Emergence Biology 361 = Computer Science 361 Bryn Mawr College Spring 2006

Ways/reasons to engage with "emergence"

• yours? in blogs ...
• enjoyment/appreciation of play (Mitchel Resnick)
• similar patterns/phenomena in quite different contexts, hence requiring explanation in some way not specific to individual contexts
• phenomena that are "mysterious" when looked at from narrower perspectives (Adam Smith)
      (see also for both Grobstein, 1988)
• puzzlement at the existence of diversity/complexity (Darwin, atomists)
  
  => distributed organization, self-organization: no conductor, no architect?
  
• Doug on Wednesday
• Paul next Monday
• Regardless of motivations/aspirations, contemporary interest in/explorations of emergence strongly depend on computer modelling (computer as telescope/microscope)

Computers as telescope/microscope for exploring emergence: John Conway's Game of Life as an example

• simple rules of interaction among simple things that
  • have outcomes that are "surprising" given knowledge of the rules
  • have consequences that cannot be characterized except by trying them out

    (Turing: no solution to halting problem for a universal computer; properly configured the Game of Life a universal computer; therefore, for at least some configurations, no "closed form solution", absolute? "computational irreducibility" (Wolfram))

Computer modelling in the emergence context
(Some Tips for Further Exploration of and Drawing Conclusions from Computer Models and Reflections and Further Considerations)

• Objective is not to show that a model will mimic some pattern/phenomenon since it is presumed that all patterns/phenomena can be mimicked by some computer model and that there are multiple different ways to achieve this. To put it differently, computer models are not capable of nor aimed at determining what is "real".
• Objective instead is to be "surprised", to "surprise" others, to establish that some pattern/phenomenon that is presumed to depend on complexity/planning/a directive element can be produced without that. To show what might be, rather than what is.
• Good computer models, in addition, provide for ongoing exploration of the the degree of dependence of observed phenomena/patterns on particular aspects of the model.