  Empirical research has shown performance improvement of many different
technological domains occurs exponentially but with widely varying improvement
rates. What causes some technologies to improve faster than others do? Previous
quantitative modeling research has identified artifact interactions, where a
design change in one component influences others, as an important determinant
of improvement rates. The models predict that improvement rate for a domain is
proportional to the inverse of the domain interaction parameter. However, no
empirical research has previously studied and tested the dependence of
improvement rates on artifact interactions. A challenge to testing the
dependence is that any method for measuring interactions has to be applicable
to a wide variety of technologies. Here we propose a patent-based method that
is both technology domain-agnostic and less costly than alternative methods. We
use textual content from patent sets in 27 domains to find the influence of
interactions on improvement rates. Qualitative analysis identified six specific
keywords that signal artifact interactions. Patent sets from each domain were
then examined to determine the total count of these 6 keywords in each domain,
giving an estimate of artifact interactions in each domain. It is found that
improvement rates are positively correlated with the inverse of the total count
of keywords with correlation coefficient of +0.56 with a p-value of 0.002. The
empirical results agree with model predictions and support the suggestion that
domains with higher number of artifacts interactions (higher complexity) will
improve at a slower pace.
