Posts

Was a PhD necessary to solve outstanding math problems? 2020-07-10T18:43:17.342Z · score: 20 (6 votes)
Was a terminal degree ~necessary for inventing Boyle's desiderata? 2020-07-10T04:47:15.902Z · score: 30 (7 votes)
Survival in the immoral maze of college 2020-07-08T21:27:27.214Z · score: 36 (13 votes)
An agile approach to pre-research 2020-06-25T18:29:47.645Z · score: 13 (5 votes)
The point of a memory palace 2020-06-20T01:00:41.975Z · score: 18 (8 votes)
Using a memory palace to memorize a textbook. 2020-06-19T02:09:18.172Z · score: 55 (22 votes)
Bathing Machines and the Lindy Effect 2020-06-17T21:44:46.931Z · score: 15 (6 votes)
Two Kinds of Mistake Theorists 2020-06-11T14:49:47.186Z · score: 8 (5 votes)
Visual Babble and Prune 2020-06-04T18:49:30.044Z · score: 38 (10 votes)
Trust-Building: The New Rationality Project 2020-05-28T22:53:36.876Z · score: 38 (17 votes)
My stumble on COVID-19 2020-04-18T04:32:30.987Z · score: 40 (19 votes)
How superforecasting could be manipulated 2020-04-17T06:47:51.289Z · score: 24 (14 votes)
Alarm bell for the next pandemic, V.2 2020-04-15T06:47:59.415Z · score: 9 (4 votes)
Curiosity: A Greedy Feeling 2020-04-11T04:38:09.544Z · score: 41 (12 votes)
Would 2014-2016 Ebola ring the alarm bell? 2020-04-08T02:01:47.031Z · score: 16 (7 votes)
Would 2009 H1N1 (Swine Flu) ring the alarm bell? 2020-04-07T07:16:11.367Z · score: 33 (11 votes)
An alarm bell for the next pandemic 2020-04-06T01:35:03.283Z · score: 50 (15 votes)
Has LessWrong been a good early alarm bell for the pandemic? 2020-04-03T09:44:39.205Z · score: 14 (15 votes)
Forecasting an 80% chance of an effective anti COVID-19 drug (probably Remdesivir) 2020-03-15T19:21:31.187Z · score: 11 (3 votes)
Raw Post: Talking With My Brother 2019-07-13T02:57:42.142Z · score: 25 (9 votes)
AI Alignment "Scaffolding" Project Ideas (Request for Advice) 2019-07-11T04:39:11.401Z · score: 9 (4 votes)
The I Ching Series (2/10): How should I prioritize my career-building projects? 2019-07-09T22:55:05.848Z · score: 14 (4 votes)
The Results of My First LessWrong-inspired I Ching Divination 2019-07-08T21:26:36.133Z · score: 20 (12 votes)
Here Be Epistemic Dragons 2019-07-04T22:31:44.061Z · score: 9 (5 votes)
Archive of all LW essay contests 2019-05-30T06:40:02.587Z · score: 13 (3 votes)
Seeking suggestions for EA cash-prize contest 2019-05-29T20:44:35.311Z · score: 16 (6 votes)

Comments

Comment by allamericanbreakfast on Was a PhD necessary to solve outstanding math problems? · 2020-07-11T02:00:50.877Z · score: 1 (1 votes) · LW · GW

No idea, but I didn't see that for any of the people who I sampled for this study. I don't think it's common.

Comment by allamericanbreakfast on Was a PhD necessary to solve outstanding math problems? · 2020-07-11T02:00:08.672Z · score: 1 (1 votes) · LW · GW

That means that someone who's put years of work into a mathematical field has a strong advantage over someone who hasn't

This claim as stated stands in tension with the idea that mathematics is a young person's game. If true, we'd expect either a positive or no correlation between age and mathematical output.

By contrast, in the biological sciences we'd expect to see a phenomenon of accelerating individual output (as knowledge and resources accumulate) followed by a sharp decline (as the techniques an individual biologist is an expert in become obsolete).

My guess is that it's a mix. Some older biologists will indeed get outmoded, while others will continue to invest in new techniques. Mathematics doesn't have this burden. Another reason to expect it to have picked more of the high-hanging fruit on the tree of mathematical knowledge.

Comment by allamericanbreakfast on Was a PhD necessary to solve outstanding math problems? · 2020-07-10T23:58:54.149Z · score: 1 (1 votes) · LW · GW

I agree that a math PhD is probably mostly for the sake of convenience and companionship and mentorship.

Some of the math discoveries seem to have been the PhD work. Others were produced many years after the discoverer completed their PhD. It's a mix.

I did this research as much to find out whether I should see a PhD as attractive, as much as whether it's necessary. I hear lots of people bemoaning their PhD or criticizing the system as a gatekeeping tool. My conclusion is that yes, the PhD system is gatekeeping and yes, it is hard, but that's because producing new original academic knowledge is hard and the system far from perfect at identifying likely candidates. It's risky, and many fail, and failure sucks and generates complaints.

The successful ones just continue their work and don't bother to air their opinions on the system that they're a part of.

Comment by allamericanbreakfast on Was a PhD necessary to solve outstanding math problems? · 2020-07-10T23:48:36.980Z · score: 1 (1 votes) · LW · GW

It's interesting to consider to what extent mathematics is different from other fields. Perhaps groundbreaking biological research also requires a PhD, but for different reasons.

The "young man's game" conjecture posits that math is a race to fill your brain with knowledge before it expires. Perhaps the lack of empirical constraints means that many more of the fruits on the tree of mathematical knowledge have been picked. Sheer individual energy, stamina and intellectual ability is all that matters. Getting accepted to a math PhD mainly buys you time to do focused work during your youth.

Other fields have more intellectually-low-hanging fruit.

One possible reason is that the data takes so long to gather that sheer intellectual ability matters less than opportunity (access to training, lab space, collaborators and funding). That's not to say these researchers are less intelligent, but that intelligence brings diminishing marginal returns in their line of work and is not the bottleneck for faster progress. Getting accepted to a PhD in other non-math scientific fields mainly buys you resources and contacts to develop into a long-term research career.

Another possible reason is that scientists lack the resources or incentive to invest in efficiency. They use the same old tools rather than trying to invent better ones. They do conservative research that's easy to turn into a paper, rather than what's hard but truly useful.

If this model is true, then it suggests three avenues for speeding scientific progress.

  • To speed mathematical progress, assuming that IQ is real and fixed, we should scour the world for child mathematical prodigies in countries that don't have the capacity to identify them and give them access to opportunity. Mathematicians Without Borders? Is this already a thing?
  • To speed data-gathering, we should automate, encourage specialization in data collection vs. analysis vs. engineering, expand the number of PhD positions, increase funding, create tools that diminish ethical issues (e.g. organoids, which could replace some animal testing, and iPSCs, which avoid some of the ethical issues with embryonic tissue), and remove red tape.
  • To encourage efficiency, more funding should be awarded in the form of bounties for certain specific tools, techniques, or applications that are yet to be invented. Scientists who have perfected a certain rare and useful technique should create startups and commercialize their work, rather than try to further their career by being a sort of glorified technician on future projects. Outsiders should create companies that hire scientists to train others in the techniques they've perfected, increasing the division of labor between education/training/technique and creative research.
Comment by allamericanbreakfast on Was a terminal degree ~necessary for inventing Boyle's desiderata? · 2020-07-10T22:18:59.682Z · score: 1 (1 votes) · LW · GW

I'd chalk up the lack of a smoking/lung cancer Nobel to the fact that a rule of the Nobel Prize is that it a prize may not be shared among more than three individuals, nor awarded posthumously. I think it makes more sense to assume there just were too many contributions to select 1-3 individuals who should be credited with "discovering that smoking causes cancer."

Interpreted that way, the lack of a Nobel for smoking/lung cancer is actually evidence against your assertion that the Nobel Prize is about credentialism and against the phenomenon of credit-stealing by high-status individuals. Who wouldn't want to claim personal credit for discovering that smoking causes cancer if they could get away with it?

I'll check out the Portal podcast interview when I get a chance. Can you find a source for your claims about NLP and EMDR? It sounds like it needs an in-depth treatment to tease out the issues.

Remember, the overwhelming preponderance of PhD-holders among prize winners and discovery-makers means that there has to be a lot of mere credentialism and credit-stealing in order for those factors to explain the phenomenon. We should expect there to be plenty of clear-cut stories of out-and-out theft if that is true. Salient examples shouldn't be hard to find. Frankly, I just don't see it.

Comment by allamericanbreakfast on Was a terminal degree ~necessary for inventing Boyle's desiderata? · 2020-07-10T19:57:24.992Z · score: 1 (1 votes) · LW · GW

Here's an article re-analyzing Mueller's paper. It finds that "The quality of the group comparison was modest and it did not add qualitatively new knowledge compared to a report published 8 years earlier." So I'm not prepared to accept him as a candidate path-breaker.

Overall, I'm just not convinced that we have the levels of intellectual parasitism that would justify the idea that scientific credit is doled out to, or stolen by, people merely based on their credentials. Nor that credentials and connections are little-correlated with actual contribution to the discovery in question.

I just feel like if that were the case, we'd hear more modern stories of stolen science, with a significant number being clear-cut cases of "An non-credentialed amateur discovered this, and some PhD came in and stole all the credit."

In this article on 10 famous instances of "stolen science," the cases are examples of:

  • Sexism
  • Science not actually getting stolen
  • Credit going to the perfected model, rather than the poor prototype
  • Two competing researchers/inventors (sometimes both PhDs) who published at almost the same time
  • Spying
  • Early death, leaving others to carry on the work

In the few cases here where it's clear-cut that credit was being unfairly stolen, it was sexism, not lack of a PhD, that was the real underlying problem.

This is just the first article that popped up on Google. Maybe there are lots more cases of stolen scientific credit that weren't sexy enough for journalism, where lack of a PhD was the root vulnerability that permitted the theft. If you can find them, I'm all ears!

Comment by allamericanbreakfast on Was a terminal degree ~necessary for inventing Boyle's desiderata? · 2020-07-10T18:11:37.119Z · score: 1 (1 votes) · LW · GW

You and I are in perfect agreement. The whole motivation for my investigation is that I think that in modern times, a degree is ~necessary for groundbreaking work, even though it clearly wasn't in the 1800s and early 1900s.

Comment by allamericanbreakfast on Was a terminal degree ~necessary for inventing Boyle's desiderata? · 2020-07-10T15:08:12.267Z · score: 6 (2 votes) · LW · GW

I don’t necessarily disagree, but why are you confident that STEM Nobels are heavily credentialized? Can you give some examples of breakthroughs in bio, chem, physics, medicine, or math that you feel deserved the prize, didn‘t win, and were discovered by a non-PhD/MD?

Comment by allamericanbreakfast on Was a terminal degree ~necessary for inventing Boyle's desiderata? · 2020-07-10T05:52:49.874Z · score: 3 (2 votes) · LW · GW

Well, as a costly signal of me appreciating it, I just held my mouse button down an extra couple of seconds to give you a strong upvote! Thanks habryka :)

Comment by allamericanbreakfast on Survival in the immoral maze of college · 2020-07-09T23:27:38.020Z · score: 1 (1 votes) · LW · GW

Another way to measure things might be to take a historical list of important discoveries-yet-to-be-made, and look at the credentials of the person who made them. This sounds like it needs a longer fact post, so I'll have that up when I get a chance.

Comment by allamericanbreakfast on Survival in the immoral maze of college · 2020-07-09T20:11:07.027Z · score: 1 (1 votes) · LW · GW

Sorry, but that's major cherry-picking. Let me pre-register a micro-study.

In the last 3 years, there were 22 Nobel Prize winners in physics, chemistry, biology, and economics. I'm willing to bet that of them, at least 20 have PhDs, which is what I meant by " If you want access to the colleagues, tools, money, position, and credibility to do groundbreaking innovative work, you're going to have to go through the maze."

Result:

All 22 STEM Nobel Prize winners from 2018-2019 had PhDs or MDs (which is a PhD equivalent).

Discussion:

This is some evidence that a terminal degree is ~necessary to do groundbreaking STEM work. It makes sense. Scientific equipment is expensive, data gathering is hard, it helps not to have to spend 20-40 hours per week on other forms of work, the PhD pipeline makes it easier to network and learn practical on-the-job scientific skills, and having a PhD makes others more likely to trust your work.

For follow-up studies, it would be useful to use other metrics of who's done groundbreaking STEM work. Examples might include:

  • Fields medal winners (all 4 2018 winners started a PhD, and 3 appear to have completed it)
  • Open Philanthropy grant winners (first 3 individuals mentioned in Scientific Research/Human Health and Wellbeing grants are all PhDs - just the first place I looked)

Just based on poking around like this, I feel quite confident that I am correct. A PhD is virtually a requirement to do groundbreaking work in STEM. You could say that Eliezar Yudkowsky, who's never completed high school, is doing groundbreaking work. But he has no proven results, and from what I've seen, virtually everyone else at MIRI has a PhD (correct me if I'm wrong).

List of STEM Laureates 2018-2019:

Arthur Ashkin: Cornell University (MS, PhD)

Gérard Mourou: Pierre and Marie Curie University (PhD)

Donna Strickland: University of Rochester (MS, PhD)

Frances H. Arnold: University of California, Berkeley (MS, PhD)

George P. Smith: Harvard University (PhD)

Greg Winter: Trinity College, Cambridge (MA, PhD)

James P. Allison: University of Texas, Austin (BS, MS, PhD)

Tasuku Honjo: Kyoto University (BS, MD, PhD)

William Nordhaus: Massachusetts Institute of Technology (Ph.D.)

Paul Romer: University of Chicago (SB, PhD)

James Peebles: Princeton University (MS, PhD)

Michel Mayor: University of Geneva (PhD)

Didier Queloz: University of Geneva (MS, DEA, PhD)

John B. Goodenough: University of Chicago (MS, PhD)

M. Stanley Whittingham: New College, Oxford (BA, MA, DPhil)

Akira Yoshino: Osaka University (PhD)

William Kaelin Jr.: Duke University (BS, MD)

Peter J. Ratcliffe: College, Cambridge (MB BChir, MD)

Gregg L. Semenza: University of Pennsylvania (MD, PhD)

Abhijit Banerjee: Harvard University (PhD)

Esther Duflo: Massachusetts Institute of Technology (PhD)

Michael Kremer: Harvard University (AB, AM, PhD)

Comment by allamericanbreakfast on Survival in the immoral maze of college · 2020-07-09T19:22:30.478Z · score: 2 (2 votes) · LW · GW

Your comment adds substance and nuance, so thank you for writing it.

I do think your first paragraph is reductive, and the point of this post was to create concepts to allow us to get beyond the reductionist dualism of "half-assing vs whole-assing." In particular:

When you have the rare opportunity to do scaffolding or practical learning, take it. Spend your slack figuring out even better deals on your credentials and making life as sustainable for yourself as possible.

I believe thought-patterns like these are common among students:

"I'm only taking this course for my graduation requirements; I'm gonna half-ass it"
"I'm never gonna use 90% of what I'm learning in this class, but it's still relevant to my future career, so I need to whole-ass it."

I'd like to see shifts to thought-patterns like these:

"This course is pure credentialism; I'm going to focus on the fun parts and otherwise do the minimum required to get an A."
"This biology course is almost entirely for credentialism and familiarity, but I really need to focus on the part about viruses. That'll be a combination of scaffolding and even some practical learning, because I want to make a career in pandemic prevention."

The point is to cultivate discernment about the personal relevance of the course content, and drop the moralistic self-judgment.

Comment by allamericanbreakfast on Survival in the immoral maze of college · 2020-07-09T19:02:23.806Z · score: 1 (1 votes) · LW · GW

Good call.

Comment by allamericanbreakfast on Survival in the immoral maze of college · 2020-07-09T19:00:56.036Z · score: 2 (2 votes) · LW · GW

That’s the distinction I’m trying to draw. I think that CS is unusual in that it has characteristics of academia and a trade. So is math, because AFAIK the undergraduate course content is directly applicable to many fields. Of course, some people who hate CS and will never use it are still forced to take classes in it. For them, it's almost all credentialism and a bit of familiarity; no genuine scaffolding or practical learning.

By contrast, many other disciplines are inherently “survey courses” to some extent, even if not labeled as such. You’ll only use a tiny subset of the content as you specialize, and you’ll forget the rest. Others are just not very useful for an actual job: nurses taking o-chem for example.

Comment by allamericanbreakfast on DARPA Digital Tutor: Four Months to Total Technical Expertise? · 2020-07-08T15:02:46.921Z · score: 1 (1 votes) · LW · GW

I have no trouble believing this software works. More power to you for wanting to spread this technology!

It seems like a hard problem will be that the idiosyncratic curriculums used throughout the school system will make interoperability a challenge. Students won’t want to sink a lot of time studying on this software if it’s not directly helping them prepare for their exam in two weeks.

I’m sure it’s not insurmountable. For example, you could design a chemistry curriculum that’s focused on a particular textbook. Students could use their syllabus to tell the software the order in which chapters will be taught and what their upcoming exam will cover. Alternatively, you could do it by topic so that it’s divorced from any particular textbook.

Comment by allamericanbreakfast on How to Find Sources in an Unreliable World · 2020-07-04T04:36:37.591Z · score: 3 (2 votes) · LW · GW

It seems like some questions might seem heavily researched, but are in fact either so hazy that no amount of research will produce clarity, or so huge that even a lot of research is nowhere near enough.

An example of the latter might be “what caused the fall of Rome?”

Ideally, you’d want numerous scholars working on each hypothesis, modeling the complex causal graph, specializing in various levels of detail.

In reality, it sounds like there are some hypotheses that are advanced by just one or a handful of scholars. Without enough eyes on every aspect of the problem, it’s no surprise that you’d have to become an expert to really evaluate the quality of the arguments on each side.

Comment by allamericanbreakfast on How to Find Sources in an Unreliable World · 2020-07-03T03:29:44.802Z · score: 1 (3 votes) · LW · GW

It seems like your approach would work well in fields like programming. It's a practical skill with a lot of people working in it and huge amounts of money at stake to figure out best practices. Plus, the issue he's addressing doesn't seem to be controversial.

Outside that safe zone, prose quality isn't a proxy for the truth. And I think it's these issues that Elizabeth's worried about.

For example, how many windows are there in your house? If you wanted to answer that question without getting out of your chair, you'd probably form a mental image of the house, then "walk around" and count up the windows.

At least, that's what the picture theorists think. Others think there's some other process underlying this cognition, perhaps linguistic in nature.

Reading their diametrically opposed papers on the same topic, I'm sure I couldn't tell who's right based on their prose. It's formal academic writing, and the issue is nuanced.

Comment by allamericanbreakfast on How to Find Sources in an Unreliable World · 2020-07-01T21:06:50.114Z · score: 5 (3 votes) · LW · GW

Also, I think you might have missed a word here: "The latter group fills me with anger and sadness; at least the people trying to convert you believe in something (maybe even the thing they’re trying to convince you of)."

Comment by allamericanbreakfast on How to Find Sources in an Unreliable World · 2020-07-01T20:26:34.479Z · score: 5 (5 votes) · LW · GW

I wonder if a good pre-reading strategy is to search for, or ask experts about, the major controversies and challenges/issues related to the topic in question.

Your first step would be to try and understand what those controversies are, and the differences in philosophy or empirical evaluation that generate them. After you've understood what's controversial and why, you'll probably be in a better position to interpret anything you read on the subject.

One way you could potentially further your work on epistemic evaluation is to find or create a taxonomy of sources of epistemic uncertainty. Examples might include:

  • Controversy (some questions have voluminous evidence, but it's either conflicting, or else various factions disagree on how to interpret or synthesize it).
  • Lack of scholarship (some questions may have little evidence or only a handful of experts, so you have limited eyes on the problem)
  • Lack of academic freedom (some questions may be so politicized that it's difficult or impossible for scholars to follow the evidence to its natural conclusion)
  • Lack of reliable methods (some questions may be very difficult to answer via empirical or logical methods, so that the quality of the evidence is inevitably weak).

You can find papers addressing many of these issues with the right Google Scholar search. For example, searching for "controversies economic inequality" turns up a paper titled "Controversies about the Rise of American Inequality: A Survey." And searching for "methodological issues creativity" turns up "Methodological Issues in Measuring Creativity: A Systematic Literature Review."

My guess is that even just a few hours spent working on these meta-issues might pay big dividends in interpreting object-level answers to the research question.

Comment by allamericanbreakfast on The point of a memory palace · 2020-06-24T12:12:17.289Z · score: 1 (1 votes) · LW · GW

No, this is entirely based on my experience so far! I’m more trying to decide for myself how useful this seems and whether to pursue it further.

Comment by allamericanbreakfast on Half-Baked Products and Idea Kernels · 2020-06-24T01:44:37.683Z · score: 2 (2 votes) · LW · GW

That's great to know. I'm learning to code but am getting all my advice off the internet - never worked in the industry. Guess it's been some bad advice I've been reading!

Comment by allamericanbreakfast on Half-Baked Products and Idea Kernels · 2020-06-24T01:12:20.121Z · score: 3 (2 votes) · LW · GW

M̶u̶c̶h̶ ̶o̶f̶ ̶t̶h̶e̶ ̶a̶d̶v̶i̶c̶e̶ ̶o̶n̶ ̶w̶r̶i̶t̶i̶n̶g̶ ̶s̶o̶f̶t̶w̶a̶r̶e̶ ̶i̶s̶ ̶t̶h̶a̶t̶ ̶t̶h̶e̶ ̶s̶o̶o̶n̶e̶r̶ ̶y̶o̶u̶ ̶s̶t̶a̶r̶t̶ ̶c̶o̶d̶i̶n̶g̶,̶ ̶t̶h̶e̶ ̶l̶o̶n̶g̶e̶r̶ ̶t̶h̶e̶ ̶p̶r̶o̶j̶e̶c̶t̶ ̶i̶s̶ ̶g̶o̶i̶n̶g̶ ̶t̶o̶ ̶t̶a̶k̶e̶.̶ ̶T̶h̶e̶y̶ ̶a̶d̶v̶i̶c̶e̶ ̶a̶ ̶l̶e̶n̶g̶t̶h̶y̶ ̶p̶r̶o̶c̶e̶s̶s̶ ̶o̶f̶ ̶d̶e̶t̶e̶r̶m̶i̶n̶i̶n̶g̶ ̶t̶h̶e̶ ̶u̶s̶e̶r̶'̶s̶ ̶n̶e̶e̶d̶s̶,̶ ̶e̶s̶t̶a̶b̶l̶i̶s̶h̶i̶n̶g̶ ̶t̶h̶e̶ ̶f̶e̶a̶t̶u̶r̶e̶ ̶s̶e̶t̶,̶ ̶d̶e̶c̶i̶d̶i̶n̶g̶ ̶h̶o̶w̶ ̶y̶o̶u̶'̶l̶l̶ ̶s̶t̶r̶u̶c̶t̶u̶r̶e̶ ̶t̶h̶e̶ ̶c̶o̶d̶e̶,̶ ̶a̶n̶d̶ ̶o̶n̶l̶y̶ ̶s̶t̶a̶r̶t̶i̶n̶g̶ ̶w̶r̶i̶t̶i̶n̶g̶ ̶t̶h̶e̶ ̶t̶h̶i̶n̶g̶ ̶w̶h̶e̶n̶ ̶t̶h̶a̶t̶'̶s̶ ̶a̶l̶l̶ ̶p̶r̶e̶t̶t̶y̶ ̶c̶l̶e̶a̶r̶.̶ ̶I̶ ̶c̶o̶u̶l̶d̶ ̶s̶e̶e̶ ̶t̶h̶e̶ ̶h̶a̶l̶f̶-̶b̶a̶k̶e̶d̶ ̶p̶r̶o̶d̶u̶c̶t̶ ̶b̶e̶i̶n̶g̶ ̶p̶a̶r̶t̶ ̶o̶f̶ ̶t̶h̶e̶ ̶"̶d̶e̶t̶e̶r̶m̶i̶n̶i̶n̶g̶ ̶t̶h̶e̶ ̶u̶s̶e̶r̶'̶s̶ ̶n̶e̶e̶d̶s̶"̶ ̶s̶t̶e̶p̶.̶

Comment by allamericanbreakfast on [META] Building a rationalist communication system to avoid censorship · 2020-06-23T22:19:02.132Z · score: 11 (8 votes) · LW · GW

This seems like seeking security through obscurity.

If a blogger wanted to be fully anonymous, they could do so by excluding personal details and using software to disguise their IP address.

The trouble with SCC is that Scott wants to be able to talk about his job and personal life, be known for his blog among friends, and cultivate IRL community around the blog. He just wants to avoid mass-media attention/fame. He wants a readership that finds their own way to the blog. He also wants to avoid creating a perception of shadiness.

It seems to me that The Whisper would both look shady (it already sounds that way), and not actually accomplish this goal. The only way to accomplish it is if mass-media outlets abstain from outing bloggers. In fact, The Whisper would probably attract additional media attention.

Comment by allamericanbreakfast on SlateStarCodex deleted because NYT wants to dox Scott · 2020-06-23T21:38:33.929Z · score: 15 (7 votes) · LW · GW

Tl;dr: A boycott is the central case here, not cancel culture. We need to promote a measured response and keep the Times' perspective charitably in mind.

Is there a difference between cancel culture and a boycott? I think so. Cancel culture inflicts 1) significant emotional, financial, or potentially physical harm on a 2) a specific individual who 3) never signed up for a position of responsibility to field these kinds of threats and 4) can't walk away from the cancellation.

Boycotting uses a much narrower set of tactics, primarily protests and advocating that people not buy a certain product. Typically they target an organization, not an individual. When specific individuals are on the receiving end, their professional role typically is in part to deal with those problems. They can quit if they choose and seek employment elsewhere.

This distinction has its grey areas:

Consider entrepreneurs. They can't necessarily just quit their business, and they're the face of it so even if they did, the accusations might follow them. They didn't start the business to field protests, but to sell products, often when the business was so small that the prospect of the former was remote. Sometimes, they do receive death threats and have their lives permanently constrained for safety reasons.

Furthermore, a successful boycott can get out of control, attracting the attention of psychopaths who'll try to personally intimidate the target. When a group of people coordinates to put a BAD GUY sticker on a corporation, there's no guarantee that the boycott won't lead to a lunatic with a weapon waiting outside the business in question. Nobody organizing the boycott is taking responsibility for the possibility that the boycott spirals out of control, a feature shared with cancel culture.

However, part of being an entrepreneur is shouldering the risks of the business. That includes the risk that it gets big and incites a boycott from which they can't extricate themselves. In exchange for this, successful entrepreneurs are heavily rewarded.

A boycott's not a legal entity, so there's no way for the organizers to be shouldered with the responsibility of even minimal accountability for any potential harmful outcomes. But at the same time, a boycott doesn't come with the possibility of profit.

In this case, the NY Times isn't owned by its founder. So the main reason not to boycott is the threat of it spiraling out of control. A catastrophic result might be that personally-targeted violence is visited on someone at the Times by a psychopath who uses the boycott as their excuse. Another bad outcome would be that we damage our own aspiring culture of measured thought and action, high valence for free speech, and charity for those we see as our opponents.

I've already made my decision about how to respond, but I'll leave it up to the individual conscience of other readers to decide if they accept this reasoning or not, and how it leads them to act.

Comment by allamericanbreakfast on New York Times, Please Do Not Threaten The Safety of Scott Alexander By Revealing His True Name · 2020-06-23T20:43:50.153Z · score: 17 (12 votes) · LW · GW

My open letter to the NY Times:

Hello,

I'm a reader of the blog SlateStarCodex. Both the writing and the community is very important to me. As you probably know by now, Scott deleted his blog because he's afraid that he will lose his job, and possibly have his life threatened, if you reveal his full name in print.

If this goes forward, it will change my perception of the New York Times. Right now, I see mainstream journalism as an important and relatively unbiased source of information, a contributor to the ideal of free speech and democracy. If the outcome of your policies and need for content is shutting down a blogging community that's so important to my life, then I will start to see the NYT as limiting free speech by making successful, anonymous bloggers who want to stay that way unable to publish.

There are probably times when involuntarily revealing the true identity of an anonymous blogger is appropriate. For example, if they are advocating violence, have links to terrorist groups, and so on. This isn't one of those times. It feels invasive, a sort of journalistic "peeping tom" behavior.

Generally, we should be able to speak the truth widely and freely, without worrying about being punished. Although I know Scott does not want the attention of national news media, you are able to publish your article with 99.9% of the content by omitting his last name. Your freedom of speech is not being seriously infringed.

Furthermore, you are the ones initiating the decision to pursue a story about an anonymous blogging community in the first place. If that's a topic you want to cover, when the members of that community don't want their identities revealed, then the least you can do is respect the wishes of the members of that community to remain anonymous. These policies you have in place are not laws. They're just your decisions. You have full responsibility for their outcomes, both as a person and as a news organization. You can choose to soften or change them, or at least advocate for this.

My personal action in response to this doxxing would be to boycott your newspaper, and ask friends and family to do so as well.

The news media's reputation as a foundation of democracy is already under attack from one segment of the political spectrum. Please don't create reasons to undermine that perception more broadly.

Sincerely,

X

Comment by allamericanbreakfast on The point of a memory palace · 2020-06-23T20:11:55.522Z · score: 1 (1 votes) · LW · GW

I'm also terrible at visualizing things. Except when I'm stoned. Or when I practice.

What's really hard for me is trying to force myself to visualize a specific, stable image in a high level of detail. But if I just close my eyes and allow myself to visualize, exploring the mental experience in an open-ended way, I can do much better. Figuring out how to understand and control the experience better is what I'm working on now.

Comment by allamericanbreakfast on The point of a memory palace · 2020-06-23T20:09:34.239Z · score: 1 (1 votes) · LW · GW

I just don't want to get too sucked into my own ideas when they're so fresh.

Comment by allamericanbreakfast on Fight the Power · 2020-06-22T03:35:15.146Z · score: 12 (8 votes) · LW · GW

This post would benefit from a treatment of the role friendship plays in these dynamics. It’s one thing to be cancelled by anonymous strangers. It’s quite another to be silenced or cancelled by your friends.

Here are some examples.

  1. Your new partner wants to meet your friends. You now feel pressure to keep those friendships so that you can be seen not as a pariah but as popular. Your friends are rigid ideologues. What do you do?

  2. Your friend is suicidal, and also a rigid ideologue. Speaking your dissent of their ideology causes them to have suicidal thoughts, for which they blame you. Do you argue your point, even though you may be furthering their extreme anguish, or do you stop, even though you may be getting emotionally blackmailed?

  3. You are poor and stressed, and your housemates are rigid ideologues. Speaking your dissent might destabilize your housing situation, which might have unpredictable knock-on effects. Do you stay silent, or speak up?

  4. Your parents are top notch status game players, who have managed to avoid ever getting embroiled in political controversy. They judge you by your achievements, not by your struggles. Do you take on the additional challenge of sticking your neck out, or keep your head down and work on your career?

  5. You get called out by a large number of your friends on social media. Do you argue? Do you act conciliatory without actually disowning your statements? Do you apologize and tell them you’ll “educate yourself” and make reparations? Do you self-cancel and just disappear?

  6. You meet a new friend. Not knowing whether each other are rigid ideologues, you both start signaling that you are, just in case the other is (defect-defect). How do you break the cycle and get to cooperate-cooperate? What if a third person enters the mix who is a rigid ideologue, and you both start mirroring them? How can you regain your original equilibrium?

Comment by allamericanbreakfast on Coronavirus and Rents · 2020-06-22T03:10:01.847Z · score: 1 (1 votes) · LW · GW

How would you recommend negotiating with a landlord over the price of rent? In my case, we’re new tenants. The landlords work out of an office that’s right next door to our house. It seems unlikely that we could negotiate lower rent if the lease isn’t up for renewal. And they’d be able to make trouble for us if we did - our girlfriends bring dogs over, for example, and they could start harassing us about stuff like that. Better to say nothing?

Comment by allamericanbreakfast on Types of Knowledge · 2020-06-21T16:54:38.658Z · score: 4 (3 votes) · LW · GW

I'm not sure I agree with this distinction between science and engineering.

Theories are a kind of product. They're akin to an algorithm, machine, or process. They allow you to rapidly do a form of useful work: to predict experimental outcomes, design tools and interventions, and explain observed phenomena. An experiment is like a prototype. It's just a way of testing your ideas out in the real world. Just like a prototype, sometimes it takes many attempts to get an experiment to work convincingly (either to support or falsify), because there are so many details in the execution.

A scientist who studies scotopic vision in the Elephant Hawk Moth, Deilephila elpenor, is striving to build an accurate model of moth vision. This is not fundamentally different from an engineer who's designing night vision goggles or a pharmaceutical company researcher trying to develop a drug to improve night vision in people with an eye disorder. It's just a different kind of product - a conceptual, predictive product, rather than a tool or a drug. Their moth vision model doesn't have to work perfectly, either: just well enough to achieve statistical significance.

An engineer and a scientist may both be dissatisfied with imprecision when something important is at stake. If fuel efficiency doesn't matter because gas is cheap and global warming is unknown, then figuring out how to double gas mileage doesn't matter. But if we're trying to sell an electric car, it's not enough to build one that drives. It needs to go fast, far, be quick to fuel, and cheap to make. That might require investigating the fundamentals of battery technology.

Insofar as there's a difference between science and engineering, it's that scientists are making products you can't easily sell. Engineers are making business products. But scientists are still engineers in the sense that they're trying to build theories and explanations and concepts that they can "sell" to their research community.

In light of this, I might rename Elizabeth's three categories "trivia," "practice," and "innovation." Innovation builds on practice, and practice builds on trivia. Each has some key outcomes. Trivia lets you regurgitate facts and explanations. Practice lets you achieve a reliable, useful result using known tools and methods. Innovation lets you create something new, whether it's a theory, prediction, tool, or process.

Comment by allamericanbreakfast on When is it Wrong to Click on a Cow? · 2020-06-20T22:01:05.864Z · score: 13 (10 votes) · LW · GW

I account for it by status/cultural signaling. In some cultures, music practice is culturally forbidden. In others, dangerous and otherwise useless rituals are required, like bullet ant gloves. So it’s not the inherent nature of the activity.

In our own culture, playing music is a status symbol. In children, it means your parents were probably wealthy enough to afford lessons and engaged in prepping you for competition in the college signaling game. Even as adults, to defend the status that the musically educated gain from their art, we are all locked in to protecting its high status.

Video games are not high status, so everyone who holds status is likewise incentivized to prevent them from becoming a competing status symbol.

Self stimming signals deep nonconformity. So we run away from it.

Comment by allamericanbreakfast on The point of a memory palace · 2020-06-20T19:55:35.430Z · score: 1 (1 votes) · LW · GW

That's interesting. Do you have a source for that? I'd love to know what historical evidence we have about MOC.

Comment by allamericanbreakfast on Using a memory palace to memorize a textbook. · 2020-06-19T20:44:08.846Z · score: 4 (4 votes) · LW · GW

Here are some additional benefits I'm discovering as I go along:

I am interested and able to memorize long lists of things that I might formerly have glanced at for two seconds, such as a table of common ligands.

Also, the several hours of visual practice I've put in so far is allowing me to visualize complex 3D molecules, such as Co(en)3^3+. Even now, my first impulsive response is to turn away from it, as the visual details seem too complex for me to make sense of. But instead, my visualization system is going so strong that I make the effort to picture it in my mind. I am able to not only replicate the 2D image of the molecule, but translate it into a 3D representation that I can inspect from different angles.

It's not the same as having a 3D graph of it on my computer that I can rotate and inspect with perfect fidelity. But the advantage is that I can pick out salient details of the 3D structure while holding their larger context in mind. For example, I can see the relative angles of the three ethylenediamene ligands and how they are rotated relative to each other, so that each molecule makes room for the others around the central metal ion.

After picturing the molecule in my mind, when I look at the molecule on the page, it looks different. Instead of a jumbled mass of colors and shapes, I can see the three-D structure, and it feels ordered and sensible.

Comment by allamericanbreakfast on Using a memory palace to memorize a textbook. · 2020-06-19T18:42:43.340Z · score: 4 (4 votes) · LW · GW

Also, a word of caution. In navigating the educational system, it's hard to say whether you're trying to:

  • Feel confident that you've learned something.
  • Retain the material you learn.
  • Be able to apply a small subset of the material in new contexts.
  • Be fast an accurate at repeating the material on a timed exam.

All of these are valuable, but I'm not certain which of them a well-practiced memory palace technique is best for. My guess so far is that it could be useful for rapid recall, but that for a beginner, it's better for slow but broad retention.

I'm also not certain which of them is best to prioritize under what circumstances.

So it's not yet clear to me that the memory palace is a good strategy for learning. It's just an interesting approach that I'd neglected until now.

Comment by allamericanbreakfast on Using a memory palace to memorize a textbook. · 2020-06-19T15:28:01.824Z · score: 3 (2 votes) · LW · GW

Yep that's the one!

Comment by allamericanbreakfast on Using a memory palace to memorize a textbook. · 2020-06-19T14:27:05.000Z · score: 2 (2 votes) · LW · GW

Thanks for the recommendation! Your link formatting got messed up somehow. Here's a fixed link.

Comment by allamericanbreakfast on Using a memory palace to memorize a textbook. · 2020-06-19T04:49:09.226Z · score: 10 (5 votes) · LW · GW

This is just from the last three days. It also makes learning much more enjoyable. What would you rather do, read and re-read dry textbook writing about diffraction? Or stand in a room while Einstein himself shows you an epic, 3D simulation of wave diffraction at any speed or angle you like?

It's suddenly easy to get myself to read my textbook and I'm really happy about that. Best video game ever!

Comment by allamericanbreakfast on Using a memory palace to memorize a textbook. · 2020-06-19T04:47:22.316Z · score: 1 (1 votes) · LW · GW

I'll look into it! Thanks for the recommendation.

Comment by allamericanbreakfast on Bathing Machines and the Lindy Effect · 2020-06-18T05:01:01.484Z · score: 1 (1 votes) · LW · GW

If I'm reading you right (low confidence), then I think our lessons are compatible. The longer something's been around, the longer we should expect it to continue, in absolute terms. At the same time, our best outside view guess is always that the thing is getting toward the end of its life, in relative terms.

I notice the Lindy Effect getting tossed out often as a counterargument to an inside view. So for example, if John says "the Catholic church is on its last legs," Alice might say "it's been around for almost two millennia, so the Lindy Effect suggests it'll probably be around for a long time to come."

I think the way to synthesize their approaches is to start with Alice's point of view, then modify it with John's. And this makes perfect sense. If you told me that X has been around for 2,000 years, then . without knowing what X is, I'd feel pretty confident that it's not going to disappear tomorrow. But I'd also want to know what X is, so I can modify my expectations accordingly.

The Lindy Effect makes a little less intuitive sense when X is only a few seconds old. But that's because I can't stop my imagination from filling in what X might be by imagining the social circumstances. Anything you can tell me is 2 seconds old is probably something you made, and it's probably an object. Most objects don't self-destruct seconds after they were manufactured.

More generally, anything that requires work to make requires an input of energy. That means evolution's fighting entropy for it, and probably wouldn't invest in it if it was likely to be fragile. Anything the living make has a life expectancy in proportion to the energy it took to build it.

But likewise, the more energy it takes to make a thing, the smaller a fraction of the total output of things per unit time. If the Lindy Effect doesn't seem intuitive, that's because we're so used to paying attention to big, old things that we don't think to use the countless small and temporary things as examples.

Comment by allamericanbreakfast on Bathing Machines and the Lindy Effect · 2020-06-18T00:32:36.760Z · score: 1 (1 votes) · LW · GW

Another ramification of the 125% longer/40% confidence Lindy Effect is that it'll always be reasonable to expect the thing you're considering to have already ended if you're using it to establish your priors for forward-looking life expectancy. You could deal with that paradox by shrinking the early bound of what you'll consider a correct prediction and being more generous on the late bound of when the thing ends.

The Lindy Effect is thus an anti-conservative heuristic. With a generous confidence interval, It's reasonable to expect, in the absence of other information, that anything could end this year. What Lindy does is provide a maximum value on our super-low-information prior for longevity.

At least a quarter of the world's population live on farms. That's been true for the last 12,000 years. The Lindy Effect suggests that state of affairs might end this year, but that we shouldn't expect it to last more than another 4,000 years or so.

The Lindy Effect is also scope insensitive. It's clearly more likely that at least 10% of the world's population live on farms as of next year. But the Lindy heuristic generates the same estimate no matter what the cutoff is, as long as it's at least the present-day level. I suppose the scope is inside-view information, so perhaps that makes sense if we're looking for a strictly outside-view prior.

Comment by allamericanbreakfast on Bathing Machines and the Lindy Effect · 2020-06-18T00:19:16.221Z · score: 1 (1 votes) · LW · GW

If the Lindy Effect is useful for forward-looking predictions, it would seem to be a way of calibrating our priors before modifying them with evidence from the inside view.

Example: We've had Christian states since Christianity became the official s̶o̶f̶t̶ ̶d̶r̶i̶n̶k̶ religion of the Roman Empire 313 AD. To estimate how much longer they'll last, we start by recognizing that absent any inside view information, our best bet would be to estimate it'll last to around the year 2500, plus or minus 500 years. Then we move from this starting point by considering inside view evidence and heuristics.

What's the trend on proportion of world population living in a Christian state over time? What's its status in the nine nations where it's still the official state religion? What if the AI foom permanently disrupts all our institutions?

Do we think that pre-industrial institutions have demonstrated durability by transcending technological and social shifts? Are they especially fragile, since they aren't built to respond to the demands of the modern era? Or are these heuristics too ambiguous to be useful?

Comment by allamericanbreakfast on Bathing Machines and the Lindy Effect · 2020-06-17T21:28:51.041Z · score: 1 (1 votes) · LW · GW

start = 1800
end = 2000
lifespan = end - start
ci = lifespan * .25
end_i = end - ci
end_f = end + ci

p_max = 0
max_correct = 0
for i in range(10000):
p = i * .001
correct = 0
for year in range(lifespan):
end_prediction = year * p + start
if end_prediction >= end_i and end_prediction <= end_f:
correct += 1

if correct > max_correct:
max_correct = correct
p_max = p
print("correct %:", max_correct / lifespan)
print("p: ", p_max)
works_i = (end_i - start) / p_max
works_f = (end_f - start) / p_max
print("Works from", start + works_i, "to", start + works_f)

Comment by allamericanbreakfast on Two Cult Koans · 2020-06-16T17:39:44.165Z · score: 1 (1 votes) · LW · GW

Eight years later, and these criteria are no longer in the Wikipedia article. Cults are now referred to as "new religions." This taxonomy isn't even available in the article on "anti-cult movement," which does contain a taxonomy of anti-cult movements.

So I guess we're fine now that the scholarly consensus has changed /s

Comment by allamericanbreakfast on Everyday Lessons from High-Dimensional Optimization · 2020-06-15T22:56:29.479Z · score: 5 (3 votes) · LW · GW

Her baking post was in the back of my mind when I typed my last comment! I thought it was great. I had the same reaction to her post as I had to yours.

We can and often do use constraints to guide our optimization process. Here are some examples:

  • When choosing a hobby, we can brainstorm what we want to get out of our hobby before we consider concrete activities that fit the bill.
  • When solving on a math problem, we can invent some rules for how to format our work so that it'll be legible later.
  • Yelp implicitly considers factors like price, location, quality, and type of food before giving us options.
  • When building a bridge, we're going to use standardized materials, tools, and techniques, not invent our own.

These constraints might be selected through intuition, or through the design of meta-constraints that help us select which object-level constraints to use.

However, the idea behind each constraint had to be invented in the first place, and in any concrete decision, we have to improvise its application.

So how does a constraint get invented in the first place? How do we decide which ones to use for a given decision? And how do we decide how to apply them? Probably with more constraints, but then the same questions arise about those. At some point, we are just picking constraints at random from those that occur to us, choosing randomly from among options that fit them, and seeing how we feel about the result.

We associate good feelings with individual or combinations of constraints that have worked out well in the past or that we've been taught to use, so they're more likely to occur to us in the future.

So the process by which we decompose a problem; invent, combine, and apply constraints; or decide how to evaluate a decision; is itself a random process. Plan it all out, and you move the randomness one meta-level higher. That's not to say it's pointless. Planning and meta-level reasoning is often a powerful way to make decisions. It's just not fundamentally different from object-level explore/exploit, and it runs into similar problems and ambiguities, just on a more abstract level.

Comment by allamericanbreakfast on Everyday Lessons from High-Dimensional Optimization · 2020-06-15T02:13:58.034Z · score: 5 (3 votes) · LW · GW

I'm using SA closer to its original meaning as a metaphor for a process in chemistry rather than its precise mathematics as an algorithm. We start with lots of random trial and error, then dial it down until we just exploit. I do think that this, not just "some explore and some exploit" is how black-box optimization works in practice. It sounds like we agree that decomposition + explore/exploit is a major component of rationality.

I believe that the steering/search algorithm is also developed and applied via black box optimization. It sounds like you disagree.

Here's an example of my concept of how explore/exploit can fully account for the success of a high-dimensional optimization problem.

Jack is choosing a hobby (explore). He loves fresh bread and decides to learn to bake it. He tries for a while (exploit), but eventually decides it's not very fun, so he picks a different hobby (explore). This time, he settles on Indian food, also because it's delicious. This hobby sticks, and he learns dish after dish, sometimes choosing his favorite dishes, other times picking randomly out of a cookbook (exploit).

The question is whether Jack is doing something fundamentally different from exploring and exploiting when he chooses how to pick which bread to bake or which Indian food to cook? To pick dishes, Jack might take suggestions from his friend Ananya, an experienced Indian cook; cook the dishes he's enjoyed at restaurants before; or pick randomly out of a cookbook.

But where did these decision procedures come from?

Well, we've been trying each of them for millennia. Each depend on exploitation (of friends, restaurants, cookbooks), which in turn are the result of exploration (going to parties, searching on Yelp, looking up reviews), and so on ad infinitum.

I don't think that any other mysterious search algorithm is needed to explain how Jack optimizes his Indian food cooking hobby. At some level where there is no decision procedure to provide guidance (how does Ananya choose from among the dishes she thinks would be best to suggest to Jack?), we use some arbitrary selection method, such as random choice or a pointless rule like "choose the first thing that comes to mind."

Can you describe where in this story (or the gaps within it) explore/exploit couldn't suffice? Alternatively, is this not a high-dimensional optimization problem of the kind you had in mind?

Comment by allamericanbreakfast on Everyday Lessons from High-Dimensional Optimization · 2020-06-13T20:22:57.079Z · score: 5 (3 votes) · LW · GW

What I'm pushing on is the nature of "gearsy reasoning." I think that it operates via SA.

Sometimes SA produces solutions to that provably calculate the global optimum. For example, mathematicians exploring geometry have been able to prove that if you're trying to build a tank with the maximum volume:surface area ration, make it in the shape of a sphere.

Other times, it just works well enough. We use SA to invent a bunch of rivet and screw designs, concrete mixtures, beam angles. We testing the component solutions on other problems, try them out on model bridges, real bridges, computer simulations. Expert bridge builders spend their time exploring and exploiting heuristics for the details of the problems they routinely face. Expert bridge builder hirers explore and exploit options for who to hire. Somehow, bridges get built and rarely collapse.

So it's SA all the way down. All reasoning boils down to SA, and it works as well as it possibly can.

It sounds like you might be arguing that there is something fundamentally different going on when we employ "gearsy reasoning." If so, that is my point of disagreement. The account I've just given seems to me like an intuitive and accurate description of how I've solved every problem I've ever worked on, and how it appears to me that other people work as well.

Even in a case like dating, where the ability to explore is quite limited, I just wind up using a crappy version of SA. I find myself attracted to someone; we date and try to deepen/exploit our relationship; we either break up and try again with someone else, or we keep dating/exploiting indefinitely. It works well enough.

Comment by allamericanbreakfast on Everyday Lessons from High-Dimensional Optimization · 2020-06-13T18:05:00.299Z · score: 9 (5 votes) · LW · GW

I'm sure you understand SA better than I do. I won't argue the jargon.

And yet the tone of your post, plus the examples you use, make it sound like you're saying that SA would not be a good choice for designing something like a bridge. The example of SA's usefulness in designing computer chips seems to contradict that example.

If the intuition you're trying to defend is "for complex problems, you need to precisely model and plan your design to achieve a workable solution," then I think SA is a strong counterargument.

If instead, you're arguing that "for complex problems, only a precise internal model of the problem will achieve an optimal solution," then I'd say you're right, but that arriving at that internal model is what an SA-like process is for. As it is written, defining the problem is most of the work. I think that's what's involved when "we break up the high-dimensional system into a bunch of low-level components, and reason about their interactions with each other" or make an "expensive investment in understanding the internal gears of the system."

If I'm completely missing the point of your post, could you give a one-sentence tl;dr of the primary claim you're making?

Comment by allamericanbreakfast on Everyday Lessons from High-Dimensional Optimization · 2020-06-13T17:01:59.889Z · score: 5 (3 votes) · LW · GW

Simulated annealing might be a counterexample. There's a more conversational description of it in this 80,000 Hours podcast transcript, and here's a video on it. By starting with evaluating entirely random designs and no optimizing tweaks, then slowly decreasing the randomness of new designs while increasing the optimizing tweaks, you can do a very good job of finding t̶h̶e̶ ̶g̶l̶o̶b̶a̶l̶ ̶o̶p̶t̶i̶m̶u̶m̶ a workable solution.

This seems to work well if you have the ability to create and evaluate an enormous number of designs, at extremely low cost relative to the value of success, and high accuracy. That's possible with computer chips, and my guess is that it's possible these days with bridges and many other complex machines.

Perhaps the work of modeling internal structure is an attempt to make a problem more amenable to the SA algorithm. Examples:

  • Online dating attempts to reduce dating preferences to a questionnaire score, to more accurately and cheaply evaluate many candidates. The problem, of course, is that it's not very accurate and doesn't do a great job of increasing dating options for many people, so it's not a game changer.
  • We play with a random thought until it turns into a business model. We break a business model down into job descriptions. We break candidates down into resumes. We break career paths down into metrics. Then we jiggle things around - switching job functions, hiring and firing and quitting - until we find a workable model, at which point we exploit it for a while, forming a workable corporation. Or we learn enough about our industry to have a new idea, quit and launch a startup. Sometimes, a merger or a CEO with vision disrupts a corporation and causes it to undergo a major reorganization, explorating a new point on the optimization graph.
  • Scientific research starts with exploration: playing around with intellectual models of a problem, with lots of a priori guesses at their plausibility. The exploitation phase begins when a likely model is subjected to optimization via an iterative process of experiment and refinement by many scientists.
    • There is an important difference here from simulated annealing. In normal SA, designs don't fail, they are just not as good as others. But in scientific research, theories do fail, when they can't account for replicable data. So in normal SA, you have to gradually decrease exploration on a timer. In scientific research, you calibrate exploration in response to theory failure.
    • Simulated annealing depends on slack - the willingness to have scientists pursuing an idea even if it seems like a failed theory. So perhaps we are being too harsh when we castigate scientists for being unwilling to abandon a "disproven" idea or for "throwing papers over the wall."
Comment by allamericanbreakfast on How Going Meta Can Level Up Your Career · 2020-06-12T00:17:54.229Z · score: 5 (3 votes) · LW · GW

A second failure mode here is doing no meta at all before settling on a starting point. If all you want is to advance your career in an arbitrary field with an arbitrary success at the end, it doesn't matter. Just pick something.

But if you do care about the end result, you need to think on a strategic level first before you commit to mastering an object-level skillset.

Comment by allamericanbreakfast on Two Kinds of Mistake Theorists · 2020-06-11T22:27:37.593Z · score: 1 (1 votes) · LW · GW

There might be many types of mistake theorists and conflict theorists!

My post was more about mistake theorists who implicitly deny the existence of conflict theory, who model the minds of conflict theorists as though they were just confused mistake theorists. It's not so much about all the different ways we could assess object-level problems. More that mistake theorists should debate conflict theory as conflict theory, not reframe it as a badly-thought-out mistake theory.