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.
KeywordsPolicy design Networks Policy patching Policy packaging Policy mixes Visualization Virtual environment Decision support system Computer-aided design
I would like to acknowledge the helpful comments of the anonymous reviewers in improving the quality of this manuscript.
- Aitamurto, T. (2012). Crowdsourcing for democracy: New era in policy-making. In Publications of the Committee for the Future, Parliament of Finland. 1/2012. Helsinki, Finland.Google Scholar
- Banister, D., Stead, D., Steen, P., Åkerman, J., Dreborg, K., Nijkamp, P., et al. (2000). European transport policy and sustainable mobility. London: Spon Press.Google Scholar
- Camagni, R. (1995). Global network and local milieu: Towards a theory of economic space. In S. Conti, E. Malecki, & P. Oinas (Eds.), The industrial enterprise and its environment: spatial perspectives (pp. 195–214). Aldershot: Avebury.Google Scholar
- Conklin, J. (2005). Dialogue mapping: Building shared understanding of wicked problems. New York: Wiley.Google Scholar
- Givoni M, Macmillen J, Banister D (2010) From individual policies to policy packaging. In European transport conference (ETC), Scotland.Google Scholar
- Gunningham, N., Grabosky, P., & Sinclair, D. (1998). Smart regulation: Designing environmental policy. Oxford: Clarendon Press.Google Scholar
- Hacker, J. S. (2005). Policy drift: The hidden politics of US welfare state retrenchment. In W. Streek & K. Thelen (Eds.), Beyond continuity: Institutional change in advanced political economies (pp. 40–82). Oxford: Oxford University Press.Google Scholar
- Howlett, M. (2010). Designing public policies: Principles and instruments. Milton Park: Taylor & Francis.Google Scholar
- Janis, I. L. (1982). Groupthink: Psychological studies of policy decisions and Fiascoes. Boston: Houghton Mifflin.Google Scholar
- Jenkins-Smith, H. C., & Sabatier, P. A. (1999). The advocacy coalition framework: An assessment. In P. Sabatier (Ed.), Theories of the policy process (pp. 117–166). Boulder, CO: Westview Press.Google Scholar
- John, P. (1998). Analysing public policy. London: Pinter.Google Scholar
- Jones, P., Kelly, C., May, A., & Cinderby, S. (2009). Innovative approaches to option generation. European Journal of Transport and Infrastructure Research, 9(3), 237–258.Google Scholar
- Justen, A., Fearnley, N., Givoni, M., & Macmillen, J. (2014). A process for designing policy packaging: Ideals and realities. Transportation Research Part A: Policy and Practice, 60, 9–18.Google Scholar
- Majone, G. (2006). Agenda setting. In M. Moran et al. (Eds.), The Oxford handbook of public policy (pp. 228–250). Oxford: Oxford University Press.Google Scholar
- Matt, E., Givoni, M., & Epstein, B. (2013). A procedure to develop synergetic policy packages and assessing their political acceptability. http://www.spreeproject.com/wp-content/uploads/2013/04/Deliverable-3.2-_website.pdf.
- May, P. J. (1981). Hints for crafting alternative policies. Policy Analysis, 7(2), 227–244.Google Scholar
- McPherson, A. F., & Raab, C. D. (1988). Governing education: A sociology of policy since 1945. Edinburgh: Edinburgh University Press.Google Scholar
- Middlemist, G., Butz, E., Carter, D., & Leech, N. (2013). Towards a better understanding of organizational policy related activity on the internet, University of Colorado at Denver Report. http://www.ucdenver.edu/academics/colleges/SPA/PhD/phdstudentprofiles/carter/Documents/MJ%20Policy%20Internet%20Analysis.pdf. Accessed 24 Jan 2016.
- Milgrom, P., & Roberts, J. (1990). The economics of modern manufacturing: Technology, strategy and organization. American Economic Review, 80(3), 511–528.Google Scholar
- Nair, S., & Howlett, M. P. (2016). Policy myopia as a source of policy failure: Adaptation and policy learning under deep uncertainty. Policy & Politics. doi: 10.1332/030557316X14788776017743.
- Nash, A. (2009). Web 2.0 applications for improving public participation in transport planning. In Paper presented at the transportation research board 89th annual meeting, January 10–14, Washington, DC.Google Scholar
- Newman, M. E. J., Barabasi, A. L., & Watts, D. J. (2006). The structure and dynamics of networks. Princeton: Princeton University Press.Google Scholar
- OPTIC (2010) Inventory of measures, typology of non-intentional effects and a framework for policy packaging, Optimal Policies for Transport in Combination, Seventh Framework Programme: Theme 7 Transport, Retrieved 14/01/2016, http://optic.toi.no/getfile.php/Optic/Bilder%20og%20dokumenter%20internett/OPTIC%20D1%20-%20FINAL%20AND%20APPROVED.pdf.
- Organisation for Economic Co-operation and Development. (2007). Instrument mixes for environmental policy. Paris: Organisation for Economic Cooperation and Development.Google Scholar
- Peters, G. (1998). Policy networks: Myth, metaphor and reality, comparing policy networks. London: Open University Press.Google Scholar
- Prpić, J., Taeihagh, A., & Melton, J. (2014a). Crowdsourcing the policy cycle. Collective Intelligence 2014, Massachusetts Institute of Technology, June 10–12, 2014 http://ssrn.com/abstract=2398191.
- Prpić, J., Taeihagh, A., & Melton, J. (2014b). A Framework for policy crowdsourcing. In Oxford internet policy and politics conference (IPP 2014), University of Oxford, 26–28 September 2014. http://ipp.oii.ox.ac.uk/sites/ipp/files/documents/IPP2014_Taeihagh%20%282%29.pdf.
- Prpić, J., Taeihagh, A., & Melton, J. (2014c). Experiments on crowdsourcing policy assessment. In Oxford internet policy and politics conference (IPP 2014), University of Oxford, 26–28 September 2014. http://ipp.oii.ox.ac.uk/sites/ipp/files/documents/IPP2014_Taeihagh.pdf.
- Rhodes, R., & Marsh, D. (1992). Policy networks in British politics (pp. 1–26). Oxford: Clarendon Press.Google Scholar
- Saaty, T. L. (1996). Decision making with dependence and feedback: The analytic network process. Pittsburgh: RWS Publications.Google Scholar
- Sawyer, J. E. (1997). Information sharing and integration in multifunctional decision-making groups. In Presented at annual meeting of the society of judgment and decision making, Philadelphia, PA.Google Scholar
- Schneider. V. (2005). Policy-networks in a complex systems perspective. A new look on an old data set. University of Constance, Baden-Wurttemberg, Germany. http://www.unikonstanz.de/FuF/Verwiss/Schneider/ePapers/ChemicalSys5Dez.pdf.
- Sheffey, S., Tindale, R. S., & Scott, L. A. (1989). Information sharing and group decision-making. In Presented at midwestern psychological association, Chicago, IL.Google Scholar
- Shum, S. J. B., Selvin, A. M., Sierhuis, M., Conklin, J., Haley, C. B., & Nuseibeh, B. (2006). Hypermedia support for argumentation-based rationale: 15 years on from gIBIS and QOC. In A. Dutoit, R. McCall, I. Mistrik, & B. Paech (Eds.), Rationale management in software engineering (pp. 111–132). Berlin Heidelberg: Springer.CrossRefGoogle Scholar
- Taeihagh, A. (2011). A novel approach for the development of policies for socio-technical systems. Oxford: University of Oxford.Google Scholar
- Taeihagh A., Bañares-Alcántara R. (2014). Towards proactive and flexible agent-based generation of policy packages for active transportation. In 47th International conference on system sciences (HICSS 47), 4–9 January 2014. http://dx.doi.org/10.1109/HICSS.2014.118.
- Taeihagh, A., Wang, Z., & Bañares-Alcántara, R. (2009b). Why conceptual design matters in policy formulation: A case for an integrated use of complexity science and engineering design. In European conference on complex systems (ECCS2009), UK, September 2009.Google Scholar
- Walker, W. E. (2000). Uncertainty: The challenge for policy analysis in the 21st century. Santa Monica, CA: Rand Corp.Google Scholar
- Watthayu, W., & Peng, Y. (2004). A Bayesian network-based framework for multi-criteria decision making. In Proceedings of the 17th international conference on multiple criteria decision analysis.Google Scholar