trentmkelly/LessWrong-43k huggingface dataset:
Prediction-based medicine (PBM)
by ChristianKl — 2016-12-29T22:49:21.509Z

We need a new paradigm for doing medicine. I make the case by first speaking about the problems of our current paradigm of evidence-based medicine.

The status quo of evidence-based medicine

While biology moves forward and the cost of genetic-sequencing dropped a lot faster than Moore's law the opposite is true for the development of new drugs. In the current status quo the development of new drugs rises exponentially with Eroom's law. While average lifespan increased greatly about the last century in Canada the average life span at age 90 increased only 1.9 years over the last century. In 2008 the Centers for Disease Control and Prevention reported that life expectancy in the US declined from 77.9 to 77.8 years. After Worldbank data Germany increased average lifespan by two years over the last decade which is not enough for the dream of radical lifespan increases in our lifetime.

When it costs 80 million to test whether an intervention works and most attempts show that the intervention doesn't work we have a problem. We end up paying billions for every new intervention.

Eric Ries wrote "The Lean Startup". In it he argues that it's the job of a startup to produce validated learning. He proposes that companies that work with small batch sizes can produce more innovation because they can learn faster how to build good products. The existing process in medicine doesn't allow for small batch innovation because the measuring stick for whether an intervention works is too expensive.

In addition the evidence-based approach rests on the assumption that we don't build bespoke interventions for every client. As long as a treatment doesn’t generalize about multiple different patients, it’s not possible to test it with a trial. In principle a double-blind trial can't give you evidence that a bespoke intervention that targets the specific DNA profile of a patient and his co-morbidity works.

The ideal of prediction-based medicine

The evidence-based approach also assumes that practitioners are exchangeable. It doesn't model the fact that different physical therapist or psychologists have different skill levels. It doesn't provide a mechanism to reward highly skilled practitioners but it treats every practitioner that uses the same treatment intervention the same way.

Its strong focus on asking whether a treatment beats a placebo in double-blind studies makes it hard to compare diff...
Meetup : Cambridge/Boston Less Wrong: Planning 2017
by jimrandomh — 2016-12-29T22:43:55.164Z

Discussion article for the meetup : Cambridge/Boston Less Wrong: Planning 2017

WHEN:
01 January 2017 03:30:00PM (-0500)

WHERE:
98 Elm St Apt 1 Somerville, MA

No plan survives contact with the enemy, or in the case of new year's resolutions, with the year. But planning is nevertheless sometimes quite valuable - as long as we're aware of the limitations, and think about strategies that are flexible and generically useful.

This January first, we will plan for the year ahead, but not in the style of new year's resolutions and imposing edicts on our future selves. Instead, we'll each individually step back, consider our priorities and possible long range plans, with a spirit of curiosity and the goal of realistically determining what will happen if we choose different strategies.

Discussion article for the meetup : Cambridge/Boston Less Wrong: Planning 2017
How do you identify complex systems?
by NatashaRostova — 2016-12-29T04:39:51.637Z

As I have studied scientific inference over the past decade, there is one major class of problems that frustrates me the most. It's what I think most people here focus on understanding: How to properly identify a complex system.

We all basically know that complex systems are unpredictable, in certain types of ways, due to incredible differences in outcomes present in small changes in starting parameters. Despite this, there seem to be some complex systems that still follow a certain state or pattern identifiable to a single input. In my field of study (economics) we know that if you increase the money supply inflation follows. Sort of. Lots of people though inflation would follow from quantitative easing following the Great Recession, when it didn't (in fact, if anything the opposite).

In the graph below you see that the market based expected inflation rate in 5 years shot up right as the economy began to tank, for fear of quantitative easing and low interest rates raising inflation (as they had in the past). In reality, so the retrospective story goes, the velocity of money dropped so heavily this countered any inflationary impacts. Then under a year later the market decided it would probably converge to its mean. This is how market based predictions went in arguably the most well understood part of macro-economics.

(I actually worked on a team at the Fed to try to help do this even better later on. Our team was the Financial Econ team, so we used market based measures instead of trying to build formal structural models. Could we predict inflation expectation dynamics better than a random walk? Yeah, but only just by the skin of our teeth. Plus, to paraphrase Tetlock, t's a field rewarded more by mastery of impressive tools than for accuracy)

As far as macro-economics goes, there are few things economists more reliably understand than the relationship between money and inflation. Within simple models (and probably in reality) it's clear that if you increase the monetary supply to infinity, the value is going to drop to zero. I can't prove it based on historical experiment, but it's reasonable to use fragments of past information and models to predict that if the government dropped trillions of dollars from helicopters over major U.S. cities, inflation would shoot up.

Despite this, there seem to be complex nonlinearities that mess with our predictions outside of ...
Bargaining trade-offs in your brain
by Elo — 2016-12-29T00:18:16.596Z

Part 1: Exploration-Exploitation

Part 2: Original post: Bargaining Trade-offs to your brain.

Side post: Empirically assess your time

Part 3: The time that you have

So you are now at the point where you are thinking about your exploration and exploitation behaviours. Exploration and exploitation is a trade off between the value of what you know and the value of what you might know if you find out.

Remember the heuristic: You want to explore as much as to increase your information with regard to both the quality of the rest of the exploration and possible results and the expected returns on the existing knowledge.

You know they are trade-offs between different types of things you want to do, specifically I want to address this:

You probably make the most measurable and ongoing gains in the Exploitation phase. I mean – lets face it, these are long running goal-seeking behaviours like sticking to an exercise routine.

The exploration might be seem more fun (find exciting and new hobbies) but are you sure that’s what you want to be doing in regard to 1?

What's going on here? If you know you know where to get the most reward, say exercising but you don't find yourself doing the action you seem to know is the right thing, what's going on?

I covered increasing complexity of models, and in aid of that I propose the idea of Bargaining trade-offs to your own brain.

As long as you take it with a grain of All models are wrong.

"The most that can be expected from any model is that it can supply a useful approximation to reality: All models are wrong; some models are useful" - George Box

Bargaining with your own brain

We take the model of Daniel Kanemann, in Thinking, fast and slow, that the brain is made of up System 1 and System 2. System 1 feels like the automatic intuitive process, the process that can catch things thrown at it, or know what an apple looks like, just as *it is this thing*. System 2 is the part of you that can do complicated stepwise tasks, like multiplication of long numbers, following a recipe.

Going back to our example, it seems like we have an intellectual understanding that exercise is a good thing. Yea; pretty important for health and long term well-being. But why is that not automatically an e...
A different argument against Universal Basic Income
by chaosmage — 2016-12-28T22:35:31.696Z

I grew up in socialist East Germany. Like most of my fellow citizens, I was not permitted to leave the country. But there was an important exception: People could leave after retirement. Why? Because that meant their forfeited their retirement benefits. Once you took more from the state than you gave, you were finally allowed to leave. West Germany would generously take you in. My family lived near the main exit checkpoint for a while and there was a long line of old people most days.

And then there is Saudi Arabia and other rentier states. Rentier states(https://en.m.wikipedia.org/wiki/Rentier_state) derive most of their income not from their population. The population gets a lot more wealth from the state than the state gets from the population. States like Saudi Arabia are therefore relatively independent of their population's consent with policy. A citizen who is unhappy is welcome to leave, or to retreat to their private sphere and live off benefits while keeping their mouth shut - neither of these options incurs a significant cost for the state.

I think these facts are instructive in thinking about Universal Basic Income. I want to make a point that I haven't seen made in discussions of the matter.

Most political systems (not just democracies) are built on an assumption that the state needs its citizens. This assumption is always a bit wrong - for example, no state has much need of the terminally ill, except to signal to its citizens that it cares for all of them. In the cases of East Germany and Saudi Arabia, this assumption is more wrong. And Universal Basic Income makes it more wrong as well.

From the point of view of a state, there are citizens who are more valuable (or who help in competition with other states) and ones who are more of a burden (who make competing with other states more difficult). Universal Basic Income massively broadens the part of society that is a net loss to the state.

Now obviously technological unemployment is likely to do that anyway. But there's a difference between answers to that problem that divide up the available work between the members of society and answers that divide up society into contributors and noncontributors. My intuition is that UBI is the second kind of solution, because states will be incentivized to treat contributors differently from noncontributors. The examples are to illustrate that a state can behave...
Meetup : Washington, D.C.: Fun & Games
by RobinZ — 2016-12-28T14:42:59.528Z

Discussion article for the meetup : Washington, D.C.: Fun & Games

WHEN:
01 January 2017 03:30:00PM (-0500)

WHERE:
Donald W. Reynolds Center for American Art and Portraiture

We will be meeting in the courtyard to hang out, play games, and have fun conversations.

Upcoming meetups:

Jan. 8: Effective Altruism

Jan. 15: Half-Assing

Discussion article for the meetup : Washington, D.C.: Fun & Games
Open thread, Dec. 26, 2016 - Jan. 1, 2017
by username2 — 2016-12-28T12:23:21.661Z

If it's worth saying, but not worth its own post, 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. (Immediately before; refresh the list-of-threads page before posting.)

3. Open Threads should start on Monday, and end on Sunday.

4. Unflag the two options "Notify me of new top level comments on this article" and "Make this post available under..." before submitting.
Exploration-Exploitation problems
by Elo — 2016-12-28T01:33:01.033Z

Original post: http://bearlamp.com.au/exploration-exploitation-problems/

I have been working on the assumption that exploration-exploitation knowledge was just common. Unfortunately I did the smart thing of learning about them from a mathematician at a dojo in Melbourne, which means that no. Not everyone knows about it. I discovered that again today when I searched for a good quick explanation of the puzzle. With that in mind this is about Exploration Exploitation.

The classic Exploration-Exploitation problem in mathematics is the multi-armed bandit. Which is a slang term for a bank of slot machines. Where the player knows that each machine has a variable payoff and you have a limit number of attempts before you run out of money. You want to balance trying out new machines with unknown payoffs against exploiting the knowledge you already have from the earlier machines you tried.

When you first start on new bandits, you really don't know which will pay out and at what rates. So some exploration is necessary to know what your reward ratio in the territory will be. As your knowledge grows, you get to know which bandits are likely to pay, and which are not, and this later informs your choices as to where to place your dollars.

Mathematicians love a well specified problem like this because it allows us to make algorithm models of patterns that will return rewards or guarantee rewards given certain circumstances. (see also - the secretary problem which does similar. Where I showed how it applied to real life dating)

Some of the mathematical solutions to this problem look like:

Epsilon greedy - The best lever is selected for a proportion 1-ε of the trials, and a lever is selected at random (with uniform probability) for a proportion ε. A typical parameter value might be ε =0.1 but this can vary widely depending on circumstances.

Epsilon-decreasing strategy: Similar to the epsilon-greedy strategy, except that the value of ε decreases as the experiment progresses, resulting in highly exploratory behaviour at the start and highly exploitative behaviour at the finish.

Of course there are more strategies, and the context and nature of the problem matters. If&...
Make Putanumonit Great (for the first time)
by Jacob Falkovich — 2016-12-27T16:50:19.000Z

Good news: 2016 is coming to a close.

Bad news: “2016” is an arbitrary construct without causal agency. The decline of civilization is not the calendar’s fault, and it’s unlikely to get any better in 2017. Only one thing is guaranteed to get better in 2017 – this blog. Because you will make it better.

At the bottom of the post is a short, multiple choice, anonymous survey, designed to give me feedback on improving Putanumonit. I have very little information on what my readers like and dislike. When 1,000 people read my post and 5 of them leave comments, I have no clue what the other 995 thought of it. Please answer as many questions as you can whether you’re a dedicated reader or just discovered Putanumonit for the first time.

If you are an enthusiastic reader and want to be involved in Putanumonit beyond just seeing the finalized posts, you can check out In Cahoots with Putanumonit but please come back afterwards and fill out the survey. The more honest your responses are, the more you’ll like this blog in 2017.

2017 Reader Survey

Each question is a separate poll, so don’t forget to hit Vote after answering each one.

How much of Putanumonit have you read? I write about 4 posts a month, and 53 total so far.

Many of my posts (but not all of them) fall into 3 broad categories:

“Lifestyle” posts are about applying math to your own life, like dating, shopping or playing the lottery.

“Research” posts are about sports, science and other issues that don’t directly affect your life.

“Political rationality” posts are about staying sane on questions of

...
Empirically assess your time use
by Elo — 2016-12-27T00:01:32.176Z

Original post on my blog: http://bearlamp.com.au/empirically-assess-your-time-use/

168 hours

You and me both buddy. We both have equal access to our 168 hours. We have different demands on our hours like the need for sleep or how demanding our job is, but fundamentally we all start with the same 168 hours (24*7=168). That means we all have an opportunity to look at these hours and figure out how to get the most out of them by doing the things we want to do in the limited hours we have.

This process is about looking at your hours and discovering where they go.

Make a list of all the things you have done over the last 7 days. As a time management technique, this is a procedure to follow that will be useful for other times.

This might take 30 minutes to do, and might take 10 minutes.

Acquire writing implements

Write down the day and time.

Subtract 7 days from now, and figure out where you were at the beginning of that day.

Go through your diary, your phone call log, your sms history, your messenger histories and work out what you did, in rough chunks, from when you woke up on that day until now.

Start with sleep - You probably have a regular enough wake up time, and a regular enough time that you go to sleep. This will put upper bounds on the number of hours in your life.

Identify Routines and how long they take - regular meals, lunch breaks, shower processes.

Identify regular commitments - meetings, clubs, events.

Identify any social time.

Identify time spent deeply working, studying, reading, planning

Identify exercise.

Identify dual purpose time, i.e. I play on facebook on the bus.

Here is an example

Monday
7 wake up
7-7:30 shower and get ready (30mins)
7:30-8 drive to work (30mins)
8-10 check emails and respond to people (1hour)
10-10:20 coffee break (20mins)
10:20-1 work meeting (2hrs40mins)
1-1:30 lunch (30mins)
1:30-3:30 computer work (2hrs)
3:30-3:40 distraction food break (10mins)
3:40-4:30 work (50mins)
4:30-5 drive home (30mins)
5-6 make and eat dinner (1hr)
6-7:30 netflix (1.5hrs)
7:30-11:30 play on computer (3.5hrs)
11:30-11:45 get ready and head to bed. (15mins)

Another option might be to simplify this to:

Monday
7am wake up
7-7:30 shower and breakfast (30mins)
7:30-8 drive to work (30mins)
8-1 work(5hrs)
1-1:30 lunch (30mins)
1:30-4:30 work (3hrs)
4:30-5 drive home (30mins)
5-6 dinner (1...
Wikipedia usage survey results
by VipulNaik — 2016-12-25T13:55:33.123Z

Contents

Summary

Surveys

First SurveyMonkey survey (S1)

Second SurveyMonkey survey (S2)

Google Surveys survey (GS)

Wikimedia Foundation New Readers survey (NR)

Other surveys

Motivation

Results

S1Q1: number of Wikipedia pages read per week

S1Q2: affinity for Wikipedia in search results

S1Q3: section vs whole page

S1Q4: search functionality on Wikipedia and surprise at lack of Wikipedia pages

S1Q5: behavior on pages

S2Q1: number of Wikipedia pages read per week

S2Q2: multiple-choice of articles read

S2Q3: free response of articles read

S2Q4: free response of surprise at lack of Wikipedia pages

GS

Data validation using known total United States Wikipedia pageviews

NRQ1: Do you use the Internet? and NRQ4: Have you ever heard of Wikipedia?

NRQ7: How often do you use Wikipedia?

Comparison against United States audiences

Data validation against known total country traffic

Summaries of responses (exports for SurveyMonkey, weblink for Google Surveys)

Takeaway: Huge gap between heavy users and general US audience, plus predictors of heavy use

Confirming the gap with numbers

Qualitative differences in other aspects of Wikipedia engagement

Predictors of audiences with high proportions of heavy users

Takeaway: Effect on impact estimates for pageviews

Upgrading estimate of impact based on reader quality

Potentially downgrading estimate of impact through reach

Takeaway: Gap with elites is stronger than demographic gaps in US but comparable to gap between US and low-income countries

Gender within the United States

Age within the United States

Cross-country comparison in perspective

Further reading

The making of this post

Document source

Original version and revision history

Survey cost

License

Summary

In 2016, Issa Rice and I conducted several surveys of Wikipedia usage. We collected survey responses from Slate Star Codex readers, Vipul’s Facebook friends, and a few United States audiences through SurveyMonkey Audience and Google Surveys (known at the time as Google Consumer Surveys). Our survey questions measured how heavily people use Wikipedia, what sort of pages they read or expected to find, the relation between their search habits and Wikipedia, and other actions they took within Wikipedia.

The surveys are part of our work to understand the impact of contributing to Wikipedia. Both of us regularly contribute to the site, and we are also ...
Meetup : Role playing game based on HPMOR in Moscow
by Alexander230 — 2016-12-25T08:22:38.217Z

Discussion article for the meetup : Role playing game based on HPMOR in Moscow

WHEN:
07 January 2017 03:00:00PM (+0300)

WHERE:
Большая Дорогомиловская, 5к2

Welcome to our custom made role playing game "Hogwarts and methods of rationality", inspired by HPMOR story! It will be intellectual role playing game with rationality-based minigames. You can read more detailed announce and description here: https://goo.gl/Y8oUND

Discussion article for the meetup : Role playing game based on HPMOR in Moscow
Expected Error, or how wrong you expect to be
by ozziegooen — 2016-12-24T22:49:02.344Z

Expected value commonly refers to the mean of a distribution that represents the expectations of a future event. It’s much more specific than “mathematical mean”. If one were to ask about the "mean" of a poker hand there would be confusion, but the ‘expected value’ is obvious.

While expected value is a popular term, the fact that it describes one point value means a lot of useful information is excluded.

Say you have two analysts for your Californian flower selling empire. Both hand you forecasts for next year's revenue. One of them tells you that revenue will be between $8 and $12 Million, with an average of $10 million. The other tells you that it will be between -$50 and $70 million, with an average of $10 million. These both have expected values of $10 million, but I would guess that you would be very interested in the size of those ranges. The uncertainty matters.

One could of course use standard deviation, variance, or literally hundreds of other parameters to describe this uncertainty. But I would propose that these parameters be umbrellaed under the concept of “expected error.” Typically the expected value gets a lot of attention; after all, that is the term in this arena that we have a name for. So an intuitive counter to this focus is the “expected error,” or how much we expect the expected value to be incorrect. In a different sense, the expected error is the part of an estimate that’s not its expected value.

Expected Error: The expected difference between an expected value and an actual value.

Or, “How wrong do you think your best guess will be?”

Hypothetically, any measure of statistical dispersion could be used to describe expected error. Standard deviation, interquartile range, entropy, average absolute deviation, etc. Of these, I think that the mean absolute deviation is probably the most obvious measure to use for expected error when using continuous variables. Expected value uses a mean, so the expected error could be the "expected value" of the error between the actual value and the referenced expected value.

The mean absolute deviation could selectively be divided by the mean to get the mean absolute percentage deviation, in cases where the percentage is more useful than the absolute number. So one could say that a specific fore...