  The remarkable results of Foster and Vohra was a starting point for a series
of papers which show that any sequence of outcomes can be learned (with no
prior knowledge) using some universal randomized forecasting algorithm and
forecast-dependent checking rules. We show that for the class of all
computationally efficient outcome-forecast-based checking rules, this property
is violated. Moreover, we present a probabilistic algorithm generating with
probability close to one a sequence with a subsequence which simultaneously
miscalibrates all partially weakly computable randomized forecasting
algorithms. %subsequences non-learnable by each randomized algorithm.
  According to the Dawid's prequential framework we consider partial recursive
randomized algorithms.
