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Discovering the dynamics of smart business networks

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  • Pau, Louis-François

Abstract

Earlier research discussed the necessary evolution from smart business networks, as based on process need satisfaction and governance, into business genetics [1] based on strategic bonds or decay and opportunistic complementarities. This paper will describe an approach and diffusion algorithms whereby to discover the dynamics of emergent smart business network structures and their performance in view of collaboration patterns over time. Some real life early analyses of dynamics are discussed based on cases and date from the high tech sector. Lessons learnt from such cases are also given on overall smart network dynamics with respect to local interaction strategies, as modelled like in business genetics by individual partner profiles, goals and constraints. It shows the weakness of static “business operating systems”, as well as the possibly destabilizing clustering effects amongst nodes linked to filtering, evaluation and own preferences.

Suggested Citation

  • Pau, Louis-François, 2007. "Discovering the dynamics of smart business networks," MPRA Paper 31020, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:31020
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    References listed on IDEAS

    as
    1. Dan Braha & Yaneer Bar-Yam, 2005. "Information Flow Structure in Large-Scale Product Development Organizational Networks," Springer Books, in: Peter Vervest & Eric Heck & Louis-François Pau & Kenneth Preiss (ed.), Smart Business Networks, chapter 8, pages 105-125, Springer.
    2. Peter Vervest & Eric Heck & Louis-François Pau & Kenneth Preiss (ed.), 2005. "Smart Business Networks," Springer Books, Springer, number 978-3-540-26694-5, October.
    3. Dietmar Maringer & Tikesh Ramtohul, 2012. "Regime-switching recurrent reinforcement learning for investment decision making," Computational Management Science, Springer, vol. 9(1), pages 89-107, February.
    4. Nier, Erlend & Yang, Jing & Yorulmazer, Tanju & Alentorn, Amadeo, 2007. "Network models and financial stability," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 2033-2060, June.
    5. Giorgio Gnecco & Marcello Sanguineti, 2009. "The weight-decay technique in learning from data: an optimization point of view," Computational Management Science, Springer, vol. 6(1), pages 53-79, February.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    smart business networks; business genetics; network performance; SBN; dynamics;
    All these keywords.

    JEL classification:

    • D02 - Microeconomics - - General - - - Institutions: Design, Formation, Operations, and Impact
    • D4 - Microeconomics - - Market Structure, Pricing, and Design
    • J54 - Labor and Demographic Economics - - Labor-Management Relations, Trade Unions, and Collective Bargaining - - - Producer Cooperatives; Labor Managed Firms
    • L24 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Contracting Out; Joint Ventures
    • L14 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Transactional Relationships; Contracts and Reputation
    • D23 - Microeconomics - - Production and Organizations - - - Organizational Behavior; Transaction Costs; Property Rights
    • D74 - Microeconomics - - Analysis of Collective Decision-Making - - - Conflict; Conflict Resolution; Alliances; Revolutions
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • D92 - Microeconomics - - Micro-Based Behavioral Economics - - - Intertemporal Firm Choice, Investment, Capacity, and Financing

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