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Guiding an adaptive system through chaos

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System: Self-adjusting map dynamics with open loop parametric controls ... [12] Mitchell, M. ; Hraber, P. T.; Crutchfield J. P. Revisiting the edge of chaos: ... – PowerPoint PPT presentation

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Title: Guiding an adaptive system through chaos


1
Guiding an adaptive system through chaos
  • Alfred Hubler, Ctr for Complex Systems Research
  • Kirstin Phelps, Illinois Leadership Center
  • University of Illinois at Urbana-Champaign

2
Summary
  • System Self-adjusting map dynamics with open
    loop parametric controls
  • Problem Target of the control is time dependent
    and passes through chaos. Is the control stable?
  • Answer No, not in the chaotic regime. But the
    control is still effective, since the systems
    stays at the nearest edge chaos
  • Adaptation to the edge of chaos-self-adjusting
    systems avoid chaos

3
Chaos
Chaos is inevitable. In the sense that
perturbation is evolutionary, it's also
desirable. But managing it is essential. It's no
use for any of us to hope that someone else will
do it. Do you have your own personal strategies
in place? C. P. Brinkworth, 2006
  • plans go wildly astray
  • opposing opinions, strong emotions, and high
    stakes small events can have large unexpected
    consequences
  • - missed deadlines, cost overruns
  • thinking out of the box, rapid
    implementation of new ideas, such as Googles
    Chaos by design strategy
  • Chaos needs to be managed

4
System
  • Chaotic map F with time dependent parameter
  • xn1 F(xn, an)
  • where xn state at time step n0,1,,N-1
  • Parameter dynamics with control and
    self-adjustment
  • an1 an c(An-an)f w(xn, xn-1, )
  • where an parameter at time step n0,1,,N-1,
  • c magnitude of control force, Antarget
  • f magnitude of self-adjustment, w low pass
    filter
  • Target dynamics
  • An1 Anr
  • where r rate of change of target, xnchaos for
    a range of targets

5
Results soft control
  • Figure 1. Dynamics of the self-adjusting system
    which is guided through a chaotic region by a
    soft control the dynamics of the state (a), the
    dynamics of the parameter (b), and a histogram of
    the parameter values (c). The peaks in the
    histogram at the boundary of the chaotic region
    indicate that the system avoids chaos
    (F3.8(1-an2)xn(1-xn), wxn-xn-32).

6
Results strong control
  • Figure 2. Dynamics of the self-adjusting system
    which is guided through a chaotic region by a
    strong control the dynamics of the state (a),
    the dynamics of the parameter (b), and a
    histogram of the parameter values (c). The peaks
    in the histogram at the boundary of the chaotic
    region indicate are much less pronounced than in
    Figure 1.

7
Boundary between adaptation-to-the-edge-of-chaos
and stable control
  • Figure 3. The boundary between the adaptation to
    the edge of chaos and stable control. In the area
    labeled control stable the parameter stays
    close to the target, even in the chaotic regime.
    In the area labeled adaptation to the edge of
    chaos the parameter stays outside the chaotic
    regime, even if the target is inside the chaotic
    regime.

8
Soft controlsgood predictability
  • Figure 4. The strength of the chaos versus the
    strength of the control (a), and the deviation of
    the parameter from the target versus the control
    strength (b). If the control strength is small
    the deviation from the target is large but the
    system is more predictable.

9
Summary
  • Chaos management
  • 1Brinkworth, C. P. Managing chaos. URL as of
    10/2006 http//catherinepalinbrinkworth.com/manag
    ing-chaos.html,
  • 2 Patterson, K. Crucial Conversations Tools
    for talking when stakes are high, McGraw-Hill
    Heights Town, N.J., 2002
  • 3 Wheeler, D. J. Understanding variation The
    key to managing chaos, 2nd Rev edition SPC
    Press, Knoxville, TN, 1999.
  • 4 Schuster, H.G. Deterministic chaos, 2Rev Ed
    edition. Wiley-VCH Weinheim 1987.
  • 5 Lashinsky, A. Chaos by design. Fortune 2006,
    154. URL as of 10/2006 http//money.cnn.com/maga
    zines/fortune/fortune_archive/2006/10/02/8387489/i
    ndex.htm
  • Prediction of chaos
  • 6 Strelioff, C. Hübler, A. Medium term
    prediction of chaos. Phys. Rev. Lett., 2006, 96,
    044101-044104.
  • Control of Chaos
  • 7 Hübler, A. Adaptive control of chaotic
    systems. Helv. Phys. Acta 1989, 62, 343-346.
  • 8 Breeden, J. L. Dinkelacker F. Hübler A.
    Noise in the modeling and control of dynamical
    systems. Phys. Rev. A 1990, 42, 5827-5836.
  • 9 Ott, E. Grebogi, C. Yorke, J. A.
    Controlling chaos. Phys. Rev. Lett. 1990, 64,
    11961199.

10
  • Adaptation to the edge of chaos
  • 10 Kauffman, S. A. The origins of order
    Self-organization and selection in evolution
    Oxford University Press New York, 1993.
  • 11 Packard, N. H. in Dynamic patterns in
    complex systems, edited by J. A. S. Kelso, A. J.
    Mandell, and M. F. Schlesinger (World Scientific,
    Singapur, 1988), pp. 293301.
  • 12 Mitchell, M. Hraber, P. T. Crutchfield
    J. P. Revisiting the edge of chaos
  • Evolving cellular automata to perform
    computations. Complex Systems 1993, 7, 89-130.
  • 13 Bak, P. Tang, C. Wiesenfeld K.
    Self-organized criticality. Phys. Rev. A 1988,
    38, 364-374.
  • 14 Zhigulin, V. P. Rabinovich, M. I.
    Huerta, R. Abarbanel, H. D. I. Robustness and
    enhancement of neural synchronization by
    activity-dependent coupling. Phys. Rev. E, 2003,
    67, 021901-021904.
  • 15 Pierre, D. Hubler, A. A theory for
    adaptation and competition applied to logistic
    Map Dynamics. Physica 75D, 1994, 343-360.
  • 16 Langton, C. A. Computation at the edge of
    chaos. Physica 42D, 1990, 12-37.
  • 17 Adamatzky, A. Holland, O. Chaos,
    phenomenology of excitation in 2-D cellular
    automata and swarm systems. Solitons and
    Fractals, 1993, 9, 1233-1265.
  • Quantitative Model forAdaptation to the edge of
    chaos
  • 18 Melby, P. Kaidel, J. Weber, N. Hubler,
    A. Adaptation to the edge of chaos in the
    self-adjusting logistic map. Phys. Rev. Lett.,
    2000, 84, 5991-5993.
  • 19 Melby, P. Weber, N. Hubler A. Robustness
    of adaptation in controlled self-adjusting
    chaotic systems. Fluct. Noise Lett., 2002, 2,
    L285-L292.
  • 20 Baym, M. Hübler, A. W. Conserved
    quantities and adaptation to the edge of chaos,
    Phys. Rev. E, 2006, 73, 056210-056217.

11
This work
  • System Self-adjusting map dynamics with open
    loop parametric controls
  • Problem Target of the control is time dependent
    and passes through chaos. Is the control stable?
  • Answer No, not in the chaotic regime. But the
    control is still effective, since the systems
    stays at the nearest edge chaos
  • Soft controlshigh predictability, but large
    deviation from target

12
Applications
  • Innovation managements visionary leader versus
    peer review
  • Explain control rise and fall of social
    organizations
  • Starting a turbine
  • Flying an airplane through turbulence
  • Controlling unstable systems

13
Other complex systems paradigms
Complex system large throughput (open
dissipative), many variables, ignores simple
controls
  • Here Adaptation to the edge of chaos
  • Reacts most sensitive to complimentary dynamics
    (resonance)
  • Stationary state min. energy consumption
    (instead of min. energy) fractals, hierarchal
    ramified networks
  • Discrete models are more accurate than time
    continuous models (cellular automata, maps)
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