It’s not enough to simply call something a language game. We might be concerned about which sort of language game is being played (are there sorts? it certainly seems so.) and why. Korzybski uses language to deconfuse language. If we can point to problems with the act of pointing itself and how it might do a sort of faulty essentialism thing. Natural language performs set operations with non obvious properties/edge cases.
So, aliveness. Maybe we care about moral relevance. Maybe we care about intentionality. Maybe we care about whether any sort of interesting non obvious signal processing is going on. In general, when we have essentialism crop up in our intuitions what is it we are pointing at? At least one of the things we’re pointing at is reference class forecasting for causal structure itself. Modularity of our causal models allows transfer learning. But buyer beware. With increased portability comes an increase in hidden structure. Lost purposes, lossy compression, lack of documentation over an increasing technical debt to things we did for efficiencies sake.
The price of being a dog is comfortable, sociable boredom. The price of being a wolf is the freedom of loneliness and uncertainty.
Indeed, I don’t know why ‘once a century’ would be expected to hold with record amounts of density, travel, and absolute numbers of people in unsanitary conditions.
We look for ways that the frontier is secretly just a saddle point and we can actually push the frontier out farther than we naively modeled when we weren’t there looking at it up close. This has worked incredibly well since the start of the industrial revolution.
Two birds with one stone for a human challenge trial in which we get faster data on vaccines by inoculating the participants with corona virus after giving them the trial vaccine. If they don’t get sick we get vaccine data. If they do we get (slightly noisy) inoculation data.
Interesting. The idea here that the market is still on average underestimating the duration and thus the magnitude of the contraction?
When people are using drastically different heuristics to value things but that isn’t explicit they can get confused in the course of debate.
Real estate likely just became significantly more illiquid at least for the next few months.
Well if we had confidence in any major parameter shifting in either direction it would be tradeable, so I expect reasonable pressures on both sides of such variables.
So, the USA seems steadily on trend for between 100-200k deaths. Certainly *feels* like there’s no way the stock market has actually priced this in. Reference classes feel pretty hard to define here.
I just checked, and you’re right! From 24 hours to closing at 10pm. Looks like 6am is the next least visited time.
I am going to 24 hour grocery stores in the middle of the night once every 2 weeks to reup to 6 weeks worth of food. I imagine this sort of thing is harder for people in denser areas.
Those are direct effects, but many other people won’t receive care or will have care postponed by general capacity overwhelm.
I’m having trouble imagining a concrete scenario where it would be possible to use this information to gain an advantage.
This isn’t a random sample afaik. That said, 2x worse than a very bad flu year falls well within the current projections (flu: 8% of population, up to 60k dead, COVID maybe 15-20% infected 100-200k dead). Could also do 3x that though pretty easily.
I’d like to see a model for knock-on deaths from hospital overwhelm.
I don’t know how this would work. Trading on info always seems great until it comes time to actually do it. eg you get actual inside knowledge that suggests a trade, but wait, how do you know whether this inside knowledge is sufficiently leaked that it is priced in?
meta but why argue about this if there is high confidence that serological data will determine things one way or another over the next week or two? Is there decision relevant leverage?
Thanks for sharing this. I wish more people would share their reasoning about how to model rather than just their finished models.
I call it orthogonal sum games.
I rag on the California government a lot, but big positive update on their early response. Some will want to credit the big tech campuses for closing fast, but this is more a function of how cheap it was for them to do so relative to other businesses.