nhaliday + rat-pack   27

Overcoming Bias : Chip Away At Hard Problems
One of the most common ways that wannabe academics fail is by failing to sufficiently focus on a few topics of interest to academia. Many of them become amateur intellectuals, people who think and write more as a hobby, and less to gain professional rewards via institutions like academia, media, and business. Such amateurs are often just as smart and hard-working as professionals, and they can more directly address the topics that interest them. Professionals, in contrast, must specialize more, have less freedom to pick topics, and must try harder to impress others, which encourages the use of more difficult robust/rigorous methods.

You might think their added freedom would result in amateurs contributing more to intellectual progress, but in fact they contribute less. Yes, amateurs can and do make more initial progress when new topics arise suddenly far from topics where established expert institutions have specialized. But then over time amateurs blow their lead by focusing less and relying on easier more direct methods. They rely more on informal conversation as analysis method, they prefer personal connections over open competitions in choosing people, and they rely more on a perceived consensus among a smaller group of fellow enthusiasts. As a result, their contributions just don’t appeal as widely or as long.
ratty  postrat  culture  academia  science  epistemic  hanson  frontier  contrarianism  thick-thin  long-term  regularizer  strategy  impact  essay  subculture  meta:rhetoric  aversion  discipline  curiosity  rigor  rationality  rat-pack  🤖  success  2016  farmers-and-foragers  exploration-exploitation  low-hanging  clarity  vague  🦉  optimate  systematic-ad-hoc  metameta  s:***  discovery  focus  info-dynamics  hari-seldon 
december 2016 by nhaliday
Fact Posts: How and Why
The most useful thinking skill I've taught myself, which I think should be more widely practiced, is writing what I call "fact posts." I write a bunch of these on my blog. (I write fact posts about pregnancy and childbirth here.)

To write a fact post, you start with an empirical question, or a general topic. Something like "How common are hate crimes?" or "Are epidurals really dangerous?" or "What causes manufacturing job loss?"

It's okay if this is a topic you know very little about. This is an exercise in original seeing and showing your reasoning, not finding the official last word on a topic or doing the best analysis in the world.

Then you open up a Google doc and start taking notes.

You look for quantitative data from conventionally reliable sources. CDC data for incidences of diseases and other health risks in the US; WHO data for global health issues; Bureau of Labor Statistics data for US employment; and so on. Published scientific journal articles, especially from reputable journals and large randomized studies.

You explicitly do not look for opinion, even expert opinion. You avoid news, and you're wary of think-tank white papers. You're looking for raw information. You are taking a sola scriptura approach, for better and for worse.

And then you start letting the data show you things.

You see things that are surprising or odd, and you note that.

You see facts that seem to be inconsistent with each other, and you look into the data sources and methodology until you clear up the mystery.

You orient towards the random, the unfamiliar, the things that are totally unfamiliar to your experience. One of the major exports of Germany is valves? When was the last time I even thought about valves? Why valves, what do you use valves in? OK, show me a list of all the different kinds of machine parts, by percent of total exports.

And so, you dig in a little bit, to this part of the world that you hadn't looked at before. You cultivate the ability to spin up a lightweight sort of fannish obsessive curiosity when something seems like it might be a big deal.

And you take casual notes and impressions (though keeping track of all the numbers and their sources in your notes).

You do a little bit of arithmetic to compare things to familiar reference points. How does this source of risk compare to the risk of smoking or going horseback riding? How does the effect size of this drug compare to the effect size of psychotherapy?

You don't really want to do statistics. You might take percents, means, standard deviations, maybe a Cohen's d here and there, but nothing fancy. You're just trying to figure out what's going on.

It's often a good idea to rank things by raw scale. What is responsible for the bulk of deaths, the bulk of money moved, etc? What is big? Then pay attention more to things, and ask more questions about things, that are big. (Or disproportionately high-impact.)

You may find that this process gives you contrarian beliefs, but often you won't, you'll just have a strongly fact-based assessment of why you believe the usual thing.
ratty  lesswrong  essay  rhetoric  meta:rhetoric  epistemic  thinking  advice  street-fighting  scholar  checklists  🤖  spock  writing  2016  info-foraging  rat-pack  clarity  systematic-ad-hoc  bounded-cognition  info-dynamics  let-me-see  nitty-gritty  core-rats  evidence-based  truth 
december 2016 by nhaliday
How to do career planning properly - 80,000 Hours
- A/B/Z plans
- make a list of red flags and commit to reviewing at some point (and at some interval)
advice  strategy  career  planning  80000-hours  long-term  thinking  guide  summary  checklists  rat-pack  tactics  success  working-stiff  flexibility  wire-guided  progression  ratty 
november 2016 by nhaliday
orthonormal comments on Where to Intervene in a Human? - Less Wrong
The highest-level hack I've found useful is to make a habit of noticing and recording the details of any part of my life that gives me trouble. It's amazing how quickly patterns start to jump out when you've assembled actual data about something that's vaguely frustrated you for a while.
lifehack  productivity  workflow  rationality  advice  akrasia  quantified-self  growth  habit  discipline  lesswrong  ratty  rat-pack  biases  decision-making  🦉  wire-guided  time-use  s:null 
july 2016 by nhaliday
What is up with carbon dioxide and cognition? An offer - Less Wrong Discussion
study: http://ehp.niehs.nih.gov/1104789/
n=22, p-values < .001 generally, no multiple comparisons or anything, right?
chart: http://ehp.niehs.nih.gov/wp-content/uploads/2012/11/ehp.1104789.g002.png
- note it's CO2 not oxygen that's relevant
- some interesting debate in comments about whether you would find similar effects for similar levels of variation in oxygen, implications for high-altitude living, etc.
- CO2 levels can range quite to quite high levels indoors (~1500, and even ~7000 in some of Gwern's experiments); this seems to be enough to impact cognition to a significant degree
- outdoor air quality often better than indoor even in urban areas (see other studies)

the solution: houseplants, http://lesswrong.com/lw/nk0/what_is_up_with_carbon_dioxide_and_cognition_an/d956

https://twitter.com/menangahela/status/965167009083379712
https://archive.is/k0I0U
except that environmental instability tends to be harder on more 'complex' adaptations and co2 ppm directly correlates with decreased effectiveness of cognition-enhancing traits vis chronic low-grade acidosis
productivity  study  gotchas  workflow  money-for-time  neuro  gwern  embodied  hypochondria  hmm  lesswrong  🤖  spock  nootropics  embodied-cognition  evidence-based  ratty  clever-rats  atmosphere  rat-pack  psychology  cog-psych  🌞  field-study  multi  c:**  2016  human-study  acmtariat  embodied-street-fighting  biodet  objective-measure  decision-making  s:*  embodied-pack  intervention  iq  environmental-effects  branches  unintended-consequences  twitter  social  discussion  backup  gnon  mena4  land  🐸  environment  climate-change  intelligence  structure 
may 2016 by nhaliday

bundles : initstarsthinking

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