trentmkelly/LessWrong-43k huggingface dataset:
How deferential should we be to the forecasts of subject matter experts?
by VipulNaik — 2014-07-14T23:41:39.019Z

This post explores the question: how strongly should we defer to predictions and forecasts made by people with domain expertise? I'll assume that the domain expertise is legitimate, i.e., the people with domain expertise do have a lot of information in their minds that non-experts don't. The information is usually not secret, and non-experts can usually access it through books, journals, and the Internet. But experts have more information inside their head, and may understand it better. How big an advantage does this give them in forecasting?

Tetlock and expert political judgment

In an earlier post on historical evaluations of forecasting, I discussed Philip E. Tetlock's findings on expert political judgment and forecasting skill, and summarized his own article for Cato Unbound co-authored with Dan Gardner that in turn summarized the themes of the book:

The average expert’s forecasts were revealed to be only slightly more accurate than random guessing—or, to put more harshly, only a bit better than the proverbial dart-throwing chimpanzee. And the average expert performed slightly worse than a still more mindless competition: simple extrapolation algorithms that automatically predicted more of the same.

The experts could be divided roughly into two overlapping yet statistically distinguishable groups. One group (the hedgehogs) would actually have been beaten rather soundly even by the chimp, not to mention the more formidable extrapolation algorithm. The other (the foxes) would have beaten the chimp and sometimes even the extrapolation algorithm, although not by a wide margin.

The hedgehogs tended to use one analytical tool in many different domains; they preferred keeping their analysis simple and elegant by minimizing “distractions.” These experts zeroed in on only essential information, and they were unusually confident—they were far more likely to say something is “certain” or “impossible.” In explaining their forecasts, they often built up a lot of intellectual momentum in favor of their preferred conclusions. For instance, they were more likely to say “moreover” than “however.”

The foxes used a wide assortment of analytical tools, sought out information from diverse sources, were comfortable with complexity and uncertainty, and were much less sure of themselves—they ten

...
Scenario analyses for technological progress for the next decade
by VipulNaik — 2014-07-14T16:31:59.625Z

This is a somewhat long and rambling post. Apologies for the length. I hope the topic and content are interesting enough for you to forgive the meandering presentation.

I blogged about the scenario planning method a while back, where I linked to many past examples of scenario planning exercises. In this post, I take a closer look at scenario analysis in the context of understanding the possibilities for the unfolding of technological progress over the next 10-15 years. Here, I will discuss some predetermined elements and critical uncertainties, offer my own scenario analysis, and then discuss scenario analyses by others.

Remember: it is not the purpose of scenario analysis to identify a set of mutually exclusive and collectively exhaustive outcomes. In fact, usually, the real-world outcome has some features from two or more of the scenarios considered, with one scenario dominating somewhat. As I noted in my earlier post:

The utility of scenario analysis is not merely in listing a scenario that will transpire, or a collection of scenarios a combination of which will transpire. The utility is in how it prepares the people undertaking the exercise for the relevant futures. One way it could so prepare them is if the early indicators of the scenarios are correctly chosen and, upon observing them, people are able to identify what scenario they're in and take the appropriate measures quickly. Another way is by identifying some features that are common to all scenarios, though the details of the feature may differ by scenario. We can therefore have higher confidence in these common features and can make plans that rely on them.

The predetermined element: the imminent demise of Moore's law "as we know it"

As Steven Schnaars noted in Megamistakes (discussed here), forecasts of technological progress in most domains have been overoptimistic, but in the domain of computing, they've been largely spot-on, mostly because the raw technology has improved quickly. The main reason has been Moore's law, and a couple other related laws, that have undergirded technological progress. But now, the party is coming to an end! The death of Moore's law (as we know it) is nigh, and there are significant implications for the future of computing.

Moore's law refers to many related claims about technological progress. Some forms of this technological progress have already stalled. Other form...
Meetup : MIRIx Tel-Aviv
by  — 2014-07-14T13:58:13.429Z

Discussion article for the meetup : MIRIx Tel-Aviv

WHEN:
18 July 2014 09:00:00AM (+0300)

WHERE:
Tel Aviv Makers Insurgence, Studio 106 (Third Floor), 45 Kibbutz Galuyot Street, Tel-Aviv, Israel

We will be holding a MIRIx workshop on July 18th, 2014, at the Tel-Aviv Makers Insurgence. If you've been reading the invites to regular Tel-Aviv meetups and wondering when we're finally going to teach you to build a Friendly AI that runs on cold fusion... we're sorry, but there is not yet any such thing as cold fusion.

We will, however, be investigating how to take probabilities over sentences in expressive logics (logical probability) for use in Updateless Decision Theory (and its associated Updateless Intelligence Metrics).

Discussion article for the meetup : MIRIx Tel-Aviv
Open thread, 14-20 July 2014
by David_Gerard — 2014-07-14T11:16:39.188Z

Previous thread

If it's worth saying, but not worth its own post (even in Discussion), then it goes here.

Notes for future OT posters:

1. Please add the 'open_thread' tag.

2. Check if there is an active Open Thread before posting a new one.

3. Open Threads should be posted in Discussion, and not Main.

4. Open Threads should start on Monday, and end on Sunday.
LW Australia's online hangout results, (short stories about cognitive biases)
by Elo — 2014-07-14T06:25:15.518Z

In the Australia Mega-Online-hangout; a member mentioned a task/goal of his to write a few short stories to convey cognitive biases. After a while and a few more goals, someone suggested we actually write the short stories (the power of group resources!). So we did. They might be a bit silly, answers are at the very bottom, try to guess the biases.

We had some fun writing them up. This project was intended to be a story-per-day blog. feel free to write a short story in the discussion, or comment on how a different cognitive bias might be attributed to any of the stories.

-------------

Guess the bias in the short stories:

Cathy hates catching the train. She hates waiting in line for tickets, she hates lazy people who can't get their wallet out before they get to the front of the line, she hates missing her train because people are disorganised and carry bags of junk around with them, "why are you so disorganised", she said to the woman in front of her, who looks at her in a huff. As she gets to the front of the line she opens her bag to find her wallet, she looks under her umbrella that she keeps for a rainy day, even though its not rainy today, moves her phone to her pocket so that she can listen to a rationality audiobook when she gets on the train, moves her book away, shuffles the gum around that she never eats, rifles past the dirty tissues and finally pulls out her wallet. A grumpy man behind cathy in the line mutters, "why are you so disorganised". Which she knows is not true because she is usually very organised.

--------------------------------------------

Mark always felt like an outcast. He was always dressing a little wacky, and enjoyed hanging out with people like him. He was especially fond of wearing Hawaiian shirts! When we was walking in the mall yesterday a man in a suit and holding a clipboard came up to him and started talking to him about donating to charity. As usual he brushed him off and kept walking. Today a man in a Hawaiian shirt and shorts; also with a clipboard came up to him and started talking to him about donating to charity. But that's okay, he was just doing his job. Mark chatted to him for a few minutes and considered donating.

--------------------------------------------

Mr. Fabulous Fox was in a hurry, he had to get

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Bragging Thread, July 2014
by diegocaleiro — 2014-07-14T03:22:21.404Z

Your job, should you choose to accept it, is to comment on this thread explaining the most awesome thing you've done this since June 1st. You may be as blatantly proud of yourself as you feel. You may unabashedly consider yourself the coolest freaking person ever because of that awesome thing you're dying to tell everyone about. This is the place to do just that.

Remember, however, that this isn't any kind of progress thread. Nor is it any kind of proposal thread. This thread is solely for people to talk about the awesome things they have done. Not "will do". Not "are working on". Have already done. This is to cultivate an environment of object level productivity rather than meta-productivity methods.

So, what's the coolest thing you've done this month?
This is why we can't have social science
by Costanza — 2014-07-13T21:04:41.700Z

Jason Mitchell is [edit: has been] the John L. Loeb Associate Professor of the Social Sciences at Harvard. He has won the National Academy of Science's Troland Award as well as the Association for Psychological Science's Janet Taylor Spence Award for Transformative Early Career Contribution.

Here, he argues against the principle of replicability of experiments in science. Apparently, it's disrespectful, and presumptively wrong.

Recent hand-wringing over failed replications in social psychology is largely pointless, because unsuccessful experiments have no meaningful scientific value.

Because experiments can be undermined by a vast number of practical mistakes, the likeliest explanation for any failed replication will always be that the replicator bungled something along the way. Unless direct replications are conducted by flawless experimenters, nothing interesting can be learned from them.

Three standard rejoinders to this critique are considered and rejected. Despite claims to the contrary, failed replications do not provide meaningful information if they closely follow original methodology; they do not necessarily identify effects that may be too small or flimsy to be worth studying; and they cannot contribute to a cumulative understanding of scientific phenomena.

Replication efforts appear to reflect strong prior expectations that published findings are not reliable, and as such, do not constitute scientific output.

The field of social psychology can be improved, but not by the publication of negative findings. Experimenters should be encouraged to restrict their “degrees of freedom,” for example, by specifying designs in advance.

Whether they mean to or not, authors and editors of failed replications are publicly impugning the scientific integrity of their colleagues. Targets of failed replications are justifiably upset, particularly given the inadequate basis for replicators’ extraordinary claims.

This is why we can't have social science. Not because the subject is not amenable to the scientific method -- it obviously is. People are conducting controlled experiments and other people are attempting to replicate the results. So far, so good. Rather, the problem is that at least one celebrated authority in the field hates that, and would prefer much, much more deference to authority.
Why I Am Not a Rationalist, or, why several of my friends warned me that this is a cult
by Algernoq — 2014-07-13T17:54:51.004Z

A common question here is how the LW community can grow more rapidly. Another is why seemingly rational people choose not to participate.

I've read all of HPMOR and some of the sequences, attended a couple of meetups, am signed up for cryonics, and post here occasionally. But, that's as far as I go. In this post, I try to clearly explain why I don't participate more and why some of my friends don't participate at all and have warned me not to participate further.

Rationality doesn't guarantee correctness. Given some data, rational thinking can get to the facts accurately, i.e. say what "is". But, deciding what to do in the real world requires non-rational value judgments to make any "should" statements. (Or, you could not believe in free will. But most LWers don't live like that.) Additionally, huge errors are possible when reasoning beyond limited data. Many LWers seem to assume that being as rational as possible will solve all their life problems. It usually won't; instead, a better choice is to find more real-world data about outcomes for different life paths, pick a path (quickly, given the time cost of reflecting), and get on with getting things done. When making a trip by car, it's not worth spending 25% of your time planning to shave off 5% of your time driving. In other words, LW tends to conflate rationality and intelligence.

In particular, AI risk is overstated There are a bunch of existential threats (asteroids, nukes, pollution, unknown unknowns, etc.). It's not at all clear if general AI is a significant threat. It's also highly doubtful that the best way to address this threat is writing speculative research papers, because I have found in my work as an engineer that untested theories are usually wrong for unexpected reasons, and it's necessary to build and test prototypes in the real world. My strong suspicion is that the best way to reduce existential risk is to build (non-nanotech) self-replicating robots using existing technology and online ordering of materials, and use the surplus income generated to brute-force research problems, but I don't know enough about manufacturing automation to be sure.

LW has a cult-like social structure. The LW meetups (or, the ones I experienced) are very open to new people. Learning the keywords and some of the cached thoughts for the LW community results in a bunch of new friends and

...
Confused as to usefulness of 'consciousness' as a concept
by KnaveOfAllTrades — 2014-07-13T11:01:18.834Z

Years ago, before I had come across many of the power tools in statistics, information theory, algorithmics, decision theory, or the Sequences, I was very confused by the concept of intelligence. Like many, I was inclined to reify it as some mysterious, effectively-supernatural force that tilted success at problem-solving in various domains towards the 'intelligent', and which occupied a scale imperfectly captured by measures such as IQ.
Realising that 'intelligence' (as a ranking of agents or as a scale) was a lossy compression of an infinity of statements about the relative success of different agents in various situations was part of dissolving the confusion; the reason that those called 'intelligent' or 'skillful' succeeded more often was that there were underlying processes that had a greater average tendency to output success, and that greater average success caused the application of the labels.
Any agent can be made to lose by an adversarial environment. But for a fixed set of environments, there might be some types of decision processes that do relatively well over that set of environments than other processes, and one can quantify this relative success in any number of ways.
It's almost embarrassing to write that since put that way, it's obvious. But it still seems to me that intelligence is reified (for example, look at most discussions about IQ), and the same basic mistake is made in other contexts, e.g. the commonly-held teleological approach to physical and mental diseases or 'conditions', in which the label is treated as if—by some force of supernatural linguistic determinism—it *causes* the condition, rather than the symptoms of the condition, in their presentation, causing the application of the labels. Or how a label like 'human biological sex' is treated as if it is a true binary distinction that carves reality at the joints and exerts magical causal power over the characteristics of humans, when it is really a fuzzy dividing 'line' in the space of possible or actual humans, the validity of which can only be granted by how well it summarises the characteristics.
For the sake of brevity, even when we realise these approximations, we often use them without commenting upon or disclaiming our usage, and in many cases this is sensible. Indeed, in many cases it's not clear what the exact, decompressed form of a concept would be, or it se...
Meetup : Robin Hanson: Why is Abstraction both Statusful and Silly?
by Vika — 2014-07-13T06:18:48.396Z

Discussion article for the meetup : Robin Hanson: Why is Abstraction both Statusful and Silly?

WHEN:
14 July 2014 07:00:00PM (-0400)

WHERE:
Citadel House, 98 Elm St Apt 1, Somerville, MA

Robin Hanson will give a short informal talk followed by a discussion on the reasons why abstraction is both statusful and silly! Meetup starts at 7pm, talk starts at 7:30pm.

Discussion article for the meetup : Robin Hanson: Why is Abstraction both Statusful and Silly?
Communicating forecast uncertainty
by VipulNaik — 2014-07-12T21:30:54.053Z

Note: This post is part of my series of posts on forecasting, but this particular post may be of fairly limited interest to many LessWrong readers. I'm posting it here mainly for completeness. As always, I appreciate feedback.

In the course of my work looking at forecasting for MIRI, I repeatedly encountered discussions of how to communicate forecasts. In particular, a concern that emerged repeatedly was the clear communication of the uncertainty in forecasts. Nate Silver's The Signal and the Noise, in particular, focused quite a bit on the virtue of clear communication of uncertainty, in contexts as diverse as financial crises, epidemiology, weather forecasting, and climate change.

In this post, I pull together discussions from a variety of domains about the communication of uncertainty, and also included my overall impression of the findings.

Summary of overall findings

In cases where forecasts are made and used frequently (the most salient example being temperature and precipitation forecasts) people tend to form their own models of the uncertainty surrounding forecasts, even if you present forecasts as point estimates. The models people develop are quite similar to the correct ones, but still different in important ways.

In cases where forecasts are made more rarely, as with forecasting rare events, people are more likely to have simpler models that acknowledge some uncertainty but are less nuanced. In these cases, acknowledging uncertainty becomes quite important, because wrong forecasts of such events can lead to a loss of trust in the forecasting process, and can lead people to ignore correct forecasts later.

In some cases, there are arguments for modestly exaggerating small probabilities to overcome specific biases that people have that cause them to ignore low-probability events.

However, the balance of evidence suggests that forecasts should be reported as honestly as possible, and all uncertainty should be clearly acknowledged. If the forecast does not acknowledge uncertainty, people are likely to either use their own models of uncertainty, or lose faith in the forecasting process entirely if the forecast turns out to be far off from reality.

Probabilities of adverse events and the concept of the cost-loss ratio

A useful concept developed for understanding the utility of weather forecasting is the cost-loss model (Wikipedia). Consider that if a part...
"Dialectics"?
by CyrilDan — 2014-07-12T06:34:00.244Z

Hi, everyone.

I just started reading Total Freedom by Chris Sciabarra (warning: politics book), and a good half of it seems to be about 'dialectics' as a thinking tool, but it's been total rubbish in trying to explain it. From poking around on the internet, it seems to have been a proto-systems theory that became a Marxist shibboleth.

Am I understanding that correctly? The LW survey says about 1 in 4 of us is a communist, so I'm hoping someone can point to me resources or something. Also, I've read through most of the sequences, and it didn't use the word dialectics in there at all, which seems strange if it's such a useful thinking tool. Is there something wrong with it as an epistemological practice? Is the word just outdated?

Sorry about the (tangentially) political post, I'm just kind of confused. Help?
[Retracted] More silent deletion; LessWrong's moderation needs to change
by Will_Newsome — 2014-07-12T00:36:01.810Z

Edit: Fuck it, Rain is right. Eliezer totally unjustifiably deleted some of my upvoted comments earlier today and this post was about that, but really this is some petty drama and even the troll in me is bored of it. I won't delete this because then people will assume Eliezer did it as is his penchant lately, so I'll just leave it here so y'all can kindly downvote it into oblivion with my enthusiastic consent. Cheers.
Forecasting rare events
by VipulNaik — 2014-07-11T22:48:43.212Z

In an earlier post, I looked at some general domains of forecasting. This post looks at some more specific classes of forecasting, some of which overlap with the general domains, and some of which are more isolated. The common thread to these classes of forecasting is that they involve rare events.

Different types of forecasting for rare events

When it comes to rare events, there are three different classes of forecasts:

Point-in-time-independent probabilistic forecasts: Forecasts that provide a probability estimate for the event occurring in a given timeframe, but with no distinction based on the point in time. In other words, the forecast may say "there is a 5% chance of an earthquake higher than 7 on the Richter scale in this geographical region in a year" but the forecast is not sensitive to the choice of year. These are sufficient to inform decisions on general preparedness. In the case of earthquakes, for instance, the amount of care to be taken in building structures can be determined based on these forecasts. On the other hand, it's useless for deciding the timing of specific activities.

Point-in-time-dependent probabilistic forecasts: Forecasts that provide a probability estimate that varies somewhat over time based on history, but aren't precise enough for a remedial measure that substantially offsets major losses. For instance, if I know that an earthquake will occur in San Francisco in the next 6 months with probability 90%, it's still not actionable enough for a mass evacuation of San Francisco. But some preparatory measures may be undertaken.

Predictions made with high confidence (i.e., a high estimated probability when the event is predicted) and a specific time, location, and characteristics: Precise predictions of date and time, sufficient for remedial measures that substantially offset major losses (but possibly at huge, if much smaller, cost). The situation with hurricanes, tornadoes, and blizzards is roughly in this category.

Statistical distributions: normal distributions versus power law distributions

Perhaps the most ubiquitous distribution used in probability and statistics is the normal distribution. The normal distribution is a symmetric distribution whose probability density function decays superexponentially with distance from the mean (more precisely, it is exponential decay in the square of the distance). In other words, the prob...