  There are a number of solutions that perform unsupervised name disambiguation
based on the similarity of bibliographic records or common co-authorship
patterns. Whether the use of these advanced methods, which are often difficult
to implement, is warranted depends on whether the accuracy of the most basic
disambiguation methods, which only use the author's last name and initials, is
sufficient for a particular purpose. We derive realistic estimates for the
accuracy of simple, initials-based methods using simulated bibliographic
datasets in which the true identities of authors are known. Based on the
simulations in five diverse disciplines we find that the first initial method
already correctly identifies 97% of authors. An alternative simple method,
which takes all initials into account, is typically two times less accurate,
except in certain datasets that can be identified by applying a simple
criterion. Finally, we introduce a new name-based method that combines the
features of first initial and all initials methods by implicitly taking into
account the last name frequency and the size of the dataset. This hybrid method
reduces the fraction of incorrectly identified authors by 10-30% over the first
initial method.
