sxzxs's comments

The protocol foundation whitepaper looks like an interesting read - how far along are the open source implementations?

I'm particularly interested in applications of dynamic open-sourced metrics (ranging from corporate carbon footprinting to labour tokenization) - is this the vision for hmt? Only things I've found online so far are Grafana/Prometheus (etc) and Uber's m3, which is built on top of the former. Anything else you know of tackle that topic?

> as with javascript, Brendan Eich appears to be channeling technology up from the depths of hell. I wonder if he created the prototype in a 10-day fit of a fever dream as well.

This made me laugh! But hyperbole aside, this premise would seem to (by nature of efficient laziness) eventually result in people spam watching ads with bots - despite any ways of preventing this, people will find a way.

Thanks for the link, I'll check it out.

For me, it comes down to what kinds of these risky models are most interesting. Some can be interesting because of potential profit or minimizing loss (financial or actuarial) and others are inherently (theoretical physics).

To add to your tail risk point - I wonder how many people foresaw the Venezuelan oil crisis way back when, or even less likely, the Saudi Arabian oil complex attack in 2019. And of course, the current situation we're in with CoVID that an entire university of forward thoughtful looking people didn't call until it was a week away. As an aside, do insurance companies significantly alter their policies when such a cat-5 hurricane is imminent? What preparations would they make in the face of that sort of event?

Are you talking about chaos theory in the last paragraph? I'll read that article you linked in a bit and see what more I have to say, from skimming through it looks as though my question from the previous paragraph may be answered.

unrelated, but - why are some names green?

The comments I've read have been really strongly worded either for or against the motif of this article. All in all I thought this essay was a nice albeit not too deep train of thought through kinds of thinking - not anything worth getting into fisticuffs over.

Yet some comments make me feel like they expect, with the threat of harsh criticism, uber-deep and profoundly insightful content from PG on a highly consistent basis. Maybe it's the phenomenon where wider audiences give rise to (or amplify) polarizing views.

What kinds of techniques are used in discourse analysis? How do you approach investigating problems in pragmatics (like how can you really establish a context for asking questions about contexts?) and can you give me some examples of interesting cases? I've wanted to do deeper reading on this topic for a while, which has intensified since I've been getting deeper into pure math. Can you point me in some directions to look for book recommendations? I have this as a jumping off point: https://www.nature.com/articles/s41562-020-0924-8 but anything else is much appreciated.

Can you elaborate more on (or give an example of) "mechanical honesty"? From the outside looking in, it doesn't seem unfeasible to implement biases in mechanical systems (especially government departments!), but I haven't given it a lot of thought.