chaos theory

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    Branch of mathematics that deals with systems that appear to be orderly but, in fact, harbor chaotic behaviors. It also deals with systems that appear to be chaotic, but, in fact, have underlying order.

    Chaos theory is the study of nonlinear, dynamic systems that are highly sensitive to initial conditions, an effect which is popularly referred to as the butterfly effect.

    The deterministic nature of these systems does not make them predictable. This behavior is known as deterministic chaos, or simply chaos.

  • Edward Lorenz. Deterministic Nonperiodic Flow, 1963.

    Lorenz was a meteorologist who developed a mathematical model used to model the way the air moves in the atmosphere. He discovered the principle of Sensitive Dependence on Initial Conditions . Butterfly Effect.

    The basic principle is that even in an entirely deterministic system the slightest change in the initial data can cause abrupt and seemingly random changes in the outcome.




    Sensitivity to initial conditions

    Order in disorder

    Long-term prediction is mostly impossible


    Dynamic systems Deterministic systems

    Chaotic systems are unstable since they tend not to resist any outside disturbances but instead react in significant ways.

  • Dynamic system: Simplified model for the time-varying behavior of an actual system. These systems are described using differential equations specifying the rates of change for each variable.

    Deterministic system: System in which no randomness is involved in the development of future states of the system. This property implies that two trajectories emerging from two different close-by initial conditions separate exponentially in the course of time.

    Chaotic systems are unstable since they tend not to resist any outside disturbances but instead react in significant ways.

  • Chaotic systems are common in nature. They can be found, for example, in Chemistry, in Nonlinear Optics (lasers), in Electronics, in Fluid Dynamics, etc.

    Many natural phenomena can also be characterized as being chaotic. They can be found in meteorology, solar system, heart and brain of living organisms and so on.


    In chaos theory, systems evolve towards states called attractors. The evolution towards a specific state is governed by a set of initial conditions. An attractor is generated within the system itself.

    Attractor: Smallest unit which cannot itself be decomposed into two or more attractors with distinct basins of attraction.


    a) Point attractor: There is only one outcome for the system. Death is a point attractor for living things.

    b) Limit cycle or periodic attractor: Instead of moving to a single state as in a point attractor, the system settles into a cycle.

    c) Strange attractor or a chaotic attractor: double spiral which never repeats itself. Strange attractors are shapes with fractional dimension; they are fractals.


    b) a)

  • FRACTALS Fractals are objects that have fractional

    dimension. A fractal is a mathematical object that is self-similar and chaotic.

    Fractals are pictures that result from iterations of nonlinear equations. Using the output value for the next input value, a set of points is produced. Graphing these points produces images.

  • Benoit Mandelbrot

    Characteristics: Self-similarity and fractional dimensions.

    Self-similarity means that at every level, the fractal image repeats itself. Fractals are shapes or behaviors that have similar properties at all levels of magnification

    Clouds, arteries, veins, nerves, parotid gland ducts, the bronchial tree, etc

    Fractal geometry is the geometry that describes the chaotic systems we find in nature. Fractals are a language, a way to describe this geometry.


    "Sensitive dependence on initial conditions.

    Butterfly effect is a way of describing how, unless all factors can be accounted for, large systems remain impossible to predict with total accuracy because there are too many unknown variables to track.

    Ex: an avalanche. It can be provoked with a small input (a loud noise, some burst of wind), it's mostly unpredictable, and the resulting energy is huge.

  • ASP





    S PREDICTABILITY Computations and

    mathematical equations


    Ott-Grebogi-Yorke Method

    Pyragas Method


    The applications of controlling chaos are enormous, ranging from the control of turbulent flows, to the parallel signal transmission and computation to the

    control of cardiac fibrillation, and so forth.

    Alter organizational parameters so that

    the range of fluctuations is limited

    Apply small perturbations to the chaotic system to try

    and cause it to organize

    Change the relationship between the organization and

    the environment


    Stock market

    Population dynamics


    Predicting heart

    attacks Real time


    Music and Arts


    Random Number



    Richard Halpern, 2008. Impact of Chaos Theory and Heisenberg Uncertainty Principle on case negotiations in law

    Never rely on someone else's measurement to formulate a key component of strategy. A small mistake can cause

    huge repercussions, better do it yourself.

    Keep trying something new, unexpected; sweep the defence of its feet. Make the system chaotic.

    If the process is going the way you wanted, simplify it as much as possible. Predictability would increase and

    chance of blunders is minimized.

    If the tide is running against you, add new elements: complicate. Nothing to lose, and with a little help from Chaos, everything to gain. You might turn a hopeless

    case into a winner.


    Everything in the universe is under control of Chaos or product of Chaos.

    Irregularity leads to complex systems.

    Chaotic systems are very sensitive to the initial conditions, This makes the system fairly unpredictable. They never repeat but they always have some order. That is the reason why chaos theory has been seen as potentially one of the three greatest triumphs of the 21st century. In 1991, James Marti speculated that Chaos might be the new world order.

    It gives us a new concept of measurements and scales. It offers a fresh way to proceed with observational data.