As I said to Barry above, what I am saying is that the consensus is wrong, and that the Austrian School and those that understand
nonlinear systems theory are right.
Not exact matches
And his book suggests that scientists should address the obvious metaphysical implications of twentieth - century physics: e.g., Einstein and quantum mechanics and the more recent developments in the field of chaos
theory and
nonlinear systems.
Chaos
theory, or
nonlinear dynamics, is a mathematical way of determining the effects of small changes on
systems so complex they look random.
While in Tampere, he did research in
nonlinear systems, image recognition and classification, image correspondence, computational learning
theory, multiscale and spectral methods, and statistical signal processing.
This revolution has brought new understanding of
nonlinear dynamics, complex
systems, chaos
theory, catastrophe
theory.
Similarly, contraction
theory can be systematically and simply extended to address classical questions in hybrid
nonlinear systems.
The three - body problem is of course at the center of Chaos
theory and climate research has long acknowledged that the climate is a dynamical
system existing on the edge of spatio - temperal chaos and that the complexity of multiple interacting positive and negative feedbacks make it so particularly complex and
nonlinear.
IMO, the standard 1D energy balance model of the Earth's climate
system will provide little in the way of further insights; rather we need to bring additional physics and
theory (e.g. entropy and the 2nd law) into the simple models, and explore the complexity of coupled
nonlinear climate
system characterized by spatiotemporal chaos.
We can not solve the many body atomic state problem in quantum
theory exactly any more than we can solve the many body problem exactly in classical
theory or the set of open,
nonlinear, coupled, damped, driven chaotic Navier - Stokes equations in a non-inertial reference frame that represent the climate
system.
The cornerstone is the
theory of random dynamical
systems, which allows us to probe the detailed geometric structure of the random attractors associated with
nonlinear, stochastically perturbed
systems.