Comment author: rhys_lindmark 24 August 2017 03:59:18PM 0 points [-]

Great question. and are building decentralized prediction markets on the Ethereum blockchain. Their goal is to "match the global liquidity pool to the global knowledge pool."

I've asked them how they're thinking about hedgehogs to form a collective fox-y model (and then segmenting the data by hedgehog type).

But yeah, I think they will allow you to do what you want above: "Questions of the form: if intervention Y occurs what is the expected magnitude of outcome Z."

Comment author: poppingtonic 29 September 2017 02:52:12PM 0 points [-]

I like both of them, but I'm wondering: why wait so long? Isn't there a way some group (maybe us) could build 10% of the kind of prediction market that gets us 90% of what we actually need? I need to think about this more, but waiting for Gnosis and Augur to mature seems risky. Unless de-risking that bet means joining both projects to accelerate their advent.

Comment author: poppingtonic 29 September 2017 11:37:59AM 3 points [-]

Your link [2] points to a .docx file in a folder on a computer. It isn't a usable download link. Was that the purpose?


In response to Open Thread #38
Comment author: WillPearson 22 August 2017 10:19:04AM 3 points [-]

I've been reading Superforecasting and my take away is that to have good predictions at the world you need to have a multiplicity of view points and quantify and breakdown the estimates fermi-style.

So my question is, has there been any collective attempts at model building for prediction purposes? Try and get all the hedgehogs together with their big ideas and synthesize them to form a collective fox-y model?

I know there are prediction markets, but you don't know what information that a price has synthesized so it is hard to bet on them, if you only have a small bit of information and do not think you know better than the market as a whole.

It would seem that if we could share a pool of predictive power between us we could make better decisions about how to intervene in the world.

Comment author: poppingtonic 23 August 2017 12:12:49PM 1 point [-]

I think that the Good Judgment Project (founded by Philip Tetlock, the author of Superforecasting) is trying to build this with their experiments.

Comment author: poppingtonic 24 July 2017 01:36:04PM 2 points [-]

A complication: Whole-brain emulation seeks to instantiate human minds, which are conscious by default, in virtual worlds. Any suffering involved in that can presumably be edited away if I go by what Robin Hanson wrote in Age of Em. Hanson also thinks that this might be a more likely first route for HLAI, which suggests that may be the "lazy solution", compared to mathematically-based AGI. However, in the S-risks talk at EAG Boston, an example of s-risk was something like this.

Analogizing like this isn't my idea of a first-principle argument, and therefore what I'm saying is not airtight either, considering the levels of uncertainty for paths to AGI.

Comment author: poppingtonic 11 October 2016 09:04:24PM *  7 points [-]

Quoting Nate's supplement from OpenPhil's review of "Proof-producing reflection for HOL" (PPRHOL) :

there are basic gaps in our models of what it means to do good reasoning (especially when it comes to things like long-running computations, and doubly so when those computations are the reasoner’s source code)

How far along the way are you towards narrowing these gaps, now that "Logical Induction" is a thing people can talk about? Are there variants of it that narrow these gaps, or are there planned follow-ups to PPRHOL that might improve our models? What kinds of experiments seem valuable for this subgoal?

Comment author: poppingtonic 11 October 2016 08:39:51PM 7 points [-]

Thanks for doing this AMA! Which of the points in your strategy have you seen a need to update on, based on the unexpected progress of having published the "Logical Induction" paper (which I'm currently perusing)?