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Innovation creation and diffusion in a social network: an agent based approach

Author

Listed:
  • Lamieri, Marco
  • Ietri, Daniele
Abstract
Market is not only the result of the behaviour of agents, as we can find other forms of contact and communication. Many of them are determined by proximity conditions in some kind of space: in this paper we pay a particular attention to relational space, that is the space determined by the relationships between individuals. The paper starts from a brief account on theoretical and empirical literature on social networks. Social networks represent people and their relationships as networks, in which individuals are nodes and the relationships between them are ties. In particular, graph theory is used in literature in order to demonstrate some properties of social networks summarised in the concept of Small Worlds. The concept may be used to explain how some phenomena involving relations among agents have effects on multiple different geographical scales, involving both the local and the global scale. The empirical section of the paper is introduced by a brief summary of simulation techniques in social science and economics as a way to investigate complexity. The model investigates the dynamics of a population of firms (potential innovators) and consumers interacting in a space defined as a social network. Consumers are represented in the model in order to create a competitive environment pushing enterprises into innovative process (we refer to Schumpeter’s definition): from interaction between consumers and firms innovation emerges as a relational good.

Suggested Citation

  • Lamieri, Marco & Ietri, Daniele, 2004. "Innovation creation and diffusion in a social network: an agent based approach," MPRA Paper 445, University Library of Munich, Germany, revised 20 Oct 2006.
    • Handle: RePEc:pra:mprapa:445
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    References listed on IDEAS

    as
    1. Cowan, Robin & Jonard, Nicolas, 2004. "Network structure and the diffusion of knowledge," Journal of Economic Dynamics and Control, Elsevier, vol. 28(8), pages 1557-1575, June.
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    3. Leigh Tesfatsion, 2002. "Agent-Based Computational Economics," Computational Economics 0203001, University Library of Munich, Germany, revised 15 Aug 2002.
    4. Nigel Gilbert & Pietro Terna, 2000. "How to build and use agent-based models in social science," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 1(1), pages 57-72, March.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Innovation; small world; computational economics; network; complexity;
    All these keywords.

    JEL classification:

    • L20 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - General
    • L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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