IDEAS home Printed from https://ideas.repec.org/a/spr/comaot/v13y2007i2d10.1007_s10588-006-9002-7.html
   My bibliography  Save this article

Complexity theory and collaboration: An agent-based simulator for a space mission design team

Author

Listed:
  • Narjès Bellamine-Ben Saoud

    (University of Tunis)

  • Gloria Mark

    (University of California)

Abstract

In this paper, we investigate how complexity theory can benefit collaboration by applying an agent-based computer simulation approach to a new form of synchronous real-time collaborative engineering design. Fieldwork was conducted with a space mission design team during their actual design sessions, to collect data on their group conversations, team interdependencies, and error monitoring and recovery practices. Based on the fieldwork analysis, an agent-based simulator was constructed. The simulation shows how error recovery and monitoring is affected by the number of small group, or sidebar, conversations, and consequent noise in the room environment. This simulation shows that it is possible to create a virtual environment with cooperating agents interacting in a dynamic environment. This simulation approach is useful for identifying the best scenarios and eliminating potential catastrophic combinations of parameters and values, where error recovery and workload in collaborative engineering design could be significantly impacted. This approach is also useful for defining strategies for integrating solutions into organizations.

Suggested Citation

  • Narjès Bellamine-Ben Saoud & Gloria Mark, 2007. "Complexity theory and collaboration: An agent-based simulator for a space mission design team," Computational and Mathematical Organization Theory, Springer, vol. 13(2), pages 113-146, June.
    • Handle: RePEc:spr:comaot:v:13:y:2007:i:2:d:10.1007_s10588-006-9002-7
    DOI: 10.1007/s10588-006-9002-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10588-006-9002-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10588-006-9002-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Troy J Strader & Fu-ren Lin & Michael J Shaw, 1998. "Simulation of Order Fulfillment in Divergent Assembly Supply Chains," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 1(2), pages 1-5.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Brian W. Kulik & Timothy Baker, 2008. "Putting the organization back into computational organization theory: a complex Perrowian model of organizational action," Computational and Mathematical Organization Theory, Springer, vol. 14(2), pages 84-119, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Francesca Borrelli & Cristina Ponsiglione & Luca Iandoli & Giuseppe Zollo, 2005. "Inter-Organizational Learning and Collective Memory in Small Firms Clusters: an Agent-Based Approach," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(3), pages 1-4.
    2. Uwe Cantner & Ivan Savin & Simone Vannuccini, 2019. "Replicator dynamics in value chains: explaining some puzzles of market selection," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 28(3), pages 589-611.
    3. Gek Woo Tan & Michael J. Shaw & Bill Fulkerson, 2000. "Web-based Supply Chain Management," Information Systems Frontiers, Springer, vol. 2(1), pages 41-55, January.
    4. Roberto Dominguez & Salvatore Cannella, 2020. "Insights on Multi-Agent Systems Applications for Supply Chain Management," Sustainability, MDPI, vol. 12(5), pages 1-13, March.
    5. Luca Iandoli & Elio Marchione & Cristina Ponsiglione & Giuseppe Zollo, 2009. "Learning and Structural Properties in Small Firms’ Networks: A Computational Agent-Based Model," Research in Economics and Business: Central and Eastern Europe, Tallinn School of Economics and Business Administration, Tallinn University of Technology, vol. 1(1).
    6. Whan-Seon Kim, 2009. "Effects of a Trust Mechanism on Complex Adaptive Supply Networks: An Agent-Based Social Simulation Study," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(3), pages 1-4.
    7. Wei, Zelong & Song, Xi & Wang, Donghan, 2017. "Manufacturing flexibility, business model design, and firm performance," International Journal of Production Economics, Elsevier, vol. 193(C), pages 87-97.
    8. Emerson, Denise & Zhou, Wei & Piramuthu, Selwyn, 2009. "Goodwill, inventory penalty, and adaptive supply chain management," European Journal of Operational Research, Elsevier, vol. 199(1), pages 130-138, November.
    9. Luca Iandoli & Elio Marchione & Cristina Ponsiglione & Giuseppe Zollo, 2014. "Knowledge sharing and network emergence in small firm clusters: an agent-based model of industrial districts," Chapters, in: Charlie Karlsson & Börje Johansson & Roger R. Stough (ed.), Agglomeration, Clusters and Entrepreneurship, chapter 4, pages 66-84, Edward Elgar Publishing.
    10. Ojha, Divesh & Sahin, Funda & Shockley, Jeff & Sridharan, Sri V., 2019. "Is there a performance tradeoff in managing order fulfillment and the bullwhip effect in supply chains? The role of information sharing and information type," International Journal of Production Economics, Elsevier, vol. 208(C), pages 529-543.
    11. Fu-ren Lin & Shyh-ming Lin, 2006. "Enhancing the Supply Chain Performance by Integrating Simulated and Physical Agents into Organizational Information Systems," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(4), pages 1-1.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:comaot:v:13:y:2007:i:2:d:10.1007_s10588-006-9002-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.