copy the linklink copied!1. Envisioning the future
This chapter outlines the need for governments to think about the future in new ways – in a more dynamic, anticipatory format. Systems approaches are not value free: they are influenced by how we think and conceptualise the future. Thus, special attention should be paid to how policymakers operationalise the future: what time-frames are talked about, what kind of narratives (e.g. smart governance) are used to outline different scenarios. All of the above guides systems transformation on the ground and is an important starting point to discuss what kind of change is possible or plausible.
copy the linklink copied!Re-imagining the purpose of public sector systems
Currently, many governments are unprepared for their role in the future. For decades, there has been an obsession with efficiency and accountability of public sector organisations. These are important concepts for public sector organisations, but they should be grounded in collective accountability to the purpose of the system. Yet, little attention has been paid to the civic or collective purpose of public organisations, while the landscape of citizens’ needs and socio-economic challenges has been thoroughly changing. Hence, it is not surprising that public organisations are losing relevance in this day and age: either they do not respond to the needs of citizens (thus increasing citizen dissatisfaction with the public sector) or citizens surpass government all together to create more suitable ways to respond to very contextual problems. Public sector systems need to adapt to the new realities they inhabit, and to the future that is fast approaching. Case 5 on the Seoul 50+ Policy in Chapter 4 describes these complex challenges in a super aging society context where new, yet undefined services to a shifting demographic have to be created, while at the same time redefining work and work-life balance itself.
Systems approaches are generally good at capturing current failings within the public sector. Strategies have been developed to create time and space – among other important factors – to look at the interlinked nature of public problems and analyse the functioning of systems based on the public purpose they need to deliver (OECD, 2017). Yet, they are more difficult to deploy when there are various possible purposes, when problems have not been framed in the right way and there is no top-level consensus about the direction to take. At the same time, most public sector challenges involve this kind of ambiguity today. Changes happen very fast and a new consensus has not yet emerged regarding the fundamental purpose of government intervention. OECD (2017b) has previously directed attention to the need of a systemic approach in its Digital Governance Review of Norway, where the pressing challenges of digital change need to be addressed in a holistic manner. Part of the ‘system approach’ is to address dynamic complexity (a behaviour that arises from the interaction of a system’s agents over time) (Sweeney and Sterman, 2000; Arnold and Wade 2015). Should or can this be a ‘neutral’, objective exercise or should the governments impose a certain set of values on long-term systems change? Hence, when it comes to systems thinking, the key question is as follows: Amid an unclear future, how can we engage in a dynamic and changing context? To expand the understanding of this, first, the role of the future should be clarified.
copy the linklink copied!What is the future and can it be codified?
For the most part, we tend to think of the future as a condition that we will be subject to at some point, usually somehow exceeding the bounds of the present. Its force as an idea stems from its inevitability and is manifest in our economic system’s fundamental organisation around lending and credit. Its power also emanates from the certainty or uncertainty about what realities will be experienced once the future arrives. Prediction is one way in which we access the future. Forecasting is a more deliberative approach to thinking about the future, but because of the nature of the future, it is no less subject to distortion by preference and bias. Whatever the case, people tend to place their hopes and fears in the future, making it a highly contentious and subjective space. The future purportedly drives decision making, but more often than not, these decisions have more to do with the present than the future.
The future is rarely treated as a critical tool for driving progress. Futures, when agreed upon by a deliberative body are typically a product of consensus building. For instance, the future of the education system in the US context is to prepare students for “career and college readiness” (Malin, Bragg, and Hackmann, 2017). But for some, these objectives are not compatible. If college readiness is to be the future of education, then it must be designed accordingly with logics that operate at cross purposes to preparing students to enter into careers after high school. So in order to work toward a shared outcome, a consensus-building process is characterised as the future. But here, the future is not an idea that charts pathways forward, as much as it is indicative of an agreement about priorities for dealing with present challenges.
A variant of this kind of future-use by collections of stakeholders is the vision for the future set at a specific point in time or time horizon, such as the millennium, 2030 or 2050. By time-bounding the future, shared priorities become more action-oriented, but it is still a vehicle for communicating the outcome of a consensus-building process. It also implies that the future can, in fact, be easily predicted and is thus prone to lock-in.
In the worst case scenario, these ‘crystal ball’ processes become co-opted by special interests such as a company wanting their product to enable their preferred future to frame the future around their products and services or a political interest group that wants to see their narrowly drawn issue drive public decision making. The advent of the processed food industry in the United States and elsewhere is an apt example. The future, according to the food industry was lack of hunger and better health, the way to get there was through the application of science and industry to food systems. While industrial food systems may have helped address hunger, it certainly has not led to better health by almost any measure.
Futures, when articulated by an individual (a futurist, foresight specialists or forecaster), tend to contain elements that align well with the agenda of the individual. For instance, the prediction that technology will converge towards singularity, or that technology and automation will lead to a future without work are both ideologically-bent scenarios and obscure what will likely be a much more diverse and complicated situation once the future finally “arrives”. In this case, the stories told about the future (i.e., a stock analyst’s baseline scenario) are used to suggest actions that should be taken now, which are often skewed by a hidden agenda, such as constraining, or widening the use of artificial intelligence. Objectively, however, these futures are no more likely to play out than any other vision of the future.
However, in both cases of group and individual future forecasts, the articulation of a future does enable a debate about what should be, and what actions might be taken to move towards that future. But that debate must be taken up and somehow acted upon by a broad coalition in order to be meaningful.
More often, the future is formed when the future is grafted onto the present, with policy makers seeing the future as a more complex present shaped by various challenges. This is a form of adoption rather than rigorously thinking through and defining what kind of future scenarios are possible for a given context. For instance, many governments in middle income and lower middle income countries in Asia identify Singapore as the model of their futures. But Singapore arose out of unique circumstances that cannot be replicated. And even if they were, a Singaporean model realised in Sri Lanka may result in a model of urbanisation that is destructive to Sri Lankan culture and society. The future of Sri Lanka arguably is more likely to be found in Sri Lanka than it is in Singapore.
As Riel Miller, head of foresight for UNESCO, has described it: most future thinking suffers from a “poverty of imagination”1 which can be interpreted in a number of ways. One, as suggested above is that there are ultimately interests other than ideas about the future driving future thinking. Another interpretation is the recognition that the convergence of factors that will ultimately decide the future are exceedingly complex and beyond the imagination of most forecasters. And yet another interpretation is that it is fear or hope about the future that overrides an ability to think about what the future might be objectively.
So how can the future be used in a more effective way? What would it mean to describe the future according to a discipline or practice, rather than using it as a way to talk about the present? And how should a “better” future inform decision making? How can we use it in a systems change process?
Another clue comes from Riel Miller (2012) who called for a future-based approach to sense-making, entitling his seminal research paper on the subject “The Discipline of Anticipation”. For him, this discipline must have three core components: an anticipatory systems perspective that encompasses both animate and inanimate anticipation, allowing one to distinguish between different models that work within the anticipatory systems. Three distinct dimensions can be drawn out for anticipating the future with different methods appropriate for each: contingent (bad or good), optimised (goals, rules and resources) and novelty (the challenge of reframing). Miller (2015) argues for futures literacy knowledge labs: a learning-by-doing process that uses collective intelligence to discover and invent specific knowledge.
The Observatory of Public Sector Innovation introduced its innovation facets model defining mission-oriented, enhancement, adaptive and anticipatory innovation at the end of 2018. It outlined the need to steward different innovation practices strategically differently. Since then, OPSI has started to work on the specific mechanisms of anticipatory innovation and its governance. As part of the work the following definitions have emerged:
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Anticipation is the process of creating knowledge – no matter how tentative or qualified – about the different possible futures. This may include, but is not limited to developing not just scenarios of technological alternatives, but techno-moral (value-based) scenarios of the future. This is the traditional role of foresight (Normann 2014).
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Anticipatory governance is the process of acting on a variety of inputs to manage emerging knowledge-based technologies and socio-economic developments while such management is still possible (Guston, 2014). This may involve inputs from a variety of governance functions (foresight, engagement, policymaking, funding, regulation etc.) in a coordinated manner.
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Anticipatory innovation governance is a broad-based capacity to actively explore options as part of broader anticipatory governance, with a particular aim of spurring on innovations (novel to the context, implemented and value shifting products, services and processes) connected to uncertain futures in the hopes of shaping the former through the innovative practice (OPSI, 2019).
Thus, anticipatory innovation governance allows us to explore what types of practice would help to shape the future in a complex environment. There is a need for foresight to feed into a working system where the signals given by foresight workers are actually being followed up in time and taken into implementation.
To this end the Observatory of Public Sector Innovation has designed a program on Anticipatory Innovation Governance where the Observatory together with its partners will co-design and implement in practice anticipatory innovations to test the mechanisms needed in government to create space and mechanisms of anticipatory governance. Thus, the bottlenecks and barriers to anticipatory innovation will be uncovered from bottom up from the innovation/implementation side.
Source: OPSI 2019: Guston 2014; Nordmann, 2014.
According to Miller, the purpose of this ‘discipline’ is to find a way to make the future as clear as possible. This is perhaps the most salient point for this discussion: in order to imagine a future free of confirmation bias and other distortions, a rigorous process must be undertaken. Miller argues that those in charge of predicting the future should be as disciplined as possible. In the meantime however, describing the future would benefit from greater understanding of the problems inherent in thinking about the future and a process or set of processes that treat the future as an explicit target of inquiry. This can be described by an anticipatory innovation process (see Box 1.1. above).
No matter how the future is envisaged, one of the most difficult challenges in forecasting the future is identifying what kind of factors will make a specific future more likely to happen. At the scale of the individual, or even the family, a pathway towards a future is fairly easy to define. For instance, an individual can attain an advanced degree to improve their job prospects or a family can establish a savings plan in order to buy a home. At the other end of the spectrum, supranational organisations and federations struggle to spell out the specific details of what the future holds. Large, encompassing concepts such as human rights or the right to pursue happiness describe a future perfect state, but leave out the specific means and methods of achieving it. The more systemic the issue of the future becomes, the more difficult it is to tackle. The more complex and cascading the potential effects of future scenarios, the more paralysed policymakers become.
Yet, public sector organisations exist at the ideal nexus of scale and decision making authority for both envisioning and working towards a specified future. One source of evidence for this is the proliferation of “Vision 2030” documents produced by governments across the globe. It seems national governments and cities have identified 2030 as an important horizon for transformative ambitions and have invested time and resources in developing “vision” documents. This has invariably been influenced by the 2030 Agenda and the SDG. Collectively, they appear to have realised that critical transformations are needed to adapt to a future that is increasingly uncertain due to rising sea levels, diversifying or aging populations and declining public budgets. Rather than make policy prescriptions the first step towards that transformation, cities have asked their constituents to imagine what the future could be.
Many of these vision documents can be found at the local level. Why are cities pioneering a public discussion about the future while, with few exceptions, states are not as dominantly present in the field? Local governments are more apt to manage the future in a more effective way for the following reasons: scale, shared fate (and therefore shared priorities), better responsiveness to challenges, infrastructure resources to get constituents involved and their unrivalled ability to transform priorities into action. Given some of the advantages cities have over other governance structures in anticipating and acting on the future, how should an urban future be designed? The answers will be as varied as the cities themselves, but some common principles apply.
First, the ideas that have traditionally underpinned a political vision have revolved around a solution that is already available or at least known. If street violence is the problem and greater safety is the vision, policy prescriptions could include more robust policing and better economic opportunities for individuals in violence-plagued communities. But in an ambiguous and uncertain world, it would be ineffective to implement a predetermined solution based on an incomplete understanding of the specific details of the challenge at hand. This is because today’s challenges, even for the most tightly-knit city or local municipality, are systemic in nature. They defy easy solutions and are resistant to any singular effort at change.
Second, a future vision should not only outline what an alternate future will look like, but also describe in detail the principles that will govern how that future unfolds (the values that bind it and will be re-examined throughout the process) and what kind of systems change it requires. By describing the governing principles, stakeholders from across the spectrum of interests can set out individualised action plans for themselves. Shaping a response around governing principles (as opposed to an overarching vision) improves the chances efforts will be made synchronously, creating a total effect that is greater than the sum of its parts. The vision will be more focused and broader in scope if it is grounded by governing principles. Without this, a future vision is just that: another idea about the future that is untethered from reality
Third, future visions should anticipate a transitional strategy that spurs the shift from the status quo to a new desired state. For instance, carbon neutrality is a strong and ultimately achievable future vision for cities. But for democratic, capitalistic systems the transformative change cannot be achieved by decree. The city will need to transition gradually toward carbon neutrality. Once a consensus has been reached that the city needs to go carbon neutral, the challenge becomes tactical in nature. Should the city grow its renewable energy portfolio? Establish or expand a district heating and cooling system? Change building codes to permit only high performance construction? Should it begin working at the scale of a single building, a city block or a district? Should it prioritise behaviour change such as incentivising people to work close to where they live? Should it encourage households and businesses to make energy choices that result in a lower carbon footprint? Transitional strategies tied to future visions are necessary because a complex system such as a city cannot simply be turned off, redesigned and turned back on.
Finally, a future vision should be immune to the influence of any single actor. If climate change is driving a future, this should happen by default as the city will need to change across every domain in order to adapt. So-called winners and losers will be found in every stratum of society. For much of the rapid urbanisation in the 20th century, it was not citizens but private interests that drove the development of cities. Automobile manufacturers wanted single-owner cars to dominate the city so they painted street trolleys as old fashioned, advocating for their removal, while pushing for greater suburbanisation, which is synonymous with increasing car ownership. Whereas before they were headquartered in cities, companies moved out to the suburbs as the ‘corporate campus’ became more popular. This left downtowns to pick up the pieces of a fractured economy without the resources to invest in fostering a better future. In addition, the recent urban renaissance has brought citizens back into cities and companies have followed. However, new private players are now working to defend corporate interests (e.g., luxury housing) in this transformed urban landscape. Now that cities are on the front lines of climate change and in a global competition for talent, perhaps future visions can be better balanced between all interests, both public and private. Nevertheless, cities will need to embrace an ownership role in how they think about, and plan for, a specific future. Creating the city of the future can no longer be outsourced to those with power and influence. Many of the vision 2030 documents offered by public sector organisations suggest this transition towards shared power is already underway.
copy the linklink copied!The limits of a ‘smart’ future
One of the future visions that has recently taken centre stage is so-called ‘smart governance’. The smart governance and smart city narrative in general tends to be normative and technology-driven where data (open data, big data) – and ‘datafication’ – and digital entrepreneurship play an outsized role. OECD (2018) has created a framework to guide governments’ efforts to enhance the availability, accessibility and re-use of open government data in its Open Government Report. Yet, technology in many cases supersedes the human. While not negative, it creates many opportunities for governments to explore the problems they are facing and create solutions that users will benefit from in this digital age (see Box 1.1 below). IoT or urban control rooms are becoming an everyday reality (Townsend, 2013; Cardullo and Kitchin, 2016) – there is even talk of real-time ‘digital twins’ of cities and even national governments where reality is mirrored by a virtual construct. This makes it possible to test and simulate various situations that may happen in real life. An abundance of success stories and best practices have emerged from these developments, especially in the ‘smart city’ literature. The concept itself got its start from urban labelling, rather than scientific research into a phenomenon. Unsurprisingly, we know little about the realities or long-term effects of smart cities or smart governance on citizens. For instance, it is unclear whether ‘smart cities’ really empower their citizens (see for example, Lember et al. 2017; Lember, Brandsen and Tõnurist, 2019). Hence, it is difficult to separate fact from fiction, hype from reality. The OECD has tried, in part, to address this recently through the E-leaders Handbook on Governance (forthcoming) and consider its three facets, underlying the importance to consider the contextual factors, the institutional models and the policy levers for sound digital government.
In 2016, CBS (Statistics Netherlands) started to develop Urban Data Centres (UDC) by matching national data and data expertise with smart, data-driven city needs. The Dutch city of Eindhoven jointly developed an UDC with CBS. The centres are built around the city’s interests and needs – smaller towns and big metropolises invariably have different interests – by combining national survey, administrative and big data with city data. After launching the first CBS Urban Data Centre, seven additional centres were established in just one year. The concept can also be adapted to and implemented in developing countries, and can contribute to reaching SDGs.
Source: Statistics Netherlands.
As such, CBS uses its considerable data expertise to address real-life urban problems and inform the municipality’s policies and strategies. As a federal body, CBS works to support cities by providing them with the expertise they often lack. The resulting jointly-developed Urban Data Centres help to better understand the current situation and address problems in a city. The centres create data-driven input that is specific to the relevant location and problem, which informs local policy-making and leads to transformative change. They are not only applicable in bigger cities, but can also be applied in smaller localities:
“In the beginning we thought the urban data centres would only work in big cities, because they have the research budget. So, we heard from smaller cities that it was a nice idea, but we cannot reallocate money. Yet through practical cases we have learnt to look at the wider range of the city budget, e.g., the smarter use of social welfare to create better data for that.”
Thus, this is not only about getting data onto the digital platform. It is also about defining issues and problems as a society and finding ways to make them visible and actionable for local governments. This is challenging work as municipalities and statistics offices have not previously worked in this way. Consequently, practical challenges have emerged around bringing together staff from cities (who are often not data experts) and CBS employees (who do not have experience in reading city programs and budgets). Furthermore, most municipalities (both large and small) have limited resources in terms of both internal research funding and staff. Limited resources have led cities to get people from across a range of different city departments to work together on problems. Working in a new way is often met with resistance. As a result, strong leadership is needed when it comes to developing data platforms that match the city’s needs. Buy-in must be obtained from senior leaders, the broader organisational culture, and the mishmash of people who work together on a daily basis. Together with its partners, CBS needs to be aware and balance all of these factors to make the initiative work in practice.
Source: OECD interviews; CBS presentation.
It does not help that there is no common understanding or agreement of what smart governance or smart city really is (Box 1.2). Both are terms used to mean technology-centred governance approaches or are applied as part of regional smart specialisation strategies (e.g., OECD 2014). Therefore, what it will actually look like in practice is relatively abstract. There are many frameworks that propose typologies of the smart city and depending on the field of analysis it can be either intelligent buildings (architects), sensors and data analytics tools (computer scientists) or smart grids and zero energy buildings (energy engineers), etc. The environment and infrastructure such as transport, water and waste management (i.e., “hard domains”) have been addressed to a much greater extent than more human-centred sectors (i.e., soft domains) such as health, education or social welfare.
The European Commission’s smart city definition is fairly simple: “A smart city is a place where the traditional networks and services are made more efficient with the use of digital and telecommunication technologies, for the benefit of its inhabitants and businesses.” Yet many other definitions exist:
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Similar broad-based definitions concentrating on the effects of ICT exist: “The application of information and communications technology (ICT) with their effects on human capital/education, social and relational capital, and environmental issues is often indicated by the notion of smart city” (Lombardi et al. 2012).
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It is popular to define smart cities by sector of activity: Smart economy, smart mobility, smart governance, smart environment, smart living, and smart people (Giffinger et al. 2007). These domains can be both ‘soft’ (education, culture, policy innovations, social inclusion and government (Albino et al. 2015)) or ‘hard’ (buildings, energy grids, natural resources, water management, waste management, mobility and logistics (Neirotti et al. 2014).
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It can be framed around sustainability: “A city is smart when investments in human and social capital and traditional (transport) and modern (ICT) communication infrastructure fuel sustainable economic growth and a high quality of life, with a wise management of natural resources, through participatory governance.”(Caragliu 2011) or community: “A smart community – a community which makes a conscious decision to aggressively deploy technology as a catalyst to solving its social and business needs – will undoubtedly focus on building its high-speed broadband infrastructures, but the real opportunity is in rebuilding and renewing a sense of place, and in the process a sense of civic pride” (Eger 2009).
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See a fuller review in Albino et al. 2015.
Source: https://ec.europa.eu/digital-single-market/en/smart-cities
Smart city and smart governance discourse seems to take complex effects – e.g., democratic processes, empowerment, value, inclusiveness, accessibility, accountability, transparency and openness – as self-explanatory and something inherently part of smart city initiatives. For example, in practice, each urbanite, through mobile phones (or even just enabling others to access their data), can indeed be a mobile sensor and report on their city experience (Ratti 2010). Yet, is that acceptable to us or do we value privacy more? The future (or the practices we associate with the future but which are already applied in practice) is already here; yet, it is largely unexplored. As such, cities are adopting a plurality of strategies to contend with these challenges (see OECD, 2019) that are not always considerate of all the value trade-offs that are happening. To illustrate the variety of ways public organisations can respond to these challenges Cases 2 and 6 in Chapter 4 highlight different approaches to a new type of smart governance: one driven by user-centricity and need (Boston), the other by technological opportunities and ways to make the city a test-bed for new solutions (City of Antwerp).
References
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