  Ranking and selection (R&S) is a popular model for studying discrete-event
dynamic systems. It aims to select the best design (the design with the largest
mean performance) from a finite set, where the mean of each design is unknown
and has to be learned by samples. Great research efforts have been devoted to
this problem in the literature for developing procedures with superior
empirical performance and showing their optimality. In these efforts, myopic
procedures were popular. They select the best design using a 'naive' mechanism
of iteratively and myopically improving an approximation of the objective
measure. Although they are based on simple heuristics and lack theoretical
support, they turned out highly effective, and often achieved competitive
empirical performance compared to procedures that were proposed later and shown
to be asymptotically optimal. In this paper, we theoretically analyze these
myopic procedures and prove that they also satisfy the optimality conditions of
R&S, just like some other popular R&S methods. It explains the good performance
of myopic procedures in various numerical tests, and provides good insight into
the structure and theoretical development of efficient R&S procedures.
