trentmkelly/LessWrong-43k huggingface dataset:
Restoration of energy homeostasis by SIRT6 extends healthy lifespan
by DirectedEvolution — 2021-06-05T21:11:49.729Z

This is just a link post for an article that came out in Nature on May 28th. Open access.

https://www.nature.com/articles/s41467-021-23545-7

“ SIRT6 overexpression in C57BL mice led to a 27% and 15% extension in median lifespan, in males and females, respectively (p = 7.1 × 10−6 and 1.1 × 10−6). Likewise, in comparison to their WT littermates, SIRT6 overexpression induced a 11% and 15% extension in maximal lifespan in males and females, respectively (p = 0.007 and 0.001). Similarly, SIRT1 + 6-tg mice exhibited a 25% and 20% extension in median lifespan (p = 1.1 × 10−6 and 1.2 × 10−8), and 13% and 15% extension in maximal lifespan (p = 0.01 and 0.001), in males and females, respectively. Relative to WT littermates, overexpression of SIRT1 alone did not affect median or maximal lifespan, in accordance with previous data.”

“ Here, we show that male and female C57BL/6JOlaHsd mice overexpressing SIRT6, but not SIRT1, live significantly longer than WT littermates. SIRT6-tg mice maintain young-like physical activity and metabolic flexibility, along with reduced aging-related pathologies. Moreover, further analyses showed that old SIRT6-tg mice show a young-like liver metabolite profile. Notably, SIRT6 enables energy production under limited energy conditions, such as fasting and aging. To mediate this function, SIRT6 promotes hepatic β-oxidation, lactate and glycerol shuttling and hepatic utilization, NAD+/NADH ratio in the liver, and glycerol release from adipose tissue. These SIRT6-regulated metabolic pathways coordinate to maintain young-like TCA cycle and GNG activities in old age (Fig. 7e). Thus, the positive effect of SIRT6 on healthy lifespan is strain and sex independent and requires SIRT6 regulation of energy production in at least two sites, liver and adipose tissues. Together, this emphasizes the potential of SIRT6-based therapeutic approaches in addressing age-related frailty and other diseases.”
Rules for Epistemic Warfare?
by Gentzel — 2021-06-05T13:30:00.896Z

In partisan contests of various forms, dishonesty, polarization, and groupthink are widespread. Political warfare creates societal collateral damage: it makes it harder for individuals to arrive at true beliefs on many subjects, because their social networks provide strong incentive to promote false beliefs. To escape this situation, improving social norms and technology may help, however if only one side of a conflict becomes more honest, the other side may exploit that as a weakness, just as conquerors could exploit countries were less violent. Coming up with rules analogous to rules of war, may help ratchet partisan contests toward higher levels of honesty and integrity over time, enabling more honest coalitions to become more competitive. What follows is a naïve shot at an ethos of what such rules and norms might look like within a community:
Do not lie, deceive, or otherwise speak dishonestly, unseriously, or insincerely. Do not tolerate others that do. Beyond the context of silly banter, comedy, games, pranks, and jokes, where such communication is permissible, deception is harmful and should only be directed at valid enemies.
When justified, lies should be told to enemies, *not* about enemies. When everyone lies about their enemies, one can only trust information about potential enemies gained from direct experience. This leaves the faithful isolated from friends they should have had, and the skeptical vulnerable to attack by those they had been warned about.
Many of the worst lies are those that drive innocent people to action in harmful ways. Lies to the masses, lies to well intentioned decision makers, and especially lies about matters of life and death. The reputation of those who converse unseriously and deceitfully on such matters should be utterly and unmercifully destroyed should they not attempt to seriously evaluate the truth of their own statements and to right the wrongs caused by their deceit. Disproportionate and indiscriminate deception is epistemic war crime.
Though it should be avoided when possible, sometimes when it is necessary to deceive adversaries, it may also be necessary to deceive the innocent. In such circumstances, proportionality must be applied, and distortions imposed on the innocent must be justified in concrete, harm mitigating gains. Harms imposed on the innocent must be compensated by the imposer and lies to the innocent...
Hypothesis: lab mice have more active transposons then wild mice
by ChristianKl — 2021-06-05T12:36:06.664Z

As johnswentworth recounts in Core Pathways of Aging, as an organism ages active transposons within it's stem cells duplicate and that mechanism might lead to increased average transposons count in stem cells. Those transposons then produce DNA damage which in turn leads to cell senescence.

If that hypothesis is true, there's evolutionary pressure to keep the count of active transposons low. That evolutionary pressure is greater in organism that reproduce at a later age then for organisms that reproduce at an earlier age.

As Bret Weinstein describes, breeding protocols for lab mice have lab mice reproducing at an earlier age then mice that live in the wild because it's economical to make the mice reproduce at a young age. Weinstein made the hypothesis that this leads to laboratory mice having elongated telomeres.

I hereby make the hypothesis that if we investigate the average amount of active transposons in laboratory mice and lab mice, we will find that the wild mice have less active transposons then the wild mice, because there's less evolutionary pressure in the laboratory mice to remove mutations that lead to increased active transposon count.

If investigation finds this hypothesis to be true, approaches to reduce transposon count should get more attention by antiaging researchers.
The Nature of Counterfactuals
by Chris_Leong — 2021-06-05T09:18:07.979Z

I'm finally beginning to feel that I have a clear idea of the true nature of counterfactuals. In this post I'll argue that counterfactuals are just intrinsicly a part of how we make sense of the world. However, it would be inaccurate to present them as purely a human invention as we were shaped by evolution in such a way as to ground these conceptions in reality.

Unless you're David Lewis, you're probably going to be rather dubious of the claim that all possibilities exist (ie. that counterfactuals are ontologically real). Instead, you'll probably be willing to concede that they're something we construct; that they're in the map rather than in the territory.

Things in the map are tools, they are constructed because they are useful. In other words, they are constructed for a purpose or a number of purposes. So what is the purpose (or the purposes) of counterfactuals?

I first raised this question in Counterfactuals are an Answer, Not a Question and I struggled with it for around a year. Eventually, I realised that a big part of the challenge is just how abstract the question is. So I replaced it with something more concrete: "Why don't agents construct crazy counterfactuals?" One example would be expecting the world to explode if I made this post. Another would be filling in the future with randomly generated events? What shouldn't I do either of these?

I'll make a modest claim: it's not about aesthetics. We don't construct counterfactuals because we want them to be pretty or funny or entertaining. We want them to be useful. The reason why we don't just construct counterfactuals in a silly or arbitrary manner because we believe in some vague sense that it'd lead outcomes that are sub-optimal or that in expectation it'll lead to sub-optimal outcomes.
I suspect most people will agree that the answer must be something along these lines, but I've hardly defined it very precisely. So let's attempt to clarify. To keep this discussion as general as possible, note that we could have stated similar sentiments in terms of achieving good outcomes, avoiding bad outcomes, achieving better outcomes. But regardless of how we word it, we're comparing worlds and deciding that one is better than another. It's not about just considering one world and comparing it to a standard because we can't produce such a standard without constructing a world non-identical to the first.

Essentially...
Social behavior curves, equilibria, and radicalism
by Eric Neyman — 2021-06-05T01:39:22.063Z
Link: https://ericneyman.wordpress.com/2021/06/05/social-behavior-curves-equilibria-and-radicalism/

I.

Here are some hypotheticals to consider, with a common theme. Note that in each case I’m asking what you would do, rather than what you should do.

In the fall, COVID cases drop to 10% of their current level. You’re back to working/studying in person. You’re vaccinated, as is everyone else. Mask-wearing isn’t required, but 25% of your co-workers wear one anyway. Would you wear a mask too? What if 75% of your co-workers wear a mask? What if it’s literally everyone else?
You’re having dinner with a party of 10 at a Chinese restaurant. Everyone else is using chop sticks. You know how to use chop sticks but prefer a fork. Do you ask for a fork? What if two other people are using a fork?
(Inspired by Scout Mindset) For anyone who chose to have kids, or wants kids: if only 30% of adults had kids, would you still choose to have a kid? What if only 2% did? For anyone who doesn’t want kids: if 90% of adults had kids, would you? What if it were 99%?
You join a group video call with 20 co-workers/classmates. Everyone except the presenter/teacher has their video turned off. Do you turn yours off too? What if everyone has their video on? Typically, what fraction of other participants need to have their video on for you to keep yours on?

Your answers to these questions depend on your personal circumstances. How uncomfortable is wearing a mask? Do you find a fork slightly or significantly easier to use? How strong is your preference to have kids? Is there a Justin Bieber poster in your background?

But — unless your answer to each question didn’t depend on the specific percentage — your decision also depends on others’ behavior. If everyone else has their video on, you’ll probably feel obligated to keep yours on too. Maybe you’ll move the poster first. This applies not just to behavior, but also to preferences, beliefs, and opinions.

Figure 1: xkcd #185

So far, everything I’ve said is pretty obvious. But let’s throw a model at this observation and see if we can discover anything interesting.

Let’s take our example with 20 people in a video call and line all of the participants up, from bottom to top, based on how many other people need to have their video on in order for them to choose to keep their video on.

Figure 2: video chat, scenario 1

In this example, Alex and Betty will keep their video on no matter what; on the other hand, Riley, Steve, and Tara will turn their video o...
The underlying model of a morphism
by Stuart_Armstrong — 2021-06-04T22:29:49.635Z

I've already talked about generalised models. The aim is not only to have a universal system for modelling any agent's mental model - universality is pretty easy to get - but a system where it's easy to recreate these mental models. And then analyse the transition between models.

This post will show that if there is a morphism r between two models (say, between ideal gas laws and models of atoms bouncing around), then there is an underlying model for that morphism.

Specifically, if r is a morphism between M0=(F0,Q0) and M1=(F1,Q1), then there is a generalised model Mr defined from r. The features of this model are the combination of the features of the two models: F0⊔F1, and there are natural morphisms r0 and r1 from this underlying model to M0 and M1:

Now, if W0 and W1 are the sets of possible worlds for M0 and M1, then W0×W1 is the set of possible worlds for Mr. Then since r is a relation between W0 and W1, it can be seen as subset of W0×W1. And the Qr is a probability distribution over this subset r.

What this means is that Qr measures how probability 'flows' from worlds in M0 to worlds in M1. If (w0,w1) is an element of r, then Qr(w0,w1) measures how much probability is flowing from w0 to w1. The actual probability of w0 is the sum of all probability flowing out of it; that of w1, the sum of the probability flowing into it.

See for example this diagram, where the Q0 probabilities are indicated in blue, those of Qr in black, and those of Q1 in red. The probabilities Q0 and Q1 are the sum of the relevant probabilities Qr on the "edges" connecting to those points:

The distribution Qr is non-unique, though. The following two examples show situations with the same Q0 and Q1, but different Qr:

The rest of this post will be dedicated to prove the existence of the underlying model for the morphism r; it can be skipped if you aren't interested.

Proof of underlying model

Definitions

Previous posts on generalised models defined them as triplets M=(F,E,Q), with F a set of features, W=2¯¯¯¯F the set of possible worlds for those features, E⊂W a subset of environments, and Q a probability distribution on E.

But E was mainly superfluous, as Q can be extended to a probability distribution on all of W just by setting it to be zero on W−E. Thus E was dropped from the definition.

The original definition allowed Q to be a partial probability distribution, but here we'll assume it's ...
Unattributed Good
by Filip Dousek — 2021-06-04T20:47:58.146Z

Cross-posted from 10-year Horizon.
If a good deed is done and no one is around to see it, does it make a sound?

In 1939, Nicholas Winton found himself on a volunteer mission in pre-war Prague. Recognizing the darkening position of the Jews, he helped organize train transports that saved 669 children from concentration camps. Today, many of us have heard this fascinating story of good character. But the story was only uncovered in 1988 by the BBC, nearly 50 years later. Sir Nicholas is said to have never talked about it himself. To most of us, those uncredited decades are powerful and puzzling.

If a good deed is done and no one is around to see it, does it make a sound?

"I used to believe that a legacy was solely based on your achievements."

Dayana Sabatin

On an everyday scale, this happens to all of us. It’s the goodwill gesture that goes unnoticed in a relationship. The overlooked patience. The brushed-off compliment.
From the trivial to the heroic, there is a yawning mismatch between what’s done and what’s appreciated. It makes a glaring contrast with the visible measures of appreciation today: namely, social media likes. Who gets thousands of likes? What for? And who goes through life helping others, perhaps by simply being a little nicer than strictly necessary, yet stays invisible?

As with economic and other resources, the judgement of others is very unevenly distributed. Some are rich with recognition, applause, goodwill, trust, reputation and others are starved of a good word.

Ziyad Marar - Judged

Unattributed good is the delta between merit and credit. What an enormous amount of misallocated energy it represents! What an amount of inner tension in people! Especially considering that both merit and popularity are so highly prized today. Imagine a social network where the most popular people were the ones who did the most good. What would that feed look like? Very different from Instagram. We can bridge that. We don’t even have to look for the next Sir Nicholas. By noticing and acknowledging everyday kindness, we fix a little bit of it today.

Network Credit

But there is a twist to the story of Nicholas Winton. He wasn’t the lead architect of the operation. The idea of transporting children to safety was actually conceived by Doreen Warriner, who started putting together the list of names and called for volunteers. Winton came to Pragu...
Alcohol, health, and the ruthless logic of the Asian flush
by dynomight — 2021-06-04T18:14:08.797Z
Link: https://dynomight.net/alcohol/

Say you're an evil scientist. One day at work you discover a protein that crosses the blood-brain barrier and causes crippling migraine headaches if someone's attention drifts while driving. Despite being evil, you're a loving parent with a kid learning to drive. Like everyone else, your kid is completely addicted to their phone, and keep refreshing their feeds while driving. Your suggestions that the latest squirrel memes be enjoyed later at home are repeatedly rejected.

Then you realize: You could just sneak into your kid's room at night, anesthetize them, and bring them to your lair! One of your goons could then extract their bone marrow and use CRISPR to recode the stem-cells for an enzyme to make the migraine protein. Sure, the headache itself might distract them, but they'll probably just stop using their phone while driving. Wouldn't you be at least tempted?

This is an analogy for something about alcoholism, East Asians, Odysseus, evolution, tension between different kinds of freedoms, and an idea I thought was good but apparently isn't.
Finite Factored Sets: Introduction and Factorizations
by Scott Garrabrant — 2021-06-04T17:41:34.827Z

This is a longer, more mathematically dense introduction to "Finite Factored Sets." For a shorter introduction, see my Topos talk/transcript.

Abstract: We propose a new approach to temporal inference, inspired by the Pearlian causal inference paradigm—though quite different from Pearl's approach formally. Rather than using directed acyclic graphs, we make use of factored sets, which are sets expressed as Cartesian products. We show that finite factored sets are powerful tools for inferring temporal relations. We introduce an analog of d-separation for factored sets, conditional orthogonality, and we demonstrate that this notion is equivalent to conditional independence in all probability distributions on a finite factored set.

1. Introduction

1.1. Pearlian Causal Inference

Judea Pearl's theory of inferred causation (e.g., as presented in chapter 2 of Causality: Models, Reasoning, and Inference) was a deep advance in our understanding of the nature of time. The Pearlian paradigm allows us to infer causal relationships between variables using statistical data, and thereby infer temporal sequence—in defiance of the old adage that correlation does not imply causation.

In particular, given a collection of variables and a joint probability distribution over those variables, the Pearlian paradigm can often infer temporal relationships between the variables.

The joint probability distribution is usually what gets emphasized in discussions of Pearl's approach. Quite a bit of work is being done, however, by the assumption that we are handed "a collection of variables" to reason about. The Pearlian paradigm is not inferring temporal relationships from purely statistical data, but rather inferring temporal relationships from statistical data together with data about how to factorize the world into variables.[1]

A doctor who misdiagnoses their patient or misidentifies a symptom may base their subsequent reasoning on a wrong factorization of the situation into causally relevant variables. We would ideally like to build fewer assumptions like this into our model of inference, and instead allow the reasoner to figure such facts out, consider the merits of different factorizations into variables, etc.

Instead of beginning with a collection of variables and a joint probability distribution over those variables, one could imagine starting with just a finite sample space and a probabil...
SIA is basically just Bayesian updating on existence
by Stuart_Armstrong — 2021-06-04T13:17:20.590Z

I love the sleeping Beauty problem, but I think it leads people astray, by making them think that anthropic reasoning is about complicated situations with multiple duplicates. That leads to people saying erroneous things, like "SIA implies there are a lot of observers".

But anthropic reasoning isn't that complicated; SIA, especially, is mostly just Bayesian updating.

Specifically, if there are no exact duplicates of yourself in your universe, SIA is just Bayesian updating on the fact that you exist; this is the same update that an outside observer would make, if informed of your existence. So, if theory Ti has prior probability pi and gives you a qi probability of existing, then SIA updates Ti's probability to qipi (and then renormalises everything):

PSIA(Ti)=P(Ti|existence)=P(Ti) P(existence|Ti)∑jP(Tj) P(existence|Tj).

This result is easy to see - since SIA is independent of reference class, just restrict the reference class to exact copies of you. If there is only on one such copy in the universe, then the update rule follows.

Even if there are multiple exact copies of you, you can still mostly see SIA as Bayesian updating over your future observations. See this footnote[1] for more details.

Indirect effect on population size

So, what does this mean for the number of observers in the universe? Well, SIA can have an indirect effect on population size. If, for instance, theory T0 posits that life is likely to happen, then our existence is more likely, so T0 gets a relative boost by SIA compared with most other theories.

So, SIA's boosting of other observers' existence is only an indirect effect of it boosting our existence. The more independent our existence is of them, or the more independent we suspect it might be, the less impact SIA has on them.

Suppose that there are N exact copies of you, and that they are going to make n independently random observations. Then as soon as n is much bigger than log2(N), you can expect that each copy will make a different observation; so, ultimately, you expect there to be only one exact future copy of you.

So if you Bayesianly update for each possible future copy (weighted by the probability of that future observation), you will get SIA. This is the trick that full non-indexical conditioning uses.

This can be seen as a partial solution to the Boltzmann brain problem: Boltzmann brains won't diverge, because they won

...