David Rostcheck, 9/1/2022

This note expands the basic analysis articulated in Introducing Broad Information Theory: A Cross-Domain Informational Framework into a more nuanced algorithm:

graph TD
  A[Identify a pattern in a domain]
  A --> B[Search for and identify parallel patterns in other domains]
  B --> C[Refine the identified pattern, identifying structure and substructure]
  C -->|Refinement Loop| B
  C --> D[Form theses for test]
  D --> E[Form computational simulations to test those theses]
  E --> F[Extract general principles from the simulations]
  F --> G[Form a more complete and more general theory]
  G --> H[Provide predictions for further validation and refinement]
  H -->|Prediction-Testing Loop| E
  H --> I{Sufficient?}
  I -->|Yes| J[End]
  I -->|No| B

Diagram by ChatGPT 4.0

The general flow of a Broad Information Theoretical analysis is:

1. Identify a pattern in a domain
   • Ex. biological organisms with higher complexity are built out of large numbers of cells that are specialized from a common base structure and are independently replaceable
2. Search for and identify parallel patterns in other domains
   • Information Technology: Large computing infrastructures are built out of replaceable components of commodity hardware
   • Business: Corporations are built out of individual workers, surviving the employment separation of any individual worker
   • Sociology: Social institutions such as states and religions are build out of individual people, surviving the loss of any individual member.
   • Ecology: Animal populations continue to sustain their function in the ecological web although individual members die
3. Refine the identified pattern, identifying structure and substructure, by cross-correlating and contrasting observations:
   • Organisms that are capable of self-repair, like living organisms, achieve this by being themselves made up of individual cells that are replaceable and can be self-generated
   • By contrast, cybernetic systems may be durable to the loss of individual components but are not currently capable of self-repair
   • In all these systems, the individual elements are themselves sophisticated and can perform complex tasks
4. Form theses for test:
   • Being composed of smaller individual elements that can compute and respond to stimuli seems to give the overall system the informational advantage of being able to resolve information at a finer level of detail (the scale of the individual element) and respond to it, increasing the overall system’s ability to extract information from and respond to a complex environment.
5. Form computational simulations to test those theses:
   • Build a computational model where organisms are composed of sub-parts and complete on a grid with fine-grained distributed properties. Make some organisms composed of more smaller elements and others composed of fewer larger elements that can sample only averages of the smaller grid elements they cover. Test over many runs which wins in competition.
6. Extract general principles from the simulations:
   • Varying the simulation over scales, create curves for how competitive advantage changes with scale difference between opponents and characterize the curves parametrically if possible
7. With the principles observed, seek to form a more complete and more general theory. Where possible it should produce testable predictions in other domains for further validation and refinement.
   • Ex. provide predictions for the limitations of cybernetic vs. biological systems and explore if those limitations are on a path to being overcome or not; form predictions of crossover and results should crossover be achieved.
8. A pattern is sufficiently well-characterized when we stop finding significant improvements to it structure. At this point it can cataloged as a known common solution to a known informational problem.