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Network-centric policy design

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Abstract

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.

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Notes

  1. Substantive policy measures directly affect the production, consumption, and distribution of goods and services while procedural policy measures modify or alter the nature of policy processes used in the implementation (Howlett 2010).

  2. Unlike in policy design, networks have been used extensively in policy studies for the examination of policy actors and communities (e.g. Rhodes and March 1992; Hermans and Cunningham 2013). These forms of network analysis (Freeman et al. 1991; Wasserman and Faust 1994) are an important tool for the systematic description and analysis of relational dimensions in politics and society (Schneider 2005). Moreover, with recent advancements in network science (Newman et al. 2006; Milo et al. 2002; Boccaletti et al. 2006) and with the innovative applications of networks in assessing issues such as policy capacity (Craft et al. 2013; Middlemist et al. 2013) the use of networks analysis is becoming even more popular. It must be pointed out that the use of a policy networks approach has not been free from critique. These critiques range from criticizing the policy networks approach for not paying attention to factors that motivate policy actors, to the charge that they are descriptive rather than explanatory, and metaphorical rather than theoretical (John 1998; McPherson and Raab 1988; Dowding 1995; Jenkins-Smith and Sabatier 1999; Peters 1998).

  3. Multi-criteria Decision Analysis is a method used for comparing different alternatives using different criteria to help the decision maker towards a judicious choice through application of a set of techniques and procedures for structuring the decision-making process (Roy 1996).

  4. The term expert in this paper refers to policy-makers, domain specialists, or analysts.

  5. Givoni et al. (2010) and OPTIC (2010) used two types of relations preconditions, and synergies/facilitations (as an interchangeable single type); Givoni et al. (2013) and Justen et al. (2014) used three policy measures types of precondition, synergy/facilitation (interchangeable), contradiction/potential contradiction (interchangeable); Matt et al. (2013) used precondition, synergy, facilitation and potential contradiction; and Taeihagh et al. (2014); Taeihagh and Banares-alcantara (2014) and Champalle et al. (2015) have used five types of policy measure relations defined so as to capture the interactions between policy measures in different capacities such as building frameworks and methodologies, or for analyses or visualizations.

  6. It is important to point out that the group decision-making literature mainly focuses on how alternatives are selected, rather than on how groups learn about and examine relationships between different alternatives. The method proposed requires discussion among group members. A method such as Delphi can be used to decide the relations among particular measures or to examine key properties for policy measures. However, discussion which seeks to understand and analyze the policy measure relations is important. Given the number of policy measures considered in relation to modern policy problems and the tendency to reach quick agreement on known solutions it might be useful to use support systems in order to manage the information. Using a facilitator might also be helpful. We believe studies examining various approaches, such as the ones highlighted for expert group decision-making in the classification of policy measures, need to be carried out in the future, in collaboration with psychologists.

  7. Multiplex networks are sets of nodes that link to other nodes with more than one type of relation (Wasserman and Faust 1994).

  8. The centrality of a node is a measure of its importance or influence in a network (Freeman 1979).

  9. Givoni (2014) points out that, unlike in network theory and network analysis, when ranking the individual policy measures the interest in policy formulation is in the policy measures themselves and not the network. Therefore, using various indices commonly used in network analysis is not as valuable for the ranking of policy measures. The information provided from a network analysis of policy measures can be better used in order to understand the interactions and to design policy packages.

  10. In two-mode networks two sets of node types (in this case, policy measures and policy packages) constitute the nodes of the network and a relation type (edge) that connects the two types of nodes. In Taeihagh et al. (2014) the two-mode network demonstrates the policy measures selected by each policy package. Furthermore, the characteristics of policy packages based on the number of policy measures they connect to (policy measures they have selected) can be illustrated by adjusting the size of this node type (e.g. if total cost is being represented the size of the package node will be bigger if it costs more).

  11. Provision of access to information during discussions (rather than relying on memory) has been demonstrated to be beneficial in decision-making (Sawyer 1997; Sheffey et al. 1989).

  12. Orphaned policy measures might even contradict the new goal and policy measures.

  13. Even in the case of online surveys the speed at which a worker can carry out a micro-task is much faster than an online survey (Prpić et al. 2014c).

  14. Expert crowdsourcing mainly through competition-based platforms and non-expert crowdsourcing through the use of virtual labour markets. Open collaboration platforms provide access to both expert and non-expert crowds but require a more sustained effort in attracting and maintaining crowds.

  15. This is while recognising that decisions and policies to attain desirable futures are essentially questions of social values and political choices and that different stakeholders given their diverse set of objectives and values have different preferences to alternative solutions (Robinson et al. 2006; Stirling 2003).

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Taeihagh, A. Network-centric policy design. Policy Sci 50, 317–338 (2017). https://doi.org/10.1007/s11077-016-9270-0

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