The Long Way Up, Part 2

Not too much to say this week. The long way up is, in fact, long. Open Logic Project has a section on the Lowenheim-Skolem theorem, which could be useful as Langan mentions it. The most recent build also has a section on temporal logic as an application for normal modal logic (Chapter 56). I thought about doing a section on naive set theory and propositional logic from OLP, but anyone reading this can probably handle that better for themselves than I can, and it seems like a waste of time. Better to get as close to the real subject matter as possible. Right now I seem to have a loose path in mind. I’m thinking to hit modal logic and tableaux in modal logic (ideally with the temporal logic application). Then I’m thinking of checking out what simulation means in the context of model theory. Brushing up on any basics of computation that I’ve missed. Then try building Nested Simulation Tableaux as best I can for inspiration before heading back to the main body of work (Major Papers). I think that will give me the best intuition.

Only 23 weeks left so things are feeling crunchy. I suppose they have from the beginning. It’s also that part of summer where it’s hot and humid so I’m not really motivated to do anything. I found a text-to-speech pdf reader, which is helpful - though not as much for the math. Hopefully I can get sufficient mastery of the logic by the end of summer, then power through the main philosophy with better background knowledge. That means 6 weeks of logic giving us 17 weeks remaining. Ideally, I’d have a few weeks to spare so that really gives us 14 weeks if we do 6 weeks of logic. However, this seems impossible. Based on a point system (see agile methodology), the most I can manage looks like it’s about 80 points. Just the sections on modal logic with the temporal application looks to be about 32 points worth of effort. This would leave only 48 points for everything else. Just the main CTMU papers themselves look like they may be about 70 points. And by the way, these point estimates assume I can just jump straight there, which may not be the case. Plus, none of this includes output like these posts, or practice like trying to code NeST. The lower bound on the total number of points is 102, but this is likely an extreme underestimate.

How many points would have been available if I were maximally efficient? The highest I can imagine right now is 312. Which means at maximal efficiency I could probably pull off this project if I had done it perfectly from the beginning. But that’s not really what it’s all about, is it? Now that I’m in an unwinnable scenario, it’s time to try and meta-break.

So, what can I do? In context it seems to come down to three things: The number of points available, the return on investment per point, and utilizing ultra-learning strategy. Three things main things come to mind when I think about maximizing the number of effort-points available. Maximizing recovery, minimizing distractions, and min-maxing eustress over distress. One main thing comes to mind when I think about maximizing the return on investment: System 1 vs System 2 thinking.

How can I maximize recovery, minimize distractions, and min-max eustress? Maximizing recovery is pretty simple, if a bit difficult. It’s harder to recover when you’re very depleted because all the little distractions that don’t help you recover (e.g. doom scrolling) tend to snipe you better when you’re need to avoid them most. I will probably just have to plan for it as I already know what things help me recover and what generally don’t. (For me it’s generally going outside, listening to birds, light exercise, not over-eating junk food - the basics). I think loosely planning the week is also the way to minimize distractions. Min-maxing eustress over distress can probably be combined with the ultra-learning principles of directness and feedback. By centralizing focus on the CTMU papers themselves and only jaunting out for specific bits of background knowledge with specific applications I can hit a few ultra-learning principles, most notably directness.

I think the real challenge is maximizing return on investment. At some point learning and demonstrating a thing takes a minimum amount of effort or brainpower and there’s no different angle, no system, no methodology, no trickity-trick-trick that’ll change that. However, most of my effort is measured assuming I’m using system 2 thinking. One would think learning something as in depth as the CTMU would require system 2 thinking - and it most certainly does - but there may be a way to utilize system 1 thinking, which could increase the relative value of system 2 effort-points. I have no idea how to do this, but will ask some friends what they think.

Apologies for the scattered thoughts, it’s been a week.