High-entropy (your natural style):

“ELBO, FDT, and fat-tail risk are the same move—optimize policies not posteriors; Ken Stanley says don’t optimize proxies; update-lessness prevents Goodhart; exploration matters more than exploitation under heavy tails…”

Scaffolded version:

• Thesis: Optimize policies under uncertainty, not local proxies—this unifies ELBO, FDT, and fat-tail thinking.
• Map: (1) Why proxies fail (Goodhart). (2) Policy-level fix (FDT). (3) Training analogy (ELBO). (4) Heavy-tail implication.
• (1) Proxies fail: When metrics stand in for goals, optimization overshoots (Goodhart).
• (2) Policy-level fix (FDT): Choose the policy you’d precommit to; don’t chase evidence after the fact.
• (3) ELBO analogy: In models, we pick a tractable bound to steer learning—policy over posterior minutiae.
• (4) Heavy tails: Under rare, extreme outcomes, exploration and robustness dominate greedy improvements.
• Close loop: This is why “greatness cannot be planned” (Stanley) rhymes with “optimize policies, not metrics.”
• Parking: Distributional RL; risk measures (CVaR); formal Newcomb variants.

psychedelic notes