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

From Self-Organization Wiki
Jump to: navigation, search
Line 1: Line 1:
 +
==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==
 
==Modeling the Structure, Dynamics and Quantification  - Group 2==
  

Revision as of 16:17, 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
Retrieved from "https://mobile.aau.at/selforganized-wiki/index.php?title=Modeling_Techniques_for_Self-Organizing_Systems&oldid=113"