Essential Incompleteness in Program Modeling
summary
Claim: any sufficiently powerful model cannot both be complete and consistent so programs cannot perfectly model reality; prefer incompleteness to inconsistency or accept exhaustion/halting. Uses puppet (server automation written in Ruby) as a concrete system that models users, packages, services across seven platforms and shows where models diverge from reality. Uses math metaphors (Ramachandran energy landscapes, asymptotes, fractals) as decision tools. Recommendation: build the least powerful model that solves the problem, iterate high-resolution only where users demand it.
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chapters (26)
- 0:00Introduction
- 3:31completeness and consistency
- 4:05Kirk Godel
- 9:10Bertrand Russell
- 12:16Simple Solutions
- 14:45Power to Decide
- 15:16Puppet
- 15:39Platform Models
- 17:19Solutions
- 19:41Energy Curves
- 22:58Energy Curve
- 24:48Decision Making
- 25:39Agile Programming
- 25:53Asymptotes
- 26:14Refine
- 26:27Refinement
- 27:02Fractals
- 29:15Conclusion
- 31:36Logic Block
- 35:29Metaphor
- 36:33Approaching Mission
- 36:59Sharing Models
- 40:13Moving Targets
- 40:44High Resolution
- 41:58Accuracy
- 42:12Accuracy vs Precision
