Complexity and Hierarchy
Concept of Complexity
• “whole is more than the sum of its parts”– Holism
• new properties not found in subsystems• “mechanistic explanations of emergence rejected”
• Weaker view of emergence– Parts in complex system have mutual
relations not existing for isolated parts• Consider terms with i in circuits
– Allows for scientific exploration of emergence
• Gödel, Escher, Bach– Aunt Hilda
Studying Complexity
• Interactions between components often slower than interactions within components– Approximations of internal behavior can often be
described independent of interactions among subsystems.
– Approximations of interactions among subsystems can often be described independent of internal behavior of subsystems.
Catastrophe Theory
• Classification of nonlinear systems according to their behavior– Stable states include static equilibria and periodic
cycles– Small perturbation can send to another stable
state or unstable state• Example: budworm population• Not applicable to many contexts so not
discussed much today
Chaos Theory and Chaotic Systems
• Deterministic dynamic systems whose paths change radically based on minor changes in input– Their detailed behavior is unpredictable due to the influence
of small changes/error• Most engineers learn – Linear differential equations– Design of systems where
these are good models• Chaos theory can be used
to predict when behavior switches from orderly to chaotic
Complexity and Design
• Chaos should not be assumed to be present or lacking
• Details may not be predictable but manageable as aggregate phenomena– Example of designing for
turbulence • Feedback mechanisms can
be used to restrict movement to within noise levels
Complexity and Evolution
• Genetic Algorithms– Features/combinations providing fitness multiply more
rapidly– Build system to model evolution with specified
mutation rate and crossover• Self-replicating systems– Need proper representation (feature selection and
abstraction)– Can be used for education/simulation (Core wars)– Example of computer viruses
Back to Hierarchic Systems
• Many types of hierarchic systems besides organizations– Biological: nucleus, cell, tissue, organ, organism– Physical: subatomic particals, atoms, molecules, …
suns, solar systems, galaxys– Social: families, villages, states, countries– Symbolic: letters, words, sentences, paragraphs
Evolution of Complex Systems• Parable of watchmakers– The existence of stable intermediate
subsystems– Intelligence is not (necessarily) hierarchy
by assembly from components but hierarchic structure through specialization
• Problem solving as natural selection– Trial and error where partial result plays
role of a stable subassembly– Evaluation of trials plays role of selectivity– Past successful paths used as starting points
• Complex systems will evolve much more rapidly if there are stable intermediate forms
Nearly-Decomposable Systems• Interactions between subsystems are weak but not
negligible– Short run behavior is independent of other components– Long run behavior depends
on aggregate behavior of other components
• Example of heating a building with rooms and cubicles
• Representation – sparse matrix with large numbers in submatrices along diagonal
Comprehension of Systems
• Nearly-decomposable systems are easier to discover/comprehend
• Non-decomposable systems may escape our detection/ observation
Description of Complexity
• State description vs. process description– Theory that “ontogeny recapitulates phylogeny”• States of embryo mimic
evolutionary transitions because genetic code is a process model
• Largely discredited biological hypothesis
• Recapitulation still considered plausible in other fields
• Perceived complexity is influenced by representation
Conclusions
• Perceived complexity does not imply internal complexity
• Many complex systems can be described as nearly-decomposable systems
• Selection of representation of problems/systems is crucial
• Design of complex systemsrelies on similar properties
• Need to teach all of these skills