8. Anticipatory innovation

Anticipatory innovation creates and implements value-shifting changes in environments of deep uncertainty, particularly for the purpose of exploration and shaping future priorities (Tõnurist and Hanson, 2020[1]). Anticipatory innovation involves picking up on signals of change, exploring emergent issues, testing assumptions, and exploring radically different possibilities. It means implementation and learning that respond to the future iteratively as it unfolds.

Anticipation does not mean predicting the future; it is about asking questions about plausible futures, so that we can act in the present to bring about the kinds of futures we want (Guston, 2014[2]). It is a capacity to engage with alternative futures, based on sensitivity to weak signals, and an ability to visualise their consequences in the form of multiple possible outcomes (Miller, 2018[3]). As change agents, governments should recognise their role in introducing new technologies and innovations to grapple with upcoming challenges. The main contribution of anticipation lies in shaping people’s perceptions about the future and developing their capacity to make sense of novelty (Miller, 2018[3]).

To make policy is to think about the future. Governments require future-oriented innovations to respond in real time to complex challenges, such as climate change, aging societies and digital transformation. Every policy carries implicit or explicit notions of the context in which it will be implemented, the intended consequences and its potential effectiveness. These notions are often based on expectations, forecasts, predictions and assumptions – mental models – about how the world will look and work (Wack, 1985[4]).

Mental models facilitate decision-making, but they can also contain biases and blind spots (Pain et al., 2014[5]). Forecasts and predictions are not suited to situations of volatility, uncertainty, complexity and ambiguity because they project the future in a linear way not reflected in reality (Ramírez and Wilkinson, 2016[6]). It is 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 an organisation.

Policymakers face a challenge maintaining continuity and confidence in the public system while rapidly adapting to quickly and constantly evolving demands, volatility and complex problems. For example, the deployment of new and disruptive technologies and digitalisation transform the production and distribution of goods and services, changing the status quo for economies and societies, and resulting in new inequalities (OECD, 2019[7]). This carries implications for the future of employment, skills, income distribution, trade and well-being (OECD, 2015[8]). 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 the capacity of governments to adapt to change are being questioned. This requires an increasingly agile public sector, able to exploit the opportunities offered by technological change to improve rule-making and adapt to new realities and risks (OECD, 2018[9]). 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[10]; Tõnurist and Hanson, 2020[1]).

This section of the report highlights the main themes of anticipatory innovation in the public sector: (1) main drivers of anticipatory innovation in the public sector; (2) support structures; (3) tools and methods; (4) skills and capacities needed; (5) policy and public service challenges; and (6) unanswered questions.

Anticipatory innovation derives from foresight and futures thinking, the increased influence of which underpins a “future-readiness” approach entering policymaking (SOIF, 2021[11]). To reap the benefits, governments must learn to anticipate – to create knowledge about futures ahead – but also to make that knowledge actionable through concrete innovation in practice. To do so, governments need a governance approach to support future-oriented learning based on empirical experimentation.

This chapter analyses anticipatory innovation as a purpose-oriented intervention (it is to a degree normative in nature), whose value can only be realised when built into decision-making processes. Because ideas about the future have no intrinsic value alone, strategic foresight treats the future as a set of ideas to be used for specific purposes by specific organisations in specific contexts (Ramírez and Wilkinson, 2016[6]). In this action-orientation, anticipatory innovation differs from traditional futures approaches. This emphasis on purpose and application requires consideration of how the actors (in this case government) will use the insights generated – especially the decision-making processes that take the future into consideration.

Anticipatory innovation can overlap with adaptation. The distinction is that adaptive resilience or anti-fragility address unexpectedness in the known world, while anticipatory innovation focuses on preparing for and shaping the unexpected world (Nordmann, 2014[12]).

“It is impossible to forecast the future, and it is foolish to try to do so. Most of the time, forecasts are quite good, and this is what makes forecasts so dangerous. […] The danger of forecasts is that usually they are right. Forecasts fail you just when you would need them most. Forecasts fail to anticipate major changes and major shifts […]. Shifts that make whole strategies obsolete.” (Wack, 1985[4])

Forecasts allow better understanding and anticipation of trends by analysing the factors underlying them and envisaging trajectories they could follow (Saffo, 2007[13]). However, forecasting has limitations. Some developments simply cannot be forecast because too little is known about the relevant factors. Many problems such as multilateral negotiations or the consequences of a pandemic are “undecidable”: their outcomes can never be predicted by an algorithm regardless of the information inputs (Bianchini, 2018[14]). Many high-quality have turn out to contain errors (Pain et al., 2014[5]). Knowledge in a subject area is poorly correlated with the ability to predict the future within that domain, which makes it unwise to base decisions on predictions even by experts (Tetlock and Gardner, 2015[15]).

To address some limitations of the approach, forecasts can use probability or multiple projections to estimate the range of likelihood of an outcome. But this is often misinterpreted, and people can assume the middle of a range of outcomes is the ‘real future’, or discount improbable outcomes as not worth considering. Forecasts might also limit their scope to very specific events or outcomes in order to assess and learn from their accuracy (Tetlock and Gardner, 2015[15]). For example, a forecast might seek to estimate the probability that a country might experience a violent coup d’état within the next two years. But such specificity also limits how broadly the findings can be generalised for use in policy making; and still does not guarantee future success of the method.

Strategic foresight is the ability of an organisation to constantly perceive, make sense of and act upon ideas about future change emerging in the present (OECD, 2021[16]).

Foresight analysis is not about predicting one future but learning from a range of plausible, possible, and probable futures (Burrows and Gnad, 2018[17]). There are two stances: (1) predictive policy stances aim to project different future alternatives (exploratory foresight), while (2) prescriptive policy stances argue for taking action towards a particular result (normative foresight) (Patton, Sawicki and Clark, 2012[18]).

Strategic foresight involves identifying signals of change, making them instructive and considering the implications. The purpose is to challenge assumptions about the future and provoke reflection on new ways to achieve success.

Foresight abandons the idea that the future is ever fully knowable, and it accepts that there are always multiple versions of the future – some of them assumptions, some of them hopes and fears, some of them projections, and some of them emerging signals of change. All of them are incomplete and still forming in the present. Strategic foresight makes wise decisions possible despite uncertainty by generating and exploring different futures that could arise, and the opportunities and challenges they could entail. Organisations use those ideas to make better decisions and act in the present (Box 8.1).

In the anticipatory innovation model, strategic foresight is a driver of insight and knowledge to inform experimentation and innovation, but is not enough on its own. With anticipatory innovation, the emphasis is on acting in the present with a future mindset (Mallard and Lakoff, 2011[19]). The aim is to steer development and technology while analysing and testing the boundaries of ethical, legal and social aspects of change (McGrail, 2012[20]). Anticipatory innovation governance should consider uncertainty (as opposed to risk) over extended timeframes and develop the capacity to mitigate it adaptively by changing actions today.

Strategic foresight is not a method, tool or decision support system. It is distinct from forecasting, risk assessment and strategic planning. Strategic foresight is different from traditional approaches to policy making in several important respects (Box 8.2).

Anticipatory innovation is more prospective and proactive than adaptation; it invites governments to explore and act towards desired futures rather than just adequately predicting or reacting to them. There is a connection between anticipatory innovation governance and adaptive management as there will always be risks that emerge suddenly, requiring government response. While adapting to changes in the current system, anticipatory innovation must explore options that could challenge how current systems function.

Organisations often turn to futures studies during crises in the hopes of faring better next time. No discipline can make such a promise, but futures studies were developed to respond to times of turbulence, unpredictability, novelty and ambiguity (Ramírez and Wilkinson, 2016[6]). These themes inspire the sections that follow, as main drivers of anticipatory innovation in the public sector: (1) responding to novel societal and technological developments; (2) decision-making and planning in conditions of unpredictability; (3) making sense of complex policy problems; and (4) the cost of doing nothing in the face of rapid change.

The need for anticipatory innovation arises when governments must make decisions in environments where the direction and impact of change are unprecedented and unclear. For example, when commoditisation of GPS and mobile devices created the conditions for peer-to-peer economies and platforms, the impacts on social security, housing markets, tax gaps and fuel emissions took years to be understood. This is especially true for the deployment of new and disruptive technologies – such as the Internet of Things (IoT), gene editing, neuro-technologies, blockchain, platform technologies, advanced robotics and machine-to-machine learning etc. – which transform the production and distribution of goods and services with significant impacts for society and individuals (Love and Stockdale-Otárola, 2017[26]) (OECD, 2020[27]). Furthermore, the operating models digitalisation creates (platforms like Uber, AirBnB, SocietyOne, WeChat) challenge the status quo of both economies and societies. This process is not only characterised by the creation of new services and products, but also by creative destruction (Schumpeter, 1942[28]; 1934[29]). New technologies introduce new inequalities in society (e.g., Bertot, Estevez, and Janowski (2016[30])) which are as complex and uncertain as the underlying technological change. Thus, future employment, skills, income distribution, trade and well-being will look substantially different and are challenges for which governments must prepare.

Technologies themselves do not have a normative stance, but their ‘design’ limitations can positively or negatively influence individuals and society, and change them in fundamental ways. The potential impact of genetically modified organisms or the effect of nuclear energy on society are two examples. Governments must deal with not only the effects of these, but also unexpected societal reactions and impacts. Here the past might not be a good predictor of the future. Long-standing trends might cease and incremental change could be superseded by non-linear transformations: disruptive technologies, systemic financial failures, natural disasters or pandemics, or abrupt climatic shifts might fundamentally alter a nation’s trajectory (Boston et al., 2019[31]). The uncertainty and risks created by rapid (technological) change cannot be directed by the private sector alone. Governments must take an active role in the change process, create partnerships and share risks.1

One important mechanism to enhance anticipatory innovation governance is engaging in societal technology assessment prior to formal regulatory process that raise questions about the potential benefits and costs and their distribution, the consequences for intellectual property, the pathways for greatest social benefit, and the sources of uncertainty in assessing the technology (OECD, 2020[27]).

Governments adopt anticipatory innovation to make decisions and plan under unpredictable conditions. For example, predecessors of some approaches to anticipation and foresight were developed in US strategic defence planning. Defending all US interests simultaneously would be prohibitively expensive, so decisions were needed about potential threats to focus on and prepare for. Administrations “selected” scenarios based on their strategic priorities and perceptions of the global state of affairs (Larson, 2019[32]).

Used in a sustained and systematic manner, scenario planning gave Royal Dutch Shell the ability to prepare for disruptions such as the 1973 energy crisis, the oil price shock of 1979, the collapse of the Soviet Union and the increasing pressure on companies to address environmental and social problems. It is not prescience that made these strategic foresight undertakings valuable, but their ability to challenge and change leaders’ mental models before it was too late (Kleiner, 2003[33]; Wack, 1985[4]).

Governments have undertaken efforts since the 1980s to upgrade their institutional anticipatory capacity and proactiveness at several junctures (Fuerth and Faber, 2012[34]). Their interest in strategic foresight appears to grow in the aftermath of disruptions. Several foresight activities came from the 2008 financial crisis. Others emerged from the lessons of the COVID-19 pandemic. At least one consultation of expert opinion considered that “there is a sense of uncertainty and lack of clarity about where the world is going. […] Demand for the capabilities and expertise of foresight units and practitioners is growing” (SOIF, 2021[11]).

Governments increasingly face complex, ‘wicked’ challenges characterised by diversity, complexity and uncertainty (Camillus, 2008[35]). These features are termed “VUCA” (Stiehm, 2002, p. 6[36]), which refers to a world that is increasingly Volatile, Uncertain, Complex and Ambiguous. Complexity can result from both up- and down-stream challenges: from the global scale in which challenges manifest (e.g., the spread and cascading effects of the COVID-19 crisis) and the localised impacts and contextualised issues of production, jobs and public services. Awareness of these fundamental uncertainties has increased in society, industry and policymaking circles (Kuhlmann, Stegmaier and Konrad, 2019[37]). For example, climate change requires the expertise and coordination of policymakers involved in issues related to agriculture, water, and food security as well as immigration, diplomacy and defence (Kaufman, 2012[38]). Changes in one factor cascade through other systems and create uncertainties around outcomes. Global communications infrastructure and the social media environment that sits on top of it raise issues around traditional utility and telecommunications regulation, and thornier questions of cultural cohesion, individual rights, national security and information warfare (Ventre, 2016[39]). Questions surrounding the creation, storage and ownership of the massive amounts of data generated through modern business and consumer technology require expertise in commerce and trade as well as in privacy, autonomy, and criminal liability (Braun et al., 2018[40]). Ongoing advances in artificial intelligence and augmented reality systems will have considerable impact on what (and how) public services are delivered while introducing as yet unknown challenges for the public sector (Berryhill et al., 2019[41]).

Governments turn to anticipatory approaches to make sense of intersecting and potentially conflicting challenges. Challenges that cut across multiple subject domains also require multi-faceted but coherent innovation (Box 8.3). One example is the IMAJINE Scenario Sketches developed by the European Commission. These scenarios capture the possible developments and consequences of spatial injustice in Europe, considering factors such as migration, climate change and political unrest. These “rich and useful visions” help regional policymakers develop more multidimensional and systemic solutions to tackle and anticipate geographic disparities.

Failing to embrace and respond to complexity can come at a high cost for governments. Simplistic answers or quick fixes do not fit with changing reality (Burrows and Gnad, 2018[17]). The more change accelerates, the less certain and more difficult it becomes to forecast, creating a need to understand the consequences and implications of change and feed this back into decision-making (Ramos, 2017[43]). As technology (especially digital technologies) tends to develop faster than policy, structures and operating models can lag the problems they try to address. This calls for anticipatory innovation as an ex ante, real-time and iterative policymaking to influence the design of solutions.

Policymakers’ interest in futures thinking and foresight methods intensified over the last decade (Minkkinen, 2019[44]). Futures thinking has been integrated into policy processes through explicit foresight or more implicit anticipation practices. At the same time, there seems to be fatigue and criticism of simple “futures talk” (Nordmann and Schwarz, 2010[45]) and future as an object of technical design (Nordmann, 2010[46]), as well as the aim to “future-proof” in practice. Consequently, consensus is emerging on the need to be more proactive: to improve government’s ability to act in the face of change. But what will work in practice remains uncertain. Anticipatory innovation connects futures insights with action.

Support structures are prerequisite to anticipatory innovation in the public sector. Foresight ecosystems are the broader institutional and social context in which anticipatory innovation is situated. Anticipatory innovation governance is the institutional capacity to support and deploy anticipatory innovation. Working methods are the daily processes that characterise and promote the practice of anticipatory innovation

Research is emerging around the organisational capacities and broader context of an organisation (ecosystem) that are conducive to foresight and anticipatory innovation (OECD, 2019[47]; SOIF, 2021[11]). Studies identify several characteristics of an ecosystem, and its actors and the roles they play. Common features include:

  • Culture, behaviour and embeddedness – the mainstreaming of anticipatory innovation into everyday working practices

  • Processes – the use of purpose-oriented interventions to generate futures knowledge and put it to use in innovation through prototyping and experimentation

  • Structures and institutions organisations that favour and reward the practices of anticipatory innovation

  • People, capacity, and skills – the individual and collective mindset and experience to embrace uncertainty, explore alternatives and put anticipatory innovation into action

  • Leadership and demand – decision-makers able and willing to engage with the (sometimes discomforting) knowledge and implications of anticipatory innovation

It is also common to see networks of practice in government foresight in ecosystems where anticipation is widely practiced (Box 8.4).

Anticipatory innovation governance (Tõnurist and Hanson, 2020[1]) is how governments operationalise and use anticipation. It embeds the practice of strategic foresight into their way of working and makes it relevant to initiatives that create change. It is the capacity to explore options and spur novel and value-shifting products, services and processes.

Engaging in anticipatory innovation requires mechanisms inside government’s core architecture and public sector innovation portfolios (Biermann et al., 2009, p. 31[50]; Fuerth and Faber, 2012[34]). Anticipatory innovation governance can be a systemic, interlocking web of widely shared principles, institutions and practices that shape decisions at all levels. This system should be able to function over time and adapt to changes.

The effectiveness of policy and policy systems depends on the ‘appropriateness’ of policymaking, which can be seen along three dimensions: analytical, political and operational (Bali, Capano and Ramesh, 2019[51]). Across these, policymakers need agency: the belief and channels to operationalise their actions (Hitlin, Jr. and G., 2007[52]), and an authorising environment that gives them the legitimacy to undertake anticipatory innovations that challenge established values (Alford, 2008[53]). These make up the general frame for anticipatory innovation governance mechanisms (Figure 8.3, Box 8.5).

Governments develop protocols to practice and implement anticipation, foresight and anticipatory innovation. Some of these are broad frameworks guiding the overall process. For example, the Centre for Strategic Futures in Singapore developed a multi-phase process of Scout-Challenge-Grow to help the government go beyond prevailing assumptions, better manage risk and uncertainty, and improve resilience to possible shocks (Kwek and Parkash, 2020[60]):

  • Scout: it is important that governments detect emerging trends, and define and name them so that everyone understands the trends in the same way and conversations and ideas can happen on that basis. Scouting in Singapore gave rise to thinking about gig-economy concepts before the phenomenon had emerged.

  • Challenge: governments need the capacity and structures to challenge legacies, which are particularly hard-wired in governments. While it may be possible to point out problems, changing things requires policymakers to immerse themselves in the problem, imagine possible futures and empathise with pain-points to change legacy systems.

  • Grow: a growth mindset encourages officials to think about how to plan for the far future. Formal training and experiential learning, and a revolving-door policy in the Centre for Strategic Futures mean that many people across the Singaporean government advocate for this anticipatory approach – including now-senior leaders trained in foresight decades ago.

Other governments produce guidance, and detailed and comprehensive methodologies and standardised approaches to anticipation and strategic foresight. Examples include the Horizons Foresight Method of Policy Horizons Canada (2016[61]) and the Futures Toolkit of the UK Government Office for Science (2017[62]).

The future can never be empirically studied by any tool and is therefore a socially constructed phenomenon with multiple perspectives and stances. As a result, diverse methodologies exist to capture the ways in which the future can be perceived, analysed, understood and acted upon (Masini and Goux-Baudiment, 2000[63]). These methods can have a passive or active stance on future developments. While some tools take an exploratory and descriptive stance, others incorporate a more prescriptive stance (Kreibich, Oertel and Evers-Wölk, 2011[64]).

There are methods and tools for each part of the process of perceiving, making sense of and acting on emerging futures (Box 8.6). These three parts are not sequential but rather aspects of an iterative process where each informs the others. Literature on tools and methods concerning ideas about plausible futures – particularly as applied to technological speculation (the perceiving and sense-making aspects) – outweighs the literature on tools and methods for converting insight into action (the action aspect).

Anticipatory innovation needs knowledge to underpin potential developments. Indicators can help policymakers track events, spot trends and separate relevant information from noise (Burrows and Gnad, 2018[17]). Usually indicators must be observable, reliable, stable over time, valid and unique to the specific phenomena. Data requirements to track uncertain futures are difficult to define; especially, as one does not know what one should look for. However, information can be obtained, used and codified to support anticipation.

Horizon scanning is the foundation of any anticipatory process. It involves seeking and researching signals of change in the present and their potential impacts. However, horizon scanning alone is never a complete or impactful process; it can only increase awareness. The action-oriented and value-shifting aspects of strategic foresight and anticipatory innovation are carried out in response (Cuhls, 2020[66]).

Anticipation relies on strategic intelligence, and signal detection and classification (Lesca and Lesca, 2011[67]). Signal detection can involve active and passive scanning for signals – either sending out probes/questions and listening for answers, or periodically observing what is happening in general or what people are talking about – and aims to predict change in order to exploit new opportunities and avoid threats (Rossel, 2009[68]). Signals can be retrieved from experts and futurists, scientists and consultants etc., and increasingly from text-based or online data (e.g., through text mining). In technology, patent data remain the most widely applied sources for signal detection (Kim and Lee, 2017[69]).

It is noteworthy that effective signal detection depends on complexity, sense-making and strategic decision-making, which all effect the types and significance of signals captured. For example, the Cynefin framework (Kurtz and Snowden, 2003[70]) contextualises signals among five domains: (1) reliable causes and effects; (2) knowable causes and effects; (3) unknowable causes and effects (except in hindsight); (4) no cause or effect; and (5) transition (ibid). Interpreting signals in chaotic or complex situations can be improved when causal relationships can be described or determined with expert interpretation.

Weak signals are knowledge of changes that appear unsurprising or insignificant in the present, but could become surprising and significant in the future. Weak signals capture disruptions and developments not yet foreseen in “strong” evidence like trend data or experiments. Weak signals can be events, new technologies or practices pointing towards important discontinuities, warning signs, or new possibilities that can strengthen or wither over time. Examples of weak signals are found in technology developments, societal innovations, conflicts, demographic shifts, new rivals, new regulations, etc. (Kim and Lee, 2017[69]; Saritas and Smith, 2011[71]).

The abundance of information available online represents a quantity of futures knowledge beyond any one person’s or team’s ability to search and curate. Increasingly sophisticated tools such as AI-driven horizon scanning and topic analysis work by gathering large amounts of text-based data from areas of the internet such as social media, blogs, news channels or government websites (Antons et al., 2020[72]; Lewis, 2020[73]). Such methods allow a broader search for knowledge than manual searching (Kayser and Blind, 2016[74]), while potentially avoiding cognitive biases that a human searching for information might experience, such as confirmation or availability bias.

Nevertheless, such methods bring challenges, including the signal-to-noise dilemma whereby a computer is not necessarily able to identify what a decision-maker might consider useful or irrelevant (Krigsholm and Riekkinen, 2019[75]). Improvements to the technology and its deployment seek to address these challenges (Jiang et al., 2016[76]).

The RAND Corporation developed the Delphi method in the 1950s to forecast the impact of technology on warfare. The method entails experts anonymously replying to questionnaires and receiving feedback in the form of a statistical representation of the group response, after which the process repeats itself (Gordon, 1994[77]; Linstone, 1985[78]). The goal is to reduce the range of responses and arrive at something closer to consensus. The Delphi Method was widely adopted and is still in use (Helmer-Hirschberg, 1967[79]).

Signal detection in anticipatory innovation governance requires real-time monitoring so governments can act and innovate quickly based on received signals. The concept of real-time monitoring systems is known in the field of disease outbreaks (Ramalingam, 2016[80]) and bolstered by the COVID-19 pandemic and associated track-and-trace technologies.

The recent push for data-based anticipation through predictive analytics and machine learning that utilise big data is unprecedented. It can provide new insight into the events, life experiences and trends in society as digital signals (Kowalkiewicz, Safrudin and Schulze, 2017[81]). Predictive analytics forecast what might happen in the future with an acceptable level of reliability and include what-if scenarios and risk assessment (Tate et al., 2018[82]). Examples include crowdsourcing maps for natural disasters, forecasting battlefield casualties, anticipating terrorism, predicting gang-related crimes or ‘predictive policing’ (Webb, Sellar and Gulson, 2019[83]). In education, learning-analytics platforms capture data from children’s educational activities to track and algorithmically optimise their experience, predicting the future performance of the system and the student (Williamson, 2016[84]). In the Netherlands, predictive data dashboards make crime patterns visible. In this project, data supports a preventative approach to so-called systemically ‘subversive crime’ (ondermijning) by gaining insights into local and regional patterns within organised subversive crime, recognising possibilities and vulnerable sectors and areas, and recognising the lack of social resilience.

‘Thick data’ allows researchers and policymakers to reflect on contextual complexity: why people do what they do or why certain things happen in certain contexts. Thick data is typically qualitative data that is ethnographically collected or analysed observational data. The UK policy lab is an example of the use of both ‘big data’ and thick data.

Thick data and other interpretations can be collected in many way, including crowdsourcing and user-generated data. Some examples of crowdsourcing projects include Magic Box, Futurescaper, HunchWorks and Futurium.

Though in name a set of methods around deciding and acting based on known inputs, principles of multiple-criteria decision analysis can be used for selecting and weighting factors and issues of relevance in anticipatory exercises such as scenario-building (Montibeller, Gummer and Tumidei, 2006[86]).

Futures work must help understand and create the future. This requires techniques to understand change in the macro-environment, the operating environment and the organisation or community at hand. It also requires a shared vision for the organisation or community. The Institute for Alternative Futures evolved "aspirational futures" as techniques to enable this. While it shares similarities with other approaches, it emphasizes development of ‘likely’, ‘challenging’ and ‘visionary’ scenarios (Bezold, 2009[87]).

An important question for policymakers beyond what futures are plausible is which of them are acceptable to citizens: Which options should be explored? Anticipatory tools encourage people to ask whether and how innovations and moral principles interact and shape one another over time (Stahl and Coeckelbergh, 2016[88]). However, it is difficult to get people to envision morally challenging situations in the future (Lehoux, Miller and Williams-Jones, 2020[89]). Existing moral values influence which innovations are more likely to become embedded in society, while some innovations may challenge values of the public good or ethical acceptability (Boenink, Lente and Moors, 2016[90]). Morality and ethics can be included in the anticipatory innovation toolbox through ethical impact assessment (e.g., Wright (Wright, 2011[91])), ethical technology assessment (Kiran, Oudshoorn and Verbeek, 2015[92]), anticipatory technology ethics (Brey, 2012[93]), techno-ethical scenarios approaches (Swierstra, Stemerding and Boenink, 2009[94]) and moral plausibility frameworks (Lucivero, 2016[95]). In this way, emphasis on public engagement and process inclusivity can align science and technology with societal goals and needs (OECD, 2020[27]).

There are more formative frameworks that set boundaries for future paths, such as the ‘responsible research and innovation’ (RRI) framework (Box 8.8). While it may seem obvious that innovation processes should respond to fundamental values in society, the implementation of ICT technologies demonstrates multiple cases of negligence in the right to privacy and data protection (von Schomberg, 2013[98]). Responsible anticipatory innovation would understand the dynamics influencing developments and avoid harmful consequences (Stilgoe, Owen and Macnaghten, 2013[99]).

Many of the tools connected to creativity and imagination encourage speculation. They usually blend approaches between design, fiction and social dreaming (Dunne and Raby, 2013[102]) to bring forth a new ‘discursive space’. These derive from a new generation of design thinking that is trans-disciplinary, commons-oriented, collaborative and participatory in nature (Ramos, 2017[43]). Some of these approaches present practitioners with living narrative context (stage craft, actors and scripts) that provoke people to question different types of futures (Ramos, 2017[43]).

There are many ways in which signals of emerging change can be developed through exploration and speculation. Examples include:

  • Futures wheels – the futures wheel is a method of structured speculation about potential future developments and their consequences. It involves thinking of a small number of consequences for a given signal of change, then second-order consequences (the consequences of the consequences), third-order consequences, and so on. The consequences are discussed sequentially in cascade fashion, hence the term “cascade diagram” for a largely identical method (Policy Horizons Canada, 2016[61]).

  • Cross-impacting – cross-impacting involves considering what might be the combined effect of two signals co-occurring, recognizing the complexity that makes future developments difficult to analyse and predict in isolation (Fuerth and Faber, 2012[34]).

  • Road-mapping and technology assessment – predicting the path of new technologies is notoriously difficult, whether the context is government regulation, venture capital or academic research. Various approaches to technology forecasting, assessment and foresight exist to this end (Figure 8.7).

Technology assessment takes a stance on normative matters such as democracy (Grunwald, 2019[104]). Though the discipline has existed for decades, a range of new anticipatory and upstream governance approaches have emerged. These can help explore, deliberate and steer the consequences of innovation at an early stage (OECD, 2020[27]). They allow responses to public concerns or changing circumstances along the development trajectory. From an industry perspective, upstream approaches can incorporate public values and concerns, potentially mitigating public backlash against technology (OECD, 2020[27]). In OECD countries, frameworks for upstream governance have entered policy debates, e.g. in the context of the Anticipatory Governance pillar within the U.S. Nanotechnology Initiative (OECD, 2012[105]).

Future-oriented technology assessment is a particular form, focused less on risk assessment and more on innovation governance with regard to emerging technologies (Nazarko, 2017[103]). Use of various forms of futures thinking, such as scenarios, visions, and alternative perspectives is becoming common in technology assessment (Lösch et al., 2019[106]).

The future is so full of possibilities that it is impossible for individuals or teams to make sense of even a small proportion of them adequately and determine which actions to take. Framing narrows the possibilities to focus on the most significant potential developments (Mukherjee, Ramirez and Cuthbertson, 2020[107]). Framing is often done unconsciously in the form of stories or ‘mental models’ (Wack, 1985[4]). However when unquestioned and untested, mental models of the future can make omissions and distortions that can be remedied in the present by ‘reframing’ (Ramírez and Wilkinson, 2016[6]). Several methods exist to challenge and reshape mental models of the future; the best-known of these is scenario planning.

Scenarios are alternative futures (usually in sets of three or four for comparison) in the form of snapshots or stories giving an image of a future context. They are deliberately fictional and should not be interpreted as predictions or recommendations (OECD, 2020[24]). Therefore, scenarios themselves have no intrinsic value; it is the process of creating and using them in the context of strategic dialogue that makes them worthwhile (Gordon and Glenn, 2018[108]). They are constructed for learning and taking action in the present (Ramírez and Wilkinson, 2016[6]). This is achieved by generating, testing and reframing ideas about how the future might be. Scenarios are more than just an extrapolation of a given trend, though they take trends into account by describing how the future might look if one or more trends were to continue (or change).

Scenarios are particularly widespread in the practice of strategic foresight, and multiple schools of thought exist on how they should be developed and used. Scenarios used at the OECD have three characteristics (Polchar, 2021[109]):

  • Exploration scenarios offer a safe space for experts to disagree and challenge each other’s assumptions. Knowing that a scenario is not a future expected to occur frees discussion. Scenario dialogue discourages trying to be ‘right’ about what will happen. This is partly why scenarios come in sets. Exploring the future allows letting go of deeply held assumptions that can be unfounded and harmful if left unchallenged.

  • Context scenarios encourage consideration of how the future will feel; how it would be if the paradigms that govern thinking change. Whereas forecasting and predictions focus on individual metrics or events, scenarios allow consider the future as a whole, “the big picture”.

  • Narrative – scenarios can become powerful tools for creation and shared understanding about how to act within an organisation. By creating a set of experiences about the future with their own characters, events and logic, good scenario narratives are memorable enough to become part of an organisation’s way of thinking.

Practitioners disagree on how actionable scenario analysis should be for policy guidance. For some, community learning is more important in framing assumptions and creating expectations of future action (Talberg et al., 2018[110]).

The concept of megatrends can be traced back to Naisbitt (1984[112]), since when a plethora of organisations and publications adopted the term and variants of the associated analysis. Megatrends are broad, gradual shifts in multiple domains such as politics, economics, society, technology and the environment. They reflect more than one trend and are deeply rooted, representing a trajectory from the past into the future. Megatrends include climate change, rising inequalities, digitalisation and shifting geopolitical power.

Megatrends are not the only way to think about the future and cannot capture all relevant information or developments to make decisions. Disruptions and short-term shifts are important too. Megatrends also do not consider how shifting values could change the level of importance of given issues.

A means of contextualising futures knowledge and framing it in terms of desired changes, Causal Layered Analysis examines the complex relationships between litany, causes, structure, discourse, metaphor, and myth (Inayatullah, 2004[113]).

Anticipatory innovation only makes sense if it leads to action: tools are needed to operationalise the futures that are explored. Moving beyond simple experimentation and innovation methodologies requires approaches such as the Anticipatory Action Learning, which merges participatory approaches and futures studies and opens “a transformational space of inquiry, the long-term and planetary future, with the everyday and embodied world of relating and acting” (Ramos, 2017, p. 830[43]). This includes anticipatory action learning (Inayatullah, 2006[114]), Inayatullah’s (2008[115]) Six Pillars approach, and José Ramos’ (2017[43]) Futures Action Model.

In the context of anticipatory innovation governance, futures tools must work in combination with innovation tools and methods so that different possibilities can be worked on in practice. Futures toolkits have existed in the private sector for some time (see e.g., Nodklapp’s Actionable Futures Toolkit), and started to enter the public sector. An example is the Policy Horizons Canada method (Policy Horizons Canada, 2016[61]) or the Futures Toolkit launched by the UK’s Government Office for Science in 2017.

However, anticipatory innovation needs a stronger linkage than some of these tools describe, with more direct routes from anticipation to experimentation and innovation. Anticipatory innovation needs different types of tools: ones that enhance creativity and imagination (e.g., visioning, historical analogy, gaming); promissory tools and methods giving licence to explore options (scenarios, course of action analysis); operational tools that allow testing in practice (e.g., adaption pathways); and epistemic tools that generalise and validate knowledge (e.g., developmental evaluation).

One mechanism through which anticipation delivers value is the possibility to rehearse future situations that have not materialised (Bason, 2017[116]; Ramírez and Wilkinson, 2016[6]). Policy stress-testing is used to see how well a set of policies or objectives stand up to a range of conditions. These objectives might already exist – in which case the exercise tests whether they are robust enough to deliver in a range of future market conditions – or stress-testing might be part of developing new objectives (Government Office for Science, 2017[117]).

Emanating from increased interest in design thinking and practice in policy (which lies beyond the scope of the present analysis) is the possibility to link anticipation and innovation through prototyping (Bason, 2016[118]). In the policy domain, a prototype is a small-scale concept of how to advance an objective in a way that can be implemented quickly, tested and learned from. Prototypes enable a policy to be viewed and experienced as material reality (Howard, Senova and Melles, 2015[119]; Ollenburg, 2019[120]). Prototypes have an advantage in anticipatory innovation because they can be implemented in advance of being needed (Buchanan, 2018[121]).

OPSI deploys prototyping in interventions using strategic foresight and anticipatory innovation to spur ideas about what could be possible and desirable to respond to future challenges and opportunities (Box 8.6).

How the types of data connected to anticipatory innovation get used influences the outcomes. Organisations must monitor their interaction with their social and ecological context as much as looking at external signals (Pickering, 2018[122]). Furthermore, how signals should be evaluated is not usually clear. Data processing is reflexive when assessment processes involve competing methods and perspectives, dialogue and deliberation (Dryzek and Pickering, 2017[123]).

Organisations use anticipatory processes to implement so-called early-warning systems that incorporate surveillance of emerging disruptions into the day-to-day decision-making of an organisation so that urgency does not displace importance in the prioritisation of issues (van der Steen, Scherpenisse and van Twist, 2018[124]). Such systems require close and ongoing integration, and interface between strategic framing to determine indicators; data gathering and analysis; identification of emerging disruptions; and communication to decision-makers (Schwarz, 2005[125]).

The benefits of anticipatory activity are notoriously difficult to evaluate (Grim, 2009[126]). We do not have access to alternate realities in which multiple futures explored and acted upon through foresight can be compared. The benefits of foresight are often indirect, difficult to measure, rarely attributable solely to foresight interventions, and sometimes in the form of an absence of something negative rather than the presence of something positive (OECD, 2021[16]).

In these circumstances, practitioners argue that the clearest observable impacts of strategic foresight are in changes in the mental models of leaders, observable through the reframing of their dialogue (Flowers, 2003[22]; Wack, 1985[4]). Some scholars attempt to operationalise the concept of strategic reframing and measure the extent to which problems have been recast, irrespective of the solutions generated (Mukherjee et al., 2020). Others take the view that outcomes are too difficult to judge in terms of foresight impact, and advocate evaluating processes and structures in terms of a ‘foresight maturity model’ for organisations (Grim, 2009[126]).

Work by OPSI connects strategic foresight to anticipatory innovation through prototyping and innovation. Initiatives derived from such exercises offer the opportunity to test and evaluate effectiveness in terms of predefined objectives, and potentially to select control cases for comparison.

Individuals and institutions have an inherent sense of time and future, though it is usually unconscious and unstructured (Zimbardo and Boyd, 2008[127]). Anticipation and foresight are based on skills and capacities to make futures thinking conscious and deliberate (Polchar, 2021[109]). Governments must be able to draw on the intellectual capacity and skills to implement strategic foresight thinking and apply it to policymaking (OECD, 2019[47]). The needed skills and capacities are subject-matter expertise, imagination, appreciation of emergence and complexity, leadership and implementation, and communication.

Familiarity with the factors and dynamics of a particular subject are essential as part of horizon scanning and framing of a set of future possibilities. However expertise in a particular subject does not correlate with prescience, and adequate explanations in hindsight are unreliable for prediction (Kahneman, 2011[128]). It is possible to train and improve the ability to forecast, but the skills associated not the same as those needed to develop subject matter expertise (Tetlock and Gardner, 2015[15]). Subject matter expertise must be complemented with anticipatory capacity (Box 8.10).

There are many approaches to spurring creativity and imagination around which types of futures might be – some more normative and some less. Scenario methods can break through communication barriers between participants. They bring a variety of perspectives and development paths for the future (Gordon and Glenn, 2018[108]; OECD, 2020[24]). However, these methods risk a lack of action-orientation as they usually have limited capacity to identify practical strategies towards futures. Techniques for anticipatory innovation include De Bono’s (1999[129]) Six Thinking Hats Methodology.

Futures literacy is the “capacity to explore the potential of the present to give rise to the future” (Miller, 2007[130]). Foresight capacity is about skills, knowledge and tools, and an attitudinal willingness to engage with the abstract nature of the future (OECD, 2019[47]). It requires appreciation that future developments are not always a linear extrapolation of trends and that complex interactions engender deep uncertainty that make decisions irreducibly difficult (Marchau et al., 2019[131]), and that conventional tools and instincts are often inadequate (OECD, 2017[132]).

This requires individuals trained in the theory of multiple futures and their development, and the use of foresight methods such as horizon scanning and scenario planning. Foresight capacity further requires the skills to design and facilitate strategic dialogue with the purpose of using foresight to look ahead, challenge assumptions, and draw out implications for policy and strategy (SOIF, 2021[11]). Examples of specialist or semi-specialist roles might include (OECD, 2019[47]):

  • foresight specialists to develop multiple plausible futures

  • foresight process specialists to design and facilitate foresight interventions, processes and strategic dialogue

  • policy researchers and programme managers to gather signals of change

  • policy analysts to design and test policy proposals against multiple futures.

Several strategies are used to build these capacities in government: hiring public servants with expertise in strategic foresight or other fields emphasising systems thinking, complexity and the tools to recognise uncertainty; providing introductory and specialised training courses to public servants; and providing learning-by-doing opportunities for public servants at all levels to engage in foresight processes within and beyond their workplaces. In Singapore, a common practice is to place officials in central foresight institutions to gain experience, then deploy them across government to propagate their expertise. The Centre for Strategic Futures serves a training and consultancy role to support foresight mainstreaming across government (Centre for Strategic Futures, 2021[133]).

Any new approach or organisational change relies on sources of legitimacy and support to initiate action and provide the resources and changes to established practices that sustain the effort (Moore, 1995[56]; Moore and Khagram, 2004[136]). Sustained demand for foresight from senior levels of government and the public service can ensure that institutional changes, resource allocations and practices are put in place to enable the anticipatory innovation required for sound policies. Sustained high-level demand for anticipatory innovation can counterbalance the tendency to limit the work of considering and preparing for the future because of immediate daily pressures or reporting requirements (Fuerth and Faber, 2012[34]).

High-level support can also provide the permission for anticipatory innovation to explore provocative issues that challenge existing assumptions and policies. Adequate demand ensures that foresight is not carried out as an academic exercise, but rather informs the priorities and decision-making processes of government (OECD, 2019[47]).

Sources of high-level demand for strategic foresight in government include legislative commitments, parliamentary oversight, political commitments, championing by senior civil servants, institutionalised demand through committees or other bodies, or a combination of these (OECD, 2019[47]).

Anticipatory knowledge is inevitably abstract and does not lend itself to actions in the present. It is often a challenge to communicate futures knowledge in a way that balances comprehensiveness and comprehension (Hajkowicz et al., 2018[137]). Indeed, it can be effective to deliberately leave analysis incomplete to leave space for decision-makers to add their own agency and actions to the narrative (Flowers, 2003[22]). This agency and instrumental capacity are key to the empathy and empowerment leaders need so their actions require anticipation and have value (Wilkinson and Flowers, 2018[138]).

Challenges to implementing anticipatory innovation in policy and public service practice range from translating foresight insights into action (the impact gap) and avoiding issues that are not imminent, to the Collingridge and innovator’s dilemmas.

The deployment of an anticipatory system should generate useful and relevant foresight, and implementation based on the findings. But governments face barriers to the development and use of strategic foresight in the context of a culture dominated by forecast-based policy planning. As a result, high-quality policy-driven foresight is underused. There are numerous foresight publications from before 2008 about financial crises (Cooper, 2015[141]), from before 2016 about rising populism (Ministerie van Defensie, 2010[142]) and from before 2019 about pandemics that start in animals and bring the whole world to a standstill (U.S. National Intelligence Council, 2017[143]). Similarly, there are many more foresight works that imagine events that have not come to pass but can be used to help organisations prepare. The issue in all these cases is not a lack of useful foresight but a lack of use of foresight.

This challenge is dubbed the ‘impact gap’ (Polchar, 2021[109]). Some of the most common barriers to the use of foresight in the public sector include those relating to authorising environments and to individual agency. To overcome these, it is essential to implement strategic foresight in a context of anticipatory innovation that translates foresight and futures insights into action.

Practitioners of anticipatory innovation note that just as trends, like design thinking, systems approaches, and innovation labs, are valuable, the forces that keep public-sector organisations in check as fundamentally stability-seeking, bureaucratic institutions are strong (Bason, 2016[118]).

Governments are generally known as risk-averse, rules-driven, based on stable structures and predictable decision-making (Brown and Osborne, 2013[144]). This is known as “minimal squawk behaviour” (Leaver, 2009[145]) – avoiding 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 governments are not able to act quickly in the face of new challenges or be proactive in the face of new opportunities. Governments’ response to transformative change is generally reactive at best. Governments are pushed from the position of ‘wait and see’ when hazards (moral, ethical or physical) materialise, or they are called upon to resolve issues between industry incumbents and new business models.

To find a different path, practitioners advocate exploring new approaches, organisational arrangements and leadership, and thinking about how to change the way the public sector operates.

The task is to lower barriers that pertain to inadequacies in the anticipatory innovation governance mechanisms explored earlier. These barriers take multiple forms (OECD, 2021[16]):

  • cultural barriers – unreasonable expectations that experts will “reveal the future”

  • corporate barriers – official ‘zombie’ futures that are believed implicitly without justification (Polchar, 2020[10]), insufficient support from leadership, insufficient learning loops

  • communication barriers – experts of different disciplines lacking common language (Kekkonen, 2015[146])

  • competency barriers – limited futures literacy

  • cognitive barriers and biases (Schirrmeister, Göhring and Warnke, 2020[147]) – time silos, difficulty recognising complexity, avoidance of uncertainty, groupthink, recency and availability bias, lack or ‘poverty’ of imagination about the future (Miller, 2018[3]).

A barrier to adopting anticipatory innovation in governments is the Collingridge Dilemma, a concept described by Professor David Collingridge in 1980. Collingridge posited that there is always a trade-off between 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. According to Collingridge (1980[148]), "When change is easy, the need for it cannot be foreseen; when the need for change is apparent, change has become expensive, difficult and time consuming".

From a governance perspective, this means that the point at which a new technology can most easily be regulated is also the point at which the least is known about the potential impact of that technology or the act of regulating that technology.2 Hence, governments are in a double-bind situation. For example, governments currently aim to steer the application of facial recognition technologies and algorithmic biases before these technologies are ubiquitous and create new challenges at a societal scale (Grace, 2019[149]). However, governments lack insights into how these technologies will impact their structures and activities.

Furthermore, the benefits and risks of new technologies do not fall on the same people. Governments must consider what kind of public values are important to persevere in the change process and how public value is affected through technological change (OECD, 2017).

Increased complexity and uncertainty per se do not disqualify traditional policy tools. However, they are unreliable when it is unclear which direction technological innovation will take. New tools are needed, such as normative codes of conduct, regulatory sandboxes and real-time technology assessments (Stilgoe, Owen and Macnaghten, 2013[99]). Regulatory sandboxes were adopted in Australia, Hong Kong, Malaysia, Singapore, the United Arab Emirates and the United Kingdom, especially for financial technology (FinTech). This requires governments to operationalise anticipatory insights and increase upstream engagement with technology developers and lead users.

Another challenge to anticipatory innovation is that organisations naturally concentrate on the immediate needs of their customer base and the presently feasible technological developments. This conundrum is coined the “Innovator’s Dilemma” (Christensen, 1997[150]). The theory argues that investing in disruptive technologies is not a rational financial decision in established firms since disruptive technologies initially interest the least-profitable user base in the market. In the context of government, there is no incentive to invest in disruptive technologies that will initially benefit a minority of citizens or public servants.

As such, essential organisational dynamics devalue disruption and anticipatory innovation activity:

  • Resistance to change – there can be resistance to radical innovations inside organisations if they conflict with established practices. Usually innovations that create new areas of engagement are more easily adopted.

  • Strategic intent – current activities invariably have bigger financial portfolios than initiatives in development. In organisational terms, they outweigh new, smaller, radical projects.

  • User focus – feedback from users and customers can steer organisations away from new products and services as they initially underperform in comparison to established products and services.

Consequently, there are many examples, from Kodak to Nokia, of market leaders who lost their positions in the market due to the abovementioned dynamics (Bouwman et al., 2014[151]; Lucas and Goh, 2009[152]).

As an emerging field, anticipatory innovation and the related discipline of strategic foresight contain numerous incomplete or unexplored avenues of further research. These include the limitations of strategic foresight, measuring value, interactions with other disciplines and barriers to implementation.

Strategic foresight on its own does not solve problems, produce strategies or guarantee success. It enlarges but does not complete the picture of considerations for decision-makers and cannot force them to take notice. Strategic foresight requires a long, sustained effort to bear fruit and rarely generates breakthroughs in a single exercise. Futures studies can be implemented in unhelpful ways. For example, some foresight processes undertake excessive gathering and pondering over signals of future change, which comes at the expense of relevant selection, purpose-driven sense-making, strategic reframing, and generation of ideas for concrete innovation and experimentation (OECD, 2021). There is a need to develop mechanisms to translate foresight knowledge into policy making processes.

There is no consensus on whether and how to measure the impact of anticipatory innovation in public governance. The fundamental problem is the lack of a control case for policy makers evaluate what could have happened or did not happen as a result of anticipatory innovation. Other points of discussion include whether it is possible to form ex-ante expectations of something whose true value can only be realised ex-post, and whether impacts should concern direct and concrete changes to policy action or can include changes in mind-set and framing of issues. Attempts are lacking to systematically assess the value of foresight and anticipatory innovation in theory and practice.

As one of the four facets of public sector innovation, anticipatory innovation is related to the other three in terms of its directedness and the level of uncertainty it addresses. How anticipatory innovation interacts with other forms of innovation within an overall innovation portfolio has not yet been researched.

Likewise, while strategic foresight has clear and extensive relevance for anticipatory innovation, there are ways it can add value to the other three facets of public sector innovation. For example the aspirational nature of mission-oriented innovation could be guided by the visioning and back-casting methods of strategic foresight. Adaptive innovation could be informed by the signal-reading of horizon scanning. Study will be required to reveal all the possible connections and evaluate them.

Furthermore, there is intellectual cross-pollination between anticipatory innovation, strategic foresight and other disciplines, such as resilience (Goldstein, 2012[153]; Kaufman, 2012[38]; Linkov et al., 2018[154]; OECD, 2016[155]), systems thinking (Hodgson and Midgley, 2014[156]; Hynes, Lees and Müller, 2020[157]), behavioural insights (Ciriolo, 2019[158]) and risk assessment (Linstone, 1985[78]; OECD, 2019[47]). While distinct in their scope and practice, each of these exhibits synergies and trade-offs with the others. A comprehensive overview is lacking of how these activities combine and interact in the overall portfolio of knowledge-based public governance.

A final field of incomplete research relates to implementing and embedding of anticipatory innovation within governments and the broader civic and societal ecosystems they inhabit. The barriers outlined above are one avenue for this research, particularly relating to the institutional, cognitive and communication conditions needed for anticipatory innovation to deliver value (OECD, 2019[47]; Schirrmeister, Göhring and Warnke, 2020[147]; SOIF, 2021[11]). This is relevant where policymakers prioritise issues according to subjective or intuitive perceptions of urgency rather than critical analysis of when action is needed – making the exploration of issues not already on the agenda a hard ‘sell’ (Fuerth and Faber, 2012[34]; OECD, 2021[16]).

As anticipatory innovation feeds into and off of imagination and creativity (Miller, 2018[3]), anticipatory innovation demands that governments encourage creativity. Extensive research for harnessing the creative capacity of the human mind (Baird et al., 2012[159]; Schooler et al., 2015[160]; Zedelius and Schooler, 2015[161]) exists but has not been tested in the public sector context.


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← 1. See, for example, the OECD (2019) brochure “Regulatory effectiveness in the era of digitalisation”: https://www.oecd.org/gov/regulatory-policy/Regulatory-effectiveness-in-the-era-of-digitalisation.pdf).

← 2. See OECD work on technology governance and regulating emerging technologies in the OECD Principles on AI and the Recommendation on Responsible Innovation in Neurotechnology (OECD, 2018[162]).

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