Born to do Math 49 – Metaprimes (Part 15)
Author(s): Scott Douglas Jacobsen and Rick Rosner
Publication (Outlet/Website): Born To Do Math
Publication Date (yyyy/mm/dd): 2017/04/25
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Rick Rosner: You can start to build a time out of association. Where you’ve got atom A and atom B interacting a lot, we also see that atom B and atom C interact a lot. But as you look the different interactors, that you can further order things so that you can make further efficiencies because A and B may interact a lot at a given time and A and C may interact a lot at a different time.
I don’t know how you pull time out of it. Anyway, the universe is built on space and time, and space and time are built on efficient arrangements of association, of highly associated particles.
Scott Douglas Jacobsen: So they’re aren’t maximally then, as a closing statement, but they are optimally efficient given various constraints.
RR: They are sloppily efficient. You’ve got these interactions. You have these informational efficiencies and rules for informational efficiency, or for the efficient structuring of space based on the interactions – space and time based on associative interactions. Based on interactions, which are themselves associative, those—you can assume that there’s going to be some of those principles of ordering space and time are going to be efficient without being maximally efficient.
Because they probably depend on local efficiencies. But there is a multi-model approach here too. You can represent the information here in various ways. There’s that underlying efficiency.
SDJ: There are the higher-order efficiencies too.
RR: There’s the “Travelling Salesman Problem” or the salesperson problem. You have to figure out the order of cities that minimizes the overall distance the salesperson has to travel. It turns out to be a problem that blows up computationally the more cities that you have. There’s not an algorithm that can find you the overall shortest distance without doing a huge amount of calculation.
Let’s say, and I don’t know the math exactly, this is probably not the case, but computationally it is similar to the case that you have to look at all 11 factorial paths. 12 factorial path, among the cities to find the shortest one, that is a number that blows up hugely when you go to 20 cities and 100 cities. To find the absolute shortest path would eat up a lot of computer time.
But there are some algorithms that find you some good paths based on just comparing a few cities at a time, like 3 or 4 and building the shortest path among those 4 proximate cities, then the next 4 proximate cities until you’ve established a locally minimal path among each set.
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