Modeling the formation of R\&D alliances: An agent-based model with empirical validation

We develop an agent-based model to reproduce the size distribution of R\&D alliances of firms. Agents are uniformly selected to initiate an alliance and to invite collaboration partners. These decide about acceptance based on an individual threshold that is compared with the utility expected from joining the current alliance. The benefit of alliances results from the fitness of the agents involved. Fitness is obtained from an empirical distribution of agent's activities. The cost of an alliance reflects its coordination effort. Two free parameters $a_{c}$ and $a_{l}$ scale the costs and the individual threshold. If initiators receive $R$ rejections of invitations, the alliance formation stops and another initiator is selected. The three free parameters $(a_{c},a_{l},R)$ are calibrated against a large scale data set of about 15,000 firms engaging in about 15,000 R\&D alliances over 26 years. For the validation of the model we compare the empirical size distribution with the theoretical one, using confidence bands, to find a very good agreement. As an asset of our agent-based model, we provide an analytical solution that allows to reduce the simulation effort considerably. The analytical solution applies to general forms of the utility of alliances. Hence, the model can be extended to other cases of alliance formation. While no information about the initiators of an alliance is available, our results indicate that mostly firms with high fitness are able to attract newcomers and to establish larger alliances

Newcomers vs. incumbents: How firms select their partners for R&D collaborations

This paper studies the selection of partners for R&D collaborations of firms both empirically, by analyzing a large data set of R&D alliances over 25 years, and theoretically, by utilizing an agent-based model of alliance formation. We quantify the topological position of a firm in the R&D network by means of the weighted k-core decomposition which assigns a coreness value to each firm. The evolution of these coreness values over time reconstructs the career path of individual firms, where lower coreness indicates a better integration of firms in an evolving R&D network. Using a large patent dataset, we demonstrate that coreness values strongly correlate with the number of patents of a firm. Analyzing coreness differences between firms and their partners, we identify a change in selecting partners: less integrated firms choose partners of similar coreness until they reach their best network position. After that, well integrated firms (with low coreness) choose preferably partners with high coreness, either newcomers or firms from the periphery. We use the agent-based model to test whether this change in behavior needs to be explained by means of strategic considerations, i.e. firms switching their strategy in choosing partners dependent on their network position. We find that the observed behavior can be well reproduced without such strategic considerations, this way challenging the role of strategies in explaining macro patterns of collaborations

A model of dynamic rewiring and knowledge exchange in R&D networks

This paper investigates the process of knowledge exchange in inter-firm Research and Development (R&D) alliances by means of an agent-based model. Extant research has pointed out that firms select alliance partners considering both network-related and network-unrelated features (e.g., social capital versus complementary knowledge stocks). In our agent-based model, firms are located in a metric knowledge space. The interaction rules incorporate an exploration phase and a knowledge transfer phase, during which firms search for a new partner and then evaluate whether they can establish an alliance to exchange their knowledge stocks. The model parameters determining the overall system properties are the rate at which alliances form and dissolve and the agents' interaction radius. Next, we define a novel indicator of performance, based on the distance traveled by the firms in the knowledge space. Remarkably, we find that - depending on the alliance formation rate and the interaction radius - firms tend to cluster around one or more attractors in the knowledge space, whose position is an emergent property of the system. And, more importantly, we find that there exists an inverted U-shaped dependence of the network performance on both model parameters

The Rise and Fall of R&D Networks

ISSN:0960-6491ISSN:1464-365

Fragile, yet resilient: Adaptive decline in a collaboration network of firms

The dynamics of collaboration networks of firms follow a life cycle of growth and decline. That does not imply they also become less resilient. Instead, declining collaboration networks may still have the ability to mitigate shocks from firms leaving and to recover from these losses by adapting to new partners. To demonstrate this, we analyze 21.500 R&D collaborations of 14.500 firms in six different industrial sectors over 25 years. We calculate time-dependent probabilities of firms leaving the network and simulate drop-out cascades to determine the expected dynamics of decline. We then show that deviations from these expectations result from the adaptivity of the network, which mitigates the decline. These deviations can be used as a measure of network resilience