Comparing Strategies of Collaborative Networks for R&D: An Agent-Based Study

• Published in: Computational economics 2013-06, Vol.42 (1), p.1-22
• Main Authors: Campos, Pedro, Brazdil, Pavel, Mota, Isabel
• Format: Article
• Language: English
• Subjects: Analysis; Behavioral/Experimental Economics; Collaboration; Computer Appl. in Social and Behavioral Sciences; Cooperation; Economic theory; Economic Theory/Quantitative Economics/Mathematical Methods; Economics; Economics and Finance; Evolution; Information sharing; Innovations; Knowledge; Math Applications in Computer Science; Operations Research/Decision Theory; R&D; Research & development; Simulation; Social networks; Studies
• ISSN: 0927-7099; 1572-9974
• DOI: 10.1007/s10614-013-9376-9

In this work we analyze the evolving dynamics of different strategies of collaborative networks that emerge from the creation and diffusion of knowledge. An evolutionary economic approach is adopted by introducing decision rules that are applied routinely and an agent-based model is developed. Firms (the agents) can collaborate and create networks for research and development purposes. We have compared three collaboration strategies (A—peer-to-peer complementariness, B—concentration process and C—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 twofold: (i) to analyze the importance of the networking effects; and (ii) to test the differences among collaboration strategies. It was 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.