trentmkelly/LessWrong-43k huggingface dataset:
Setting an example too good
by KatjaGrace — 2014-06-26T12:21:06.000Z

Jeff Kaufman points to a kind of conflict: as he optimizes his life better for improving the world, his life looks less like other people’s lives, so makes a less good example showing that other people could also optimize their lives to make the world better. This seems similar to problems that come up often, regarding whether one should really do the (otherwise) best thing, if it will discourage onlookers from wanting to do the best thing.

These conflicts seem to be a combination of at least two different kinds of problems. Likely more, but I’ll talk about two.

One of them seems like it must be a general problem for people wanting others to follow them in doing a new thing. Since people are not doing the thing, it is weird. If you do the thing a little, you show onlookers that people like them can do it. When you do it a lot, they start to suspect you are just a freak. This might even put them off trying, since they probably aren’t the kind of person who could really succeed.

For instance, if you can kind of juggle, it suggests to an observer that they too could learn to kind of juggle. However if you can juggle fifty burning chairs, they begin to think that you are inherently weird. They also think that they are not cut out for the juggling world, since as far as they know they are not a freak.

This is a problem that both you and the observer would like to resolve – if it is really not very hard to become a good juggler, both of you would like the observer to know that.

The other kind of problem is less cooperative. Instead of observers thinking they can’t reach the extremes you have attained, they may just not want to. It looks weird, after all. You suspect that if they became half as weird as you, they would then want to be as weird as you, so want them to ‘take the gateway drug’. They may also suspect they would behave in this way, and don’t want to, and so would like to avoid becoming weird at all. At this point, you may be tempted to pretend that they would only ever get half as weird as you, because you know they would be happy to be half as weird, as long as it didn’t lead to extreme weirdness. So you may hide your weirdness. In which case you have another fairly general problem: that of wanting to deceive observers.

While there are many partial solutions to the second problem, it is a socially destruc...
General-purpose forecasting and the associated community
by VipulNaik — 2014-06-26T02:49:51.005Z

This post includes some lists gathered by web surfing and link-traipsing, as well as some broad qualitative impressions. The qualitative impressions are not always thoroughly grounded, and some of them may be plain wrong.

As contract work for MIRI, I've been reviewing forecasting in many different domains. My output is currently scattered across many posts, and I'm planning to do a summarization post once I've finished a few more. I thought it'll be good to provide some background on the general ideas related to forecasting and the community that surrounds forecasting.

General ideas related to forecasting

There are a number of important type distinctions in forecasting. The jargon differs somewhat across domains, so ignore the terminology I use and concentrate on the ideas:

Judgmental forecasts (forecasts relying on individual or group judgment) versus model-based forecasts (forecasts that rely on an explicit model)

Statistical models versus conceptual/causal models in the forecast: Statistical models start with a relatively general functional form (without necessarily having a strong theoretical justification) and use regression-like approaches to find the parameters of the model. Causal or conceptual models start with a fairly specific functional form with a conceptual justification for it, and with either no parameters or a very small number of parameters. In the case that the underlying processes are physical processes, the conceptual models are called physical models.

Simulations versus closed form expressions: Simulations require one to run the entire model through time (and possibly space) in order to come up with predictions for values at a given later point in time. With simulation-based models, one cannot directly make a forecast for the value after a fixed time interval. Rather, one needs to simulate how the system evolves over the whole time period. Closed form expressions may allow one to directly compute predictions at later points in time. Simulations are generally far more computationally intensive but could produce more accurate forecasts, or a more representative and comprehensive ensemble of forecasts, than simple closed functional forms.

Time series forecasts versus explanatory forecasts: Time series forecasts simply use the previous values of the same variable to predict future values of the variable. Typical techniques here include weight

...
Separating the roles of theory and direct empirical evidence in belief formation: the examples of minimum wage and anthropogenic global warming
by VipulNaik — 2014-06-25T21:47:07.424Z

I recently asked two questions on Quora with similar question structures, and the similarities and differences between the responses were interesting.

Question #1: Anthropogenic global warming, the greenhouse effect, and the historical weather record

I asked the question here. Question statement:

If you believe in Anthropogenic Global Warming (AGW), to what extent is your belief informed by the theory of the greenhouse effect, and to what extent is it informed by the historical temperature record?

In response to some comments, I added the following question details:

Due to length limitations, the main question is a bit simplistically framed. But what I'm really asking for is the relative importance of theoretical mechanisms and direct empirical evidence. Theoretical mechanisms are of course also empirically validated, but the empirical validation could occur in different settings.
For instance, the greenhouse effect is a mechanism, and one may get estimates of the strength of the greenhouse effect based on an understanding of the underlying physics or by doing laboratory experiments or simulations.
Direct empirical evidence is evidence that is as close to the situation we are trying to predict as possible. In this case, it would involve looking at the historical records of temperature and carbon dioxide concentrations, and perhaps some other confounding variables whose role needs to be controlled for (such as solar activity).
Saying that your belief is largely grounded in direct empirical evidence is basically saying that just looking at the time series of temperature, carbon dioxide concentrations and the other variables can allow one to say with fairly high confidence (starting from very weak priors) that increased carbon dioxide concentrations, due to human activity, are responsible for temperature increases. In other words, if you ran a regression and tried to do the usual tricks to infer causality, carbon dioxide would come out as the culprit.
Saying that your belief is largely grounded in theory is basically saying that the science of the greenhouse effect is sufficiently convincing that the historical temperature and weather record isn't an important factor in influencing your belief: if it had come out differently, you'd probably just have thought the data was noisy or wrong and wouldn't update away from believing in the AGW thesis.

I also posted to Fa...
Doubt regarding basic assumptions
by KatjaGrace — 2014-06-25T19:05:00.000Z

Robin wonders (in conversation) why apparently fairly abstract topics don’t get more attention, given the general trend he notices toward more abstract things being higher status. In particular, many topics we and our friends are interested in seem fairly abstract, and yet we feel like they are neglected: the questions of effective altruism, futurism in the general style of FHI, the rationality and practical philosophy of LessWrong, and the fundamental patterns of human behavior which interest Robin. These are not as abstract as mathematics, but they are quite abstract for analyses of the topics they discuss. Robin wants to know why they aren’t thus more popular.

I’m not convinced that more abstract things are more statusful in general, or that it would be surprising if such a trend were fairly imprecise. However supposing they are and it was, here is an explanation for why some especially abstract things seem silly. It might be interesting anyway.

Lemma 1: Rethinking common concepts, and being more abstract tend to go together. For instance, if you want to question the concept ‘cheesecake’ you will tend to do this by developing some more formal analysis of cake characteristics, and showing that ‘cheesecake’ doesn’t line up with the more cutting-nature-at-the-joints distinctions. Then you will introduce another concept which is close to cheesecake, but more useful. This will be one of the more abstract analyses of cheesecakes that has occurred.

Lemma 2: Rethinking common concepts and questioning basic assumptions look pretty similar. If you say ‘I don’t think cheesecake is a useful concept – but this is a prime example of a squishcake’, it sounds a lot like ‘I don’t believe that cheesecakes exist, and I insist on believing in some kind of imaginary squishcake’.

Lemma 3: Questioning basic assumptions is also often done fairly abstractly. This is probably because the more conceptual machinery you use, the more arguments you can make. e.g. many arguments you can make against the repugnant conclusion’s repugnance work better once you have established that aversion to such a scenario is one of a small number of mutually contradictory claims, and have some theory of moral intuitions as evidence. There are a few that just involve pointing out that the people are happy an...
A new derivation of the Born rule
by MrMind — 2014-06-25T15:07:56.333Z

This post is an explanation of a recent paper coauthored by Sean Carroll and Charles Sebens, where they propose a derivation of the Born rule in the context of the Many World approach to quantum mechanics. While the attempt itself is not fully successful, it contains interesting ideas and it is thus worthwhile to know.

A note to the reader: here I will try to enlighten the preconditions and give only a very general view of their method, and for this reason you won’t find any equation. It is my hope that if after having read this you’re still curious about the real math, you will point your browser to the preceding link and read the paper for yourself.

If you are not totally new to LessWrong, you should know by now that the preferred interpretation of quantum mechanics (QM) around here is the Many World Interpretation (MWI), which negates the collapse of the wave-function and postulates a distinct reality (that is, a branch) for every base state composing a quantum superposition.

MWI historically suffered from three problems: the absence of macroscopic superpositions, the preferred basis problem, the Born rule derivation. The development of decoherence famously solved the first and, to a lesser degree, the second problem, but the role of the third still remains one of the most poorly understood side of the theory.

Quantum mechanics assigns an amplitude, a complex number, to each branch of a superposition, and postulates that the probability of an observer to find the system in that branch is the (squared) norm of the amplitude. This, very briefly, is the content of the Born rule (for pure states).

Quantum mechanics remains agnostic about the ontological status of both amplitudes and probabilities, but MWI, assigning a reality status to every branch, demotes ontological uncertainty (which branch will become real after observation) to indexical uncertainty (which branch the observer will find itself correlated to after observation).

Simple indexical uncertainty, though, cannot reproduce the exact predictions of QM: by the Indifference principle, if you have no information privileging any member in a set of hypothesis, you should assign equal probability to each one. This leads to forming a probability distribution by counting the branches, which only in special circumstances coincides with amplitude-derived probabilities. This discrepancy...
The representational fallacy
by DanielDeRossi — 2014-06-25T11:29:05.763Z

Basically Heather Dyke argues that metaphysicians are too often arguing from representations of reality (eg in language) to reality itself.

It looks to me like a variant of the mind projection fallacy. This might be the first book length treatment teh fallacy has gotten though. What do people think?

See reviews here

https://www.sendspace.com/file/k5x8sy

https://ndpr.nd.edu/news/23820-metaphysics-and-the-representational-fallacy/

To give bit of background there's a debate between A-theorists and B-theorists in philosophy of time.

A-theorists think time has ontological distinctions between past present and future

B-theorists hold there is no ontological distinction between past present and future.

Dyke argues that a popular argument for A-theory (tensed language represents ontological distinctions) commits the representational fallacy. Bourne agrees , but points out an argument Dyke uses for B-theory commits the same fallacy.
The representational fallacy
by DanielDeRossi — 2014-06-25T11:28:55.734Z

Basically Heather Dyke argues that metaphysicians are too often arguing from representations of reality (eg in language) to reality itself.

It looks to me like a variant of the mind projection fallacy. This might be the first book length treatment teh fallacy has gotten though. What do people think?

See reviews here

https://www.sendspace.com/file/k5x8sy

https://ndpr.nd.edu/news/23820-metaphysics-and-the-representational-fallacy/

To give bit of background there's a debate between A-theorists and B-theorists in philosophy of time.

A-theorists think time has ontological distinctions between past present and future

B-theorists hold there is no ontological distinction between past present and future.

Dyke argues that a popular argument for A-theory (tensed language represents ontological distinctions) commits the representational fallacy. Bourne agrees , but points out an argument Dyke uses for B-theory commits the same fallacy.
Artificial Utility Monsters as Effective Altruism
by  — 2014-06-25T09:52:34.481Z

Dear effective altruist,

have you considered artificial utility monsters as a high-leverage form of altruism?

In the traditional sense, a utility monster is a hypothetical being which gains so much subjective wellbeing (SWB) from marginal input of resources that any other form of resource allocation is inferior on a utilitarian calculus. (as illustrated on SMBC)

This has been used to show that utilitarianism is not as egalitarian as it intuitively may appear, since it prioritizes some beings over others rather strictly - including humans.

The traditional utility monster is implausible even in principle - it is hard to imagine a mind that is constructed such that it will not succumb to diminishing marginal utility from additional resource allocation. There is probably some natural limit on how much SWB a mind can implement, or at least how much this can be improved by spending more on the mind. This would probably even be true for an algorithmic mind that can be sped up with faster computers, and there are probably limits to how much a digital mind can benefit in subjective speed from the parallelization of its internal subcomputations.

However, we may broaden the traditional definition somewhat and call any technology utility-monstrous if it implements high SWB with exceptionally good cost-effectiveness and in a scalable form - even if this scalability stems form a larger set of minds running in parallel, rather than one mind feeling much better or living much longer per additional joule/dollar.

Under this definition, it may be very possible to create and sustain many artificial minds reliably and cheaply, while they all have a very high SWB level at or near subsistence. An important point here is that possible peak intensities of artificially implemented pleasures could be far higher than those commonly found in evolved minds: Our worst pains seem more intense than our best pleasures for evolutionary reasons - but the same does not have to be true for artifial sentience, whose best pleasures could be even more intense than our worst agony, without any need for suffering anywhere near this strong.

If such technologies can be invented - which seems highly plausible in principle, if not yet in practice - then the original conclusion for the utilitarian calculus is retained: It would be highly desirable for utilitarians to facilitate the invention and implementat...
[LINK] seL4, secure operating system kernel is being open-sourced
by  — 2014-06-24T23:02:12.003Z

seL4 is the world's first (and only?) operating-system kernel with an end-to-end proof of implementation correctness and security enforcement. In 34 days it is going open source:

http://sel4.systems/

Now if only we could get a provably-correct implementation of the Amoeba operating system kernel on top of this, it'd be the perfect base for a boxed AI software stack.
An overview of forecasting for politics, conflict, and political violence
by VipulNaik — 2014-06-24T22:10:39.093Z

Global politics is a high-stakes affair, and being able to predict it, prepare for it, and in some cases manipulate it, could be a game-changer. Political forecasting, construed broadly as forecasting of events that affect the structure of political systems and the configurations of political power, is therefore quite an important activity.

In relatively stable, prosperous, democratic, and developed countries, political forecasting largely involves two related forecasting activities:

Predicting what individual or political parties will win elections

Predicting what sort of policies will be implemented by the ruling parties

The tools used here are relatively simple: public opinion (both on who people will vote for and what policies they want to see implemented) is tracked through opinion polls. For forecasts made far out in the future, the opinion poll results are combined with some models about how changing economic or political conditions are likely to affect people's voting choices. (In fact, as of the time of writing this post, the Wikipedia page on political forecasting somewhat narrowly defines it as election forecasting, thereby taking the perspective of a relatively stable democratic country).

A blog post on the SAS Business Forecasting blog reviews the main methods used in election forecasting, and compares their performance on US presidential elections. It identifies three broad categories of models that seem to make somewhat reliable and high-quality predictions:

Prediction markets such as Intrade (website, Wikipedia) and the Iowa Electonic Markets (website, Wikipedia).

Combination models such as PollyVote (website, Wikipedia), created by J. Scott Armstrong. A paper by Armstrong with Andreas Graefe claims that despite being quite stable and accurate, PollyVote has not received much media coverage, and speculates as to the reasons.

Polling aggregators such as FiveThirtyEight (website, Wikipedia) (run by Nate Silver) and Votamatic (website, Wikipedia). See also the blog post Was Nate Silver the Most Accurate 2012 Election Pundit? by Luke Muehlhauser and Gwern Banwen, November 9, 2012.

When considering global politics, however, the narrow focus on public opinion and election performance is misguided, for two reasons. First, the interactions between nation-states are governed by rules somewhat different from those that govern local politics, and they...
How do you notice when you are ignorant of necessary alternative hypotheses?
by  — 2014-06-24T18:12:47.164Z

So I just wound up in a debate with someone over on Reddit about the value of conventional academic philosophy. He linked me to a book review, in which both the review and the book are absolutely godawful. That is, the author (and the reviewer following him) start with ontological monism (the universe only contains a single kind of Stuff: mass-energy), adds in the experience of consciousness, reasons deftly that emergence is a load of crap... and then arrives to the conclusion of panpsychism.

WAIT HOLD ON, DON'T FLAME YET!

Of course panpsychism is bunk. I would be embarrassed to be caught upholding it, given the evidence I currently have, but what I want to talk about is the logic being followed.

1) The universe is a unified, consistent whole. Good!

2) The universe contains the experience/existence of consciousness. Easily observable.

3) If consciousness exists, something in the universe must cause or give rise to consciousness. Good reasoning!

4) "Emergence" is a non-explanation, so that can't be it. Good!

5) Therefore, whatever stuff the unified universe is made of must be giving rise to consciousness in a nonemergent way.

6) Therefore, the stuff must be innately "mindy".

What went wrong in steps (5) and (6)? The man was actually reasoning more-or-less correctly! Given the universe he lived in, and the impossibility of emergence, he reallocated his probability mass to the remaining answer. When he had eliminated the impossible, whatever remained, however low its prior, must be true.

The problem was, he eliminated the impossible, but left open a huge vast space of possible hypotheses that he didn't know about (but which we do): the most common of these is the computational theory of mind and consciousness, which says that we are made of cognitive algorithms. A Solomonoff Inducer can just go on to the next length of bit-strings describing Turing machines, but we can't.

Now, I can spot the flaw in the reasoning here. What frightens me is: what if I'm presented with some similar argument, and I can't spot the flaw? What if, instead, I just neatly and stupidly reallocate my belief to what seems to me to be the only available alternative, while failing to go out and look for alternatives I don't already know about? Notably, it seems like expected evidence is conserved, but expecting to loca...
Meetup : Tel Aviv: Choosing research topics
by JoshuaFox — 2014-06-24T08:33:55.779Z

Discussion article for the meetup : Tel Aviv: Choosing research topics

WHEN:
26 June 2014 07:00:00PM (+0300)

WHERE:
Derech Menachem Begin 7, 15th floor, Ramat Gan, Israel

At the next LessWrong meeting, on Thursday, June 26 at 19:00, we'll prepare for an upcoming MIRIx session. We will hear short talks on open problems in FAI and choose some problems to tackle.

VisionMap has kindly agreed to host us: Gibor Sport House, 15th Floor, 7 Menachem Begin St., Ramat-Gan.

If you can't find us, call Vadim Kosoy at 0542600919 or me at 0545691165.

Discussion article for the meetup : Tel Aviv: Choosing research topics
Meetup : Tel Aviv Meetup: Choosing research topics
by JoshuaFox — 2014-06-24T08:33:28.945Z

Discussion article for the meetup : Tel Aviv Meetup: Choosing research topics

WHEN:
26 June 2014 07:00:00PM (+0300)

WHERE:
Menachem Begin Road 7, 15th floor, Ramat Gan, Israel

(This is a duplicate generated by a bug. Now trying to resolve the bug. See meeting notice at http://lesswrong.com/meetups/11p)

At the next LessWrong meeting, on Thursday, June 26 at 19:00, we'll prepare for an upcoming MIRIx session. We will hear short talks on open problems in FAI and choose some problems to tackle.
VisionMap has kindly agreed to host us: Gibor Sport House, 15th Floor, 7 Menachem Begin St., Ramat-Gan.
If you can't find us, call Vadim Kosoy at 0542600919 or me at 0545691165.

Discussion article for the meetup : Tel Aviv Meetup: Choosing research topics