In recent work I have explored the role of
finite precision computation, in physics in
analog form and in mathematics in
digital form as models of physics, typically as
differential equations of the form
where F is given forcing (input), A is a differential operator and U the corresponding system state (output).
In physics the input generates the output by some physical mechanism, viewed as some form of
analog computation, which is represented mathematically by digital computation with the output U being computed from the input F in computational solution of the differential equation.
The stability of the computational process, in analog physical or digital mathematical form, reflects how perturbations of input transform through the computational solution process to perturbations in output. If small perturbations of input transform to small perturbations of output, then the process is stable, while if small perturbations in input may result in large
perturbations in out, the problem is unstable.
As a basic example let us consider Newton's 2nd Law
where M is the mass of a particle moving with velocity V(t) under the action of the force F(t)
as functions of time t. With F(t) input and V(t) output this is a stable problem, since the solution process corresponds to integrating F(t), which is a stable summation process.
However, if we turn data around and view V(t) as input and F = M dV/dt as output, then the
process is unstable, since differentiation is unstable because it involves division by the small
quantity dt: Small perturbations in V(t) are magnified by the large factor 1/dt in the computation of dV/dt.
We now connect to the old question of cause-effect in a relation between two entities F and
U connected by an equation A(U) = F. The question is what is the cause and what is the effect,
F or U?
If the problem A(U) = F with F input and U output is a stable problem, then it is natural to view F as the cause and U the effect: The effect will then be essentially the same under small perturbations of the cause. On the other hand if it is an unstable problem, then it is not natural to view F as the cause and U as the effect, since small variations in F may give vastly different effects.
This argument has led us to identify cause-effect from stability point of view into a new analysis of the physics and mathematics of the 2nd law of thermodynamics and blackbody radiation, as developed in
In the setting of Newton's 2nd law M dV/dt = F, we may thus view the force F as cause and
the velocity V as the effect, but not the other way around.
In climate science a basic problems concerns the relation between global temperature and
level of atmospheric CO2, which with temperature as input and CO2 as output may very well be stable, while CO2 as input and temperature as output may very well be unstable.
A diffusion process like heat conduction is stable with heat source and initial temperature as input and temperature at a later time as output. On the other hand, "backdiffusion" with
temperature input at a later time than output is unstable without cause-effect and thus unphysical. Softening a sharp image in Photoshop into a blurred image by diffusion is a stable
process, while sharpening by "backdiffusion" is unstable.