We present a systematic approach toward the development of nonlocal ro
bustness measures. Our approach offers the advantage of being far more
versatile than local or ''worst case'' nonlocal methods, and provides
the user with models appropriate to many practical situations in comp
uting average nonlocal robustness for the design and evaluation of var
ious algorithms. Copyright (C) 1996 Published by Elsevier Science Ltd