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.

We report here a detailed study about the formation and self-organization of nanoscale structures during ion beam implantation at room temperature of 300 keV Ge+ in Ge as a function of the ion fluence in the range between 1×1014 to 4×1016 cm−2. “Microexplosions” characterize the morphology of the swelled material; a random cellular structure consisting of cells surrounded by amorphous Ge ripples has been observed and studied in details by combining atomic force microscopy, scanning electron microscopy, and transmission electron microscopy.

Drawing on a large database of publicly announced R&D alliances, we empirically investigate the evolution of R&D networks and the process of alliance formation in several manufacturing sectors over a 24-year period (1986-2009). Our goal is to empirically evaluate the temporal and sectoral robustness of a large set of network indicators, thus providing a more complete description of R&D networks with respect to the existing literature. We find that most network properties are not only invariant across sectors, but also independent of the scale of aggregation at which they are observed, and we highlight the presence of core-periphery architectures in explaining some properties emphasized in previous empirical studies (e.g. asymmetric degree distributions and small worlds). In addition, we show that many properties of R&D networks are characterized by a rise-and-fall dynamics with a peak in the midnineties. We find that such dynamics is driven by mechanisms of accumulative advantage, structural homophily and multiconnectivity. In particular, the change from the "rise" to the "fall" phase is associated to a structural break in the importance of multiconnectivity.1 Besides, the hypothesis about the path-dependent character of R&D network evolution also underlies another stream of theoretical works, which in the latter years have tried to account for the observed properties of R&D networks and their dynamics (see in particular the works of Cowan among firms; see also Newman, 2003) and the presence of "nested" core-periphery architectures (see Bascompte et al., 2003). In this way, we refine the existing knowledge on R&D networks by detecting new stylized facts about the structural features of those networks and shed further lights on the drivers of the process of alliance formation.Third, building on the above-mentioned structural analysis, we perform a longitudinal analysis of the determinants of R&D alliance formation. In this analysis, our dependent variable is a firm dyad, and the observation unit is every potential pair of firms in the R&D network. We then investgate which combination of attachment rules provides a good description of the empirical evolution of alliances in the sample considered. We focus on the mechanisms of alliance formation which have received more attention in the literature so far, namely: (i) accumulative advantage, (ii) structural homophily (or diversity) and (iii) multiconnectivity (see Powell et al., 2005;Rosenkopf and Padula, 2008). Moreover, we conduct regression analyses for different time periods. In this way we are able to check if different attachment rules may account for different evolutions of the network over the observed sample. Finally, we also consider separately alliances formed among incumbent firms in the network, and alliances where at least one firm is entering the network, in order to check whether drivers of alliance formation are different across incumbents and entrants.Our results show, first, that the evolution of R&D networks has been universal across different sca...

We propose a model that reflects two important processes in R&D activities of firms, the formation of R&D alliances and the exchange of knowledge as a result of these collaborations. In a data-driven approach, we analyze two large-scale data sets extracting unique information about 7500 R&D alliances and 5200 patent portfolios of firms. This data is used to calibrate the model parameters for network formation and knowledge exchange. We obtain probabilities for incumbent and newcomer firms to link to other incumbents or newcomers which are able to reproduce the topology of the empirical R&D network. The position of firms in a knowledge space is obtained from their patents using two different classification schemes, IPC in 8 dimensions and ISI-OST-INPI in 35 dimensions. Our dynamics of knowledge exchange assumes that collaborating firms approach each other in knowledge space at a rate µ for an alliance duration τ . Both parameters are obtained in two different ways, by comparing knowledge distances from simulations and empirics and by analyzing the collaboration efficiencyĈ n . This is a new measure, that takes also in account the effort of firms to maintain concurrent alliances, and is evaluated via extensive computer simulations. We find that R&D alliances have a duration of around two years and that the subsequent knowledge exchange occurs at a very low rate. Hence, a firm's position in the knowledge space is rather a determinant than a consequence of its R&D alliances. From our data-driven approach we also find model configurations that can be both realistic and optimized with respect to the collaboration efficiencyĈ n . Effective policies, as suggested by our model, would incentivize shorter R&D alliances and higher knowledge exchange rates.