Design as a Multi-Scale Network, Risk as Network Structure

Seed idea: Can modeling a design project as a network provide underpinnings to better estimate risk and sequence problem solving? With connections to multi-scale systems, Alexander's idea of an unfolding process, and thin vs. fat-tailed variables.

Part 1: 00:12
Network theory, multi-scale networks, example of building a clustering feature on a side project, two scales of design: the feature level and the implementation level


Part 2: 13:54
Unfolding as a network dynamic, learning at the fine scale under constraints from the large scale


Part 3: 20:05
Risk, thin-tailed vs. fat-tailed variables, how underlying structure gives rise to different shapes of distributions, orthogonality and interdependence

  • Nassim Taleb. See Probability, Risk, and Extremes (PDF) on thin vs fat-tailed variables. Re: the relationship between distributions and  underlying structure: "... we cannot rule out that it is not fat tailed unless we understand the process." (emphasis added)
Part 4: 30:14
Patchiness of risk in a design problem, observation in science vs. active control in design, targeting unknowns, redesigning at the feature scale based on information from the implementation scale, example of designing for independence

Part 5: 38:35
Scopes in Shape Up as tangled network neighborhoods, structure vs. opacity in the network, alternation between identifying structure and removing "the fog"


Outro: 46:05
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