Computational Economics

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

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

Article

Abstract

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.

Keywords

Collaborative networks Multi-agent system  Collaboration strategies Knowledge 

JEL Classification

D85 C63 L29 

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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

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