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Reasoning From First Principles: The Dumbest Thing Smart People Do
Most middle-class Americans at least act as if:
- Exactly four years of higher education is precisely the right level of training for the overwhelming majority of good careers.
- You should spend most of your waking hours most days of the week for the previous twelve+ years preparing for those four years. In your free time, be sure to do the kinds of things guidance counselors think are impressive; we as a society know that these people are the best arbiters of arete.
- Forty hours per week is exactly how long it takes to be reasonably successful in most jobs.
- On the margin, the cost of paying for money management exceeds the cost of adverse selection from not paying for it.
- You will definitely learn important information about someone’s spousal qualifications in years two through five of dating them.
-Human beings need about 50% more square feet per capita than they did a generation or two ago, and you should probably buy rather than rent it.
- Books are very boring, but TV is interesting.

All of these sound kind of dumb when you write them out. Even if they’re arguably true, you’d expect a good argument. You can be a low-risk contrarian by just picking a handful of these, articulating an alternative — either a way to get 80% of the benefit at 20% of the cost, or a way to pay a higher cost to get massively more benefits — and then living it.[1]
techtariat  econotariat  unaffiliated  wonkish  org:med  thinking  skeleton  being-right  paying-rent  rationality  pareto  cost-benefit  arbitrage  spock  epistemic  contrarianism  finance  personal-finance  investing  stories  metameta  advice  metabuch  strategy  education  higher-ed  labor  sex  housing  tv  meta:reading  axioms  truth  worse-is-better/the-right-thing 
16 days ago by nhaliday
Ask HN: Learning modern web design and CSS | Hacker News
Ask HN: Best way to learn HTML and CSS for web design?:
Ask HN: How to learn design as a hacker?:

Ask HN: How to learn front-end beyond the basics?:
Ask HN: What is the best JavaScript stack for a beginner to learn?:
Free resources for learning full-stack web development:

Ask HN: What is essential reading for learning modern web development?:
Ask HN: A Syllabus for Modern Web Development?:

Ask HN: Modern day web development for someone who last did it 15 years ago:
hn  discussion  design  form-design  frontend  web  tutorial  links  recommendations  init  pareto  efficiency  minimum-viable  move-fast-(and-break-things)  advice  roadmap  multi  hacker  games  puzzles  learning  guide  dynamic  retention  DSL  working-stiff  q-n-a  javascript  frameworks  ecosystem  libraries  client-server  hci  ux  books  chart 
29 days ago by nhaliday
Estimating projects with power law completion times
With fat tail, I.e. power law, project times, late projects are likely to be even later,
powerlaw  pareto  law  cook  consulting  estimates  management  math  estimation  interesting  probability  project-management 
7 weeks ago by xer0x
Laurence Tratt: What Challenges and Trade-Offs do Optimising Compilers Face?
It's important to be realistic: most people don't care about program performance most of the time. Modern computers are so fast that most programs run fast enough even with very slow language implementations. In that sense, I agree with Daniel's premise: optimising compilers are often unimportant. But “often” is often unsatisfying, as it is here. Users find themselves transitioning from not caring at all about performance to suddenly really caring, often in the space of a single day.

This, to me, is where optimising compilers come into their own: they mean that even fewer people need care about program performance. And I don't mean that they get us from, say, 98 to 99 people out of 100 not needing to care: it's probably more like going from 80 to 99 people out of 100 not needing to care. This is, I suspect, more significant than it seems: it means that many people can go through an entire career without worrying about performance. Martin Berger reminded me of A N Whitehead’s wonderful line that “civilization advances by extending the number of important operations which we can perform without thinking about them” and this seems a classic example of that at work. Even better, optimising compilers are widely tested and thus generally much more reliable than the equivalent optimisations performed manually.

But I think that those of us who work on optimising compilers need to be honest with ourselves, and with users, about what performance improvement one can expect to see on a typical program. We have a tendency to pick the maximum possible improvement and talk about it as if it's the mean, when there's often a huge difference between the two. There are many good reasons for that gap, and I hope in this blog post I've at least made you think about some of the challenges and trade-offs that optimising compilers are subject to.

Most readers will be familiar with Knuth’s quip that “premature optimisation is the root of all evil.” However, I doubt that any of us have any real idea what proportion of time is spent in the average part of the average program. In such cases, I tend to assume that Pareto’s principle won't be far too wrong (i.e. that 80% of execution time is spent in 20% of code). In 1971 a study by Knuth and others of Fortran programs, found that 50% of execution time was spent in 4% of code. I don't know of modern equivalents of this study, and for them to be truly useful, they'd have to be rather big. If anyone knows of something along these lines, please let me know!
techtariat  programming  compilers  performance  tradeoffs  cost-benefit  engineering  yak-shaving  pareto  plt  c(pp)  rust  golang  trivia  data  objektbuch  street-fighting  estimate  distribution  pro-rata 
july 2019 by nhaliday
What happens when everyone in a room keeps giving dollars to random others? • Decision Science News
Annie Duke:
<p>When we were giving a talk at the Department of Electrical Engineering and Computer Science at Northwestern we met Uri Wilensky, who shared with us a simulation he likes to assign.
<p>Imagine a room full of 100 people with 100 dollars each. With every tick of the clock, every person with money gives a dollar to one randomly chosen other person. After some time progresses, how will the money be distributed?</p>

If on quick reflection you thought “more or less equally”, you are not alone. I asked 5 super-smart PhDs this question and they all had the same initial intuition.

How does the distribution look? Play the movie above to see. [You'll have to click through; the video doesn't have an embed.] Here’s how it works.

The movie shows 5,000 clock ticks in less than a minute.

The Y axis shows the number of dollars each person has. It starts at 45 dollars each.

On the x-axis we have 45 people.

The red bars show the wealth of each person at each tick of the clock.

The blue bars are the same as red bars, but sorted to show how wealth is distributed. The rightmost blue bar is the height of the highest red bar, and so on down.

Don’t believe it? Play with R and tidyverse and gganimate code yourself.

Inequality can arise from seemingly innocuous policies — you need to keep an eye on it.</p>

Ah, hello, Mr Pareto. The penthouse suite as usual? (From Decision Science News, a once-weekly signup newsletter.) There's more discussion <a href="">here</a>.
economics  simulation  statistics  inequality  pareto 
october 2018 by charlesarthur
(PDF) Goodbye Pareto Principle, Hello Long Tail: The Effect of Search Costs on the Concentration of Product Sales
Many markets have historically been dominated by a small number of best-selling products. The Pareto principle, also known as the 80/20 rule, describes this common pattern of sales concentration. However, information technology in general and Internet markets in particular have the potential to substantially increase the collective share of niche products, thereby creating a longer tail in the distribution of sales. This paper investigates the Internet's "long tail" phenomenon. By analyzing data collected from a multichannel retailer, it provides empirical evidence that the Internet channel exhibits a significantly less concentrated sales distribution when compared with traditional channels. Previous explanations for this result have focused on differences in product availability between channels. However, we demonstrate that the result survives even when the Internet and traditional channels share exactly the same product availability and prices. Instead, we find that consumers' usage of Internet search and discovery tools, such as recommendation engines, are associated with an increase the share of niche products. We conclude that the Internet's long tail is not solely due to the increase in product selection but may also partly reflect lower search costs on the Internet. If the relationships we uncover persist, the underlying trends in technology portend an ongoing shift in the distribution of product sales. This paper was accepted by Ramayya Krishnan, information systems.
longtail  pareto  8020 
october 2018 by bartcelona

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