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Simulating Knowledge Dynamics In Innovation Networks (Skin)

In: Industry And Labor Dynamics The Agent-Based Computational Economics Approach

Author

Listed:
  • PETRA AHRWEILER

    (Research Centre Media and Politics, Institute for Political Science, University of Hamburg, Germany)

  • ANDREAS PYKA

    (University of Augsburg, Institute of Economics, Universitaetsstr. 16, D-86135 Augsburg, Germany)

  • NIGEL GILBERT

    (School of Human Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom)

Abstract

An agent-based simulation model representing a theory of the dynamic processes involved in innovation in modern knowledge-based industries is described. The agent-based approach allows the representation of heterogeneous agents that have individual and varying stocks of knowledge. The simulation is able to model uncertainty, historical change, effect of failure on the agent population, and agent learning from experience, from individual research and from partners and collaborators. The interactions between the agents occur on two levels: through a market with firms supplying and consuming goods for a price, and through the exchange of knowledge. A brief description of the implementation of the model and its user interface is given.

Suggested Citation

  • Petra Ahrweiler & Andreas Pyka & Nigel Gilbert, 2004. "Simulating Knowledge Dynamics In Innovation Networks (Skin)," World Scientific Book Chapters, in: Roberto Leombruni & Matteo Richiardi (ed.), Industry And Labor Dynamics The Agent-Based Computational Economics Approach, chapter 14, pages 284-296, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9789812702258_0014
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    References listed on IDEAS

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    1. d'Aspremont, Claude & Jacquemin, Alexis, 1988. "Cooperative and Noncooperative R&D in Duopoly with Spillovers," American Economic Review, American Economic Association, vol. 78(5), pages 1133-1137, December.
    2. N. Gilbert, 1997. "A Simulation of the Structure of Academic Science," Sociological Research Online, , vol. 2(2), pages 91-105, June.
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    Cited by:

    1. Ruiz, Walter & Spinola, Danilo & Villalba, Maria Luisa, 2025. "Simulating Innovation Systems and STI Policy: An Agent-Based Perspective," CAFE Working Papers 32, Centre for Accountancy, Finance and Economics (CAFE), Birmingham City Business School, Birmingham City University.
    2. Korber Manuela & Paier Manfred, 2014. "R&d networks and regional knowledge production: an agent-based simulation of the Austrian competence centres programme," Экономика региона, CyberLeninka;Федеральное государственное бюджетное учреждение науки «Институт экономики Уральского отделения Российской академии наук», issue 2, pages 264-275.
    3. Andreas Pyka & Nigel Gilbert & Petra Ahrweiler, 2006. "Simulating Knowledge-Generation and -Distribution Processes in Innovation Collaborations and Networks," Discussion Paper Series 287, Universitaet Augsburg, Institute for Economics.
    4. Gilbert, Nigel & Ahrweiler, Petra & Pyka, Andreas, 2007. "Learning in innovation networks: Some simulation experiments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(1), pages 100-109.
    5. Flavio Lenz-Cesar & Almas Heshmati, 2010. "Agent-based Simulation of Cooperative Innovation," TEMEP Discussion Papers 201052, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Jan 2010.
    6. Anna Varga-Csajkás & Tamás Sebestyén & Attila Varga, 2023. "Dynamics of collaboration among high-growth firms: results from an agent-based policy simulation," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 70(2), pages 353-377, April.
    7. Tur, Elena M. & Azagra-Caro, Joaquín M., 2018. "The coevolution of endogenous knowledge networks and knowledge creation," Journal of Economic Behavior & Organization, Elsevier, vol. 145(C), pages 424-434.
    8. Guido Fioretti, 2005. "Agent-Based Models of Industrial Clusters and Districts," Urban/Regional 0504009, University Library of Munich, Germany.
    9. Santiago Quintero & Diana P. Giraldo & William Orjuela Garzon, 2022. "Analysis of the Specialization Patterns of an Agricultural Innovation System: A Case Study on the Banana Production Chain (Colombia)," Sustainability, MDPI, vol. 14(14), pages 1-12, July.

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    More about this item

    Keywords

    Simulation; Agent-Based; Computational Economics; Labor; Industrial Dynamics; Innovation; Cluster; Firm Behavior;
    All these keywords.

    JEL classification:

    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • L22 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Organization and Market Structure

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