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Comment by roughly | Hacker News Reader

There's a lot of ink in this spent on how Poverty, Climate Change, Urban Decay, and Financial Markets are Complex Hard Complicated problems.

The problem with these is they're also problems where there are actors profiting from the failure to fix the system - the issue isn't that we don't understand the complex nature of the domain, it's that the components of the system actively and agentically resist changes to the system. George Soros called this Reflexivity - the fact that the system responds to your manipulations means you can't treat yourself and the system as separate agents, and you can't treat the system as a purely mechanistic/passive recipient of your changes. It's maybe the biggest blind spot for people who want to apply the rules and methods of physics to social issues - the universe may be indifferent, but your neighbors are not.

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This is the strongest point in the thread. The article treats poverty, climate, and markets as though the obstacle is insufficient model capacity. But these systems contain agents with values and motivations who actively resist interventions. A billion-parameter model of a system whose components are trying to game the model will never be a theory of that system. The agents will simply route around it.

More broadly, the article assumes that scaling model capacity will eventually bridge the gap between prediction and understanding. I have pre-registered experiments on OSF.io that falsify the strong scaling hypothesis for LLMs: past a certain point, additional parameters buy you better interpolation within the training distribution without improving generalization to novel structure. This shouldn't surprise anyone. If the entire body of science has taught us anything at all, it is that regularity is only ever achieved at the price of generality. A model that fits everything predicts nothing.

The author gestures at mechanistic interpretability as the path from oracle to science. But interpretability research keeps finding that what these models learn are statistical regularities in training data, not causal structure. Exactly what you'd expect from a compression algorithm. The conflation of compression with explanation is doing a lot of quiet work in this essay.

Literally countries with so much surplus land: Canada, Australia etc. have housing crisis where most of the top 10-20% of the population has become speculators in housing and openly NIMBY with no interest in supply side solutions unless forced down.

Rentseeking is the super-capitalism in the room.

The problem we've got is that 10-20% of the population are speculating while another 50% of the population have almost their entire net worth stuffed into their family home. We're finding it difficult to rein in the top without ruining the middle too.

there are plenty of examples of the top getting reined in, and if people have their wealth in their house then don't take their houses.

this isn't a complicated problem, and it's not difficult to build guillotines

If there’s surplus land, why build something unwanted in someone’s backyard? I’m a suburban NIMBY homeowner and I feel like you’re actually making my argument without realizing it. I’m all for building new houses on unused land. Can you please just do it without ruining my neighborhood? Build nice new neighborhoods and make them as dense as you’d like, but don’t try to force density on older, established neighborhoods that can’t support it.

The empty land is not very valuable. Suburban homeowners are sitting on relatively valuable land, and it's valuable because of access to jobs and services.

In my personal experience, adding density to established neighborhoods improves those neighborhoods' character. Sometimes it gets those afraid of change to move out, improving it even more.

I'm actually curious - have you spent time in cities like Bern or Bilbao? I think urbanism's been a hard sell in the US because we don't really have a lot of great examples of it - New York's maybe the closest we've got to a European style city, but that's only in certain places and it's still a bit much. I was in Europe last year and I was surprised how calm some of the cities were - green, walkable, a lot of nice cafes and parks, good public transit, and it never really felt overwhelming the way that, say, Chicago or LA does. I grew up in the suburbs, and I felt like some of the smaller European cities delivered the suburban sales pitch better than a lot of places I've been in the US.

(Don't take this as an attack or critique - genuine curiosity.)

most of the surplus land is marginally habitable, and costs increase dramatically when you get rural.

plus "land" doesn't mean anything if you're not near the people and things you want do to, places to work, etc.

do you want to do a 1.5 hour commute and hustle to live around Toronto, or do you want to live in Outer Nowhere, Manitoba, population 400, and where it regularly gets to -40C?

Reflexivity is nodded to in the definition of complex systems in the piece!

I think what you're saying is poverty is actually simple, and the solution is to stop the bad actors causing poverty? But at the same time, you are correctly recognizing that attempts to stop bad actors from causing poverty triggers reflexive responses and cascading repercussions. Which sounds mighty like a complex system?

I think you need to distinguish between complex systems, and byzantine systems. You can have complex systems where every piece shares a common goal, but feedback loops are hard. You can also have systems which, if a common goal was shared, wouldn't be that hard to understand, modelize and optimize, but where the actors of the system are not acting in good faith.

And I agree with the above poster: often, a problem is described as "hard" as a way to make an excuse for the agents. Sure, the problem is hard. The reason why it's hard isn't some esoteric arcane complexity, it's that some of the agents aren't even trying.

No, I'm not saying the problem is simple, but I'm saying that in many of these cases a systematic understanding of the problem isn't what we're lacking in pursuit of fixes - the reason the problem seems so intractable is because parts of the system benefit from perpetuating the problem and take agency to ensure the problem does not get fixed.

Poverty is one of these, but I think Climate Change is the most direct - the climate is complex, but climate change is simple: we're releasing too much carbon into the atmosphere, we have been for a century, and we've known that for at least half a century*. The issue isn't that we don't have the capacity to model or understand the problem, the issue is that powerful actors have used the leverage available to them within the system to prevent us from making changes to fix the problem.

And, you're right, that makes the problem difficult, because the system includes those actors resisting changes to the system, but again, it's not difficult because we don't understand it, it's difficult because we're being actively resisted by people who do not want to solve the problem, and that should be acknowledged by people looking to make it an abstract mathematical modeling problem.

* This isn't a conspiracy theory: https://en.wikipedia.org/wiki/ExxonMobil_climate_change_deni...

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