Computational Economics

, Volume 42, Issue 1, pp 1–22 | Cite as

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



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.


Collaborative networks Multi-agent system  Collaboration strategies Knowledge 

JEL Classification

D85 C63 L29 


  1. Alkemade, F., & Castaldi, C. (2005). Strategies for the diffusion of innovations on social networks. Computational Economics, 25, 3–23.CrossRefGoogle Scholar
  2. Audretsch, D. B., & Feldman, M. P. (2006). R &D spillovers & the geography of innovation & production. American Economic Review, 86(3), 630–640.Google Scholar
  3. Axelrod, R., & Benett, S. (1997). A landscape theory of aggregation. In R. Axelrod (Ed.), The complexity of cooperation. Princeton, NJ: Princeton University Press.Google Scholar
  4. Barabasi, A.-L. (2002). Linked: The new science of networks. Cambridge, MA: Perseus.Google Scholar
  5. Baumol, W. J. (2002). Free market innovation machine: Analyzing the growth miracle of capitalism. Princeton, NJ: Princeton University Press.Google Scholar
  6. Burt, R. S. (1992). Structural holes: The social structure of competition. Cambridge, MA: Harvard University Press.Google Scholar
  7. Campos, P., Brazdil, P., & Brito, P. (2006). Organizational survival in cooperation networks: The case of automobile manufacturing. In L. Camarinha-Matos, H. Afsarmanesh, & M. Ollus (Eds.), Network-centric collaboration & supporting frameworks (pp. 77–84). Heidelberg : Springer.Google Scholar
  8. Carayol, N., & Roux, P. (2005). Collective innovation in a model of network formation with preferential meeting. In Nonlinear dynamics & heterogeneous interacting agents—lecture notes in economics & mathematical systems (Vol. 550, Part III, pp. 139–153). Berlin, Heidelberg: Spinger-VerlagGoogle Scholar
  9. Carroll, G., & Hannan, M. (1992). The demography of corporations & industries. Pinceton, NJ: Princeton University Press.Google Scholar
  10. Cointet, J. P., & Roth, C. (2007). How realistic should knowledge diffusion models be? Journal of Artificial Societies & Social Simulation, 10(3), 5.Google Scholar
  11. Cowan, R., Jonard, N., & Zimmerman, J. B. (2004). Networks as emergent structures from bilateral collaboration. MERIT–Infonomics Research memorandum series, 2004–17.Google Scholar
  12. Csermely, P. (2006). Weak links: Stabilizers of complex systems from proteins to social networks. Berlin: Springer.Google Scholar
  13. D’Agata, A., & Santangelo, G. (2003). Cognitive distance, knowledge spillovers & localisation in a duopolistic game. Catania: Mimeo.Google Scholar
  14. D’Aspremont, C., & Jacquemin, A. (1988). Cooperative & noncooperative R &D in duopoly with spillovers. American Economic Review, 78(5), 1133–1137.Google Scholar
  15. Dyer, J. H. (1996). Specialized supplier networks as a source of competitive advantage: Evidence from the auto industry. Strategic Management Joumal, 17, 271–291.CrossRefGoogle Scholar
  16. Ebers, M. (1997). Explaining inter-organizational network formation. In M. Ebers (Ed.), The formation of inter-organizational networks (pp. 3–40). New York: Oxford University Press.Google Scholar
  17. Fujita, M., Krugman, P., & Venables, A. (1999). The spatial economy: Cities, regions & international trade. MIT, MA: The MIT Press.Google Scholar
  18. Gulati, R., Nohria, N., & Zaheer, A. (2000). Strategic networks. Strategic Management Journal, 21, 203–215.Google Scholar
  19. Hakansson, H. (1987). Industrial technological development: A network approach. London: Croom Helm.Google Scholar
  20. Hakansson, H., & Snehota, I. (1995). Business networks. London: Routledge.Google Scholar
  21. Jackson, M., & Wollinsky, A. (1996). A strategic model of social & economic networks. Journal of Economic Theory, 41, 44–74.CrossRefGoogle Scholar
  22. Jaffe, A. B. (1989). Real effects of academic research. American Economic Review, 79(5), 957–970.Google Scholar
  23. Jaffe, A. B., Trajtenberg, M., & Henderson, R. (1993). Geographic localization of knowledge spillovers as evidenced by patent citations. Quarterly Journal of Economics, 108(3), 577–598.CrossRefGoogle Scholar
  24. Latora, V., & Marchoiri, M. (2003). Economic small-world behavior in weighted networks. The European Physical Journal B, 32(2), 249–263.CrossRefGoogle Scholar
  25. Leskovec, J., Kleinberg, J., & Faloutsos, C. (2005). Graphs over time: Densification laws, shrinking diameters & possible explanations. In International conference on knowledge discovery & data mining KDD’05. New York: ACM Press.Google Scholar
  26. Lozano, S., Arenas, A., & Sanchéz, A. (2008). Community connectivity & heterogeneity: Clues & insights on cooperation on social networks. Journal of Economic Interaction & Coordination, 3(2), 183–199.CrossRefGoogle Scholar
  27. Milgram, S. (1967). The small-world problem. Psychology Today, 1, 62–67.Google Scholar
  28. Mitchell, T. (1997). Machine learning. New York: McGraw Hill.Google Scholar
  29. Nelson, R., & Winter, S. (1982). An evolutionary theory of economic change. Cambridge, MA: Belknap Press.Google Scholar
  30. Nooy, W., Mrvar, A., & Batagelj, V. (2005). Exploratory social network analysis with Pajek structural analysis in the social sciences. Cambridge, MA: Cambridge University Press.CrossRefGoogle Scholar
  31. Quinlan, J. R. (1993). C4.5: Programs for machine learning. San Francisco, CA: Morgan Kaufmann Publishers Inc.Google Scholar
  32. R Development Core Team. (2010). R: A language & environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
  33. Schumpeter, J. A. (1996). [1942].Capitalism, socialism & democracy. London: Routledge.Google Scholar
  34. Simon, H. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 1, 99–118.CrossRefGoogle Scholar
  35. Scitovsky, T. (1954). Two concepts of external economies. Journal of Political Economy, 62, 142–151.Google Scholar
  36. Swaminathan, A., Hoetker, G., & Mitchell, W. (2002). Network structure & business survival: The case of U.S. automobile component suppliers. Working Paper—University of Illinois at Urbana Champagne.Google Scholar
  37. Tesfatsion, L. (2006). Agent-based computational economics: A constructive approach. In L. Tesfatsion & K. Judd (Eds.), Handbook of computational economics (Vol. 2, pp. 831–880). Amsterdam: Elsevier.Google Scholar
  38. Varian, H. (2004). Microeconomic analysis (3rd ed., 1992). New York: W. W. Norton. Company, Inc.Google Scholar
  39. Wang, S., & Archer, N. (2004). Supporting collaboration in business-to-business electronic market places. Information Systems & E-Business Management, 2(2–3), 269–286.CrossRefGoogle Scholar
  40. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440–442.CrossRefGoogle Scholar
  41. Wersching, K. (2005). Agglomeration in an innovative & differentiated industry with heterogeneous knowledge spillovers. In Workshop on regional agglomeration, growth & multilevel governance: The E.U. in a comparative perspective. Ghent: University of Gent.Google Scholar
  42. Wilhite, A. (2006). Economic activity on fixed networks. In L. Tesfatsion & K. Judd (Eds.), Handbook of computational economics (Vol. 2, pp. 1015–1045). Amsterdam: Elsevier.Google Scholar
  43. Young, L., & Wilkinson, I. F. (1989). The role of trust & co-operation in marketing channels: A preliminary study. European Journal of Marketing, 23(2), 109–122.CrossRefGoogle Scholar
  44. Young, H. P. (1998). Individual strategy and social structure: An evolutionary theory of institutions. Princeton, NJ: Princeton University Press.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.LIAAD (Laboratory of Artificial Intelligence and Decision Support)INESC TECPortoPortugal
  2. 2.FEP (Faculty of Economics)University of PortoPortoPortugal
  3. 3.CEF.UP (Center for Economics and Finance at UP) PortoPortugal

Personalised recommendations