  The dichotomy between noise-stable and (completely) noise-sensitive
stochastic models is of recent interest in probability theory. Of particular
interest is the study of lattice models coming from statistical physics. The
Fourier transform of noise-sensitive lattice models is concentrated on high
eigenvalues and is described by "large" stochastic geometric objects. Noise
sensitivity occurs quite surprisingly in various models like critical
percolation, and is forced by certain symmetry conditions.
  It appears that basic models from high-energy physics are noise stable; This
is the impression from the basic mathematical frameworks used for describing
them, and also from the description in terms of particles and interactions
involving a small number of particles.
  More general stochastic models with noise-sensitive components will not make
a difference in measurements involving particles and their interactions, but
may provide additional modeling power to proceed where current models are
insufficient.
