Scenes form a developmental partially ordered sequence. So a question like “where do you go if you leave tech role in Silicon Valley?” is well-posed. You’re unlikely to go get an MBA and join Wall Street for eg, but likely to go to LA and get into entertainment media.
Conversation
Finance scene is upstream of tech, which is upstream of media. Fin tech is post-tech and ironically-fin.
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It’s a partial order so you can move to any scene that is not-upstream. Even reactionary moves are fundamentally ironic. Tech to trad is not the same as just being trad. So not-upstream.
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I’m currently very curious about “post-tech” scenes. I have an inventory but not a map: crypto, entertainment, trad, waldenponding, neoreaction, local politics, biotech. Each is post in a different way.
Tech is a bottleneck scene. Everything is either pre-tech or post-tech.
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It is locally total order. Only 1 scene at a time is the bottleneck usually. Other scenes don’t induce total pre/post cuts.
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Replying to
Well if there’s a net direction locally (A to be is more likely than B to A) for every pair with significant flow, you’ll get at least a local order. I suspect if you impose a minimum net flow threshold to filter random noise you’ll get a proper DAG.
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Replying to
Yeah, the overall graph of all flows, including both low absolute and net levels, probably has no structure, since almost any pair in any direction will have *some* flow due to pure randomness. The question is how to threshold to get to robust flow patterns.
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My these is basically that there is a historical direction to the grain of living scenes that’s independent of their youth/maturity. We have aging rock groupies and young trad knitters, so it looks confusing, but the exit patterns are a revealed preference sign of growth vectors

