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> We should be learning alternative models

In addition to learning models from other subjects, we also need to understand that the complexity and often chaotic nature of human behavior might mean some subject can only be modeled superficially.

> economics

Varoufakis[1] and Blyth[2] argue that it might be impossible to create models of the economy. They both warn that there is a seductive quality to math, but that "elegance" is an oversimplification that can easily unravel when given the chaos of the real world.

[1] https://www.youtube.com/watch?v=L5AUAIzciLE&t=1355

[2] https://www.youtube.com/watch?v=hmWbkPezgtU

It is certainly impossible to create realistic models of the economy, if one's economics is based on assumptions of linearity and equilibrium. Mark Buchanan's book _Forecast_ [0] makes the case for more realistic models, using techniques from the natural sciences, that would have better predictive value.

[0] https://www.amazon.com/Forecast-Physics-Meteorology-Sciences...

Is he rich from his superior models?

Good question. The book's blurb says he is a science writer; I don't know how rich he is.

I guess it's easier to point out the flaws in linear, equilibrium models, and to point to better results in, say, meteorology, from using better models based on different assumptions, than it is to construct and validate completely new economic models.

>we should focus on problems where our resources can go further - e.g. in helping the global poor.

I'm not convinced that the right people to help the "global poor" are Silicon Valley technologists. Scientifically-minded entrepreneurs focused on profit but with idealistic rhetoric attempting to "help" cultures they know nothing about has, historically, not worked out great.

Colin Woodward wrote "American Nations: A History of the Eleven Rival Regional Cultures of North America (ISBN-13: 978-0143122029)" and basically, the people who settled the center of the tech industry were Yankee Progressives, the sort who send third sons to be missionaries around the world. You have to accept the premise that somehow, the past stains present-day thinking more than is easily explainable, but this does not stop people who have demographically interesting professions from using this sort of thing.

From over here in India it looks pretty good.

In spite of the radically different culture, Uber has improved transportation. It turns out putting Indians into a car and charging them money works a lot like doing the same for Americans.

Facebook and Whatsapp have improved communication.

At work, Slack, Salesforce and Jira work the same as any western office.

Why, exactly, do you think other cultures and the global poor can't use SV technologies?

All those technologies were first developed in the US, for an American target audience, then exported to other countries. Other things also developed in the West and then exported: electricity, vaccines, et cetera. I agree that that's generally been a good thing.

The parent comment, however, seemed to imply that technologists should focus on "solving global poverty" instead of solving local problems. This represents a fundamental shift, because at that point SV technologists are attempting to solve problems they personally do not have experience with.

All the examples you provide solved problems that SV itself had- transportation, communication, etc.

I didn't interpret the article as defining things like Uber as solving "local" problems. But if you do, then the article is simply wrong because SV is manifestly solving local problems.

I agree that silicon valley technologists are not well informed about the problems of the poor (or other important problems) and too often use the rhetoric of doing good whilst not thinking carefully enough about how to actually do that.

But I don't think concentrating on local problems is the right solution given that there are much more important non-local problems to solve. Perhaps entrepreneurs should concentrate on profit and then donate what they earn. Alternatively, they could learn about important problems and partner with experts in those problems to avoid naively doing more harm than good.

> overuse models from programming when thinking about other aspects of the world

Arguably we already do this when we try to map problem-domains to class-hierarchy structures (OOP)...

When/if we see these flaws and start using general data structures to model the world, we'll have moved forward...

You pushed almost ALL of my buttons...note that I on every point I almost agree with your points (which of course meant I have to nitpick them:).

1. I agree that it is important to broaden your horizon and not blindly think everything works like software. But IMO, not enough people learn to attack problems like a good hacker. I'd argue that for understanding and modeling,the fundamental principles that drive hard sciences ( best discussed by Karl Popper and Feynman in my opinion) are as of yet the only methods which have proved themselves. For doing and building complicated things, (software) engineers have developed a way of zipping between layers of abstractions which is again in this domain of fundamental principles, but less fleshed out. Economics and other "soft" sciences is then (when its done well) about properly applying these principles from hard science and engineering to domains with incredibly scarce data and no real way to do experiments(because, you know, ethics). History and other "exploratory" sciences is then about gathering more data and cleaning it. And finally, philosophy and the arts are about pushing the boundaries of our imagination and to inspire us, so we can apply all of those other tools to new domains and cross fertilize.

2. To AI Risk: I said it before, i say it again, I am incredibly disappointed in the current AI risk movement. There is way to much focus on the vague "tail risk" of a rogue AI (be it by chance of by ill will) and some sort of "paperclip" scenario, and almost no mention of the very real, very right now structural risk of continued wealth concentration, mass surveillance (which is where we agree) and mass unemployment. The tail risk in my opinion is negligible, due to simple physical constraints computing faces right now, and to the fact that we have EMP. An actual AI apocalypse just isn't going to happen. And even if you handwave that with "tail risk"/rogue actors, then I ask: why not go after bioweapons? They are much easier (since we know they can work with our current tech, unlike AI) and about as dangerous. Now, the structural problems will combine with demographical change, a regress from the brief period of increased equality (and freedom) and other factors. That stuff is happening right now. There are enough resources pointing out how many jobs will simply be made redundant,yet there are only some movements talking about UBI and other schemes to move to this post-work society. Disproportionately more noise is made about the scary death robots.

3. Death: I hope research into immortality, healthy aging (i.e. "dying with a young body at 70" ) and the likes continue. But it is very much a first world problem, nay, a millionaires and above problem. I don't buy the argument of stopping death being infinitively important on account of if making every other intervention more important. A lot of the current problems in the world are artificial (or at least not mandated by the laws of physics) and due to too much power in the hand of too few, without checks and balances. Let us fix that first, then make our overlords immortal. Tangentially, there was a great article+ discussion on HN here (https://news.ycombinator.com/item?id=9523231) on the topic of Peter Thiel and his "libertarian future"

I apologize for going into rant mode, I hope I managed to make it somewhat congruent

"more noise is made about the scary death robots."

Likely because they make for better stories.