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Network-centric policy design


  • Araz Taeihagh

    () (Singapore Management University)


Two important challenges in policy design are better understanding of the design space and consideration of the temporal factors. Moreover, in recent years it has been demonstrated that understanding the complex interactions of policy measures can play an important role in policy design and analysis. In this paper, the advances made in conceptualization and application of networks to policy design in the past decade are highlighted. Specifically, the use of a network-centric policy design approach in better understanding the design space and temporal consequences of design choices are presented. Network-centric policy design approach has been used in classification, visualization, and analysis of the relations among policy measures as well as ranking of policy measures using their internal properties and interactions, and conducting sensitivity analysis using Monte Carlo simulations. Furthermore, through use of a decision support system, network-centric approach facilitates ranking, visualization, and selection of policies using different sets of criteria, and exploring the potential for compromise in policy formulation. The advantage of the network-centric approach is providing the ability to go beyond visualizations and analysis of policies and piecemeal use of network concepts as a tool for different policy design tasks to moving to a more integrated bottom–up approach to design. Furthermore, the computational advantages of the network-centric policy design in considering temporal factors such as policy sequencing and addressing issues such as layering, drift, policy failure, and delay are presented. Finally, some of the current challenges of network-centric design are discussed, and some potential avenues of exploration in policy design through use of computational methodologies, as well as possible integration with approaches from other disciplines, are highlighted.

Suggested Citation

  • Araz Taeihagh, 2017. "Network-centric policy design," Policy Sciences, Springer;Society of Policy Sciences, vol. 50(2), pages 317-338, June.
  • Handle: RePEc:kap:policy:v:50:y:2017:i:2:d:10.1007_s11077-016-9270-0
    DOI: 10.1007/s11077-016-9270-0

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    Cited by:

    1. Wei Yang & Wijnand Veeneman & Martin De Jong, 2018. "Transport Demand Management Policy Integration in Chinese Cities: A Proposed Analysis of Its Effects," Energies, MDPI, Open Access Journal, vol. 11(5), pages 1-22, May.
    2. Li, Lili & Taeihagh, Araz, 2020. "An in-depth analysis of the evolution of the policy mix for the sustainable energy transition in China from 1981 to 2020," Applied Energy, Elsevier, vol. 263(C).
    3. Yanwei Li & Araz Taeihagh & Martin De Jong, 2018. "The Governance of Risks in Ridesharing: A Revelatory Case from Singapore," Energies, MDPI, Open Access Journal, vol. 11(5), pages 1-21, May.
    4. Shqipe Buzuku & Andrzej Kraslawski & Javier Farfan & Kari Harmaa & Tuomo Kässi, 2019. "A Case Study of Complex Policy Design: The Systems Engineering Approach," Complexity, Hindawi, vol. 2019, pages 1-23, January.
    5. Araz Taeihagh, 2017. "Crowdsourcing: a new tool for policy-making?," Policy Sciences, Springer;Society of Policy Sciences, vol. 50(4), pages 629-647, December.


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