Working from a description of what policy analysis entails, we review the emergence of the recent field of analytics and how it may impact public policy making. In particular, we seek to expose current applications of, and future possibilities for, new analytic methods that can be used to support public policy problem-solving and decision processes, which we term policy analytics. We then review key contributions to this special volume, which seek to support policy making or delivery in the areas of energy planning, urban transportation planning, medical emergency planning, healthcare, social services, national security, defence, government finance allocation, understanding public opinion, and fire and police services. An identified challenge, which is specific to policy analytics, is to recognize that public sector applications must balance the need for robust and convincing analysis with the need for satisfying legitimate public expectations about transparency and opportunities for participation. This opens up a range of forms of analysis relevant to public policy distinct from those most common in business, including those that can support democratization and mediation of value conflicts within policy processes. We conclude by identifying some potential research and development issues for the emerging field of policy analytics.
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Abi-Zeid, I., & Tremblay, J. (2015). Value-based argumentation for policy decision analysis—Methodology and an exploratory case study of a hydroelectric project in Québec. Annals of Operations Research. doi:10.1007/s10479-014-1774-4.
Albright, S., & Winston, W. (2014). Business analytics: Data analysis and decision making. Boston: Cengage Learning.
Alfaro, C., Cano-Montero, J., Gómez, J., Moguerza, J. M., & Ortega, F. (2015). A multi-stage method for content classification and opinion mining on weblog comments. Annals of Operations Research. doi:10.1007/s10479-013-1449-6.
Althaus, C., Bridgman, P., & Davis, G. (2007). The Australian policy fandbook (4th ed.). Crows Nest: Allen and Unwin.
Aringhieri, R., Carello, G., & Morale, D. (2015). Supporting decision making to improve the performance of an Italian Emergency Medical Service. Annals of Operations Research. doi:10.1007/s10479-013-1487-0.
Banks, D., Rios, J., & Rios Insua, D. (2015). Adversarial risk analysis. Boca Raton: Francis Taylor.
Barnard, C., & Simon, H. (1947). Administrative behavior. A study of decision-making processes in administrative organization. New York: Free Press.
Blackett, P. M. S., & Blackett, B. (1962). Studies of war, nuclear and conventional. Westport, CT: Greenwood Press.
Brennan, A., Meier, P., Purhouse, R., Raifia, R., Meng, Y., & Hill-McManus, D. (2015). Developing policy analytics for public health strategy and decisions—The Sheffield alcohol policy model framework. Annals of Operations Research. doi:10.1007/s10479-013-1451-z.
Capriolo, E., Warmpler, D., & Rutherglen, J. (2012). Programming hive. Sebastopol: O’Reilly.
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36, 1165–1188.
Clemen, R., & Reilly, T. (2014). Making hard decisions. Boston: Cengage Learning.
Cohen, M. D., March, J. G., & Olsen, J. P. (1972). A garbage can model of organizational choice. Administrative Science Quarterly, 17, 1–25.
Daniell, K. A. (2014). The role of national culture in shaping public policy: A review of the literature. HC Coombs Policy Forum discussion paper. Canberra: The Australian National University.
De Marchi, G., Lucertini, G., & Tsoukiàs, A. (2015). From evidence based policy making to policy analytics. Annals of Operations Research. doi:10.1007/s10479-014-1578-6.
Dryzek, J. S. (2000). Deliberative democracy and beyond: Liberals, critics, contestations. Oxford: Oxford University Press.
Dunn, W. (1994). Public policy analysis: An introduction (2nd ed.). Englewood Cliffs: Prentice-Hall.
Fischer, F., Miller, G. J., & Sidney, M. S. (Eds.). (2007). Handbook of public policy analysis: Theory, politics and methods. Boca Raton: CRC Press.
French, S., Papamichail, N., & Maule, J. (2009). Decision behaviour, analysis and support. Cambridge: Cambridge University Press.
Forester, J. (1993). Critical theory, public policy, and planning practice: Towards a critical pragmatism. Albany, NY: State University of New York.
Giacomelli, P. (2013). Apache Mahout cookbook. Birmingham: Packt.
Gigerenzer, G., Swijtink, Z., Porter, T., Daston, L., Beatty, J., & Krueger, L. (1989). The empire of chance: How probability changed science and everyday life. Cambridge: Cambridge University Press.
Goldsmith, S., & Crawford, S. (2014). The responsive city: Engaging communities through data-smart governance. New York: Wiley.
Hewson, P. J., Halliday, J., Gibson, A., & Asthana, S. (2015). Policy analytics need more than a spreadsheet: A case study in funding formulae. Annals of Operations Research. doi:10.1007/s10479-013-1475-4.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. Berlin: Springer.
Jann, W., & Wegrich, K. (2007). Theories of the policy cycle. In F. Fischer, G. J. Miller, & M. S. Sidney (Eds.), Handbook of public policy analysis: Theory, politics and methods. Boca Raton: CRC Press.
Kingdon, J. W. (1984). Agendas, alternatives, and public policies. Boston: Little, Brown.
Kumar, A., Nguyen, V. A., & Teo, K. M. (2015). Commuter cycling policy in Singapore: A farecard data analytics based approach. Annals of Operations Research. doi:10.1007/s10479-014-1585-7.
Kunreuther, K., & Heal, G. (2003). Interdependent security. Journal of Risk and Uncertainty, 26, 231–249.
Lasswell, H. D. (1936). Who gets what, when and how. New York: Whittlesey House.
Lasswell, H. D. (1956). The decision process: Seven categories of functional analysis. Bureau of Governmental Research,College of Business and Public Administration, University of Maryland.
Lindblom, C. E., & Woodhouse, E. J. (1968). The policy-making process (Vol. 4). Englewood Cliffs, NJ: Prentice-Hall.
Lloyd, C. J. (2011). Data driven business secisions. New York: Wiley.
MacKenzie, C. A., Baroud, H., & Barker, K. (2015). Static and dynamic resource allocation models for recovery of interdependent systems: Application to the deepwater horizon oil spill. Annals of Operations Research. doi:10.1007/s10479-014-1696-1.
Matthews, M. (2009). Fostering creativity and innovation in cooperative federalism—The uncertainty and risk dimensions. In J. Wanna (Ed.), Critical reflections on Australian Public Policy (pp. 59–70). http://epress.anu.edu.au/anzsog/critical/pdf/ch06.pdf. Accessed June 13, 2014.
Matthews, M. (2014a). Innovation and the productivity challenge in the public sector, talk given at the inaugural Policy Reflection Forum at the Department of Communications, Wednesday 5 March 2014, http://marklmatthews.files.wordpress.com/2014/02/dept-coms-talk-05-03-14-text-final.pdf. Accessed June 13, 2014.
Matthews, M. (2014b). Is it time to shift from evidence-based policymaking to intelligence-based policymaking? HC Coombs Policy Forum poster presentation. Canberra: The Australian National University.
Mayer, I. S., Van Daalen, C. E., & Bots, P. W. G. (2004). Perspectives on policy analyses: A framework for understanding and design. International Journal of Technology, Policy and Management, 4(2), 169–191.
MIT Technology Review. (2013). Big data will save politics. MIT Technology Review, 116(2). http://www.technologyreview.com/magazine/2013/01/. Accessed January 13, 2015.
O’Reilly, (2012). Big data now: 2012 edition, current perspectives from O’Reilly Media. Sebastopol: O’Reilly.
Pollock, S. M., & Maltz, M. D. (1994). Operations research in the public sector: An introduction and a brief history. In S. M. Pollock, M. H. Rothkopf, & A. Barnett (Eds.), Operations research and the public sector (pp. 1–22). Amsterdam: North Holland.
Pollock, S. M. (1994). Operations research and the public sector. Amsterdam: North Holland.
Provost, F., & Fawcett, T. (2013). Data science for business. Sebastopol: O’Reilly Media.
Quade, E. (1975). Analysis for public decisions. Skokie: Rand Co.
Rios Insua, D., & French, S. (Eds.). (2010). e-Democracy: A group decision and negotiation perspective. Advances in group decision and negotiation series, Vol. 5, Part 2. Dordrecht: Springer.
Rosenhead, J., & Mingers, J. (2001). Rational analysis for a problematic world revisited: Problem structuring methods for complexity, uncertainty and conflict. Revised edn. Chichester: Wiley.
Sabatier, P. A., & Jenkins-Smith, H. (1993). Policy change and learning: An advocacy coalition approach. Boulder, CO: Westview Press.
Scharaschkin, A., & McBride, T. (2015). Policy analytics and accountability mechanisms: Judging the ‘value for money’ of policy implementation. Annals of Operations Research. doi:10.1007/s10479-014-1723-2.
Shive, B. (2013). Data engineering. Westfield: Technics.
Stigler, S. (1990). The history of statistics: The measurement of uncertainty before 1900. Cambridge: Belknap Press.
Stokey, E., & Zeckhauser, R. (1978). A primer for policy analysis. New York: Norton.
Tsoukiàs, A., Montibeller, G., Lucertini, G., & Belton, V. (2013). Policy analytics: An agenda for research and practice. EURO Journal on Decision Processes, 1, 115–134.
Wasserman, S. (1994). Social Network Analysis. Cambridge: Cambridge University Press.
White, T. (2012). Hadoop: The definitive guide. Sebastopol: O’Reilly Media.
WIRED. (2008). The end of theory: The data deluge makes the scientific method obsolete. Wired Magazine, 16(07). http://archive.wired.com/science/discoveries/magazine/16-07/pb_theory. Accessed January 13, 2015.
Wolf, C. (1993). Markets or governments: Choosing between imperfect alternatives. Cambridge: MIT Press.
Xu, J., & Zhuang, J. (2015). Modeling costly learning and counter-learning in an defender-attacker game with private defender information. Annals of Operations Research. doi:10.1007/s10479-014-1722-3.
Xiang, Y., & Zhuang, J. (2015). A medical resource allocation model for serving emergency victims with deteriorating health conditions. Annals of Operations Research. doi:10.1007/s10479-014-1716-1.
Xu, J., Zhuang, J., & Liu, Z. (2015). Modeling and mitigating the effects of supply chain disruption in an attacker-defender game. Annals of Operations Research. doi:10.1007/s10479-015-1810-z.
Zhang, L., Hu, G., Wang, L., & Chen, Y. (2015). A bottom-up biofuel market equilibrium model for policy analysis. Annals of Operations Research. doi:10.1007/s10479-013-1497-y.
The idea for this special volume was sparked by a workshop on Policy Analytics organized at LAMSADE-CNRS, Paris, in December 2011 as a joint initiative between LAMSADE and DIMACS, where discussions with Alexis Tsoukiàs, Valerie Belton and a number of our other colleagues, both during and after the workshop, have supported the development of our thinking around the topic. The work of Katherine Daniell was supported by the HC Coombs Policy Forum. The HC Coombs Policy Forum and the Australian National Institute for Public Policy (ANIPP) received Australian Government funding under the ‘Enhancing Public Policy Initiative’. The work of David Ríos is supported by the AXA-ICMAT Chair in Adversarial Risk Analysis, the AESA-RAC Agreement on Operational Safety, and the MINECO project MTM2014-56949-C3-1-R. Discussions with colleagues at the ESF-COST IS1304 action on Expert Judgment and the HC Coombs Policy Forum are gratefully acknowledged. We are also grateful to the reviewers of the papers contained in this special volume, who, while they must remain anonymous, have generously contributed their time and expertise, and without whom the special volume would not be possible.
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Daniell, K.A., Morton, A. & Ríos Insua, D. Policy analysis and policy analytics. Ann Oper Res 236, 1–13 (2016). https://doi.org/10.1007/s10479-015-1902-9
- Public policy
- Policy analysis
- Big data
- Decision support