Why Failing on Invalid Data Beats Silent Fallbacks

This account reveals a critical bug pattern in optimization systems: when invalid input silently defaults to conservative action, it creates false confidence in faulty analysis. The lesson is universal—data validity must be treated as a hard requirement before business logic runs, not as a soft constraint that can be papered over with fallbacks. Systems that collapse invalid input into safe recommendations risk users trusting analyses built on invalid foundations.