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

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  • Pedro Campos

    ()
    (LIAAD – INESC Porto, L.A and Faculdade de Economia do Porto)

  • Pavel Brazdil

    ()
    (LIAAD – INESC Porto, L.A and Faculdade de Economia do Porto)

  • Isabel Mota

    ()
    (CEF.UP and Faculdade de Economia do Porto)

Abstract

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.

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Bibliographic Info

Paper provided by Universidade do Porto, Faculdade de Economia do Porto in its series FEP Working Papers with number 405.

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Length: 37 pages
Date of creation: Mar 2011
Date of revision:
Handle: RePEc:por:fepwps:405

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Keywords: Collaborative networks; Multi-Agent System; Collaboration Strategies; Stock of Knowledge;

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  1. Floortje Alkemade & Carolina Castaldi, 2005. "Strategies for the Diffusion of Innovations on Social Networks," Computational Economics, Society for Computational Economics, vol. 25(1), pages 3-23, February.
  2. Tibor Scitovsky, 1954. "Two Concepts of External Economies," Journal of Political Economy, University of Chicago Press, vol. 62, pages 143.
  3. Adam B. Jaffe & Manuel Trajtenberg & Rebecca Henderson, 1992. "Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations," NBER Working Papers 3993, National Bureau of Economic Research, Inc.
  4. Jaffe, Adam B, 1989. "Real Effects of Academic Research," American Economic Review, American Economic Association, vol. 79(5), pages 957-70, December.
  5. Jean-Philippe Cointet & Camille Roth, 2007. "How Realistic Should Knowledge Diffusion Models Be?," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(3), pages 5.
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