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 alternative I've been increasingly using isn't as familiar because it doesn't have an obvious mathematical analogy in quantitative models. The key is to decouple assessments of what is important from mechanisms of propagation.
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... you decide what's important overall, in a cradle to grave sense, and track it through peaks and troughs of situational importance, whether or not it's doing anything interesting at a given time
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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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