This is a very interesting part of an interview with Freeman Dyson where he talks about how computation could go on forever even if the universe faces a heat death scenario. https://www.youtube.com/watch?v=3qo4n2ZYP7Y
In the same vein, I would highly recommend John Maynard Smith's "Evolution and the Theory of Games". It has many highly motivated examples of Game Theory in Biology by a real biologist. The later chapters get dense but the first half is readable with a basic knowledge of calculus (which was in fact my background when I first picked up this book).
CellBioGuy all your astrobiology posts are great I'd be happy to read all of those. This may be off the astrobiology topic but I would love to see a post with your opinion on the foom question. For example do you agree with Gwern's post about there not being complexity limitations preventing runaway self-improving agents?
Still reading minor nitpick: for point 2 you don't want to say NP (since P is in NP). It is the NP-hard problems that people would say can't be solved but for small instances (which as you point out is not a reasonable assumption).
So your first and second point make sense to me, they together make the nominal interest rate. What I don't understand is your point about growth. The price of a stock should be determined by the adjusted future returns of the company right? The growth you speak of should be accounted for already in our models of the future returns. So if the price going up that means the models are underestimating future returns right?
People in finance tend to believe (reasonably I think) that the stock market trends upward. I believe they mean it trends upward even after you account for the value of the risk you take on by buying stock in a company (i.e. being in the stock market is not just selling insurance). So how does this mesh with the general belief that the market is at least pretty efficient. Why are we systematically underestimating future returns of companies?
About 20/50, I don't know if that can be unambiguously converted to diopters. I measure by performance by sitting at a constant 20 feet away and when I am over 80% correct I shrink the font on the chart a little bit. I can currently read a slightly smaller font than what corresponds to 20/50 on an eye chart.
Does anyone know of some good program for eye training. I would like to try to become a little less near-sighted by straining to make out things which are at the edge of my range of good vision. I know near-sighted means my eyeball is squashed, but I am hoping my brain can fix a bit of the distortion in software. Currently I am doing random printed out eye charts, and I have gotten a bit better over time, but printing out the charts is tedious.
This is a really fascinating idea, particularly the aspect that we can influence the likelihood we are in a simulation by making it more likely that simulations happen.
To boil it down to a simple thought experiment. Suppose I am in the future where we have a ton of computing power and I know something bad will happen tomorrow (say I'll be fired) barring some 1/1000 likelihood quantum event. No problem, I'll just make millions of simulations of the world with me in my current state so that tomorrow the 1/1000 event happens and I'm saved since I'm almost certainly in one of these simulations I'm about to make!
I believe Dyson is saying there could indeed by an infinite amount. Here is a wikipedia article about it https://en.wikipedia.org/wiki/Dyson%27s_eternal_intelligence and the article itself http://www.aleph.se/Trans/Global/Omega/dyson.txt