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“Fischer Black thought that got the right price within a factor of two about 90 per cent of the time… I knew Fischer pretty well, and he didn’t throw numbers like those around lightly; he thought long and hard about them, even though they sound like the kind of rough figures other people would come up with quickly. We argued quite a bit about them, and I pushed for ‘within a factor of two about half the time’.” —Aaron Brown

*Financial Risk Management for Dummies* chapter 4 = 🔥

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“Taking less risk than is optimal is not safer; it just locks in a worse outcome. In competitive fields, doing less than the best often means failing completely. Taking more risk than is optimal also results in a worse outcome, and often leads to complete disaster.”
—Aaron Brown, “Red-Blooded Risk”

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Friends, I'm a terribly incompetent social medialyte—I don't see many toots since I follow almost no one, don't check local or federated timelines, don't get follow/fave/boost notifications—just at some point I lost the taste for being plugged in.

But! I'm more than happy to read, learn, discuss, answer, and mentor:

or whatever 🤗!

(I'll pin this toot, so don't link to it!)

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Ideas I frequently invoke:

- Philip Guo on silent technical privilege

- Bryn Hammond on silenced history

- Duncan Watts on cumulative advantage, or, MusicLab, in the top ten scientific experiments ever

- AnimeFeminist for their critiques and season guides

(Don't link to this toot, it'll be edited+deleted, but it'll be pinned to my profile on

Related, in theme & influence on me:

"You can passively consume hundreds of articles and podcasts and learn far less than shipping one side project a year. … I think it's much, *much,* harder to ship a project and not learn a ton. ESPECIALLY if that project is product." — (2017, but I think I saw a similar statement earlier that burned into me the importance of shipping stuff, not letting it lie around 80% done)

Having a friend/family member as a user is very effective.

"If you are an interested and easily intrigued person, it takes an enormous amount of energy to keep yourself focussed on the product in front of the technology. This energy is precious and very hard to come by. It can come from

- blind belief,
- future-customer-driven belief, or
- the fact that your code is being used or at least acknowledged by real people

and so has become a product first and foremost whether you like it or not." —

Bookmarked in 2013 but it's stuck.

Has anyone any experience writing backend that runs on… ? Instead of ?

I’m sure this community Is much smaller than the Node commutity but I’m sure it exists and want to find it.

A friend introduced me to this word:

via just usage (no definitions), like. “Boss gives you a lot of jansori but I only sense love behind it.” Or, “I don’t want to give you jansori but have you thought about not doing XYZ”. And I’ve begun using it because it’s useful to name that brand of harangue that seeks your improvement. tries to explain it too.

Appendix 4: "5 hours to assess an employee's performance will have lots of false positives / false negatives… companies drive all of their business decisions with data; recruiting shouldn't be any different."

( if you're lucky enough to not know, FAANG=FaceAppAmazNetflGoog, darling tech stocks/employers)

This thread has hopefully prepared you to evaluate this steaming pile of poo—if big tech firm blow (& accept) cash for ads, recruiting likely too.

22 boosted

@22 So true! In academia it is similar: the average academic will not be good at being a manager, because we are researchers and/or educators, and that is a very different skillset. But there is for most academics no non-management route to promotion. So we get ineffective management by people who never actually wanted it.

Appendix 3: for those who want to study , see

- this 1990s-style page is a good summary, you have to click the various sub-links:

- for a review of John Gall's book.

Appendix 2—from the linked

"In-campaign optimization breaks PSA experiments: to maximize performance of the focal ad and PSA, the ad platform will assign different types of consumers to be exposed to the PSA or the focal ad."

Translation—they can measure ad effectiveness by using ads for charities as a control group. But most campaigns first optimize WHO GETS SHOWN THE CHARITY AD AND WHO GETS SHOWN THE REAL AD.

What kind of unethical scum dev would sell this??

Here's the "appendices" section of the toot thread.

"Decisions have to be made, somebody has to lay out a strategy, doubt must stop at some point. For that reason, companies hire overconfident people who act like they know what they cannot possibly know."

I love this statement of fact: likely we all know of that one guy (or several guys—invariably guys) whose overconfidence got them power. Of course they see that power as meritocratic reward for talent.

In the end, of course the value of something is what people pay for it (ads, bonds, and boat shoes). But this thread shows a couple of big areas where people pay for stupid things, because doing something stupid as a group can help belonging.

So one takeaway I think is, as you build your toolkit to detect and measure things (statistics, code, ETLs, etc.), make it bullshit-proof. Aggressively science your findings—be costantly open to the possibility you've completely fooled yourself.

"It might sound crazy, but companies are not equipped to assess whether their ad spending actually makes money. It is in the best interest of a firm like eBay to know whether its campaigns are profitable, but not so for eBay’s marketing department. … The fact that management often has no idea how to interpret the numbers is not helpful either."

"Marketers are often most successful at marketing their own marketing."

Or, in :

> The System Itself Does Not Do What It Says It Is Doing

This article is fire. I have to pay someone for this: their inline footnotes are amazing and constantly link to published papers, e.g.

'When these experiments showed that ads were utterly pointless, advertisers were not bothered in the slightest. They charged gaily ahead, buying ad after ad.

> See Rao, Justin M., and Andrey Simonov. "Firms’ reactions to public information on business practices: The case of search advertising." Quantitative Marketing and Economics 17.2 (2019)'

and charts too!

'Advertising rationally, the way it’s described in economic textbooks, is unattainable. Then how do advertisers know what they ought to pay for ads? "Yeah, basically they don’t know," Lewis said in one of those throw-away clauses that kept running through my head for days after.'


"Following the news about the millions of dollars eBay had wasted, brand keyword advertising only declined by 10%. The vast majority of businesses proved hell-bent on throwing away their money."


"Economists at conducted 15 experiments… A large retailer launched a Facebook campaign. Initially it was assumed that the retailer’s ad would only have to be shown 1,490 times before one person actually bought something. But the experiment revealed that many of those people would have shopped there anyway; only one in 14,300 found the webshop because of the ad. … selection effects were almost 10 times stronger than the advertising effect"

The willful blindness to waste—jawdropping.

'Algorithmic targeting may be technologically ingenious, but if you’re targeting the wrong thing then it’s of no use to advertisers. Most advertising platforms can’t tell clients whether their algorithms are just putting fully-automated teenagers in the waiting area (increasing the selection effect) or whether they’re bringing in people who wouldn’t have come in otherwise (increasing the advertising effect).'

I love the teenagers handing out flyers in the restaurant lobby visual.

'Picture this. Luigi’s Pizzeria hires three teenagers to hand out coupons to passersby. After a few weeks of flyering, one of the three turns out to be a marketing genius. Customers keep showing up with coupons distributed by this particular kid. … "I stand in the waiting area of the pizzeria."'

Lovely image to understand selection effect vs advertising effect.

"the brightest minds of this generation are creating algorithms which only increase the effects of selection"—aka 'targeted ads'.

“What was the effect of pulling the ads? Almost none. For every dollar eBay spent on search advertising, they lost roughly 63 cents… [in sidebar] The 95% confidence interval is between negative $0.03 and negative $1.24.”

And bravo for citing confidence intervals in a lay piece!

"I kind of had the belief that most economists have: businesses are advertising, so it must be good. Because otherwise why would they do it? … But after my experience at eBay that’s all out of the window."

As I was thinking about the costs of conventional wisdom and unexamined societal defaults, "The new dot com bubble is here: it’s called online advertising" landed:

This explains the insane, expensive, poisonous unexamined beliefs about 's (non-)efficacy.

“all the traffic that had previously come from paid links was now coming in through ordinary links. … Annually, eBay was burning a good $20m on ads targeting the keyword ‘eBay’.”


I'm rereading my copy of the first edition of Gall's (which I first picked up more than fifteen years ago) and it's so ! At least in its statement of the problem:

> Things (Things Generally/All Things/The Whole Works) Are Indeed Not Working Very Well. In Fact, They Never Did.

and its call to resign yourself to that problem

> Large Complex Systems Are Beyond Human Capacity To Evaluate.

but Gall didn't quite reach Buddha's level in suggesting a meta-solution!

Obligatory reference to Peter Principle that I'm glad they teach kids in college these days—people are rewarded for skill in one area by being moved to another they're woefully unskilled in (I had to read it in Gall's hilarious ).

I'm trying to unindoctrinate devs in my circle to not assume management is some default next life step—if your company lacks a technical non-management route (which may or may not be a big red flag), you could always leave for the many companies that do.

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