more recent data suggests that the successes of the extremizing algorithm during the forecasting tournament were a fluke.
Do you have a link to this data?
I haven’t looked through your links in much detail, but wanted to reply to this:
Overall I would suggest to approach this with some intellectual humility and study existing research more, rather then try to reinvent large part of network science on LessWrong. (My guess is something like >2000 research years were spent on the topic often by quite good people.)
I either disagree or am confused. It seems good to use resources to outsource your ability to do literature reviews, distillation or extrapolation, to someone with higher comparative advantage. If the LW question feature can enable that, it will make the market for intellectual progress more efficient; and I wanted to test whether this was so.
I am not trying to reinvent network science, and I’m not that interested in the large amount of theoretical work that has been done. I am trying to 1) apply these insights to very particular problems I face (relating to forecasting and more); and 2) think about this from a cost-effectiveness perspective.
I am very happy to trade money for my time in answering these questions.
(Neither 1) nor 2) seems like something I expect the existing literature to have been very interested in. I believe this for similar reasons to those Holden Karnofsky express here.)
Seems like a sensible worry, and we did consider some version of it. My reasoning was roughly:
1) The questions feature is quite new, and if it will be very valuable, most use-cases and the proper UI haven’t been discovered yet (these can be hard to predict in advance without getting users to play around with different things and then talking to them).
No one has yet attempted to use multiple questions. So it would be valuable for the LW team and the community to experiment with that, despite possible countervailing considerations (any good experiment will have sufficient uncertainty that such considerations will always exist).
2) Questions 1⁄2, 3 and 4 are quite different, and it seems good to be able to do research on one sub-problem without taking mindshare from everyone working on any subproblem.
See this post for a good, simple mathematical description of the discrete version of the phenomenon.
Me and Ben Pace (with some help from Niki Shams) made a Guesstimate model of how much information cascades is costing science in terms of wasted grant money. The model is largely based on the excellent paper “How citation distortions create unfounded authority: analysis of a citation network” (Greenberg, 2009), which traces how an uncertain claim in biomedicine is inflated to established knowledge over a period of 15 years, and used to justify ~$10 million in grant money from the NIH (we calculated the number ourselves here).
There are many open questions about some of the inputs to our model as well as how this generalises outside of academia (or even outside of biomedicine). However, we see this as a “Jellybaby” in Douglas Hubbard’s sense—it’s a first data-point and stab at the problem which brings us from “no idea idea how big or small the costs of info-cascades are”, to at least “it is plausible though very uncertain that the costs can be on the order of magnitude of billions of dollars, yearly, in academic grant money”.
This might be an interesting pointer.
In Note-8 in the supplementary materials, Greenberg begins to quantify the problem. He defines an amplification measure for paper P as the number of citation-paths originating at P and terminating at all other papers, except for paths of length 1 flowing directly to primary data papers. The amplification density of a network is the mean amplification across its papers.
Greenberg then finds that, in the particular network analysed, you can achieve amplification density of about 1000 over a 15 year time-frame. This density grows exponentially with a doubling time of very roughly 2 years.
Here’s a quick bibliography we threw together.
Information Cascades and Rational Herding: An Annotated Bibliography and Resource Reference (Bikchandani et al. 2004). The best resource on the topic, see in particular the initial papers on the subject.
Y2K Bibliography of Experimental Economics and Social Science Information Cascades and Herd Effects (Holt, 1999. Less thorough, but catches some papers the first one misses.
“Information cascade” from Wikipedia. An excellent introduction.
“Understanding Information Cascades” from Investopedia.
Previous LessWrong posts referring to info cascades:
Information cascades, by Johnicholas, 2009
Information cascades in scientific practice, by RichardKennaway, 2009
Information cascades, LW Wiki
And then here are all the LW posts we could find that used the concept (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
. Not sure how relevant they are, but might be useful in orienting around the concept.
Schelling cafe? What Schelling cafe?
One you went to before, maybe?
And who on earth do you talk to? Maybe the guy who sat with us in a cab after EA Global Lonon last fall...
I found myself in a situation like: “if this is common knowledge within econ, writing an explanation would signal I’m not part of econ and hence my econ opinions are low status”, but decided to go ahead anyway.
It’s good you found it helpful. I’m wondering if equilibria like the above is a mechanism preventing important stuff from being distilled.
I really appreciate you citing that.
I should have made it clearer, but for reference, the works I’ve been exposed to:
Hal Varian’s undergrad textbook
Marginal Revolution University
Some amount of listening to Econ Talk, reading Investopedia and Wikipedia articles
MSc degree at LSE
For section III. it would be really helpful to concretely work through what happens in the examples of divorce, nuclear war, government default, etc. What’s a plausible thought process of the agents involved?
My current model is something like “my marriage is worse than I find tolerable, so I have nothing to loose. Now that divorce is legal, I might as well gamble my savings in the casino. If I win we could move to a better home and maybe save the relationship, if I lose we’ll get divorced.”
People who have nothing to lose start taking risks which fill up the merely possibly bad outcomes until they start mattering.
In the broader economy, it’s not the case that “If buying things reduced your income, people stop buying things, and eventually money stops flowing altogether”.
So the only way that makes sense to me is if you model content as a public good which no user is incentivised to contribute to maintaining.
Speculatively, this might be avoided if votes were public: because then voting would be a costly signal of one’s epistemic values or other things.
though I’m not sure how that is calculateed from one’s karma
I believe it’s proportional to the log of your user karma. But I’m not sure.
One can get high karma from a small amount of content that a small number of sufficiently high karma users that double up vote it.
There is still an incentive gradient towards “least publishable units”.
Suppose you have a piece of work worth 18 karma to high-karma user U. However, U’s strong upvote is only worth 8 karma.
If you just post one piece of work, you get 8 karma. If you split your work into three pieces, each of which U values at 6 karma, you’re better off. U might strong-upvote all of them (they’d rather allocate a little too much karma than way too little), and you get 24 karma.
To the extend the metaphor in the original question: maybe if the world economy ran on the equivalent of strong upvotes there would still be cars around, yet no one could buy airplanes.
Do you have details on when and why that was removed? Or past posts discussing that system?