Difference between revisions of "Modeling Techniques for Self-Organizing Systems"

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==Modeling Techniques for Self-Organizing Systems - Group 1==
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In general: Three methodologies for designing (self-organizing) systems.
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* Bottom up modeling
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** System consists of units
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** Modeling structure and dynamics of units (agents), topology, and interaction
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*** Model I/O behavior of agents
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**** Transfer function
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**** Local rule set
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** Advantage: straightforward implementation
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** Disadvatage: Emergence cannot been foreseen
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** Examples:
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*** Cornway's Game of Life (Cellular Automaton)
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*** Micro economics
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* Top down modeling
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** Model entire system from observations
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** Observe time series and predict behavior by finding regularities in pattern's using for instance:
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*** Markov Models
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*** Neural Networks
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** Advantage: Covers emergence and goal orientation
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** Disadvantage: Analyzing system as black box, difficult to verify obtained system model
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** Example:
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*** Chart Analysis in stock market
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*Recursive bootstrapping
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** Cyclic adaptation of both models
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** Increase understanding in each iteration
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==Modeling the Structure, Dynamics and Quantification  - Group 2==
 
==Modeling the Structure, Dynamics and Quantification  - Group 2==
  

Revision as of 17:17, 15 July 2009

Modeling Techniques for Self-Organizing Systems - Group 1

In general: Three methodologies for designing (self-organizing) systems.

  • Bottom up modeling
    • System consists of units
    • Modeling structure and dynamics of units (agents), topology, and interaction
      • Model I/O behavior of agents
        • Transfer function
        • Local rule set
    • Advantage: straightforward implementation
    • Disadvatage: Emergence cannot been foreseen
    • Examples:
      • Cornway's Game of Life (Cellular Automaton)
      • Micro economics
  • Top down modeling
    • Model entire system from observations
    • Observe time series and predict behavior by finding regularities in pattern's using for instance:
      • Markov Models
      • Neural Networks
    • Advantage: Covers emergence and goal orientation
    • Disadvantage: Analyzing system as black box, difficult to verify obtained system model
    • Example:
      • Chart Analysis in stock market
  • Recursive bootstrapping
    • Cyclic adaptation of both models
    • Increase understanding in each iteration


Modeling the Structure, Dynamics and Quantification - Group 2

Looked at modeling the topology and connections between agents.

Techniques for dynamic processes (microscopic rules of behaviour) and their strengths/weaknesses regarding robustness:

  • Cellular Automaton
    • Easy to form, broad range of patterns
    • But: Dependence on synchronization
  • Evolutionary Algorithms
    • Genetic Algorithms
      • Efficient search through high dimensional state
      • However: Need to specify a "fitness landscape" a priori (need to know what to find a good solution to)
      • History dependence (converges to different solutions)
      • Good, but no optimal solutions
      • Discussion mentioned that this may be more design than modeling (since it designs the solution)
  • UML (Modelling specification language)
  • Finite State Machines / Hidden Marcov Models
    • Flexible
    • Model at the system level (macroscopic)
  • Control Theory
    • Real-Time, but has some centralized, global requirements
  • Agent-based approaches
    • Local synch. algorithms
    • Local geometric constructions
    • Game theory (high complexity once N>2)
    • Swarm behaviour / intelligence (scalable, but has limited modeling capabilities)

Group Work - Group 4

  • Modeling Techniques
    • Use differential/difference equations to model Self Organizing Systems that correlate at some point e.g. for synchronization
      • Lyapunov-Exponent – evaluate how different start points change the systems evolution through a phase space
      • Sensitivity to the initial conditions of the differential equations
    • (Hidden) Markov models
  • Properties
    • Emergent properties are often not easy to measure/understand
    • Sensitivity to the initial conditions of the differential equations might help with adaptivness
    • Adaptivness/goal-orientedness: in Markov models we can remove some nodes and re-evalute. Can give us an idea about effectiveness etc.
  • Limitations/Potentials
    • “classical methods” are well researched and evaluated but not specifically tailored towards SOS
    • very hard to measure
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