This outcome is mathematically relevant, but it may not in fact be a possible outcome at all (the expected value of a die toss is 3.5), but averaged over
a larger number of iterations it becomes closer to expected outcomes.
Some such iterative sequences only «blow up» after a very
large number of iterations, or there may be huge numbers of potential and indistinguishable sequences among a candidate set only a few of which will blow up soon, and that no good filter can anticipate short of testing them all.
Not exact matches
The Equihash algorithm makes a variable
number of iterations over a
large list.
Furthermore the modelling approach is inherently
of no value for predicting future temperature with any calculable certainty because
of the difficulty
of specifying the initial conditions
of a
large number of variables with sufficient precision prior to multiple
iterations.
see Section 1 at http://climatesense-norpag.blogspot.com/2014/07/climate-forecasting-methods-and-cooling.html Here's an excerpt: «The modelling approach is also inherently
of no value for predicting future temperature with any calculable certainty because
of the difficulty
of specifying the initial conditions
of a sufficiently fine grained spatio - temporal grid
of a
large number of variables with sufficient precision prior to multiple
iterations.
It is long past time for everyone to recognize that GCMs are inherently
of no value for predicting future temperature with any calculable certainty because
of the difficulty
of specifying the initial conditions
of a sufficiently fine grained spatio - temporal grid
of a
large number of variables with sufficient precision prior to multiple
iterations.