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Someone should write an engineering management book that presents a series of pairs of case studies. Each pair consisting of two initially similar projects: one a success, the other a failure. We aren't learning enough from failure
Arguably you'd need to review more failures than successes, as the number of potential bad decisions is larger than the number of good ones
Arguably because you might do better by imitating successes than by trying to avoid infinite possible ways of failing
Realistically a smart person will start building internal heuristics based on both positive and negative experiences
One of the reasons I don't like the "5 habits of successful people" kind of books: we often have no clue "why" something worked well/ failed - especially in complex systems. Every anecdote might just miss the one critical factor.
This reminds me of how everyone in academia talks about the need for a "Journal of Null Studies"
Can we train a network with only failures?
Current AI paradigm needs to review "more failures" or anomalies because the architecture is designed for success requiring infinite loop of updating after quantitative analysis. Next AI's model should be designed for failure, able to qualitatively validate the good ones only
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