Comparing Strategies of Collaborative Networks for R&D: an agent-based study

In this work we analyze the evolving dynamics of different collaboration network strategies that emerge from the creation and diffusion of knowledge. In addition, we aim at describing their most relevant network properties over time. An evolutionary economic approach has been adopted by avoiding the profit-maximization behavior of firms and introducing decision rules that are applied routinely. A Multi-Agent Model with cognitive attributes where agents learn to make their own decisions has been developed. Firms (the agents) can collaborate and create networks for Research and Development (R&D) purposes. We have compared five collaboration strategies (A - Peer-to-Peer complementariness, B –Concentration process, C –Reinforcement Strategy, D - Virtual Collaboration Networks and E - Virtual Cooperation Networks) that were defined on the basis of literature and on empirical evidence. Strategies are introduced exogenously in the simulation. The aims of this paper are threefold: (i) to analyze the importance of the networking effects; (ii) to test the differences among collaboration strategies; and, finally, (iii) to verify the effect of learning. It has been possible to conclude that profit is associated with higher stock of knowledge and with smaller network diameter. In addition, concentration strategies are more profitable and more efficient in transmitting knowledge through the network. These processes reinforce the stock of knowledge and the profit of the firms located in the centers of the networks. Such dynamics is supported by the learning mechanism that generates a kind of collective cognition: in fact, if more firms connect to a particular network, then the center of the network is reinforced, producing feedbacks to all nodes.