Simulated Annealing Thinking
Updated 2026-08-13
INTRODUCTION
English translation pending.
CORE DEFINITION
Simulated annealing thinking borrows the annealing metaphor from metallurgy: start at high temperature with large random exploration, then cool gradually so the search converges. The core proposition is that a greedy search gets trapped in whatever good option it encounters first, while tolerating temporary worsening early on keeps better solutions reachable. The key qualification is that the cooling schedule matters more than any single move: too slow and the process never converges, too fast and it locks in prematurely.
SCAFFOLDING EFFECT
Reduce cognitive load
- Explore phase: deliberately introduce random variation and try options far from the current best. - Setback budget: accept short-term deterioration within a defined limit to escape local optima. - Cooling plan: define when exploration narrows and how the final choice is locked in.
Anchor fast decisions
Accepting worse moves occasionally lets the search leave a local optimum that would otherwise be absorbing, because improvement alone can never justify the first step out. As the tolerance for worsening decreases, the search settles into the best region it has found. The schedule therefore trades exploration against commitment over the life of the search.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Simulated_annealingverified
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