Comment by rachofsunshine | Hacker News Reader

Comment by rachofsunshine | Hacker News Reader

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The problem with data comes in two parts. First, data is inherently abstractive and abstractions are leaky. And second, we have culturally elevated data to a truth beyond what we can see with our own eyes.

Or, to paraphrase Douglas Adams: "The data is definitive. Reality is frequently inaccurate."

Because abstractions are leaky, it's easy to create false apparent patterns in data. And when the data is trusted more than the reality it is supposed to describe, those false apparent patterns become unfalsifiable, because the only way to falsify them is the data that created them in the first place.

The goal to avoid human bias and compensate for human fallibility is a good one. So I don't think that "data" is a bad idea. What I think is that data is a tool, to be used alongside intuition and experiment, as a means for understanding the world. If your beliefs are wildly inconsistent with the data, it is worth asking why and lowering your confidence in those beliefs (but you need not immediately abandon them). If your beliefs are consistent with the data, it's worth asking what other beliefs might be. If you did an experiment and predicted one set of observations but observed another, that's proof that at a minimum your prediction failed (even if your theory was not a bad one), and that deserves investigation. These are legitimate uses of data.

And, to add to that, there's a certain fetish for data that can be quantified. Which leads us straight to the McNamara fallacy.

An empirical mindset alone won't save you from this problem. Quantification bias is a self-confirming bias.

> Or, to paraphrase Douglas Adams: "The data is definitive. Reality is frequently inaccurate."

Having worked with public sector data in Denmark this part is particularly hilarious to encounter in the wild. Even something as “simple” as an organisational chart is something will multiple realities depending on who you ask. Often the people working within context of the different realities will be quite fanatical about their reality.

The place I worked had an employee registry which became the foundation for more and more purposes as the digital services grew. Typically being the foundation for rights to the 300+ different IT systems. It was based on the payment system, which was sort of natural when it was build because that is the one place every employee is registered. Of course this became an issue. For one, teams can only have one manager in basically every Danish HR system, I’m not sure why that is, because a lot of teams have multiple managers performing different roles. Sometimes some of the manager roles where delegated, sometimes the responsibilities were simply spilt. In any case, because there was no data on this hierarchy it was hilariously hard to do things like default who would have access rights to approving vacation, audit and so on. Then you had healthcare, which works three shifts with a different amount of people on each shift. Especially the night shift was a challenge, because they needed access to the whole house and every patient. Which might’ve been easy if there was a regular night shift team, but heathcare personal rotate shifts. Even the specifically designed patient registry which was solely build for patient care couldn’t handle this because nobody had thought about it before they build it (or the data laws like GDPR).

Anyway, there was a billion different things where data didn’t represent a single reality. I can’t get into the stuff involving citizens, but let’s just say that it will be horrible when different departments use the data with AI as though their own reality is the only reality.

Didn't you encounter the problems because you tried to simplify reality by using an abstraction?

The classic blunder. Another one of their problems was that they had to use all those bits. If only they had access to a 2!

Software is always simplifying reality by using abstractions. What else could it possibly do? Completely simulate reality?

It's not just software. Law and policy do the same thing. So does science - even ostensibly fundamental concepts such as "temperature" are really just a simplifying stochastic model of a complex physical system. This is what natural language does, too.

As another commenter pointed out, "The map is not the territory."

The full Korzybski quote is perhaps more insightful, if less pithy: "A map is not the territory it represents, but, if correct, it has a similar structure to the territory, which accounts for its usefulness."

Right, "the map is not the territory" is just half of the quote, and the worse half at that. It's like saying "well, you never know" to everything. Okay, thanks for your help.

> The full Korzybski quote is perhaps more insightful, if less pithy: "A map is not the territory it represents, but, if correct, it has a similar structure to the territory, which accounts for its usefulness."

Right, nobody is expecting a map to actually be the territory. The only question is whether it's useful. We do have a pithier quote for that; one of my favorite quotes of all time:

"All models are wrong, but some models are useful."

Yes. That was the point of my original post: abstractions generate problems.

But abstractions are also useful. You can't just not abstract anything at all.

I took a look at my company's metrics this morning. Approximately 30% of the candidates we send to clients (and who have not ended up out of the process for reasons outside of quality, e.g. the company hired someone else) have ended up getting an offer. That's an important piece of information: it tells me that my company does not have a problem with failing to screen out weak candidates.

Is that leaving out some important details? Yeah, of course! One of our candidates failed an interview because he was too aggressive in questioning his interviewer about their company's prospects. That's a useful piece of information, too; it was (along with a couple other anecdotes) a clue that we should try to do more basic coaching for candidates before interviews.

The data tells me how common the problem is, and suggests which problems are most critical to solve first. The anecdotes can tell me in detail about the nature of the problems, and suggest to me possible interventions. Both of those things matter.

The map is not the territory.

That should be engraved in the mind of anyone that works with data/numbers.

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