Why no prediction markets for large infrastructure projects?

Been reading this excellent piece on why prediction markets aren't popular. They say that without subsidies prediction markets won't be large enough; the information value of prediction markets is often nog high enough. 

Large infrastructure projects undertaken by governments, and other large actors often go overbudget, often hilariously so: 3x,5x,10x or more is not uncommon, indeed often even the standard.

One of the reasons is that government officials deciding on billion dollar infrastructure projects don't have enough skin in the game. Politicians are often not long enough in office to care on the time horizons of large infrastructure projects. Contractors don't gain by being efficient or delivering on time. To the contrary, infrastructure projects are huge cashcows. Another problem is that there are often far too many veto-stakeholders. All too often the initial bid is wildly overoptimistic. 

Similar considerations apply to other government projects like defense procurement or IT projects.

Okay - how to remedy this situation? Internal prediction markets theoretically could prove beneficial. All stakeholders & decisionmakers are endowed with vested equity with which they are forced to bet on building timelines and other key performance indicators. External traders may also enter the market, selling and buying the contracts. The effective subsidy could be quite large. Key decisions could save billions. 

In this world, government officials could gain a large windfall which may be difficult to explain to voters. This is a legitimate objection. 

A very simple mechanism would simply ask people to make an estimate on the cost C and the timeline T for completion.  Your eventual payout would be proportional to how close you ended up to the real C,T compared to the other bettors. [something something log scoring rule is proper]. 

I don't know what you mean by 'general intelligence' exactly but I suspect you mean something like human+ capability in a broad range of domains. I agree LLMs will become generally intelligent in this sense when scaled, arguably even are, for domains with sufficient data. But that's kind of the sticker right? Cave men didn't have the whole internet to learn from yet somehow did something that not even you seem to claim LLMs will be able to do: create the (date of the) Internet.

(Your last claim seems surprising. Pre-2014 games don't have close to the ELO of alphaZero. So a next-token would be trained to simulate a human player up tot 2800, not 3200+. )

I would be genuinely surprised if training a transformer on the pre2014 human Go data over and over would lead it to spontaneously develop alphaZero capacity. I would expect it to do what it is trained to: emulate / predict as best as possible the distribution of human play. To some degree I would anticipate the transformer might develop some emergent ability that might make it slightly better than Go-Magnus - as we've seen in other cases - but I'd be surprised if this would be unbounded. This is simply not what the training signal is.

Could you train an LLM on pre 2014 Go games that could beat AlphaZero?

I rest my case.

In my mainline model there are only a few innovations needed, perhaps only a single big one to product an AGI which just like the Turing Machine sits at the top of the Chomsky Hierarchy will be basically the optimal architecture given resource constraints. There are probably some minor improvements todo with bridging the gap between theoretically optimal architecture and the actual architecture, or parts of the algorithm that can be indefinitely improved but with diminishing returns (these probably exist due to Levin and possibly.matrix.multiplication is one of these). On the whole I expect AI research to be very chunky.

Indeed, we've seen that there was really just one big idea to all current AI progress: scaling, specifically scaling GPUs on maximally large undifferentiated datasets. There were some minor technical innovations needed to pull this off but on the whole that was the clinger.

Of course, I don't know. Nobody knows. But I find this the most plausible guess based on what we know about intelligence, learning, theoretical computer science and science in general.

My timelines were not 2026. In fact, I made bets against doomers 2-3 years ago, one will resolve by next year.

I agree iterative improvements are significant. This falls under "naive extrapolation of scaling laws".

By nanotech I mean something akin to drexlerian nanotech or something similarly transformative in the vicinity. I think it is plausible that a true ASI will be able to make rapid progress (perhaps on the order of a few years or a decade) on nanotech. I suspect that people that don't take this as a serious possibility haven't really thought through what AGI/ASI means + what the limits and drivers of science and tech really are; I suspect they are simply falling prey to status-quo bias.

Can somebody explain to me what's happening in this paper ?

Beautifully illustrated and amusingly put, sir!

A variant of what you are saying is that AI may once and for all allow us to calculate the true counterfactual     Shapley value of scientific contributions.

( re: ancestor simulations

I think you are onto something here. Compare the Q hypothesis:    

https://twitter.com/dalcy_me/status/1780571900957339771

see also speculations about Zhuangzi hypothesis here  )

Why do you think there are these low-hanging algorithmic improvements?

I didn't intend the causes $C_j$ to equate to direct computation of \phi(x) on the x_i. They are rather other pieces of evidence that the powerful agent has that make it believe \phi(x_i). I don't know if that's what you meant.

I agree seeing x_i such that \phi(x_i) should increase credence in \forall x \phi(x) even in the presence of knowledge of C_j. And the Shapely value proposal will do so.

(Bad tex. On my phone)