The more VUCA (volatility, uncertainty, complexity, ambiguity) in the world, the less useful the state-propagation model of foresight. State-propagation model: define boundary of 'important', book-keep what is in/out, propagate the 'in' bits via some model, ignore 'out' bits.
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...the situational foresight problem then turns into a 2-step problem: detect "interaction bundles" of individual important dynamics, and propagate past mixing events (kinda like billiard ball collisions). This is like tracking a bunch of interacting subplots of a story.
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Act like a CEO, in other words.
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...much harder to set up, but once you start thinking in these terms, the modeling and propagation become easier and more useful. There is stuff you track longitudinally, and stuff you track situationally because it affects individual mixing events. Bg/fg interact more gracefully
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