There’s a question related to what Gil asks that I have wanted to look at for a 
while.

I would put her suggestion in the category of “better democracies provide their 
citizens with better information for decision-making”, which I also wanted to 
look into.  It also brings to the forefront another dimension of what one wants 
a democracy to do, if one wants to argue in favor of democracy as a framework.


But the thing for this post was a bit different (though related).  Redo the 
basis for pricing and economic decision theory based on real-goods 
input-output, and whole lifecycle material and energy constraints.

So the background was: we know that marginalist pricing in economics (what we 
all use right now) commits two big distortions:
A: it doesn’t clear markets very well, and the problem is not just the 
incompleteness but the skew.  Rich people can bid up prices for luxury 
consumption to a level where many more ordinary or poor people can’t afford 
necessities or near-necessities for getting-by (let me not use the word 
“survival”; it’s too melodramatic).  What form the skew takes and how bad it is 
depends on the way marginalist pricing interacts with wealth inequality in the 
population who are using the economy.
B: Not pricing in aspects of one thing can distort the prices of everything.  
The not-pricing part is what economists call “externalities”; what one thinks 
of this term, and its suggestion of inevitability, can be discussed as a 
separate point.  But the main use case here is energy sources.  The main things 
not-priced in are climate and other ecological impacts now, and scarcity 
consequences later.  Oil is the big one, but coal and natural gas are in there 
too; and equally well we could look at ores that are needed for electrification 
under current technologies, and much else.

The goal in the re-do, then, is to make real input-output models of the economy 
that look more like chemical-reaction schemata, in the sense that we know a 
production event can’t happen unless all its required inputs are available, and 
all the unavoidable outputs have somewhere to go.  von Neumann started this in 
1937 and a few years after, and people worked on it for a while.  He did it the 
way economists do (and also for the sake of ensuring a problem he could solve), 
with the requirement of all-positive prices and the assumption of “free 
disposal”, meaning if there is a pollutant you produce but don’t want, you just 
walk away from it and it doesn’t cost [you] anything.  Within the last year or 
two, we redid the same framework, but allowing prices to become negative (you 
have to pay somebody to take things that neither of you want), and no free 
disposal (everything has to go concretely somewhere, and the economy should do 
a thorough accounting of those somewheres).  It’s a trivial construction, of 
course (a human could do it), but I don’t think anybody has bothered to pursue 
it before.

von Neumann’s point is that you can put a decision theory on this system and 
derive prices from that, the same way as you can for marginalist pricing.  But 
the underlying market-clearing model is very different.  Same goes for the 
version with both-sign prices and no free disposal.  In the era of “smart” 
contingent contracts that are popular in the blockchain and proof-of-stake 
world, there is lots of other already-in-use method that could be built with 
the information that this kind of data-foundation supplies, beyond just 
dollar-prices.

Then the whole-lifecycle thing, which can be done independently of, or as part 
of, a von Neumann representation, is to take account of inescapable baseline 
costs in such quantities as free energy, so that grades of ores have physical 
relations to the energy sources used to extract them and the alternatives such 
as more or less complicated and costly recycling.  This too has some precedent, 
but by hand it is too costly and labor-consuming, so it was never going to 
scale that way.


Both of these remind me of things like insurance, where decreased costs of 
integrative computation and communication can take something that we know 
perfectly-well how to do, and transition it from being something nobody wants 
to pursue, to something that there is a valid model for somebody to develop.  
Used to be that if you wanted to ensure a ship, you had to go to Lloyds of 
London and get a custom contract written, with big cost, delay, etc.  Now 
insurance for very big things is a commonplace with standard models.  It was 
just increased scale and decreased cost for finance, accounting, computation, 
and communication, that made that tip possible.  The law around it could then 
follow.  The combination of comprehensive and also fine-grained data collection 
that the big-compute companies already do, with the integrative capabilities 
and custom-rendering that the LLMs do, and the ability to generate large 
consistent models (as with the Lean applications to big complicated theorems 
and conjectures) make it seem to me like all the capabilities are already in 
place to try to prototype something like this.  Incentives, not so much, but 
that identifies a concrete task.

Eric


> On Sep 5, 2026, at 8:58, Gillian Densmore <[email protected]> wrote:
> 
> Ai Comp Sci Question: How come there is a need for arms race to get insanely 
> dense model..so as I am to understand it the big guys have massive amounts of 
> parameters (30bn from Ali labsqwen) just as layman it seems like we can make 
> the Neral Nets, unets, and GANS, more...compressed. Can anyone kindly explain 
> to me what the heck we're doing here, where models can speek idiot human like 
> me. (suposed to speek )  where the need for rediculous computes  goes up, but 
> how good the hardware (GPUs and memory etc) have to go up x amount more to 
> run them. Where is the part where the software is made much more...correct 
> and elegant.
> 
> Or is this just the nature of an exloding new field where we don't know what 
> the hell we're doing half the time.
> 
> 
> Speculative question: Where's the generative ai that can do more than make 
> pervy pictures and cringe adds for coca cola I was just thinking about this: 
> it'd be so...handy: if generative ai could be used to create something where 
> its much much lighter for one thing. And also: It'd be so handy for maybe 
> videos showing over time what a importat meadow, or some other...analog thing 
> for want of a better phrase fromthe real world fead real GIS data and then 
> extrapolate and create videos, semi-interactive reality based simulations for 
> real big picture: Litium, and silicon aren't infinite (on 5 year scale) at 
> least so I understand: showing a very acruate simulation of: gen what comes 
> after Z? oO well them having to using pretty old tech: day to day life (what 
> ever that looks like) so...the normies with a very americana tight read get 
> shown in a extreme visceral way: no actuall not just dry but important nerdy 
> pre-rendered simulations. But: you could actually ask a ---hyper real person 
> who has simulated 5 year world: is it like? etc and get...dead on right 
> answers because very sophisticated agents. that's the world I'd love to have 
> for future gens...if it'd be a improvement.
> What do folks think?
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