1. Need for a new future-oriented model of governance

Abstract

More than ever, policy environments are characterised by complex, uncertain, multi-causal contexts, where risk taking is limited and short-termism prevails. There is a need to introduce long-term perspectives and future-oriented decision making into policy and use strategic foresight to anticipate different futures.

    

Today the need for governments to respond to emerging challenges is particularly acute. More widespread events connected to climate change, migration, pandemics and other quickly developing issues are likely to emerge. In this environment, where complex systems and the problems they contain have become the norm rather than the exception, a purely reactive approach to setting policy is proving increasingly inadequate. Waiting until crises strike to respond has far less value than anticipating and acting in an innovative way before issues have emerged. Governments need both the ability to respond to unforeseen challenges in an expedient manner – adapt – and the ability to anticipate different (probable, plausible and possible) futures and prepare for these realities. This is not about introducing more strategic foresight or innovation into various pockets of government, but about building a system that helps policy makers to leverage both adaptation and anticipation.

Based on a review of existing research (Tõnurist and Hanson, 2020[1]), the policy environment today is characterised by:

• Complexity. In the policy context, complexity can derive both from underlying characteristics of wicked problems, and also due to competing interests in a policy area (Peters, 2005[2]). Wicked problems are characteristically open-ended, inter-connected and without clear, predetermined pathways to solutions (Rittel and Webber, 1973[3]).

• Multi-causality. Policy makers often rely on simulations and predictions based on linear causality, drawing on the dominant pattern within the policy field. This makes futures “closed” as they are extrapolated from past events and continuation of specific values and norms. This does not have to be the case and often is also not desirable, when transformation is actually deemed desirable, necessary or unavoidable. Here multi-causality means that there are many future possibilities and they are layered. This starting point enables policy makers to consider “open futures,” i.e. a multiple and open-ended understanding of future possibilities (Bussey, 2014[4]).

• Uncertainty. Uncertainty stems from the fact that policy problems and their solutions are often unquantifiable and their risks cannot be calculated (whereas with risk the probability distribution is known or predictable) (OECD, 2017[5]). When faced with uncertainty, not taking action is in some cases easier than intervention: it frees authorities from having to justify risky or uncertain interventionist policies until the future catches up with policy makers and negative outcomes arrive (Guler and Demir, 2020[6]).

• Diverging pace of change. Governments are often slow to respond to changed circumstances in their environments and face a ‘pacing problem’ (Marchant, 2011[7]): given the speed of innovation, challenges can evolve and change at unexpected points during the policy cycle. Traditional policy making often involves making decisions and judging priorities based on past information and existing evidence, and thus responds reactively to rapid change and unexpected events. Not all developments can be predicted or reduced to manageable practices within a single policy field; they must be continuously explored in real time and in an iterative manner.

• Technological change. The far-reaching impacts of technological change tend to be unpredictable. The Collingridge Dilemma captures this challenging trade-off between clearly understanding the impact a given technology will have on society, and the ease with which interested parties are able to influence the social, political and innovation trajectories of this technology. When change is easy (at early development stage of a technology), the need for it cannot be foreseen; when the need for change is apparent (when technologies have already diffused), change has become expensive, difficult and time consuming (Morozov, 2012[8]).

• Crises and short-termism. Policy makers today are often driven by events rather than visionary or forward-looking practices (Burrows and Gnad, 2018[9]). Crises can sometimes act as 'focusing events' – as is the case with COVID-19 – which can allow for major policy resets. Yet, this way of making policy depends on chance rather than an intentional process; it is an ad hoc and not a systematic practice. There is a continuous pressure to seek out quick wins towards political imperatives and manage crises rather than preparing for uncertain futures. Meanwhile, governments defer decisive action on long-term trends such as climate change, rising world population, demographic changes, urbanisation, and unsustainable consumption patterns.

• Risk avoidance. Governments are generally known to be risk-averse, rule-driven, and based on stable structures and predictable decision-making (Brown and Osborne, 2013[10]). This is also known as ‘minimal squawk' behaviour’ (Leaver, 2009[11])– trying to avoid drawing attention to rising issues if there is no immediate pressure to do so. Avoiding risks is often justified for political and reputational reasons; however, it means that by design, governments are not able to take action quickly when confronted with new challenges or to act proactively in the face of new opportunities. Governments’ response to transformative change has generally been reactive at best. From the position of ‘wait and see’, governments are pushed to act when hazards (moral, ethical or even physical) materialise, or they are called upon to resolve issues arising between industry incumbents and new business models.

Recent OECD work laid out a principled framework on how governments can start addressing these challenges by integrating anticipatory capacities into public governance and policy steering (Tõnurist and Hanson, 2020[1]). Research shows that simplifying these issues into discrete models1 does allow governments to take decisive action, but often creates blind spots. Adequate action starts with the willingness to embrace radical uncertainty and complexity, and to put forward the right tools to make sense of new developments as they emerge.

To make policy is to think about the future. Governments require future-oriented innovations in order to respond to complex challenges, such as climate change, aging societies and digital transformation, in real time. Every policy designed and delivered carries implicit or explicit notions of the context in which it will be implemented, the intended consequences, and its potential effectiveness. Often these notions are based on expectations, forecasts, predictions, and assumptions – mental models – about what the world will look like and how it will work (Wack, 1985[12]).

These mental models facilitate decision-making, but they can also contain biases and blind spots (Pain et al., 2014[13]). Forecasts and predictions are not well suited to situations of volatility, uncertainty, complexity, and ambiguity because they project the future in a linear way that is not reflected in reality (Ramírez and Wilkinson, 2016[14]). It may be possible to follow the line of an indicator such as GDP into the future, but that will not necessarily give an appreciation of the factors affecting or affected by it, or what they mean for a given organisation.

Policy makers face a difficult task of maintaining continuity and confidence in the public system, while rapidly adapting to a new environment of fast-changed and constantly evolving demands, volatility and complex problems. The deployment of new and disruptive technologies and digitalisation are transforming the production and distribution of goods and services, changing the status quo for economies and societies, and resulting in new inequalities (OECD, 2019[15]). This has serious implications on future employment, skills, income distribution, trade and well-being (OECD, 2015[16]).

Governments need to understand and anticipate the impacts of technology, change and innovation as well as the shifting expectations of citizens, companies and innovators and their implications for public policy. The validity of existing regulatory frameworks and, indeed, the capacity of governments to adapt to change are being questioned. This requires an increasingly agile public sector, able to exploit the many opportunities offered by technological change to improve rule-making and adapt to new realities and risks (OECD, 2018[17]). Governments need to guide society through uncertainty and technological change, which requires new forms of innovation governance that allow policy makers to respond to unforeseen events and technological change in real time (Polchar, 2020[18]; Tõnurist and Hanson, 2020[1]).

OECD research indicates that government responses to these challenges increasingly depend on the system’s ability to harness futures thinking, anticipation and innovation. While strategic foresight can help governments understand the possible spaces within which to take action, it often lacks the connection to what this action can look like in practice. It is impossible to determine the most effective responses to a complex problem without testing them out in practice by innovating. An important aspect of effective policy making is the ability to learn from innovation and to feed the insights gathered back into the system. Thus, effective government action increasingly depends on the public sector’s ability to harness futures thinking and anticipation, and to test innovations on the ground. These capacities lie at the core of the anticipatory innovation governance (AIG) model described in the next section.

References

[10] Brown, L. and S. Osborne (2013), “Risk and Innovation: Towards a framework for risk governance in public services”, Public Management Review, Vol. 15/2, pp. 186-208.

[9] Burrows, M. and O. Gnad (2018), “Between ‘muddling through’ and ‘grand design’: Regaining political initiative – The role of strategic foresight”, Futures, Vol. 97, pp. 6-17, https://doi.org/10.1016/j.futures.2017.06.002.

[4] Bussey, M. (2014), “Concepts and effects: ordering and practice in foresight”, Foresight, Vol. 16/1, pp. 1-16, https://doi.org/10.1108/fs-04-2013-0017.

[6] Guler, A. and F. Demir (2020), “Identifying Security Challenges in the IoT for the Public Sector: A Systematic Review”, in Public Administration and Information Technology, Beyond Smart and Connected Governments, Springer International Publishing, Cham, https://doi.org/10.1007/978-3-030-37464-8_4.

[11] Leaver, C. (2009), “Bureaucratic Minimal Squawk Behavior: Theory and Evidence from Regulatory Agencies”, American Economic Review, Vol. 99/3, pp. 572-607, https://doi.org/10.1257/aer.99.3.572.

[7] Marchant, G. (2011), “Addressing the Pacing Problem”, in The International Library of Ethics, Law and Technology, The Growing Gap Between Emerging Technologies and Legal-Ethical Oversight, Springer Netherlands, Dordrecht, https://doi.org/10.1007/978-94-007-1356-7_13.

[8] Morozov, E. (2012), “What is your favorite deep, elegant, or beautiful explanation?”, https://www.edge.org/response-detail/10898 (accessed on 13 June 2022).

[15] OECD (2019), Under Pressure: The Squeezed Middle Class, OECD Publishing, Paris, https://doi.org/10.1787/689afed1-en.

[17] OECD (2018), OECD Regulatory Policy Outlook 2018, OECD Publishing, Paris, https://doi.org/10.1787/9789264303072-en.

[5] OECD (2017), Systems Approaches to Public Sector Challenges: Working with Change, OECD Publishing, Paris, https://doi.org/10.1787/9789264279865-en.

[16] OECD (2015), In It Together: Why Less Inequality Benefits All, OECD Publishing, Paris, https://doi.org/10.1787/9789264235120-en.

[13] Pain, N. et al. (2014), “OECD Forecasts During and After the Financial Crisis: A Post Mortem”, OECD Economics Department Working Papers, No. 1107, OECD Publishing, Paris, https://doi.org/10.1787/5jz73l1qw1s1-en.

[2] Peters, G. (2005), “The Problem of Policy Problems”, Journal of Comparative Policy Analysis: Research and Practice, Vol. 7/4, pp. 349-370, https://doi.org/10.1080/13876980500319204.

[18] Polchar, J. (2020), “Unboxing the Future: Finding the futures hidden in plain sight”, European Union Institute for Security Studies, Vol. Brief 19, p. 8, https://www.iss.europa.eu/sites/default/files/EUISSFiles/Brief%2019%20Foresight.pdf.

[14] Ramírez, R. and A. Wilkinson (2016), Strategic Reframing: The Oxford Scenario Planning Approach, Oxford University Press, Oxford, New York.

[3] Rittel, H. and M. Webber (1973), “Dilemmas in a general theory of planning”, Policy Sciences, Vol. 4/2, pp. 155-169, https://doi.org/10.1007/bf01405730.

[1] Tõnurist, P. and A. Hanson (2020), “Anticipatory innovation governance: Shaping the future through proactive policy making”, OECD Working Papers on Public Governance, No. 44, OECD Publishing, Paris, https://doi.org/10.1787/cce14d80-en.

[12] Wack, P. (1985), “Scenarios: Uncharted Waters Ahead”, Harvard Business Review, https://hbr.org/1985/09/scenarios-uncharted-waters-ahead (accessed on 21 June 2021).

Note

← 1. Models that only account for a set number of values.