  We develop an agent-based model of strategic link formation in Research and
Development (R&D) networks. Empirical evidence has shown that the growth of
these networks is driven by mechanisms which are both endogenous to the system
(that is, depending on existing alliances patterns) and exogenous (that is,
driven by an exploratory search for newcomer firms). Extant research to date
has not investigated both mechanisms simultaneously in a comparative manner. To
overcome this limitation, we develop a general modeling framework to shed light
on the relative importance of these two mechanisms. We test our model against a
comprehensive dataset, listing cross-country and cross-sectoral R&D alliances
from 1984 to 2009. Our results show that by fitting only three macroscopic
properties of the network topology, this framework is able to reproduce a
number of micro-level measures, including the distributions of degree, local
clustering, path length and component size, and the emergence of network
clusters. Furthermore, by estimating the link probabilities towards newcomers
and established firms from the data, we find that endogenous mechanisms are
predominant over the exogenous ones in the network formation, thus quantifying
the importance of existing structures in selecting partner firms.
