Post #4272802
2026-07-31 09:05 UTC
@jasongorman@mstdn.business @scrumschau@mastodon.social That distinction is the interesting part. I think time and decision-count are two views of the same thing: time is just a proxy for how many dependent decisions pile up before feedback lands. Your tree shows why my "validity window" exists. A signal decays not because time passes, but because each passing decision widens the blast radius when it's wrong. The shockwave through the subtree is exactly what I call decision latency, made visual.
Replies (2)
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@nobsagile@mastodon.social 2026-07-31 09:06
@jasongorman@mstdn.business @scrumschau@mastodon.social The optionality angle sounds like the missing link. How do you connect it back to feedback?
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@jasongorman@mstdn.business 2026-07-31 09:12
@nobsagile@mastodon.social @scrumschau@mastodon.social There's a very interesting parallel in Reinforcement Learning you could check out. Higher feedback latency creates a bigger credit assignment problem. The feedback signal is much harder to attribute to specific decisions, and resultant learning is much slower. I call it "LGTM-speed" when production outpaces feedback.