An Introduction to Residuality Theory
summary
Residuality Theory claims designers should model what remains after unpredictable events — residues — then design components so residues survive likely attractors. It uses random simulation plus K‑N‑P (Kauffman) network analysis, cites root(n) attractor scaling (e.g. n=100,000 → ≈317 attractors) and a simple heuristic: if computed K > 3 refactor. Shows an EV‑charger design with ALPR fallback, cameras and pricing changes, and measures improvement with a residual index (RI > 0 across dozens of projects).
AI-written summary · about
chapters (12)
- 0:00The problem of architecture
- 1:27Ordered vs disordered systems
- 4:23The problem of X
- 7:26Reflective practice and science
- 9:40Introduction to residuality
- 12:33Complexity tools for design
- 17:24NKP analysis
- 22:15Designing for chaos
- 25:27Case study: charging network
- 30:39Hidden couplings
- 34:33Contagion analysis
- 40:50Empirical proof and conclusion
