  Data-driven control strategies for dynamical systems with unknown parameters
are popular in theory and applications. An essential problem is to prevent
stochastic linear systems becoming destabilized, due to the uncertainty of the
decision-maker about the dynamical parameter. Two randomized algorithms are
proposed for this problem, but the performance is not sufficiently
investigated. Further, the effect of key parameters of the algorithms such as
the magnitude and the frequency of applying the randomizations is not currently
available. This work studies the stabilization speed and the failure
probability of data-driven procedures. We provide numerical analyses for the
performance of two methods: stochastic feedback, and stochastic parameter. The
presented results imply that as long as the number of statistically independent
randomizations is not too small, fast stabilization is guaranteed.
