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Complexity Primer (INCOSE) (Guiding Principles (Emergence (phase…
Complexity Primer (INCOSE)
What is Complexity?
Subjective Complexity
inability of human mind to grasp the whole of a complex problem and predict the outcome (Stillitto 2009)
Frustration (Warfield 2006)
Stillitto's Objective Complexity
uncertain, unpredictable, complicated, difficult
Complex?
prescribed behaviors do not extend well into unplanned environments
complicated systems can be described through reduction
complex systems
interactions lead to emergent self-organizing patterns of behavior.
Cannot understand from parts in isolation
complicated systems can be described through reduction
adapt by re-organizing structure, responses, patterns of parts
Characteristics of Complexity
Structure
States
multiple stable states
transient states
no lasting stable states (continuous evolution)
perterbations
recovery to former state
transition to another state
Scales
fine scales influence large-scale behavior
dynamics of parts and patterns cannot be reproduced by averages
weak ties can have a disproportionate effect on the system behavior
prepetually generating novelty
key variables are opaque
boundaries ideterminate
Duality Causes Tension (unbalance)
large and small
distributed and central
agile and planned
Multi-Dimensional
Basics
subtle bugs and surprising dynamics
unintended consequences
diversity, connectivity, interactivity, adaptivity of a system and its environment
intricate networks of evolving cause-effect relationships
observed patterns not well understood or predicted
require multi-scale descriptions
feedback loops, internal and external
Identifying the Right Level
Basics
Abstraction
Simplifying Assumtions
Generalize from one complex situation to another
Risk
diversity
connectivity
adpativity
interactivity
Tools
Agent-based modeling
Model-based systems
Einstein's Razor
"make things as simple as possible, but not simpler"
General Solutions
Inadequacy of traditional system engineering tools
Reduce subjective complexity
Avoid oversimplification
Ross Ashby's
Law of Requisite Variety
degrees of system control = degrees of freedom on environment being controlled
Identify the kinds of complexity within the environment
create appropriate new ways to think about complexity
Cook Matrix
Analyze
Diagnose
Model
Synthesize
Shalizi (2006) - new tools
Guiding Principles
Evolve a living solution (resist starting from scratch)
Accept irreducible uncertainty
Mimic living systems
Use free order, self-organization
Identify and use patterns
Zoom in and out (scale)
Apply multiple perspectives
Collaborate
Achieve balance
Learn from failure/problems
Emergence
phase transitions
cascading failures
fat-tailed distributions
Black-Swan Events (Taleb 2007)
Meta-Cognition (identify bias)
Desired regions of outcome space (range of solutions)
Motivate autonomous agents
Adaptive feedback loops
Integrate problems