[jasode]

To the author... I'd suggest a rewrite of what you're trying to communicate because your usage of "good-argument-driven" is a textbook example of Begging The Question: https://en.wikipedia.org/wiki/Begging_the_question

For discussion's sake, let's go along with excluding data/metrics/science in pushing for arguments. In this framework, what exactly is a "good" argument based on? Gut feel? Opinion?

There was a famous quote by Jim Barksdale, the former CEO of Netscape: "If we have data, let’s look at the data. If all we have are opinions, let’s go with mine."

(So the tie-breaker in competing arguments in that case was "hierarchy-of-arguer-driven".)

So Jane and Bob disagree on the next action to take. Jane thinks her argument is a "good argument" but has no data. But Bob thinks he has a "good argument" but no data.

How does this thread's blog post help resolve the above scenario? (Blog's answer: you're driven by the one that has the good argument.) ... which is circular.

[vitiral]

This is a textbook example of The Strawman Argument.

I'm pretty sure the author is talking about "data" in the context of "databases", i.e. repositories of digital information that can be queried, transformed and displayed (dashboarded).

In other words the author is assuming the value of human's more natural data processing: common sense, personal experience and conversing with others (empathy).

If a process/feature/etc doesn't make sense within how you understand your product, then you can make an argument based on that. The argument will involve data (i.e. the current architecture) but not data in any database.

[jasode]

>I'm pretty sure the author is talking about "data" in the context of "databases",

Yes I agree and the "data metrics" was the interpretation I was commenting on. Instead of straw-man, I actually steel-manned what the author was trying to communicate in my other comment. (One has to read this thread's blog post combined with his previous blog entry to understand what the author means by "good argument".)

[tdehnel]

A simple answer to this is that good explanations are hard to vary.

More here: https://www.lesswrong.com/posts/jcTsbaQ8hNc7qxwaQ/explanatio...

[jasode]

>A simple answer to this is that good explanations are hard to vary.

But the "hard to vary" explanations were built up from observing data of smashing particles. E.g. from your link:

- Frank Wilczek describes hard-to-vary-ness as follows "A theory begins to be perfect if any change makes it worse." He explains further using the Standard Model as an example of a hard-to-vary explanation: Too many gluons! But each of the eight colour gluons is there for a purpose. Together, they fulfil complete symmetry among the color charges. [...] No fudge factors or tweaks are available.

This author's blog post about "data" also links to his previous post[1] about "science" leading one astray from "good arguments" is the opposite of "hard to vary" explanations.

Here's the reason for the disconnect: The author is using the adjective "good" in his idiosyncratic way to describe the type of arguments that depend more on "storytelling" and "intrinsic motivation" -- rather than empirical science/data. Excerpt:

- >And here is a secret: in the natural sciences themselves, storytelling and bare conjecture are far more important modes of persuasion than data-based empirical argument, anyway. [...]

- >A good example of the sort of argument I think is helpful is A Philosophy of Software Design. Ousterhout defines his terms clearly, accompanies his definitions and claims with illustrative examples, and tells an occasional story. You, the reader, are free to evaluate each claim based on whether it plausibly seems to capture the essence of what you have encountered in your experiences writing software. For my part, I didn’t find most of Ousterhout’s ideas to be persuasive, as some of my colleagues did, but that doesn’t mean they aren’t good arguments,

Those types of subjective claims arguments the author is espousing are actually "easy to vary" -- because they don't require constructing a cohesive theory that reconciles data that looks contradictory (e.g. like the The Standard Model, or Theory of General Relativity reconciling the speed-of-light observations).

[1] http://twitchard.github.io/posts/2019-10-13-software-develop...

[tdehnel]

Duh, science is the interplay between observations and explanations. But that doesn’t change that what makes an explanation good or bad is whether it’s hard to vary.

I’ll use a David Deutsch example: let’s say a theory that eating 1kg of grass cures the common cold.

You could do an experiment and find it does not. But you could easily vary the theory and say actually it’s 1.1kg and so on.

But you wouldn’t actually need to do the experiment because there is no good, invariable explanation as to _why_ eating the grass cures a cold.

In that case, you wouldn’t need any data or observations at all. You could simply ask why eating exactly 1kg of grass cures the cold. What is the mechanism of action?

In this way you can see that empiricism is not sufficient in any case to give evidence to a theory. We need only a good explanation to judge whether a theory is worth considering to be true. From there, we can do further experiments/observations to rule it out. But never to prove it true.