What is over-optimization costs?

Over-optimization costs is a concept in personal epistemology: Each improvement gets harder and smaller — know when further optimization is not worth the cost.

Example: Vilfredo Pareto observed in 1896 that 80% of Italy's land was owned by 20% of the population. This pattern — later named the Pareto Principle — reappears everywhere: Microsoft found that fixing the top 20% of reported bugs eliminated 80% of crashes. The first 20% of optimization effort captures the vast majority of available gains. The remaining 80% of effort fights over scraps. A developer who spends two hours refactoring a critical hot path may cut response time by 40%. The next two hours might yield 5%. The next two, 0.3%. The function being optimized has not changed — but each unit of input now buys radically less output.

This concept is part of Phase 29 (Agent Optimization) in the How to Think curriculum, which builds the epistemic infrastructure for agent optimization.