Tuesday, October 11, 2022
Guest Blogger: Larry A. Rosenthal, Goldman School of Public Policy, UC Berkeley

In my previous post, I discussed “agile policymaking” as a vehicle for better government and how agile would be more objective and evidenced-based when it comes to traditional policy analysis (“TPA”).

As taught in leading universities and practiced across the government spectrum, TPA[3] orients analytic activity toward the predilections of “clients” (typically, elected and appointed officials, exercising democratic and delegated authority, respectively). Narrowly framed and carefully defined problems are researched, so that policy practitioners can conduct business in an informed and evidence-based manner.

Problems and evidence: Agile policymaking would reimagine both. Unexploited social opportunities would become more prevalent, and evidence-practice would move naturally from aggregate social trends toward the lived experience of the most impacted stakeholders, taking a page out of the playbooks for user-based research and human-centered design.[4]

In TPA the solution-space is built through an ideative process, constructing the alternatives for government action. Often those policy options are generated in path-dependent terms, based on extant legislation and administrative activity. Next come the performance standards for those alternatives; these social values and specialized objectives provide the selected criteria, metrics by which improvement can be meaningfully measured.

Alternatives & criteria: Agile policymaking would emphasize invention over replication. Proposed solutions would include untried approaches based on processes of social discovery. Metrics of success would be situational, both readily measurable and newly revelatory.

With alternatives and criteria in place, TPA proceeds into more technical forms of projection and calculation. Empirical methods and decision modeling allow the outcomes of various policy-options to be predicted in terms of the designated metrics. In the atypical case of dominance, a singular option prevails on all specified dimensions, making policy choice straightforward. More frequently, however, trade-offs are evident, in which case methods of weighting, social priority, and balancing attempt to triangulate the most desirable policy to recommend.

Outcomes & trade-offs: Agile policymaking can regularize data collection, make the projection of outcomes less costly, center stakeholders, and design deliberative ways to confront trade-offs and reveal undiscovered modes of consensus and cohesion.

As analysis concludes, suasion gets underway. Once the final recommendation is reached, an evidence-based narrative (telling the resulting story in written and presentation forms) is delivered to the client. The aim is to have the recommendation win the day. The narrative supports the chosen policy, demonstrates its advantages over the alternatives, reflects upon the criteria and trade-offs which were weighed, and cements in place the expected advances in the public interest.

Story: Agile policymaking would exploit innovative information networks, make communication more multilateral and interactive, and treat the evidence-based case for a recommended policy as a dynamic social conversation, not a static “official” report.

Against the canvas of TPA-informed disciplines, therefore, agile policymaking frontiers appear numerous and provocative. But reinvention shouldn’t sacrifice smart practice. The notion that social choice can be made more objective, even scientific, has a lot to recommend it. Government should remain careful and methodical, especially when the aggregate stakes are large. For these reasons, traditional policymaking is rightly considered “analytic” in a number of respects. The drive toward deep understanding should proceed apace.

Complex issues of social choice – particularly vexatious ones[5] making political consensus most elusive – can be unraveled to reveal their constituent features, causal chains, and knowledge gaps. TPA’s working parts (problems, evidence, alternatives, criteria, outcomes, and trade-offs) each require their own styles of thinking and decision-making approaches. Political considerations can be temporarily “held constant” as applied, evidence-based inquiry proceeds with rigor. At the same time, the TPA disciplines recognize the power to elevate political concerns as criteria unto themselves. And just as problematizing social conditions is the hallmark of all policy choice, the process by which recommended courses of action get approved by clients, authorized, enacted, budgeted and implemented is inescapably political in nature … and should ever remain so, in an open and inclusive democracy.

However, TPA’s known advantages in these respects also present opportunities to make policy analysis itself even more agile. Thus agile policymaking ought to build upon TPA, not usurp it. Where analysis is cumbersome, it should be streamlined. Where analysis lags behind technological change and sociocultural progress, it should accelerate. When the client’s prerogatives stand in the way, analysis should loosen the bonds and break free.

Agile policymaking sets a constructive tone for these improvements. Among the enhancements we might envision:

  • Clientele. TPA is usually undertaken with a specific elected or appointed “client.” Agile policymaking can broaden the aperture, focusing upon stakeholders and, wherever possible, the “users” of policies as designed and implemented. A ready example might be restructuring environmental baseline-standards utilizing the mutual vision of both affected industries and impacted communities.
  • Ingenuity. Optimal solutions to specific problems can evade data-focused analytics. TPA professionals spend more time learning social science than they do engineering and entrepreneurship. Discovery and inventiveness are difficult things to teach, to be sure. At minimum, agile policymaking can take the “waterfall” model in TPA – where multiple steps are followed, in order to reach ultimate, definitive results – and introduce iterative processes, frequent versions, successive-improvement routines, and simulation-based evaluation. The aim should be to seed “ingenuity environments” so that policymaking will arrive at clever insights more frequently, as a germinating mode for conducting business.
  • Anticipation. Traditional governance is often more reactive than future-oriented. And while TPA does seek to project the outcomes of various policy options if adopted, futurism remains foreign to those trained in social-scientific empiricism and retrospective data fundamentals. Agile policymaking can ply the art and craft of scenario planning and future-state modeling,[6] much as is already being done in public-private collaborative settings like climate change adaptation, population-growth analysis, energy investment, bioengineering, space exploration, and cloud computing.
  • Adept Politics. In practical terms given partisan conditions, agile policymaking would also need to master politics in certain ways. It should learn to finesse obstacles and build coalitions, both in terms of overcoming entrenched opposition to innovative approaches and earning those ideas trust and belief. One agile-policymaking toolkit[7] devises a series of processes by which agile-policymaking can gain traction in government settings and evolve. These processes include: mapping the stakeholder-based landscape of power and influence; identifying “protagonists” who can champion agile process; gauge how the political discourse is likely to take shape; monitor potential “tipping points” where progress-trajectories can launch; secure the imperative by “tuning the landscape” of perceived stakes and policy fundamentals; empower allies aligned with the agile vision; advance outlooks for cooperation and civility; and outpace sources of opposition and friction.

Viewed in appropriate relief, agile policymaking charts an important trade route for the agile-government journey writ large. At least three strategic emphases can be made explicit moving forward: increasing the adaptive capacity of policymaking bodies under conditions of not only dynamism but turbulence; [8] fostering policymaking in the promotion of technological innovation across industrial sectors and institutional settings;[9] and policy development for open data architectures and digital-government expansion.[10] As one forward-thinking observer notes, “The main point of this is that agile is a mindset, a culture, around delivering small steps forward fast and often: that’s what we need to bring into policy making ….”[11]


[3] The elements of traditional policy analysis (“TPA”) set forth below are based upon Bardach & Patashnik (id.) in a sequentially organized but iteratively undertaken method titled by the authors, and known affectionately among practitioners, as “the Eightfold Path.” The approach was invented by political scientist Eugene Bardach, now Emeritus Professor at the Goldman School of Public Policy, UC Berkeley, and is the Goldman School's hallmark policy-analytic method.

[4] See UK Design Council, et al., “Design for Public Good” (Sharing Experience Europe [keep.eu], 2013).

[5] See Head, B.W., & J. Alford, “Wicked Problems: Implications for Public Policy and Management,” Administration & Society 47(6): 711–739 (2015); Dunn, W., “Structuring Policy Problems,” in Public Policy Analysis: An Integrated Approach (Routledge, 2018 [6th ed.]), ch.3 (pp. 68-117).

[6] See Volkery, A. and T. Ribeiro, “Scenario Planning in Public Policy: Understanding Use, Impacts, and the Role of Institutional Context Factors, “ Technological Forecasting and Social Change 76(9): 1198-1207 (2009); see also Guston, D. H., “Understanding ‘Anticipatory Governance’,” Social Studies of Science 44(2): 218-242 (2014) and Tõnurist, P. and A. Hanson, “Anticipatory Innovation Governance: Shaping the Future Through Proactive Policy Making,” OECD Working Papers on Public Governance, no. 44 (2020).

[7] Room, G., “Agile Policy Making” presentation (link: https://prezi.com/brr81qqctqyi/agile-policy-making/), based on the author’s book, Complexity, Institutions and Public Policy: Agile Decision-Making in a Turbulent World (Cheltenham, UK: Edward Elgar, 2011).

[8] Howlett, M. and M. Ramesh, “Designing for Adaptation: Static and Dynamic Robustness in Policy-Making,” Public Administration [online symposium article] (Apr. 2022); see also Kasianiuk, Krzysztof, “Towards More Agile Public Policymaking,” Studia z Polityki Publicznej 3(11): 41-53 (2016).

[9] Mergel, I., “Open Innovation in the Public Sector: Drivers and Barriers for the Adoption of Challenge.Gov,” Public Management Review 20(5): 726-745 (2018).

[10] Parcell, J. and S. H. Holden, “Agile Policy Development for Digital Government: An Exploratory Case Study,” Proceedings of the 14th Annual International Conference on Digital Government Research, https://doi.org/10.1145/247972... (June 2013).

[11] Ollerhead, L., “The Limits of Agile – Can We Apply It To Policy Making?” Policy Lab Blog (Gov.UK), [https://openpolicy.blog.gov.uk...] (January 27, 2015).


This blog was first published by the National Academy of Public Administration.