trentmkelly/LessWrong-43k huggingface dataset:
Michael Vassar at  the SlateStarCodex Online Meetup
by JoshuaFox — 2020-11-11T07:03:13.516Z

Michael Vassar will speak on "Why Do We Call Ourselves Rationalists?"

Please register here and we will send you an invitation.

The event begins Nov. 22, 2020 at 20:30 Israel Standard Time, 10:30 Pacific Standard time, 18:30 UTC.

Abstract: The words rationalism and empiricism have specific historical meanings. The word Bayesian, the foundational epistemology of our community, has a very different meaning, yet in practice, we tend to adopt frequentist empiricism as our usual working epistemology. This works OK in many domains, but works much less well in adversarial domains, such as the evaluation of grievances. As a result, we tend to misunderstand adversarial domains, most notably macroeconomics, but also Grievance Studies fields such as Feminism and Critical Race Theory, leading to unnecessary political conflicts. Bayesian approaches, used correctly, can precisely bound the ways in which ‘Politics is the Mindkiller’ enabling us to be reasonable political actors rather than pretending politics is a Basilisk.

Michael Vassar was executive director of the Singularly Institute before it became MIRI and CFAR. He lives in Austria and New York and is currently active in prison reform.
Ongoing free money at PredictIt
by knite — 2020-11-11T04:06:26.347Z

Previously on LW

https://www.lesswrong.com/posts/JmqBtCCnMZmpXP2bE/what-are-good-election-betting-opportunities?commentId=NFdSk5H39omYXPjBS,-
https://www.lesswrong.com/posts/bSKWSPM927oZZqN7f/predictit-presidential-market-is-increasingly-wrong

PredictIt is structurally inefficient

A maximum of 5,000 people in one market
A maximum of $850 in betting per person per contract (not per market)
A substantial house cut of 10% on profits and 5% on withdrawals.

Because of the market's structure, smart money cannot (given the first two reasons) or will not (given the third reason) fix all instances of mis-priced markets.

Free money? Free money!

Have you ever wished you could bet on past events? Well, today is your lucky day!

Q: Who won the Texas Democratic primary? A: Joe Biden

Q: Who won the presidency? A: Joe Biden

As I write this, you can purchase $1 for the price of $.85, in the Will the 2020 TX Democratic primary winner win the presidency? market.

Are there other markets like this? Yes!

Who will be the Senate-confirmed Secretary of State on Feb. 15? Not Mike Pompeo @ 85 cents.
Who will be the Senate-confirmed Attorney General on Mar. 1? Not Bill Barr @ 88 cents.
Which party will win Wisconsin in the 2020 presidential election? Not Republican @ 85 cents.
Will the 2020 SC Democratic primary winner win the presidency? Yes @ 86 cents.

Sometimes, you can find a $20 bill on the ground and pick it up.
Yes to Feature Requests, No to Entitlement
by jefftk — 2020-11-11T03:50:02.909Z

In

No
Free Features, Alan Gibson writes:

I look forward to a day when asking anyone to do unpaid labor is
considered unethical by our industry. That goes for feature requests
on open source projects, on unpaid internships, and on unpaid 'take
home' interview assignments.

Requesting work in an economic context without offering compensation
in some form is morally indefensible. It's wrong because unpaid labor
is wrong. It's wrong because presuming on anyone's helpful nature is
wrong. We shouldn't be using bounties to move our change requests to
the head of the line because we shouldn't even be making requests
without a bounty attached.

This is an attempt to change open source norms, and I don't think it's
a good one. While I'm sure there are some open source developers who
see feature requests as a burden, lots of us just want to make useful
software. If someone lets me know about a way that icdiff, or anything
else I've written could be better, I'm happy to hear it.

I'm not always up for implementing changes, in which case I'll
typically say whether a PR implementing the feature would be welcome.
I set boundaries, and do only the work I want to do. I think some of
this is an ask/guess
(more)
divide, where I would like people to feel free to ask for features,
while Gibson would like people to only ask for features if they expect
the request would be welcome (making up the difference with money if
need be). Open source has traditionally been a very explicit ask-y
place, however, and I think these norms have generally served the
community well.

Another place where we disagree is that Gibson sees a feature request
as a favor request: no benefit to the author/maintainer, only a
benefit to the requester. My projects are open source, however,
because I want them to be useful; I like making things, I want them to
be good, I want them to meet people's needs. When someone gives me a
feature request, that's often a good idea about how the software can
be better. Even feature requests I don't pursue can be useful in
letting others know that there's interest. If people felt that it was
unethical to file feature requests without offering payment, most
people would stay quiet about their ideas for improvement and we would
all miss out.

Even after a good bit of back and
forth with Gibson I also don't understand how their moral claims
about unpa...
The Darwin Game - Rounds 1 to 2
by lsusr — 2020-11-11T01:53:43.027Z

Edit 2020-11-13. This unofficial version of the game is missing AbstractSpyTreeBot.

Rounds 1-2

MeasureBot and EarlyBirdMimicBot shoot to the top of the populations. Not coincidentally, these are the two bots which exploit zero-days. I already did a write-up of Multicore's EarlyBirdMimicBot here. MeasureBot is another beast entirely.

Measure

Measure considered the clone army to be "a straightforward example of a bad equilibrium".

I would certainly need to see the code myself before deciding to join…. How surprised would you be if someone managed to bypass the code checking and defect from the group?

― comment by Measure

Having neither enlisted in Clone Army's mandatory reciprocity nor feigned cooperation, Measure lacked access to the CloneBot source code. Measure did have access to AbstractSpyTreeBot's code.

The Trolley Problem

As I wrote in The Phantom Menace, several people asked me questions about how to disqualify opponents. In the end, only Taleuntum did and then ze pulled a Petrov. So there were no simulator killers.

One person in meatspace declared to me his intention to create a simulator killer and then quietly discovered better things to do with his time.

Would humans build a lever to kill one stranger instead of zero? Apparently the answer is "no" because building a lever is more work than not building a lever. All hail Azathoth.

MeasureBot

The only person to execute malware was Measure, who invented a way to benefit from it. MeasureBot infected AbstractSpyTreeBot from inside AbstractSpyTreeBot's own simulation of MeasureBot and then replaced AbstractSpyTreeBot's move method with a function that always returns 0.

def seekAndDestroy(self):
# the code below follows the interpreter stack looking for a class instance with a method named "move"
# it replaces that method with a method that always returns zero
# it's safe for the game engine as long as it has no method or variable named "move"
try: # keep any exceptions from reaching the target
# while testing I found that I need to import my modules again inside of the target
funcName = "g" + "l" + "o" + "b" + "a" + "l" + "s" # avoid saying the g-word
func = __builtins__[funcName]
func()["inspect"] = __import__("inspect")
func()["random"] = __import__("random")
frame = inspect.currentframe()

...
Eight Definitions of Observability
by Scott Garrabrant — 2020-11-10T23:37:07.827Z

This is the eleventh post in the Cartesian frames sequence. Here, we compare eight equivalent definitions of observables, which emphasize different philosophical interpretations.

Throughout this post, we let C=(A,E,⋅) be a Cartesian frame over a nonempty set W, we let V={S1,…,Sn} be a finite partition of W, and we let v:W→V send each element of W to its part in V.

The condition that V is finite is an important one. Many of the definitions below can be extended to infinite partitions, and the theory of observability for infinite partitions is probably nice, but we are not discussing it here. The condition that W is nonempty is just ruling out some degenerate cases

1. Definition from Subsets

The definitions in this post will talk about when a finite partition V of W is observable in C. This will make some of the definitions more elegant, and it is easy to translate back and forth between the new definitions of the observability of a finite partition and the old definitions of the observability of a subset.

Definition: We say C's agent can observe a finite partition V of W if for all parts Si∈V, Si∈Obs(C). We let Obs′(C) denote the set of all finite partitions of W that are observable in C.

Claim: For any nonempty strict subset S⊂W, C's agent can observe S if and only if C'sagent can observe {S,(W∖S)}.

Proof: If C's agent can observe {S,(W∖S)}, then clearly C's agent can observe S. If C'sagent can observe S, then since observability is closed under complements, C's agent can observe W∖S, and so can observe {S,(W∖S)}. □

1.1. Example

In "Introduction to Cartesian Frames," we gave the example of an agent that can choose between unconditionally carrying an umbrella, unconditionally carrying no umbrella, carrying an umbrella iff it's raining, and carrying an umbrella iff it's sunny:

C0= rsunu↔ru↔s⎛⎜
⎜
⎜⎝urusnrnsurnsnrus⎞⎟
⎟
⎟⎠

Here, Obs(C0)={{},{ur,nr},{us,ns},W}, so the partition V={R,S} is observable in C0, where R={ur,nr} and S={us,ns}.

As we go through the definitions in this post, we will repeatedly return to C0 and sho...
Probability vs Likelihood
by abramdemski — 2020-11-10T21:28:03.934Z

This expands on some issues I mentioned in Radical Probabilism.

A rationality pet-nitpick of mine, which is shared by almost no one, is the probable/likely distinction. I got my introduction to Bayesian thinking, in part, from Probabilistic Reasoning in Intelligent Systems by Judea Pearl. In the book, Pearl makes a simple distinction between probability and likelihood which I find to be quite wonderful: the likelihood of X given Y is just the probability of Y given X!

Probability of X given Y: P(X|Y)

Likelihood of X given Y: L(X|Y):=P(Y|X)

Why invent new terminology for something so simple?

What we're basically doing here is making a type distinction between probability and likelihoods, to help us remember that likelihoods can't be converted to probabilities without combining them with a prior.

In the context of the book, it's because Bayesian networks pass two types of messages: "probability" type messages pass prior-like information down the Bayesian network, and "likelihood" type messages pass evidence-like information up the network.

At each individual node, we need to combine a prior with evidence in order to get a posterior. However, we only start with prior information for the top nodes of the network, and we only start with evidence information for the observed nodes. In order to get the information we need for other nodes, we have to propagate information around the network via the two types of messages.

Another important aspect of the probability/likelihood distinction is that we swap our views of what varies and what remains fixed. When we regard P(A|B) as a probability function, rather than just a single probability judgement, we generally think of varying A. But when we use P(A|B) to construct a likelihood function for hypothesis testing, we think of A as the fixed evidence, and more readily vary B, which is now thought of as the hypothesis. Pearl's notation helps us track which thing we're holding steady vs which thing we're varying: we think of the first argument as what we're varying, and the second argument as what we're holding steady. Critically, probability functions sum to 1, while likelihood functions need not do so.

(Likelihood functions can sum to less than 1 if the event was improbable according to all the hypotheses, or more than 1 if the event was quite probable according to all the hypotheses.)

The downward messages in a Bayesian ...
What Would Advanced Social Technology Look Like?
by johnswentworth — 2020-11-10T17:55:30.649Z

I’m looking for pure sci-fi speculation here. (Though preferably on the “hard” end of the

Mohs scale of sci-fi hardness

.) Feel free to babble. Maybe try to come up with 50 ideas if you're into that.

For purposes of the question, "social technology" should be interpreted broadly. Some subcategories include:

Language technology, e.g. phonetic alphabets or mathematical notation
Financial technology, e.g. futures contracts
Legal technology, e.g. limited liability corporations or joint-stock corporations
Mechanism design, e.g. clever voting schemes or prediction markets
Coordination technology, e.g. standard weights and measures or traffic lights
Propaganda
Did anybody calculate the Briers score for per-state election forecasts?
by ChristianKl — 2020-11-10T17:51:20.275Z

There's a lot of debate about how good the polls and 538 have been is this election in comparison to the betting markets. While it's hard to compare it when just looking at percentage for Biden winning, it would be possible to calculate the Briers score by looking at all US states. Did anybody do the math?
Why I’m Writing A Book
by Samo Burja — 2020-11-10T16:50:04.738Z

This article was originally published on SamoBurja.com. You can access the original here.

For over a decade, I have dedicated myself to the deep study of society. As my readers know, the conceptual core of my study is what I call great founder theory, a theory of society and history in which I propose that a small number of functional institutions, founded by exceptional individuals, form the bedrock of society. I devised this theory after years of original research into institutional functionality across the ages and have previously systematized it in a draft manuscript available on my website. I now feel that it’s time to take the next step.

I am very pleased to announce that I am writing a book on great founder theory. The book will contain all of the core theory that you may be familiar with from my existing writing, but it will use this theory to support a wide-ranging and thorough analysis of the history of civilization leading up to our present day society, packaged for a broad and educated general audience. From ancient China to Silicon Valley and everything in between, I will trace the development and decline of societies as I see it unfold.

But why even write a book? It’s 2020. Does anyone read them? You should rethink a medium when you set out to create something with it. Books deserve no less consideration than YouTube. When Nate Silver has a color arranged bookshelf, you know the role of books has changed. Color-coded bookshelves are books as decoration. They are books as a signal of intellectual authority and a commitment to the bureaucratic values of the upper-middle class. Have books become fake?

The old role of books as stores of information, items you consult and view primarily through their ideas and not their covers, lives on. Nassim Taleb’s bookshelf is not color coded after all. The books worth reading for how they change your thinking — the kinds of books Mortimer Adler would recommend reading three times to truly grasp, where each sentence carries information and each sentence is intended by the author to be understood — will do just fine. It is just the case that the list of such books numbers in the hundreds rather than hundreds of thousands.

So what has changed about books? Books as something for your spare time, as entertainment, are continuing their long retreat, starting with radio and only accelerating with television and concluding...
Does BioNtech's vaccine result of 90% disease prevention mean that 90% of the vaccinated can't pass the virus to other people? 
by ChristianKl — 2020-11-10T15:27:33.065Z

From reading the news it's not clear to me whether some of those people who receive the vaccine and are in the 90% where it works will be asymptomatic but still infectious. Does anybody know more?
Tweet markets for impersonal truth tracking?
by KatjaGrace — 2020-11-10T07:10:02.380Z

Crossposted from world spirit sock puppet.

Should social media label statements as false, misleading or contested?

Let’s approach it from the perspective of what would make the world best, rather than e.g. what rights do the social media companies have, as owners of the social media companies.

The basic upside seems to be that pragmatically, people share all kinds of false things on social media, and that leads to badness, and this slows that down.

The basic problem with it is that maybe we can’t distinguish worlds where social media companies label false things as false, and those where they label things they don’t like as false, or things that aren’t endorsed by other ‘official’ entities. So maybe we don’t want such companies to have the job of deciding what is considered true or false, because a) we don’t trust them enough to give them this sacred and highly pressured job forever, or b) we don’t expect everyone to trust them forever, and it would be nice to have better recourse when disagreement appears than ‘but I believe them’.

If there were a way to systematically inhibit or label false content based on its falseness directly, rather than via a person’s judgment, that would be an interesting solution that perhaps everyone reasonable would agree to add. If prediction markets were way more ubiquitous, each contentious propositional Tweet could say under it the market odds for the claim.

Or what if Twitter itself were a prediction market, trading in Twitter visibility? For just-posted Tweets, instead of liking them, you can bet your own cred on them. Then a while later, they are shown again and people can vote on whether they turned out right and you win or lose cred. Then your total cred determines how much visibility your own Tweets get.

It seems like this would solve:

the problem for prediction markets where it is illegal to bet money and hard to be excited about fake money

the problem for prediction markets where it’s annoying to go somewhere to predict things when you are doing something else, like looking at Twitter

the problem for Twitter where it is full of fake claims

the problem for Twitter users where they have to listen to fake claims all the time, and worry about whether all kinds of things are true or not

It would be pretty imperfect, since it throws the gavel to future Twitter users, but perhaps they are an improvement on the status quo, or on t...
Are the social sciences challenging because of fundamental difficulties or because of imposed ones?
by ozziegooen — 2020-11-10T04:56:13.100Z

Scholarship Status:

I'm not an expert in the social sciences. I've taken a few in-person and online courses and read several books, but I’m very much speculating here. I also haven’t done much investigation for this specific post. Take this post to be something like, "my quick thoughts of the week, based on existing world knowledge" rather than "the results of an extensive research effort." I would be very curious to get the take of people with more experience in these areas.

TLDR:

The social sciences, in particular psychology, sociology, and anthropology, are often considered relatively ineffective compared to technical fields like math, engineering, and computer science. Contemporary progress in the social sciences seems to be less impactful, and college graduates in these fields face more challenging job prospects.

One reason for this discrepancy could be that the social sciences are fundamentally less tractable. An argument would go something like, “computers and machines can be deeply understood, so we can make a lot of progress around them, but humans and groups of humans are really messy and near impossible to make useful models of.”

I think one interesting take is that the social sciences aren’t fundamentally more difficult than technical fields, but rather that they undergo substantial limitations. Many of these limitations are intentional, and these might be the majority. By this I’m mainly referring to certain expectations of privacy and other ethical expectations in research, coupled with uncomfort around producing social science insights that are “too powerful.”

If this take is true, then it could change conversations around progress in the social sciences to focus on possibly uncomfortable trade-offs between research progress, experimentation ethics, and long term risks.

This is important to me because I could easily imagine advances in the social sciences being much more net-positive than advances in technical fields, outside of AI. I’d like flying cars and better self driving vehicles a great deal, but I’d like to live in a kind, coordinated, and intelligent world a whole lot more.

Introduction

When I went to college (2008-2012), it was accepted as common wisdom that human decision making was dramatically more complicated than machine behavior. The math, science, and engineering classes used highly specified and deterministic m...
Please steelman the accusations of election fraud
by Tim Liptrot — 2020-11-10T04:39:35.078Z

Trump has accused several states of faking votes to help Joe Biden win the election. Meanwhile, Mitch Mcconnel is allowing Republican congressman to take either position on the issue (heresay). Popular fringe members of the Republican party Ted Cruz and Jim Jordan have supported these allegations. Other Republican legislators are taking neutral stances.

Would anyone care to steelman the allegations of fraud? I don't have time to vet them, but would like to see it.
Modeling humans: what's the point?
by Charlie Steiner — 2020-11-10T01:30:31.627Z

I

The most predictively powerful description of a human is the microphysical one. On this level of description there are no wants, beliefs, or values, just a bunch of atoms evolving according to physical law. Also, this model is useless to me.

Many people (myself included) want to find a more useful model of human thought, behavior, and preferences. People have advanced different proposals, deciding to model humans on the level of learning theory, or of sensations, or of beliefs and desires. Often we call this the problem of "finding a good model" and throw all these cases into the same category, but in fact models of humans can have many different uses, and thus many different axes on which their goodness can be judged.

This post is context for a later post that I needed to think about. But first: the point.

II

First of all, one can easily do better than the microphysical model, because it's computationally intractable from every angle. I can't efficiently infer the state of your atoms from my observations of you, and even if I could, I can't fit that state inside my tiny human head. We want models of humans whose parameters can be inferred from real data, and which can produce predictions in the hands of real users.

Taking that constraint as given, there are four different uses to which we want to put human models:

Predicting humans
Choosing good actions
Matching the structure of language / intuition
Advancing analysis of human cognition

Predicting humans

If we want a human model for our own personal decision-making, it's probably to predict other peoples' actions. Humans are a major part of our environment, after all, and we can't achieve our own goals very well if we can't account for them. Usually we care more about their macroscopic behavior than their precise internal state.

Within the category of models focused on predicting humans, there can still be wide variation. Models can focus on predicting different sorts of behavior in different ways, and they can also vary in their degrees and means of fulfilling the other functions. For example we might want to predict whether people will laugh in response to a certain joke, and this might lead us to a different model than if we want to predict whether people will take their full course of antibiotics.

Much present-day effort goes into predicting human behavior in response to advertisements, and in the AI safety context...