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A methodology for analyzing decision networks, based on information theory

Author

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  • Ehsani, Maryam
  • Makui, Ahmad
  • Sadi Nezhad, Soheil

Abstract

This paper assumes the organization as a distributed decision network. It proposes an approach based on application and extension of information theory concepts, in order to analyze informational complexity in a decision network, due to interdependence between decision centers. Based on this approach, new quantitative concepts and definitions are proposed in order to measure the information in a decision center, based on Shannon entropy and its complement in possibility theory, U uncertainty. This approach also measures the quantity of interdependence between decision centers and informational complexity of decision networks. The paper presents an agent-based model of organization as a graph composed of decision centers. The application of the proposed approach is in analyzing and assessing a measure to the organization structure efficiency, based on informational communication view. The structure improvement, analysis of information flow in organization and grouping algorithms are investigated in this paper. The results obtained from this model in different systems as distributed decision networks, clarifies the importance of structure and information distribution sources effect's on network efficiency.

Suggested Citation

  • Ehsani, Maryam & Makui, Ahmad & Sadi Nezhad, Soheil, 2010. "A methodology for analyzing decision networks, based on information theory," European Journal of Operational Research, Elsevier, vol. 202(3), pages 853-863, May.
    • Handle: RePEc:eee:ejores:v:202:y:2010:i:3:p:853-863
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    References listed on IDEAS

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    1. DeCanio, Stephen J. & Watkins, William E., 1998. "Information processing and organizational structure," Journal of Economic Behavior & Organization, Elsevier, vol. 36(3), pages 275-294, August.
    2. López, Luis & F.F. Mendes, Jose & Sanjuán, Miguel A.F, 2002. "Hierarchical social networks and information flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 695-708.
    3. Pete, Andras & Pattipati, Krishna R & Kleinman, David L, 1993. "Optimal Team and Individual Decision Rules in Uncertain Dichotomous Situations," Public Choice, Springer, vol. 75(3), pages 205-230, March.
    4. Marschak, Thomas & Reichelstein, Stefan, 1998. "Network Mechanisms, Informational Efficiency, and Hierarchies," Journal of Economic Theory, Elsevier, vol. 79(1), pages 106-141, March.
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    Cited by:

    1. Wu, Chao & Huang, Lang, 2019. "A new accident causation model based on information flow and its application in Tianjin Port fire and explosion accident," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 73-85.
    2. Durugbo, Christopher & Tiwari, Ashutosh & Alcock, Jeffrey R., 2013. "Modelling information flow for organisations: A review of approaches and future challenges," International Journal of Information Management, Elsevier, vol. 33(3), pages 597-610.
    3. Kazim Topuz & Behrooz Davazdahemami & Dursun Delen, 2024. "A Bayesian belief network-based analytics methodology for early-stage risk detection of novel diseases," Annals of Operations Research, Springer, vol. 341(1), pages 673-697, October.
    4. Yang, Jie, 2014. "Supply chain agility: Securing performance for Chinese manufacturers," International Journal of Production Economics, Elsevier, vol. 150(C), pages 104-113.

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