wrong, and we should be weighing our expectations more heavily towards worst-case scenarios than what models, expert assessments, etc. tell us. The virus could suddenly mutate (like SARS did in a good way), there could be compounding natural disasters, the economic recovery could look more like an L than a U.

I don't claim to know how we should be responding to the epidemic, in terms of stopping the spread of the virus or combating its economic impacts, but whatever we do, we should assume the virus' damage will be worse than we think.



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The One Mistake Rule

5 часов 18 минут назад
Published on April 10, 2020 2:50 PM GMT

Epistemic Status: The Bed of Procrustes

If a model gives a definitely wrong answer anywhere, it is useless everywhere. 

This principle is doubtless ancient, and has doubtless gone by many names with many different formulations.

All models are wrong. That does not make them useless. What makes them useless is when they are giving answers that you know are definitely wrong. You need to fix that, if only by having the model more often spit out “I don’t know,” if you want the model to become not useless.

Of course, a wrong prediction of what is probably going to happen is not definitely wrong, in this sense. An obviously wrong probability is definitely wrong no matter the outcome.

The origin of this particular version of this principle was when me and a partner were, as part of an ongoing campaign of wagering, attempting to model the outcomes of sporting events.

He is the expert on sports. I am the expert on creating models and banging on databases and spreadsheets. My specialty was assuming the most liquid sports betting market odds were mostly accurate, and extrapolating what that implied elsewhere.

First we would talk and he would explain how things worked. Then I would look at the data lots of different ways and create a spreadsheet that modeled things. Then, he would vary the inputs to that spreadsheet until he got it to give him a wrong answer, or at least one that seemed wrong to him.

Then he’d point out the wrong answer and explain why it was definitely wrong. I could either argue that the answer was right and change his mind, or I could accept that it was wrong and go back and fix the model. Then the cycle repeated until he couldn’t find a wrong answer.

Until this cycle stopped, we did not use the new model for anything at all, anywhere, no matter what. If a new wrong answer was found, we stopped using the model in question until we resolved the problem.

Two big reasons:

If we did use the model, even if it was only wrong in this one place, then the one place it was wrong would be the one place we would disagree with the market. Fools and their money would be soon parted.

Also, if the model was obviously wrong here, there’s no reason to trust anything else the model says, either. Fix your model.

This included tail risk style events that were extremely unlikely. If you can’t predict the probability of such events in a reasonable way, even if those outliers won’t somehow bankrupt you directly, you’re going to get the overall distributions wrong.

This also includes the change in predictions between different states of the world. If your model predictably doesn’t agree with itself over time, or changes its answer based on things that can’t plausibly matter much, then it’s wrong. Period. Fix it.

You should be deeply embarrassed if your model outputs an obviously wrong or obviously time-inconsistent answer even in a hypothetical situation. You should be even more embarrassed if it gives such an answer to the actual situation.

The cycle isn’t bad. It’s good. It’s an excellent way to improve your model: Build one, show it to someone, they point out a mistake, you figure out how it happened and fix it, repeat. And in the meantime, you can still use the model’s answers to help supplement your intuitions, as a sanity check or very rough approximation, or as a jumping off point. But until the cycle is over, don’t pretend you have anything more than that.

 

 



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Organizing a Group Buy of Flour

6 часов 8 минут назад
Published on April 10, 2020 2:00 PM GMT

We just had a huge shift from people eating out to eating in, and now a lot more people are baking, so we and a lot of people I know have had trouble finding grocery stores with flour in stock. The residential supply chain is struggling to keep up with demand. On the other hand, the commercial supply chain is massively under-utilized: so many restaurants that had been buying 50lb bags of flour regularly are now buying none. What if those of us with households that go through a lot of flour bought some from commercial sources? Not only would we have flour, but there would be less pressure on residential supplies and so more for others as well!

I looked around, and I found that Webstaurant would ship a pallet of flour, 50 bags of 50lbs each, for $1,081 or $0.43/lb. I wrote to friends, wrote to work mailing lists, posted on FB, and found other people that were interested. Enough people wanted flour that I decided to place the order when it was only about half accounted for, since I was getting about 10 more people each day.

Unfortunately, between placing the order and them shipping it they ran out of stock:

Not only were they out of the flour I'd ordered, they were out of all-purpose flour entirely. I considered ordering high-gluten flour, since a lot of people wanted flour for making bread, but bread isn't the only thing people cook and coordinating back with everyone would be a lot of work.

One of my coworkers pointed out that Baldor, a local specialty foods distributor, was now making home deliveries as long as you'd order at least $250 worth. Their flour was a bit fancier (King Arthur Sir Galahad, which is commercial King Arthur All-Purpose) and so more expensive, but their shipping was cheaper so it was still only $0.56/lb. I placed an order for 27 bags on Wednesday and wrote to people letting them know about the change and asking if they wanted to cancel their order; none of them did.

Thursday morning I set an old pallet out on our porch and put plastic sheeting on it, so I'd be able to fully wrap the bags. They arrived an hour later, and they were great about doing a fully contactless delivery. They carried the bags up, put them on the pallet, and left.

I wrapped the bags in plastic to keep them dry and keep animals out:

I told people they'd arrived, people paid me via paypal/zelle/check, and people started coming to pick them up. One thing I hadn't anticipated was how interested animals would be in the flour, and how inconsistent people would be about wrapping the bags back up again. Some people were great, but others weren't. I wrote to people again asking them to please pick up today if they could and to be careful about putting the plastic back, and moved the bags of people who said they couldn't pick up today ginside.

As of this morning I still need four people to pay, and six people to pick up their bags, so we're in good shape!

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COVID-19 response as XRisk intervention

13 часов 30 минут назад
Published on April 10, 2020 6:22 AM GMT

Thank you to the reviewers of this post: Seán Ó hÉigeartaigh, Robin Hanson, Anders Sandberg, Allison Duettmann, Andre Ornish, Jessica Taylor, and Blake Borgeson.

---------

Effective Altruists and rationalists are some of the only people in the world tracking existential risks (XRisks). And so it’s understandable that some of us would ask: “Is this a distraction?” Surely a crisis that ends many lives pales in comparison to crises that could end all of them.

However, this line of reasoning might make long-termists lose sight of a large opportunity: we can build XRisk prevention capacity *through* pandemic response efforts. Through fighting COVID-19, we can:

1. Train ourselves

2. Forge alliances

3. Establish credibility

4. Grow the global risk movement

5. Create XRisk infrastructure

I’ll go through these by category.

Training Ourselves

Now that we’re dealing with a live global catastrophe, there’s a chance to develop our skills and ability to coordinate for future global catastophes.

Here are some particularly relevant areas for skill growth:

Forecasting: Create predictions of what might happen and be proven right or wrong.

Scenario-planning: Run inside-view and outside-view simulations of your proposed COVID-19 intervention, act accordingly, and then improve your simulation abilities based on what actually happened.

Coordination: Pandemic response efforts will often require that you collaborate with a wide variety of different actors, just like with other global risks.

Persuasive argumentation: This skill can be built through convincing others that your project is worth supporting, or through influencing decision-makers.

Networking: Navigate the complicated world of crisis-response to find others to aid and work with. Your project might require you to form connections with epidemiologists, with front-line responders, with public officials, with manufacturing plants, with fellow activists, and more.

Project management: Develop the ability to set and meet deadlines at a moment when they really matter. Learn how to lead other people. Find and use the best tools for tracking tasks. Document and curate best practices (this can help other projects as well).

Perhaps the greatest opportunity here is for EAs and rationalists to improve their existing comparative advantage: applied epistemology. A live crisis affords epistemic growth opportunities unmatched by many other contexts. You need to work with spotty information and a rapidly unfolding situation. You must process signal from unreliable sources. You need to make decisions over uncertainty.

This is an opportunity to:

• Get rapid feedback on your decisions and predictions.

• Ground abstract models in concrete experiences.

• Understand how various canonical tools fare in a real-life situation (back-of-envelope calculations, conducting meta-analyses on RCTs, doing reference-class forecasting)

In other words, it’s an opportunity to practice rationality with immediate practical upside.

Forging alliances

Just about every sector of society is mobilizing itself to fight the pandemic right now. This gives EAs and rationalists an “in” to form alliances that can be useful for preventing future global risks. In just a few weeks of working on the crisis, my extended network has come to include crisis-response experts, epidemiologists, philanthropic foundations, lobbyists, manufacturing firms, and intelligence community members.

Let’s say you care about AI safety in particular. Some alliances you might forge now include:

• Policy experts at the White House’s Office of Science and Technology Policy (OSTP). OSTP is now working with AI teams at Microsoft and Google to analyze thousands of scientific papers on COVID-19. Many policy experts are stretched thin and would value additional research support. And in one notable case I’m unable to post publicly, EAs are already offering it!

• Researchers across various AI groups like Deepmind that are working on AI-based pandemic-response initiatives.

• Competent volunteers you meet through activist efforts. For instance, the best initiatives tend to have at least one solid project manager or ops person. Nearly every AI safety initiative benefits from having competent PMs and ops people, so this is a chance to source new talent.

Establishing credibility

You know what’s useful for convincing people that you can competently address a global crisis? Competently addressing a global crisis. Useful resources are given to those who have demonstrated the sorts of prescience and intelligence that EAs and rationalists can demonstrate right now. Credit is already being assigned to rationalists and Silicon Valley people whose public material demonstrated better tracking of the virus than most established institutions. One attempt I like in this direction is epidemicforecasting.org from a team at the Future of Humanity Institute.

Notable demonstrations of competence on particular problems can often grant more general credibility. E.g., as with Nassim Taleb’s prediction of the 2008 financial crisis and Nate Silver’s predictions during the 2008 election.

Conversely, the EA and rationalists communities stand to *lose* credibility for inaction during the pandemic. The optics of tepidly responding to a global crisis after being the community that always ranted about global crises are...not good optics.

Of course we could also lose credibility by getting in the way of trained professionals during this one. But I believe EAs are pretty good at being helpful instead of hinderful.

Growing the global risk movement

The category of “global catastrophe” is no longer just an abstract idea. It’s a tangibly experienced reality. This ought ideally make it much easier to get more resources devoted to GCRs and XRisks in the future.

It is easy to imagine useful movement-building efforts. For example, now would be a good time to volunteer as marketing staff for Toby Ord’s new book, The Precipice.

With this crisis, there may be the potential to cause larger culture-shifts. Historians speculate that the Enlightenment emerged from the Thirty Years War and that the Renaissance emerged from the Plague. This new plague offers an opportunity for shifting culture toward a new renaissance – ideally one which has long-termism as a central value.

Importantly, this may not happen automatically. It’s possible that we’ll miss the opportunity to draw the connection between COVID-19 and the broader category of global risk. It’s also very possible that the broader culture will fail to blame COVID-19 in part on short-termism, and miss out on valuing EA ideas around long-termism. Here, we must helpfully intervene.

Global institutions like the IMF and IBRD emerged from Breton Woods immediately after World War II. Toward the end of the pandemic, there is going to be a short window for policy proposals and new global institutions. Likewise, there will be a limited window where it’s possible to introduce narratives that change the broader culture. To meet these windows, we need to start policy and narrative work during the crisis.

Creating XRisk infrastructure

There are plenty of exciting ideas out there for creating long-term XRisk infrastructure out of COVID-19 but not very many people acting on them. Here are just a few:

• Improving prediction markets: Prediction markets like Metaculus could benefit from more quality forecasters, increased organizational capacity, and extended influence media (e.g., media coverage and credibility amongst policy-makers).

• Increasing community world-modeling capabilities: This might simply include stimulating more quality analysis via a crisis which requires understanding many relevant parts of the world. Or it might include the building and improvement of tools like Guesstimate.

• Creating prize competitions: One idea I’ve seen is creating a prestigious prize like the Nobel for outstanding contributions to global risk reduction. One could start awarding these during the current crisis and build it into an ongoing institution post-crisis. This could make it not only more acceptable, but also widely virtuous to make contributions to global risks such as future pandemics, nuclear war, and AI safety.


In summary, there is ample opportunity to treat COVID-19 as an XRisk capacity-building intervention. If you find yourself interested in acting on this opportunity, here are a few resources:

The Effective Altruism Coronavirus Discussion Facebook Group

Coronavirus Research Ideas for EAs from Peter Hurford

A database of EA responses to COVID-19, organized by Michal Trzesimiech

The Resilient Socieities Initiative. We’re interested in hearing proposals from EAs and rationalists even if they’re at the idea stage.



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[U.S. Specific] Free money (~$5k-$30k) for Independent Contractors and grant recipients from U.S. government

15 часов 7 минут назад
Published on April 10, 2020 5:00 AM GMT

[crossposted from the EA Forum]

Epistemic Status: I spent the past couple of days looking into this, but there is far more that I don’t know than that I do. I may be wrong about some of this, and this certainly has gaps and omissions. If you know something I don’t, or you think something here is wrong, please say so in the comments.

tldr: The US government is giving away several hundred billions of dollars to small businesses as part of COVID-19 stimulus. If you are an independent contractor who received income in 2019, you are eligible for (on average) $10,000 to $30,000. The process for applying for that money as an independent contractor is somewhat tricky, and so this post is to guide you through it.

We expect money to be allocated on a first come, first serve basis, and to run out fast, so we recommend that you follow these steps ASAP. From what we can tell almost all banks stopped accepting applications for the business-version of this program within 6 hours of opening their applications, so time is really of the essence here. For independent contractors this program starts on April 10th (tomorrow), for everyone else it started on April 3rd.

Peter Hurford made a post about this same program two weeks ago where he mostly talked about the opportunities for charities. This post is about the opportunities for independent contractors and sole proprietors, which should also include many grant recipients.

This post is broken into two sections, the first describes how to apply for a EILD loan (likely worth at minimum something like $1000), and the second describes how to apply for the paycheck protection program (probably worth ~0.2 * your annual income for 2019).

EILD programWhat is an EIDL?

EILD stands for “Economic Injury Disaster Loan”, and is described here.

While the term used is “loan”, the “loan” in question does not have to be repaid, making this effectively a taxfree grant.

In response to the Coronavirus (COVID-19) pandemic, small business owners in all U.S. states, Washington D.C., and territories are eligible to apply for an Economic Injury Disaster Loan advance of up to $10,000. This advance will provide economic relief to businesses that are currently experiencing a temporary loss of revenue. Funds will be made available following a successful application. This loan advance will not have to be repaid.

Small Business Administration (emphasis mine)

How to apply for an EILD loan (~ 10 minutes of work)

To apply, just fill out this application on the SBA website.

The paycheck protection programWhat is the paycheck protection program?

Here is a four-page government-issued fact sheet about the program. Here is a more readable summary by one of the companies that is issuing loans:

The Paycheck Protection Program (PPP) is a key section within the recently passed Coronavirus Aid, Relief and Economic Security Act (CARES) Act that allocates $349 billion for small business (< 500 employees) loans to support payroll and certain other expenses. Loans are available for up to 2.5 times of your average monthly payroll during the year preceding the application, with a maximum loan of $10 million. If employers maintain their payroll and use loan funds for allowed expenses like payroll, rent and utilities for the first 8 weeks after the loan is issued, the loan amount is forgiven. The PPP is retroactive to February 15, 2020.

You are eligible for an amount equal to [(yearly income for 2019) / 12] * 2.5 . For many independent contractors, that could be on the order of $10,000 to $20,000,

By default, this is a loan, which you are expected to pay back. However, 100% of the principle is eligible for loan forgiveness, so long as you spend the <= 75% of the total amount on payroll costs (i.e. pay the money to yourself). Given loan forgiveness, this is essentially a tax free grant.

You can apply for forgiveness of the loan 8 weeks after you receive it, and the process seems pretty simple. (According to some article that I read 2 days ago, but can’t find at the moment.)

This page has regular updates about the program.

Instructions for independent contractor applying to the Paycheck Protection Program (~ 1 hour of work)

The PPP loans are distributed by SBA approved lenders. You’ll have to apply through one of them to receive a grant.

If you have an existing business account with a bank, apply via your bank

This is the standard way to apply for a PPP loan. Unfortunately, most banks are only serving existing customers who have a business account with them.

If you do have a business account with a Bank, follow the instructions on your bank's website.

However, most banks will have a lending cap, which will be quickly filled, therefore you may want to apply via the methods under the “if you don’t have a business account” in addition to applying with your bank.

If you don’t have a business account

Since most of us will not have a business bank account, we’ll have to apply via other methods.

First, apply via Kabbage

You can currently submit a full application via Kabbage (a fintech startup). Given that this is a full application, and not merely an interest form, it is one of the venues that I am most optimistic about. (On the other hand, the fact that they have an open application, and have for several days, suggests that many people may have applied and are “ahead of you” in line, which is reason to be less optimistic.)

  1. Go to this site: https://www.kabbage.com/paycheck-protection-program-loans/, and fill out the application.
  2. Once you have applied they will ask you to upload some documents. This includes a picture of your driver's licence, a 2019 schedule C form from your taxes, any 1099-MISC you may have received, and payroll documentation.
    1. Note: you do not have to have filed your taxes to submit a schedule C. If you have prepared your taxes, but not filed, and you submit a schedule C, that is fine.
    2. Note: I asked one of Kabbage’s phone support people about the payroll info, given that that doesn’t have much applicability to me. He said to submit whatever info I had available,

[Kabbage’s phone support was pretty quick and helpful, so you might call them if you get confused.]

Second, apply via Fountainhead

Fountainhead is another lender that has a full application available right now. As near as I can tell, it is totally legal to apply for a loan from multiple lenders, so long as you don’t accept a loan from multiple lenders. (However, I am not a lawyer or an accountant, and I may be wrong about that.) Given that supposition, you may want to apply to multiple lenders, to increase your chances of actually securing a loan.

This link: https://www.fountainheadcc.com/apply-relief-loan/

Note: I think that “2019 Payroll Cost” should be equal to your “2019 Annual Gross Revenue”, or perhaps your “2019 Annual Gross Revenue” - the business expenses you declared on your taxes. I’m pretty sure that it shouldn’t be 0.

[Unvetted] Third, apply via NorthOne

https://www.northone.com/sba-loan-application

I just found this one as I was preparing this post, so I haven’t gone through it myself, yet. They appear to have an open application form, however.

Fourth, express interest on all of these forms

All of the following are expressions of interest forms, instead of full applications, but if you fill them out, you’ll put yourself on a list, and the lender in question will contact you if/when they have capacity in their pipeline.

Each of these are single-page interest forms and it shouldn’t take more than 10 or 15 minutes to fill out all of them.

  • Celtic Bank
  • Live Oak Bank
    • They are focusing on current customers, but you can still apply.
  • US Bank
  • Umpqua Bank
    • They have “temporarily suspended” applications, but do have an interest form to fill out. It is unclear to me if you need to be an existing customer in order to be eligible.
  • First home bank
    • They have suspended applications, but you can apply to be notified if/when they reopen them)
  • Mechanics bank
    • They say that they are prioritizing folk that have an existing account)
Some additional lenders at which you might be able to apply depending on your circumstances

Lenders who haven’t opened (or reopened) applications yet, and recommend that you check back at their website regularly.

Banks with location based constraints (that might work for some of you?)

  • First United
    • Serving Texas and Oklahoma
  • Midwest Bank Centre
    • Their website says they are only serving their local area, but also, you just email them the application, so you can try it and see what happens, especially if you can say that you live in “St. Louis City, St. Louis County, St. Charles County, [or] Jefferson County.”
Moral considerations

There is a question of whether it's immoral to apply to this if you aren't one of the people who needs these stimulus funds the most. My current model is that many of us will be affected by the economic uncertainty of the upcoming recession – for example I expect OpenPhil, SFF, the LTF, and other x-risk/EA funders to see a substantially reduced funding capacity – and that I think it makes sense for you to apply. I also think many other individuals and businesses who will need the funds less than you do will apply, and that you do always have the option to later on donate an amount equivalent to the money you get from this.

How likely is this to pay off?

I don’t know.

I could try and assign a number to the probability that you will actually receive a loan/ grant from this process, but there is so much uncertainty in this whole thing, that I feel like putting a number on it would do little more than anchor you illegitimately.

If you insist, I think that if you fill out all of the above applications, sometime on April 10th, I think there’s something like a 1 to 10% chance of getting money from the EIDL, and a 10 to 40% chance of getting money from the Paycheck Protection Program. I pulled those numbers completely out of my ass, they are very likely to be overestimated or underestimated. and they are contingent on new information (like congress approving more stimulus money).

(some) Frequently Asked QuestionsI’m an independent contractor and a sole proprietor. How can it be the case that there are different deadlines for those two categories?

I don’t know. It seems like confusing poorly executed logistics to me.

This page explains the technical nuance of the two designations, but it is true that they often overlap.

Should I count myself as “1” employee, or should I say that I have 0 employees?

From the little research that I did, I got mixed messages about this, but my best guess is that you should put “1” for “number of employees” questions. It is possible that I am mistaken.


[Please ask any questions that you might have about any of this, as well as offer any corrections, confusions, or additional info that you may have in the comments. I'm figuring this out on the fly, as much as anyone else is.]



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Mozilla Hubs Virtual Meetup 10:30 PDT, April 19th

16 часов 27 минут назад
Published on April 10, 2020 3:40 AM GMT

Thanks to everyone who came to the meetup last week. Despite some organizational stumbles on my part, it seemed to work well. So I have decided to set up another meetup for the 19th of April.

I think the big thing we need for next time is more people willing to do a ~5 minute talk.

If you work in an interesting area, have expertise in an unusual programming language (like APL say), are an undergraduate or PhD in a subject you think would interest others, have a historical event or case study you want to share, or paper or result you want to explain, please consider doing a talk. Ideally, your talk should have slides but this is certainly not mandatory.

Not only will it help our meetups, but it is a very low-stakes way of improving your public speaking, too.

Please click this link to register for the April 19th meetup: https://docs.google.com/forms/d/e/1FAIpQLSffwxEO0n_okIhrE2ij0wDnvovZmZoR8CD_iA8dEpfWqDG4zQ/viewform?usp=sf_link

Also, there is a new rule that will take effect next meetup: your avatar cannot be larger than an SUV.



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Why don't we have active human trials with inactivated SARS-COV-2?

9 апреля, 2020 - 22:40
Published on April 9, 2020 7:40 PM GMT

It seems to me that it should be relatively straightforward to grow SARS-COV-2 in vitro and then inactive it via UV radiation.

After we inactivated it we could give it to test subjects to see whether we get a good immune response.

Is there a reason this is more complicated then I think? Have I missed something else? Otherwise what's the reason that there are no such trials going on?



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Testing and contact tracing impact assessment model?

9 апреля, 2020 - 20:42
Published on April 9, 2020 5:42 PM GMT

What is a good model for assessing the effect of testing and contact tracing on R0?

I'm imagining it would have parameters perhaps like:

  • testing frequency
  • time
  • viral load (varies with time)
  • testing false negatives (probably varies with viral load)
  • path through environment (varies with viral load - i.e. if very sick they self isolate)
  • number of people who intersect that path (perhaps weighted by distance and duration)
  • emission filtration (aka masks)
  • reception filtration (aka masks)
  • percentage of the population tested on a regular frequency (or more complex arrangements if you wish to build that in)
  • contact tracing density - maybe only 50% of the population is part of the network
  • how deep in the contact tracing web it is recommended to self isolate (perhaps varying with testing frequency because it spreads in time)
  • some sums or integrals over time and when the testing occurred
  • something something default parameters (some of which can be determined by the original R0 of covid-19)
  • something something initial conditions
  • maybe including the percentage of the population already infected or recovered
  • maybe something something pooled tests or randomized population testing
  • maybe something something contact graph structure
  • if very ambitious adding things like packages or perhaps other animals that may be able to catch and transmit it

Ideally it would be able to answer questions like: How much does it decrease R0 if you test 90% of people every 10 days with a false negative rate of 15%, an isolation compliance rate of 85%, a contact tracing web that is 50% dense, and a proactive quarantine over the contact tracing web that is two contacts deep?



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Why I'm Not Vegan

9 апреля, 2020 - 16:00
Published on April 9, 2020 1:00 PM GMT

While many people in the effective altruism movement are vegan, I'm not, and I wanted to write some about why. The short answer is what while I'm on board with the general idea of making sacrifices to help others I think veganism doesn't represent a very good tradeoff, and I think we should put our altruistic efforts elsewhere.

There are many reasons people decide to eat vegan food, from ethics to taste to health, and I'm just interested in the ethical perspective. As a consequentialist, the way I see this is, how would the world be different if I stopped eating animals and animal products?

One factor is that I wouldn't be buying animal products anymore, which would reduce the demand for animals, and correspondingly the amount supplied. Elasticity means that if I decrease by buying by one unit I expect production to fall by less than one unit, but I'm going to ignore that here to be on the safe side. Peter Hurford gives a very rough set of numbers for how many animals are required to support a standard American diet and gets:

  • 1/8 of a cow
  • 1/8 of a pig
  • 3 chickens
  • 3 fish
For example, a typical American consumes about a quarter of a pig per year, and these pigs live about six months, so that's 1/8 of a pig on an ongoing basis. I haven't checked his numbers in detail, but there are 78M pigs and 327M people in the US, so one pig for every four people, and once you consider that we export a lot of pork this seems in the right range.

Now, I don't think animals matter as much as humans. I think there's a very large chance they don't matter at all, and that there's just no one inside to suffer, but to be safe I'll assume they do. If animals do matter, I think they still matter substantially less than humans, so if we're going to compare our altruistic options we need a rough exchange rate between animal and human experience. Conditional on animals mattering, how many animal-years on a factory farm do I see as being about as good as giving a human another year of life?

  • Pigs: about 100. Conditions for pigs are very bad, though I still think humans matter a lot more.
  • Chickens: about 1,000. They probably matter much less than pigs.
  • Cows: about 10,000. They probably matter about the same as pigs, but their conditions are far better.
  • Fish: about 100,000. They matter much less than chickens.
These are very rough, and this is the main place where I think I differ from most ethical vegans: I think humans matter much more than these animals. Your own views may also be different!

Overall this has, to my own personal best guess, giving a person another year of life being more valuable than at least 230 Americans going vegan for a year.

The last time I wrote about this I used $100 as how much it costs to give someone an extra year of life through a donation to GiveWell's top charities, and while I haven't looked into it again that still seems about right. I think it's likely that you can do much better than this through donations aimed at reducing the risk of human extinction, but is a good figure for comparison. This means I'd rather see someone donate $43 to GiveWell's top charities than see 100 people go vegan for a year.

Since I get much more than $0.43 of enjoyment out of a year's worth of eating animal products, veganism looks like a really bad altruistic tradeoff to me.

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COVID-19: List of ideas to reduce the direct harm from the virus, with an emphasis on unusual ideas

9 апреля, 2020 - 14:33
Published on April 9, 2020 11:33 AM GMT

This is a collection of ideas I assembled on what interventions could be investigated, funded, or implemented to potentially reduce the harms from COVID-19 by intervening directly on the causal chain leading to infection and death. In particular, it has a lot of “unusual ideas” that haven’t gotten much attention yet.

Caveats: Most of the “unusual ideas” (see later section) were generated or vetted only quickly. Before implementation, they will definitely need further investigation, and some are only meant to serve as inspiration for better interventions. But capacity to implement them if they do work can generally be built in parallel with investigating whether they work. Further, it’s also possible that some things here could have consequences worse than the disease (stuff like surveillance states, long term harm from bad vaccinations, or dual use dangerous biotechnology). Therefore, the suggestion here is to pour a lot of resources into investigating those particularly risky options, rather than to immediately and unilaterally implement them.

As the surrounding context to keep in mind, we want to:

  • Stop this pandemic in its tracks
  • Minimize lives lost, economic disruption, psychological harm, and political harm done directly by the disease or our responses to it
  • Perhaps, if lucky, find a way to make humanity better off afterwards

For some related research topics check out Coronavirus Research Ideas for EAs.


A general list of things for interfering with the causal chain of infection and death

In this section, I’ll list categories of interventions to reduce the harm from COVID-19. These should ideally be investigated and implemented in parallel at speed for something as disruptive as this virus. Some of these are being implemented by varying degrees in nations fighting the pandemic.

  • Large scale prevention of exposure: Social distancing and managing packages and surfaces
  • Small scale of prevention of exposure: Physically blocking the virus before it gets to the body
    • masks
    • sanitation
    • etc.
  • Chemical interventions once it has entered the body: Vaccines, antivirals
  • Physiological support once it has entered the body: Stuff like ventilator production and supplemental oxygen
  • Meta: Investigate the alternative causal pathways to death and hypotheses about them; answering questions like: how much damage and death is due to lung damage, cytokine storms, damage to the nervous system, or interfering with oxygen transportation in the blood?
  • Miscellaneous “unusual ideas” (see later section)

Policy for accelerating vaccine development (and similar for antivirals and possibly some other potential solutions)
  • Many candidates can be tested in parallel for speed
  • To speed up the testing of a specific vaccine we can skip directly to later stage trials - exposing humans to the virus
  • We can accelerate testing for long-term negative effects by having a large and diverse set of volunteers take it at once - effects that are rare or slow to show themselves will be detected much faster this way
  • Slow and rare effects can plausibly also be detected more easily by selecting people based on weakness to the plausible rare or slow effect, or alternatively exposing people to conditions that would cause a plausible rare effect or slow effect to show itself
  • In parallel with testing, production capability and perhaps even stocks of plausible vaccine candidates can be built up and even distributed so they are ready to use on a moment’s notice if the vaccine works. (Bill Gates is now doing this.)
  • See also the this EA forum post

Note: Some of these ideas will face ethical objections or may be hard politically to implement.


Unusual ideasMinimizing environmental exposure
  • Coppering surfaces to decrease the virus lingering time (this effect may not last much beyond when the copper surface oxidizes)
  • Leaving soap residues on surfaces to decrease virus lingering time (plausible because covid-19 is surrounded by a lipid membrane, but this may not work at normal temperature and humidity)
  • Having UVc lights of the ideal wavelength in people dense areas
    • possibly having them on when people are not around
    • Maybe you could even build something to shine in the lungs
  • Assuming COVID-19 exhibits seasonality: changing the temperature, humidity, or wind patterns inside people-dense areas to induce artificial summer and “outside”
Social distancing measures and management
  • Applying even more sophisticated strategies for contact tracing and immunity
    • Remove the small-world network hubs or perhaps just wait for them to become immune (or replace them with with immune folks)
    • Highly privacy preserving contact tracing: https://covid-watch.org and https://www.novid.org
    • Take into account variance of R0 when doing contact tracing and lockdowns
    • As an idea for inspiration (not feasible as is), change the network topology of interactions
    • Another idea for inspiration (not feasible as is), move the recovered people around, integrate them into the social web, and have them act as firebreaks. You take recovered or otherwise immune people and move them to places where there is an outbreak in order to drop R0 below 1
  • Apply a more synchronous form of lockdown. In theory if everyone was in lockdown for a month at the same time we’d be able to identify essentially everyone who has it and have the mild cases non contagious by the end of that time period (we’d still have to be careful with contaminated surfaces (especially frozen or refrigerated items)). Something like this could very well be cheaper and easier than an extended partial lockdown.
  • In order to spread out the timing of when people are sick, a staggered form of quarantine where you expose some people earlier than others could be implemented. This option is not a good option and more of a last resort to spread out when people are at the hospitals and give us more time to come up with something better. It is better than just letting the epidemic spread freely though. Perhaps implement something like this controlled infection
  • Apply complicated forms of the "dance": things like varying the degree and intensity of tracing and lockdown over time and locations dynamically in response to contagion (perhaps using something like a PID controller). For some concreteness check out:
  • Ubiquitous health monitoring (temperature sensors, cough sound detectors, ...) - this can done using either attached or far away sensors
  • Figure out how to make the symptoms more apparent to people (by education, customized biotracking, something that amplifies symptoms (stuff like the opposite of a cough suppressant or fever reducer?), ...)
Decrease the initial viral loadChemical means of countering covid 19 infections
  • Put the entire population on weak immune boosters (stuff like vitamins D and C, selenium, ...)
  • Put the entire population on stronger forms of immune system boosters (I think there are some immune system boosters in certain vaccines). Alternatively maybe just hand them out to be used at the first sign of illness
  • Prime the immune system before or during the earlier stages of the disease
  • Put the entire population on other immune boosting strategies: intermittent fasting, cold showers, good sleep, getting to the ideal weight for respiratory disease, oxytocin/social support/hugs, placebo pills, ...
  • Lots of speculative chemicals like quercetin, rosemary...
  • Transfer blood plasma/antibodies from the recovered to the ill (some people are implementing this) - possibly boost this by hooking up circulatory systems or boosting the recovered antibody production (perhaps by transferring some more viral load later)
  • Transfer blood from the young to the old - probably need immune suppressors for this because you'd need to transfer immune cells and in any event this shouldn't work because you very likely need to generate new immune cells to handle the disease
  • Disperse Zinc lozenges to the population to use as soon as they even speculate they are coming down with something in order to kill the virus in their throat and perhaps upper respiratory system to decrease or perhaps eliminate their viral load.
  • Disperse a coat of antiviral "oil" deep into the lungs - Danielle Fong had this idea but I'm not sure it ended up going anywhere
  • See if chemicals useful for dealing with high altitude or boosting oxygen levels help
  • Look into what athletes use when doping to boost red blood cell count and hemoglobin levels
  • Use chemicals that decrease cytokine storms (like possibly Vitamin C, Melatonin, Tocilizumab, and drugs that help with autoimmune disorders)
  • Maybe some of the chemicals used in preventing autophagy, stroke damage, etc, can be used to prevent damage in this case
  • Maybe inject lots of AEC2 receptors into the body and lungs to divert the virus away from its target
  • Castration imitation chemicals, to regenerate the thyroid and possibly return it to a state similar to that of younger people where the death rate is lower. I recall reading a discussion on this somewhere but cannot find it again.
  • Have people take estrogen or phytoestrogens, assuming that is relevant for the difference between the male and female fatality rates: Estrogen receptor impairs interleukin-6 expression by preventing protein binding on the NF-kappaB site.
Non-chemical ways of countering covid 19 infections
  • Implement sophisticated forms of liquid breathing
  • Maybe there is a way to ultrasonically shake the lungs to clear them, similar to what is used for kidney stones
  • Maybe there is truth to the possibility that some forms of near infrared can boost healing. Maybe bathe the patient or their lungs (somehow) in it
  • See if increasing partial air pressure can help for blood oxygenation
  • Have people implement prone positions in the earlier stages of the disease or for self care if hospitals are overwhelmed

Conclusion

I again wish to emphasize that the ideas collected here range from strongly implied by science to be helpful (such as copper surfaces) to the very speculative (such as an antiviral “oil” for the lungs), and that some have the potential to cause more harm than the disease itself. Many of these ideas would need to be investigated further before implementation, though capacity to implement could be built up at the same time. And hopefully some of the "unusual ideas" can inspire more such ideas from others. In general, people need to move fast to combat COVID-19, but also need to be thoughtful, and to avoid information hazards and highly risky actions.

What do you think of these ideas; how probable, costly, and effective would they be?

Do you have additional ideas you think would be good to investigate and implement?

I hope that promising ideas from here will inspire additional progress and will be further investigated, forwarded to relevant parties, and acted upon if sufficiently vetted and developed.

Thanks to David Kristoffersson, Michael Aird, and Elizabeth Van Nostrand for editing help, to Alexey Turchin for sharing some ideas, and to the many people who have been coming up with great ideas for fighting the pandemic, many of which I've included here or have been inspired by.



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[Announcement] LessWrong will be down for ~1 hour on the evening of April 10th between 10PM-11PM PST

9 апреля, 2020 - 08:09
Published on April 9, 2020 5:09 AM GMT

LessWrong's database provider (Mlab) has been acquired by a separate company (MongoDB) and as such our database needs to be migrated to that new provider. In order to do that, there will be about half an hour of downtime on Friday (April 10th) between 10PM and 11PM. 

Sorry for the inconvenience.



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The Sandwich Argument

9 апреля, 2020 - 06:58
Published on April 9, 2020 3:58 AM GMT

Today I saw a very interesting argument pattern.

Normally I'd expect some evidence for X. I don't think there was any evidence for X apart from Y. So in conclusion, there was no evidence for X.

It's well known that people tend to remember the start and end over the middle. This pattern is optimised for people to believe there is no evidence for X.

However, the middle part is where it gets interesting. If the statement had just said that there was no evidence for X then the author would have looked like they were ignorant of Y. It would have left them vulnerable to a comment saying "What about Y?".

Mentioning Y indicates they aren't ill-informed and blocks this obvious reply. But rather than addressing Y, they have merely used some sleight-of-hand. Their argument doesn't really account for Y; it merely pretends to.

Have you seen this pattern before? If so, please comment below.

See also: Motte and Bailey, Shit Sandwich



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Ways you can get sick without human contact

9 апреля, 2020 - 01:41
Published on April 8, 2020 10:41 PM GMT

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src: local('MathJax_Vector Bold'), local('MathJax_Vector-Bold')} @font-face {font-family: MJXc-TeX-vec-Bx; src: local('MathJax_Vector'); font-weight: bold} @font-face {font-family: MJXc-TeX-vec-Bw; src /*1*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/eot/MathJax_Vector-Bold.eot'); src /*2*/: url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/woff/MathJax_Vector-Bold.woff') format('woff'), url('https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.2/fonts/HTML-CSS/TeX/otf/MathJax_Vector-Bold.otf') format('opentype')} ∞x/year and depends heavily on person; my own odds seem like ~10:1 non-COVID to COVID given a symptom, but yours are different; I'm not a doctor and don't have metis on this, maybe I say stupid things)

Now that people have been shut away for weeks, they've (reasonably) expected to stop getting colds and things. But I've now seen at least 3 cases, one with an extremely high level of quarantine, where someone still got a cold-like illness. This affords a few hypotheses:

  • Perhaps the base rate for sickness with no human contact is not as low as we thought
  • Perhaps disease spread takes more advantage of tiny amounts of contact than one would expect from a model of p(illness) ∝ amount of pathogen contacted
  • Perhaps people are terrible at quarantining

I don’t think it’s the last, and the second one is interesting but hard to investigate, so this post will be about the first hypothesis: can people easily get sick while alone?

I'm going to sketch a catalogue of the options and roughly divide these apparent illnesses into two groups: those spread by significant external amounts of pathogen vs minor internal amounts of pathogen. (Base rate estimates at the end.)

Significant external amounts of pathogen

The three main sources of these illnesses are food, fauna, and fecal.

Food poisoning can be caused by lots of different types of bacteria. For example, Campylobacter, Clostridium, Staphylococcus, and E. Coli seem to be some of the main culprits, but there are many others (and 80% of cases appear to be caused by agents we haven’t identified!). These present like "stomach flu", often for 1-6 days after 1-10 days of incubation.

Apparently a lot of the diseases dogs spread overlap with food poisoning bacteria (and even norovirus, which also presents as stomach flu).

A lot of fecal-to-oral infections appear to be from similar bacteria. Of course, we don’t care about diseases from other people’s fecal matter, and some of these are obviously spread person-to-person, like adenovirus. But the four listed above (can I call them the Big Four since they keep coming up?) appear to naturally occur in the lower digestive tract, so you can presumably self-contract them unless there’s some effect where you have serious immunity to the specific strains in yourself.

There are at least some others.

  • Fungal pneumonia looks pretty nasty
  • Streptococcus A is usually spread person-to-person but is also found on the skin and regularly causes food poisoning and can thus probably be acquired from oneself
  • Mold and “stuff in dirt” also seem like prominent options, though maybe mostly minor symptoms
  • Allergies are definitely minor but can mimic colds in some circumstances

In general these external infections seem mostly bacterial, since fungi are typically too weak to cause noticeable illness, and viruses mostly need other humans to reproduce unless they have animals. Not sure how many protozoans are in this group: I’d think lots, but haven’t heard of any.

Minor internal amounts of pathogen

This is the category for a bunch of weird things that may compound on each other, which you don’t really find much about in the medical literature.

The factors I see here are:

  1. Fluctuating native pathogens
  2. Variation in immune function
    1. Between people
    2. From health
    3. As calibration to ambient disease risk

As an example, you have a bunch of candida yeast on and in your body, not very deep because high temperature harms it. It is sometimes but very rarely invasive (maybe because mammals are specifically warm-blooded to fight fungi!). But in immunocompromised individuals, especially AIDS patients, suddenly invasive infections become vastly more common, and many of them have 50+% mortality. But they aren’t constant, so this must be responding to various fluctuations in candida infectiousness and immune function.

Obviously variation in immune function also applies to the external pathogen sources from the above section, but the application to native pathogens is especially interesting. Is it plausible that immune function has a wide enough range of function that internal pathogen sources may cause a significant number of flare-ups in various circumstances much less extreme than AIDS?

Here’s the pitch for plausibility.

Native pathogens are bacterial (especially the Big Four), fungal, and viral; mostly they’re on the surface of skin or mucosal membranes, but there is some penetration and humans may have a fair amount of internal viral load. We know highly-immunocompromised people regularly become infected by these native pathogens. But if normal people undergoing fluctuations in their viral load or immune function from health had these flare-ups, I don’t think they’d be very distinguishable from colds or stomach flu. To further complicate things, I think it’s a reasonable hypothesis that your body may calibrate its immune response to what seems like the ambient disease load of your environment. Thus in times of high disease load you may have immunosensitivity and “sicknesses” from large immune response to small pathogen challenge (similar to immunosensitivity from allergies), and in times of low disease load you may be “immunodesensitized” and be at higher risk of infection. Evidence for a high rate of phantom colds, or partial viral colds being replaced by self-infection, seems extremely hard to come by, and I don’t think the absence of evidence is much evidence of absence.

Lastly, there’s some chance with such a large virome that chronic flare-ups may be more common than we think. Herpes is a classic. But other diseases may just be finicky: supposedly 2-10% of Campylobacter bacterial infections have sequelae or chronic presentation. One highly-immunocompromised boy was shedding influenza A from his stool for >2 months and from his respiratory secretions for >1 year, strongly hinting at some sort of chronic infection even though influenza A is supposed to be incapable of this. At least a few people now think chronic viral infections are a common cause of chronic fatigue syndrome, perhaps from mutation to non-cytolytic form or abortive infection. Probably non-cytolytic ones are too weak to cause flare-ups, but this is the kind of weird edge case that, after realizing it’s very difficult to obtain evidence against, makes me wonder if viruses do lots of weird stuff we just haven’t classified yet. (Adenovirus-36 may cause a lot of obesity, h/t Adam Scholl—that should probably make us pause and consider the state of our knowledge.)

Base rates and takeaways

This is all somewhat useless without base rates that can help us infer how likely it is that our quarantine has failed vs we’re just letting food sit out too long. Unfortunately, it’s very difficult to get base rates on what we care about, so I’m going to have to resort to a lot of hand-waving.

A sampling of those that were reported: E. Coli infections are at 0.1%/yr in the US. Supposedly food poisoning is 15%/year. Norovirus is about 5%/year, but probably most of that is person-to-person. Infections from pets are supposedly 1%/year, but most aren’t serious. For comparison, flu is usually 3-15%/year.

Stomach-flu-like symptoms will be at least the food poisoning cases plus other cases. That’s 15+%. If we relax from intensive cases to include the kind of minor confusing nausea I get once a year, I’d guess the rate goes up to about once/year based on personal experience. If there is weird immune modulation, I could see this being once per few months.

Common-cold-like symptoms don’t seem to come up aside from immune modulation, allergies, and supposedly very-low-base-rate things like strep on skin. Healthy people probably have skin virus infection rates like .001-1%. Standard partially-immunocompromised people (that just get sick more than normal) may be more like .005-5%. I’d guess weird immune modulation could take that number to 1-200% if it exists (if it were more than 2x/year I’d think people would start noticing). But there’s also allergies, mold, stuff I’ve missed, and all these exacerbated by overreaction to immune challenges. Empirically, I have ~~thrice a year that I seem like I’m just starting to get sick and it turns out to be allergies or goes away mysteriously, and I think most people probably have this less but some have it substantially more. I could imagine this rate being like 5x if my body was on high alert, and even a few times a year the symptoms mimicking an actual mild cold rather rather than a tease.

Of course, if your body can’t pick up on external disease cues very well, or it only does so with respect to actual ambient pathogen load rather than leading mental indicators like disgust integration or anxiety tracking, then probably these base rates go down. If you’re someone who gets sick a lot, they might be higher.

So I expect base rates of minor flu-like symptoms not contracted from another person to be about .1-10/year depending on person, and base rates of minor cold-like symptoms not contracted from another person to also be about .1-20/year depending on person (again, if more, we’d see that).

COVID ambient rates are (my guess) around .1-5% most places (more like 5% in the Bay). If you’ve been doing a good quarantine, your rate is probably about the rate it was when you started, e.g. I think I started around .1% and so that is probably about my current rate (I could easily be asymptomatic). So I think if I were very healthy, my model didn’t expect much mental immunomodulation, and I got cold or flu symptoms, I’d be about as likely to be seeing a COVID onset as seeing a false alarm. As it stands, being not super healthy and a little more sympathetic to various psychosomatic control theories (writ large, not just placebo), I think it’s more like ~10:1 that I’m seeing a false alarm.

Given the fact that these numbers are certainly going to be off by about an order of magnitude, obviously I would still monitor very closely and take thoughtful precautions that make sense both in worlds where I have COVID and don’t have COVID (don’t want to give housemates another sickness to make them more susceptible). If I hadn’t been as careful with my lockdown I’d also consider getting sick as evidence that I should tighten things up a bit where possible. Also, how textbook the symptom match with COVID was would heavily effect this estimate. Etc etc. But I hope this helps you get a handle on how to grapple with these numbers and the implicated policies.



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English summaries of German Covid-19 expert podcast

9 апреля, 2020 - 00:00
Published on April 8, 2020 8:29 PM GMT

We'll start providing unofficial English summaries of the NDR coronavirus update podcast (see notion linked). The podcast provides weekdaily interviews with Christian Drosten.

Christian Drosten is leading the virology department at the German university hospital "Charite" in Berlin. He co-developed the PCR test that Germany is using to detect SARS-CoV2 (the virus causing Covid-19), is continuing to research Covid-19 and consulting the German government. He has been researching Coronaviruses, including the old SARS, for many years.

Important: We are no virologists, epidemologist, medics or have any other relevant expertise. We are lay people trying to write up Christian Drosten's evaluations as good as possible without verifying anything he says and we might misrepresent some things. Christian Drosten himself is not the relevant expert for some of the niche topics he talks about and gives informed guesses which he sometimes revises at later times. None of this should be taken at face value without double checking.

German transcripts (time-lag of one day) are available here.


Here is yesterday's summary written by Chau Nguyen with some editing from me:

  • Episode 29: 2020/04/07 - We need to target testing better
  • We don't know how most infections occur
    • Too early to find a good answer
    • Lockdown means most infections currently occur in private households which gives us no indication on how infections would happen in "real" daily life.
    • Early stage of the pandemic means we don't have enough data, yet

  • Ceiling for scaling up testing makes targeted testing and tracing apps more important
    • Germany
      • Last week around 350k tests were reported even though the RKI (Germany's public health institute) predicted for 500k
        • Possible explanation 1: Some tests might not be reported yet and will be reported in the upcoming days
        • Possible explanation 2: Shortage of certain resources such as reagents or swabs might be hitting the test centres as all European countries have an increased demands for these resources - there is also anecdotal evidence for this
          • Politicians started reaching out to companies asking why their country's demand is not being met
    • Global
      • Conclusion: Even if test centres are able to increase efficiency, the necessary resources (reagents, swabs) are a bottleneck, so we can't increase testing as necessary
      • Upshot: We need to target testing better
        • Catch hospital employee infections early and who already had it
        • Prioritize according to who's likely to be hospitalized to prevent them being hospitalized too late: Check-in by phone every two days with positive cases
      • This is an argument for importance of tracking via apps

  • It is unlikely that there is a high dark figure of infected people, and there are false positive antibody tests
    • Antibody tests have some cross reactions with other corona viruses: Someone recently sick with another corona cold virus might test positive in a Sars-CoV-2 antibody test
    • Germany and other countries (e.g. China) started doing antibody tests: No evidence for a surprisingly high percentage of people that have already been infected but have not noticed
    • Antibody tests can track silent infections via seroconversion number: People who have been tested in one week without antibodies and then tested in the next week with antibodies
    • Tests have to be rolled out country-wide and not just regionally to factor out coincidence

  • Potential prospects of tracking via sewage water and tab water
    • Group in the Netherlands tries to track Sars-CoV-2 in sewage water
    • First positive evidence, group did the same for Polio before
    • Note: No evidence that virus load found in sewage water would be infectious, just a potential tracker


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A Formal Justification of Emotions

8 апреля, 2020 - 22:29
Published on April 8, 2020 7:29 PM GMT

Disclaimer: I'm far from an expert in mathematics or psychology, so if you are: I apologize ahead of time for what will undoubtedly be a sloppy treatment of the subjects and I welcome constructive criticism in the comments. Otherwise, take all of this with a grain of salt.

This is an attempt to tie my lay understanding of what's been mathematically proven about neural networks to my lay understanding of how emotions are described in modern psychology. In doing so, I hope to arrive at an informal justification for emotional systems that may pave the way for a more formal proof.

What is Emotion?:

On May 5th, 2006; the podcast/radio-show Radiolab broadcast its first episode, "Where am I"[1] which explores, among other things, the phenomenon of emotion. The hosts interview Robert Sapolsky, a professor of neurology at Stanford about the nature of emotions. Sapolsky describes emotion as one's conscious recognition of the body's physiological response triggered by one's sub-conscious.

In other words: if you're preparing for a high-stakes rap battle, your subconscious will assess that you're in a dangerous situation and prepare you by triggering the release of adrenaline and cortisol and other hormones. Consciously, you'll recognize that your heart rate is elevated, your palms are sweaty, your knees feel weak, your arms feel heavy, you may even vomit some of mom's spaghetti[2]. You consciously recognize your physiological state and apply a label to it like "nervous" or "highly stressed".

But this seems like a strange, round-about way for your sub-conscious to communicate to your conscious, and the response rarely seems rational. Why vomit at a time like this? What could possibly be the advantage of such a phenomenon?

Why is Emotion?:

The Radiolab hosts explore a different scenario that brings a little clarity to the situation: Imagine you're walking through the woods and a bear jumps out of a nearby bush.

Sapolsky explains that there are two paths that your sensory data takes through your brain. One is like a bullet train, the other is much slower and represents your consciousness. The sensory data of the bear jumping out of the woods flows through both paths, but the much faster path, again; triggers a physiological response preparing your body to fight or run before you've consciously registered that a bear just appeared.

In this scenario, the advantage of emotion is more clear. The utility of vomiting still seems a bit dubious, but obviously; if conscious processing of information is slow and you have to operate in a world with situations that require you to respond quickly, it helps to have an alternate stimulus-response function that's much faster.

The low-latency path (which I've perhaps erroneously equated to the sub-conscious in my rap-battle example) seems to produce responses that are less optimal than the conscious path. If you didn't need a sub-conscious and could consciously process data as fast as the average sub-conscious (and had conscious control over your physiological responses), you would probably make more optimal decisions about how to respond to various situations. Maybe you wouldn't treat most stressful situations as equivalent to a bear jumping out of a bush. Maybe you'd decide that sweating and vomiting isn't helpful to prepare you for a rap battle. Maybe you'd realize that elevating your heart rate is a harmful response to sitting on an airplane for hours.

It seems that there might be some trade-off between the latency and the optimality of a stimulus-response path. Is that trade-off specific to the human brain or biological neural-networks in general or is it an even more general property that may be fundamentally inescapable?

Properties of Artificial Neural Networks:

We can prove (informally) that the trade-off between latency and optimality is, indeed; inescapable for Artificial Neural Networks based on two properties:

1) The Universal Approximation Theorem:

A feed-forward network with a single hidden layer containing a finite number of neurons can approximate continuous functions on compact subsets fo Rn, under mild assumptions on the activation function[3].

2) The Expressive Power of Depth Theorem:

For any given function, there is a minimal depth below which the number of required parameters grows exponentially[4].

We can model each path connecting sensory input to response as a function of the sensory input and the current output state. We assume there is an optimal continuous function, f_optimal; which outputs the best response possible for all possible sequences of inputs.

[1] https://www.wnycstudios.org/podcasts/radiolab/episodes/91524-where-am-i

[2] https://genius.com/Eminem-lose-yourself-lyrics

[3] https://www.sciencedirect.com/science/article/abs/pii/089360809190009T

[4] http://papers.nips.cc/paper/7203-the-expressive-power-of-neural-networks-a-view-from-the-width.pdf



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What should I study to understand real-world economics? (I.e., To gain financial literacy)

8 апреля, 2020 - 22:22
Published on April 8, 2020 7:22 PM GMT

I have taken a basic economics course and read some academic economic stuff here and there, but these have been viscerally “theoretical” in nature. I want to understand real-world concepts like value chains, stocks, venture capital, IPO, corporations (I can’t quite say I know what a corporation IS.) ... . The best resource I have found to date is http://stratechery.com . I am wondering if more efficient learning material exists. I don’t want advice/prediction books (on management or investing). I simply want to understand the basic stuff about these concepts. Financial literacy, I would say. I don’t know if there is a name for this ”field of study.”



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COVID-19 and the US Elections

8 апреля, 2020 - 21:25
Published on April 8, 2020 6:25 PM GMT

Saw a news story this morning about possible using mail in ballots for the elections and concerns about it not being a fair process (disadvantaging Republicans). Leaving that aside it does seem that we might want to consider a plan for November just in case. (ROK is running into some difficulties with its upcoming elections it seems.)

I can think of three possible approaches, perhaps others are possible.

1) Use the mail-in function. That is already a legal process but would need to be extended to locally present voters not just those elsewhere. Down side here would seem to be mail in votes are always questioned it seems as potentially fraudulent, perhaps without reasonable cause as well.

So if we decided we still need social distancing for the election mail in votes will accomplish that but will likely delay counting and ultimately identifying the winner as I would expect a lot challenges even in not that close a race -- you need a wide margin where all agree the result was as expected or too big a difference to be counter error or fraud.

If we take that route we probably need to tell everyone to submit their paper work and hire election staff to vet the applications.

2) On-line voting. Well, we do have electronic voting machines. There are certainly ways to make that possible but could it be done in time and securely? Suspect this would both be executed very poorly and be open to even more fraud or other manipulations than mail in voting. It attempted I would expect the final announcement might be even more delayed than for the mail in votes.

3) Election Week rather than Election Day? In this approach nothing changes in the voting process other than reducing the number of people at the polls at any given time -- so the longer period of time for people to cast their vote. Down side is that might require some type of legislation. It would also need to be planned out in advance (by whom?) so everyone knows the date (and perhaps time) when they are allowed to cast their vote.

[Note, I've not offered any reason why or any estimate on if this would be necessary. I suspect those here can make their own assessments on that. However, this is not something we (the people) would want to wait to the last minute. Additionally, perhaps this would allow the politicians to start focusing on politics rather than pandemics and get them out of the medical experts way ;-) ]



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