1. Intellectual Ancestors
   1. Computer science
      1. Artificial intelligence
      2. Cybernetics
      3. Software systems
   2. Information theory
   3. Ecology
   4. Non-linear physics
      1. Dynamical systems
         1. Chaos
   5. Mathematics
2. Key Tools
   1. Modelling and simulation
      1. analogy
         1. problematic; quality and precision matter
      2. abstraction
         1. problem: level of detail to omit
   2. Detailed case studies
      1. mapping system dynamics after the fact; letting patterns show themselves
3. Important Questions
   1. what is the value of these terms?
   2. many names for the same phenomenon
   3. says something about the nature of the science
4. Key (and vague) terms
   1. complexity
      1. interrelation
   2. emergence
      1. whole is different than parts
   3. non-linearity
      1. harder to predict
   4. sensitivity to initial conditions
   5. phase cycles
   6. phase shifts
      1. 'tipping point'
   7. agents
   8. hierarchy/panarchy/holarchy
      1. 'holons' = scales; nested systems
   9. domains of stability (attractors)
   10. uncertainty, indeterminancy
5. Key Issues
   1. if 'complexity approaches' take longer and are less certain to be successful, are they useful?
   2. are some analogies relevant? is there a standard that should be applied
   3. should complexity become like the other sciences?
      1. what is the utility in it becoming more regulated?
   4. who is validating these models?
   5. complexity isn't new, these ideas aren't new. why do we need it to be a distinct 'science'?
   6. incomplete information
      1. does a true 'complexity approach' necessitate complete information; and isn't that impossible?