  When applied to a stochastic process of interest, a restart protocol alters
the overall statistical distribution of the process' completion time; thus, the
completion-time's mean and randomness change. The explicit effect of restart on
the mean is well understood, and it is known that: from a mean perspective,
deterministic restart protocols -- termed sharp restart -- can out-perform any
other restart protocol. However, little is known on the explicit effect of
restart on randomness. This paper is the second in a duo exploring the effect
of sharp restart on randomness: via a Boltzmann-Gibbs-Shannon entropy analysis
in the first part, and via a diversity analysis in this part. Specifically,
gauging randomness via diversity -- a measure that is intimately related to the
Renyi entropy -- this paper establishes a set of universal criteria that
determine: A) precisely when a sharp-restart protocol decreases/increases the
diversity of completion times; B) the very existence of sharp-restart protocols
that decrease/increase the diversity of completion times. Moreover, addressing
jointly mean-behavior and randomness, this paper asserts and demonstrates when
sharp restart has an aligned effect on the two (decreasing/increasing both),
and when the effect is antithetical (decreasing one while increasing the
other). The joint mean-diversity results require remarkably little information
regarding the (original) statistical distributions of completion times, and are
remarkably practical and easy to implement.
