trentmkelly/LessWrong-43k huggingface dataset:
A Primer on United States Treasuries
by Annapurna — 2021-03-10T03:08:13.804Z

Original post here.

A few years into my career, I came to the realize that most investment markets, especially those most regular people invest in (equities), have strong relationships to the United States Treasury (UST) market.

Considering the recent moves in that market, I want to explain what the UST market is and how it relates to equities, loans and other investments.

A United States Treasury is a debt obligation backed by the full faith of the United States government.

Anyone can buy a United States Treasury

, but they are primarily purchased by institutional investors and governments. Today, USTs are considered the safest investments in the world.

One of the most common UST is the UST 10 year bond. Today, an investment in the UST 10 year bond will yield you about 1.5% per year. This means that if you invest $1,000, you will get $10.50 a year until it matures in 2031 and you get your $1,000 back.

The historical 10 year yield.

Source

You can buy treasuries that mature in most months and many different years, but only a select few are tracked by the markets, and are the reference rates that are used by the markets to drive expectations and investment decisions.

The most common UST rates tracked by market participants.

Source

So how do other investment assets relate to USTs? It is important to note that the relationships I am about to explain are just one input in determining the price of the assets in question. These relationships do not always hold. Treasury rates affect not only the asset classes below, but many other asset classes and financial instruments such as commodities, futures, and options.

Foreign Exchange

All else being equal, when USTs are rising across the board, it means that the United States dollar will strengthen, assuming that rates in the foreign currency in question are stable or dropping. For example, if UST rates are rising, and Canadian Government Bond rates are falling, the US Dollar will get stronger versus the Canadian Dollar. This is simply because as rates rise, it becomes more attractive to purchase United States Treasuries. Therefore major market participants would find it appealing to sell their Canadian Government Bonds, sell Canadian Dollars for US Dollars and buy USTs.

Corporate Fixed Income Securities

The simplified version of obtaining the yield for a fixed income security is benchmark rate + credit spread. In the case of U...
Deflationism isn't the solution to philosophy's woes
by Rob Bensinger — 2021-03-10T00:20:07.357Z

[epistemic status: thinking out loud; reporting high-level impressions based on a decent amount of data, but my impressions might shift quickly if someone proposed a promising new causal story of what's going on]

[context warning: If you're a philosopher whose first encounter with LessWrong happens to be this post, you'll probably be very confused and put off by my suggestion that LW outperforms analytic philosophy.

To that, all I can really say without starting a very long conversation is: the typical Internet community that compares itself favorably to an academic field will obviously be crazy or stupid. And yet academic fields can be dysfunctional, and low-hanging fruit can sometimes go unplucked for quite a while; so while it makes sense to have a low prior probability on this kind of thing, this kind of claim can be true, and it's important to be able to update toward it (and talk about it) in cases where it does turn out to be true.

There are about 6700 US philosophy faculty, versus about 6000 LessWrong commenters to date; but the philosophy faculty are doing this as their day job, while the LessWrong users are almost all doing it in their off time. So the claim that LW outperforms is prima facie interesting, and warrants some explanation.

OK, enough disclaimers.]

A month ago, Chana Messinger said:

Rob says, "as an analytic philosopher, I vote for resolving this disagreement by coining different terms with stipulated meanings."

But I constantly hear people complain that philosophers are failing to distinguish different things they mean by words and if they just disambiguated, so many philosophical issues would be solved, most recently from Sam and Spencer on Spencer's podcast.

What's going on here? Are philosophers good at this or bad at this? Would disambiguation clear up philosophical disputes?

My cards on the table: I understand analytic philosophers to be very into clearly defining their terms, and a lot of what happens in academic philosophy is arguments about which definitions capture which intuitions or have what properties, and how much, but I'm very curious to find out if that's wrong.

Sam Rosen replied:

Philosophers are good at coming up with distinctions. They are not good at saying, “the debate about the true meaning of knowledge is inherently silly; let’s collaboratively map out concept space instead.”

An edited version of my reply to Chana and Sam:

Al...
[Prediction] The Consequences of Radical Reform
by lsusr — 2021-03-09T23:05:07.747Z

Scott Alexander suggests readers make a prediction.

Under Napoleon, the revolutionary French took over large swathes of Europe. They abolished their client states' traditional systems, replacing them with the Napoleonic Code and other "modern" legal systems. Europe at the time had so many tiny duchies and principalities and so on that you can actually do a decent experiment on it - for every principality Napoleon conquered and reformed, there was another one just down the river which was basically identical but managed to escape conquest. So the authors ask: did the radically-reformed polities do better or worse than the left-to-their-traditions polities?

(admittedly, this is measured in GDP, urbanization, and other “legible” statistics - but pretty broad ones, measured over decades from a distance of centuries, in a way that seems to track very-long-term development and is hard to fake)

Take a second to make a prediction here - a real prediction, with a probability attached.

―The Consequences of Radical Reform by Scott Alexander

I am making this prediction and posting it publicly having deliberately not read the rest of Scott Alexander's post. I may edit this post for spelling, grammar and clarity. I may even take this post down. But I will NOT change my prediction after the fact.

This post is written with no research. I have consulted no friends. I have read neither others' comments nor others' predictions. The following answer comes entirely from the information stored in my own memory prior to discovering Scott Alexander's challenge.

Clarifying the Question

I interpret this question to refer to subject territories outside of France proper because the question refers to "revolution [France taking] over large swathes of Europe".

My Prediction

GDP, urbanization and other legible statistics, as measured over decades from a distance of centuries, correspond to doing "better" from the perspective of the rulers. They do not (necessarily) correspond to doing "better" from the perspective of the ruled. Importing slaves can increase GDP while harming a population's average quality of life. The Napoleonic Empire was an Empire. Enlightenment ideals were a thin veneer painted on top of a fundamentally extractive system.

Most telling about this perspective is that the question (as stated) uses "GDP" rather than "per capita GDP". GDP only matters to the ruler. Per capita ...
Defending the non-central fallacy
by Matthew Barnett — 2021-03-09T21:42:17.068Z

Aaron Bergman recently defended logical fallacies against the charge that they're bad arguments. His thesis wasn't new. Gwern highlighted "Bayesian Informal Logic and Fallacy" and "Fallacies as weak Bayesian evidence".

Defending logical fallacies sounds pretty fun, so I decided to do it myself. But, I thought, if I'm going to do it, I might as well go all the way. Why not defend the final boss of all fallacies, the so-called "worst argument in the world": the non-central fallacy.

Scott Alexander describes the non-central fallacy as such,

X is in a category whose archetypal member gives us a certain emotional reaction. Therefore, we should apply that emotional reaction to X, even though it is not a central category member.

When phrased like that, it's hard to disagree that this a bad argument. Probably not literally the worst argument in the world, but still pretty bad. However, people don't usually phrase their arguments like that. Taken literally, this description is looking terribly like a strawman.

What might the steelmanned version of the fallacy look like? Here's one possibility:

While often mistaken for being outside of morally relevant category Y, X in fact belongs to Y upon reflection. Therefore, we ought to treat X more similarly to other more typical members of Y.

Imagine the following argument.

Person A: "I think eating meat is wrong."

Person B: "Why?"

Person A: "Because animal farming is cruelty. By eating meat, you are contributing to this cruelty, and that's wrong."

Person B: "That's ridiculous. The archetypal examples of cruelty are things like torture and child abuse. Animal farming just means raising animals for food. You, my friend, are guilty of the non-central fallacy."

As much as I sympathize with Person B, I must say that Person A appears to have the better point. It kind of just looks like Person B is deflecting from the central argument. Let's examine how Person A might reply.

Person A: "I'm not saying that animal farming merely fits the dictionary definition of cruelty, and therefore we ought to treat it exactly like every other case that matches that definition.

Look, why do we think that torture and child abuse are wrong in the first place? For me, it's because those things involve involuntary suffering, and I think involuntary suffering is bad, no matter who experiences it. When I said that animal farming is cruelty, I was mer

...
De-confusing myself about Pascal's Mugging and Newcomb's Problem
by DirectedEvolution — 2021-03-09T20:45:38.299Z

Epistemic status: I've been confused by Pascal's Mugging and Newcomb's Problem for a while now. After a clarifying short-form conversation with Dagon and others, I'm ready to post my thoughts as an essay to solicit feedback.

Pascal's Mugging and Newcomb's Problem are from a class of problems of "utility-maximizing agent (UMA) vs. adversarial superior modeling." And you could look at such problems from at least three points of view.

How can the UMA...

...verify the superior/adversarial nature of the model?
...avoid being entrapped by the model?
...respond if you accept that you are inescapably entrapped?

Both of these problems try to focus your attention on (3), the problem of how the UMA should respond to inescapable entrapment. The point is that the UMA would be helpless. The superior modeler can fundamentally alter the nature of reality as experienced by the UMA, so that the concept of "choice" and "cause and effect" loses its meaning.

Roko's Basilisk points out that the superior modeler doesn't even need to exist to exert an effect. Just by considering what it might want us to do, and how it could manipulate us if it did exist, can influence our actions in the present.

It seems like the limitations on our intelligence have a protective effect. Failure to fully consider and act on this problem is not a bug, it's a feature. It prevents us from taking the whims of nonexistent adversarial superintelligences into account in our decision-making.

Sometimes, our world does contain minor superintelligences, particularly markets, with which we need to engage, yet which can have a similar paralyzing effect. Strategies for investment, entrepreneurship, career-building, and other domains are often about looking for the inadequacies of civilization, which is the same as inadequacies of the minor social superintelligences we've managed to build so far. Participating in that endeavor improves the superintelligence. It just works much more slowly than the theoretical maximum because human brains are more slower and more fragile than silicon computers.

From this perspective, the War in Iraq seems like a real-world example of a self-inflicted Pascal's Mugging. Nuclear weapons and similar measures appear as a crucial form of deterrence against PMs, by making the real threat dominate consideration of any imaginary threats. The US government might worry that North Korea will build a nuc...
Towards a Mechanistic Understanding of Goal-Directedness
by Mark Xu — 2021-03-09T20:17:25.948Z

This post is part of the research I have done at MIRI with mentorship and guidance from Evan Hubinger.

Introduction

Most discussion about goal-directed behavior has focused on a behavioral understanding, which can roughly be described as using the intentional stance to predict behavior. We briefly summarize behavioral goal-directedness, then present a parallel understanding focused on how the goal is represented and used by an agent, which we call mechanistic goal-directedness. We analyze connections between the two, then conclude with a number of open questions.

As an analogy, any nondeterministic finite automaton (NFA) can be translated into a deterministic finite automaton (DFA). What does it mean to say that a machine "is an NFA''? There is an equivalent DFA, so "NFA" cannot be a feature of the machine's input-output mapping, i.e., "being an NFA" is not strictly a behavioral property.

Converting most NFA into DFA requires an exponential increase in the state-space. Therefore, we call a machine behaviorally an NFA if its input-output mapping is more likely to be the input-output mapping of an NFA. A machine “is an NFA (behaviorally)” to the extent that such a description is simpler than describing the machine as a DFA. In contrast, we call a machine mechanistically an NFA if the internal mechanism resembles that of an NFA and mechanistically a DFA if the internal mechanism resembles a DFA. A machine “is an NFA (mechanistically)” if the internal mechanism has non-deterministic transitions. These understandings can split apart: a DFA emulated an NFA is behaviorally an NFA but mechanistically a DFA.

We roughly break down our current understanding in the following chart. Note that this chart isn’t a complete factorization of questions into subquestions, but rather a hierarchical grouping of questions into similar themes:

Behavioral Goal-Directedness

Behavioral goal-directedness suggests that modeling agents as goal-directed ought to predict behavior. Explanations of agent behavior involving goals should allow us to predict the agents actions better than other explanations.

Adam Shimi’s Literature Review on Goal-Directedness identifies five properties behaviorally goal-directed systems have. Summarized by Rohin Shah:

Restricted space of goals: The space of goals should not be too expansive, since otherwise [behavioral] goal-directedness can become vacuous.

...
Welcome to LW/SSC/EA in San Juan, Puerto Rico
by Mati_Roy — 2021-03-09T18:49:35.733Z

A group of friends am part of meets regularly (some are into LessWrong, but not all; other interests include: social dynamics and business). We sometimes do Rationalist-adjacent games, such as status games and creativity games from the book Improv. Let me know if you'd be interested in joining us and/or connecting with me / other Rationalists.
Why does Applied Divinity Studies think EA hasn’t grown since 2015?
by KatjaGrace — 2021-03-09T10:10:20.446Z

Applied Divinity Studies seeks to explain why the EA community hasn’t grown since 2015. The observations they initially call the EA community not having grown are:

GiveWell money moved increased a lot in 2015, then grew only slightly since then.

Open Phil (I guess money allocated) hasn’t increased since 2017

Google Trends “Effective Altruism” ‘grows quickly starting in 2013, peaks in 2017, then falls back down to around 2015 levels’.

Looking at the graph they illustrate with, 1) is because GiveWell started receiving a large chunk of money from OpenPhil in 2015, and that chunk remained around the same over the years, while the money not from Open Phil has grown.

So 1) and 2) are both the observation, “Open Phil has not scaled up its money-moving in recent years”.

I’m confused about how this observation seems suggestive about the size of the EA community. Open Phil is not a community small-donations collector. You can’t even donate to Open Phil. It is mainly moving Good Ventures’ money, i.e. the money of a single couple: Dustin Moskovitz and Cari Tuna.

One way that I could imagine Open Phil’s spending saying something about the size of the EA community is that the community might provide funding opportunities for Open Phil, so that its growth was reflected in Open Phil’s spending. But this would require EA growth at a scale that produced large enough funding opportunities, that met Open Phil’s specific criteria, to show up amidst hundreds of millions of dollars of annual grant-making. I think this at least requires argument.

I’m further confused when in trying to explain the purported end of growth, ADS says, ‘One possibility is that there was not a strange hidden cause behind widespread stagnation. It’s just that funding slowed down, and so everything else slowed down with it’, then go on to explore the possibility that funding from Open Phil/Good Ventures has slowed down in line with this ‘widespread’ stagnation (in different aspects of Open Phil and one Google Trends result). They find that indeed it has.

There is a strange hidden cause! Which is the underlying structural relationship between Open Phil and a combination of Open Phil twice and another thing.

(In fairness, ‘widespread’ might at that point in the post also include the stagnation since 2010 and 2013 respectively of Google searches for popular Rationalist blogs, Less Wrong, and Slate Star Cod...
Will Martian G be enough to make healthy baseline babies?
by Randomized, Controlled — 2021-03-09T03:30:46.583Z

Did you know that just changing the orientation of chicken and frog eggs while they're gestating can mess things up? Never mind actually changing the magnitude of the g vector.

The lack of basic research in this area is not good. The moon has around 1/3 g. Mars around 2/3g. Can baseline humans and other large mammals reproduce successfully at that acceleration? I'm just an internet know-nothing know-it-all, but I give 70% against lunar G being sufficient and 60% against Martian.

We don't have good ways to study this on earth. We've done fun stuff like fly human jizz on a vomet-comet and roll plants, cells, tissue samples and small animals around in Clinostats and Random Positioning Machines (big, slow-moving rotating drums and gimbaled doohickies). But apparently all we really know is that larger mammals don't have the specific vulnerabilities that birds and frogs do: having a yolk seems to make them more sensitive to the direction of G, because of chemical diffusion:

Gravity sensing in individual cells in mammals has also not yet been defined. Unlike frog, where free-living embryos arise from oocytes with maternally differentially-deposited nutritional heavy yolk, mammalian embryos rely on maternal nutrition after implantation and have little yolk. Thus, mouse embryos are not thought to sense gravity by macromolecular (yolk) distribution sensed by the cytoskeleton, as reported in frog..

AFAICT, there's no large centrifuge on the ISS -- JAXA built a module intended to house a decently sized centrifuge (specimens up to 24" tall), but the plans to launch it were cancelled. AFAIK, there's certainly nothing on the drawing board that would be big enough to actually accommodate human experiments. This seems like one of the highest value things NASA could do with ISS.

My guess is that if 0.38g ain't enough this will probably need some next-level genetic engineering to be worked around.

Elicit Prediction (elicit.org/binary/questions/0eyG9b4Bc)
CLR's recent work on multi-agent systems
by JesseClifton — 2021-03-09T02:28:47.930Z

Introduction

We at the Center on Long-Term Risk (CLR) are focused on reducing risks of cosmically significant amounts of suffering[1], or s-risks, from transformative artificial intelligence (TAI). We currently believe that:

Several agential s-risks — in which agents deliberately cause great amounts of suffering — are among the largest s-risks in expectation.

One of the most promising ways of addressing these risks involves intervening in the design of TAI systems to make them more cooperative.

Increasing the cooperativeness of powerful systems is robustly valuable for everybody interested in shaping the long-run future. (See the recent Cooperative AI and AI Research Considerations for Existential Safety (ARCHES) research agendas for examples of work motivated by considerations other than s-risks.)

This was the subject of our research agenda on cooperative, conflict, and TAI. In this post, I’d like to give an overview of some of the research that CLR has been doing on multi-agent systems. I’ll then briefly remark on the importance, tractability, and neglectedness of this work (both from a downside-focused and more mainstream longtermist perspective), though a thorough discussion of prioritization is beyond the scope of this post. The main goal is to inform the community about what we’ve been up to recently in this space.

We’re interested in supporting people who want to contribute to this work. If you might benefit from financial support to work on topics related to the research described here, you can fill out this form. If you’re interested in working with us as a temporary summer research fellow, full-time researcher, or research assistant, apply here.

Potential causes of conflict between intelligent actors

It is possible that TAI systems will find themselves in conflict, despite the fact that conflict is often seemingly Pareto-inefficient. Factors that might lead intelligent agents to become engaged in conflict include:

Uncertainty about each others’ level of resolve or ability to win a fight (and incentive to misrepresent this private information). This may lead agents to overestimate their relative resolve (Fey and Ramsay 2007), or to engage in costly conflict to signal their resolve (Sechser 2018);

Inability to credibly commit to a negotiated agreement (Powell 2006)[2];

Noisy observations of other agents’ actions, leading to punishments for falsely-

...