Bennett Conference 2020
On the 16th of April 2020 the Bennett Institute was due to hold its third annual conference, bringing participants together to discuss major challenges for public policy-makers, including the future of government, the digital state, how to achieve wellbeing, and how to develop a more sustainable economic model.
Whilst we were not sadly able to hold this event, in the context of the on-going Covid-19 crisis, we are delighted to publish reflections on these issues from some of the leading contributors we were looking forward to hosting: Dr Anna Alexandrova, Dr Tanya Filer, Professor Geoff Mulgan, and Dame Fiona Reynolds.
They have approached these themes in the context of the many questions and challenges thrown up by the Covid-19 pandemic.
At the Bennett Institute, we are also amending the ways we work and the focus of our policy and research programmes to ensure that we are able to address these issues; we are committed to providing evidence-based analyses and insights that can inform the work of governments and policy-makers.
You can read some of our researchers’ work on some of the major challenges of the current crisis, and access their thoughts on the kinds of economy, social settlement and governance that will be needed in its aftermath, in our major new blog series.
In the meantime we wish all of you the very best in this very difficult time, and look forward to the moment when we can carry on the conversation in person.
Michael Kenny and Diane Coyle
Government as a brain: how can governments better understand, think, create and remember, and avoid the traps of collective stupidity
By Geoff Mulgan
People need nature and nature needs people
By Dame Fiona Reynolds
Shaping the State in a Digital Age
By Dr Tanya Filer
Beware of ugly compromises: well-being science in the pursuit of policy influence
By Dr Anna Alexandrova
Government as a brain: how can governments better understand, think, create and remember, and avoid the traps of collective stupidity
By Geoff Mulgan
The last few have months confirmed just how much we rely on governments’ capacity to think and act, in conditions of great uncertainty and at scary speed. As lessons are learned from the very uneven performance of different governments around the world, the topic I had planned to talk about at the Bennett Conference this year – the question of how government thinks and could think better, and the need for a more developed ‘cognitive political science’ – feels even more relevant now.
The idea of government as a brain is very old. The earliest symbol of governance, the Sumerian symbol of the ruler, was the rod and line – a symbol of a surveyor not a warrior: analytical, cognitive and controlling. And for millennia government was often imagined as a head, with the ruler’s head put on coins. But what is the neuroscience of the state? What does it remember or forget? How does it create? Does it suffer delusions and fantasies? What makes some governments amplify the intelligence of their society while others do the opposite?
Government as intelligence
To answer these questions, a good starting point is to look at how governments observe, analyse, predict, remember and create. As I’ll show, one mark of effective government is that it has a high quantity and quality of feedback of all kinds which it uses to interpret the past and prepare for the future (while avoiding the risk of fetishizing just a few kinds of feedback, like poll ratings or stock price).
So we start, first, with observation as the foundation of government intelligence. From the Domesday book to the 18th century pioneers of statistics to intelligence agencies today this has been core to how governments work, and Covid-19 has reinforced how vital it is to observe the right things – with some governments incredibly energetic in testing and tracking, and others wholly lax. Taiwan’s ‘digital fences’ and Singapore’s use of mobile phone data to trace contacts show just how helpful the right kinds of observation can be (though we will increasingly need new arrangements to govern that data). They are also reminders of the changing patterns of both what government observes – with isolation a good example that in the recent past was not measured at all, but is now seen as hugely important to physical and mental health – and how they observe, for example now scraping the web or using images from microsatellites.
Second, models – a big lesson of recent neuroscience is that models precede observations; they determine what we see as well as what sense we make of it. So the mark of smart government is that it has many models, and constantly refines and improves them. Again Covid-19 has reemphasised this – and the risks of over-dependence on single models. As Scott Page put it in his brilliant book ‘Model Thinking’, for any complex phenomenon we need many models that can challenge each other, including in this case not just epidemiological ones but also economic and social ones too.
Third, creativity – Covid-19 has forced an extraordinary acceleration of innovation, from India turning 10,000 train carriages into ambulances; Bogota quintupling bike lanes, Rwanda introducing hand sanitisers in urban centres, and extraordinary efforts to accelerate production of ventilators or development of vaccines. In much of business innovation and R&D are serious activities, with big investments of money and time. Governments are far less systematic and even in the best cases innovation is piecemeal, ignored in public finance, and rarely linked to strategic priorities like solving the care crisis. The UAE remains the only government with a significant budget allocation for its own R&D. And while some public agencies – like NASA – have embraced collective intelligence, opening-up innovation tasks like designing space suits or writing code to anyone anywhere, no public agency in the UK has yet to do anything comparable.
Fourth, memory – one justification for a permanent civil service. Unfortunately, this is now quite badly organised thanks to the high turnover of civil servants and ministers and the lack of even a basic knowledge management system in Whitehall. Indeed, memory appears to have deteriorated since digitisation. The one positive is that there’s been more success in externalising memory, notably through the dozen or so What Works Centres that act as a common store of memory for fields like healthcare, policing, education and children’s social services.
Fifth, empathy. Robert MacNamara, once the boss of Ford, the Pentagon and the World Bank, commented that lack of empathy – the ability to feel and see from the perspective of another country or people, whether a villager in Vietnam or an unemployed 55 year old, often lay behind the biggest failings of governments. The rapid ditching of the government’s ‘herd immunity’ strategy was a classic example of an empathy failure – the unsustainability of this strategy would have quickly become apparent if government had done proper simulation exercises to prepare for pandemics, which had long been identified as the country’s number one risk.
More generally empathy is best cultivated by getting civil servants out and about, and it links to better observation. I’ve long advocated triangulation: that civil servants and advisers should never believe anything the system claims unless it has been checked first-hand, in a local school, business or GP’s surgery. Sitting in an office and relying on papers and emails, unfortunately, guarantees a distorted view of reality.
Finally, there is judgement and wisdom – which, as in other fields, draws on experience, ethical sensitivity and the ability to take a long view, and is often best cultivated by being quite explicit about what you expect to happen and then having open ways to assess why things turn out differently.
This very cursory description of the functional elements of intelligence (there’s much more in my book Big Mind) I hope has persuaded you that it is worthwhile to look at government as a system for thought and action, and that this provides a diagnostic framework as well as a way for thinking about how new data tools or AI can be applied.
There are of course many other aspects to government cognition – the types of learning that are vital; the ability to repel and confront misinformation or to resist groupthink, particularly at the centre of government where there are the biggest risks of error thanks to lack of sleep, the delusions of spin and sycophancy.
Here I just want to emphasise one important message of this description of the elements of intelligence which is that – as with the human brain – they work best if linked together, in real time.
This kind of ‘intelligence assembly’ which we take for granted in our personal lives, will, I believe, be critical for the future of government. At present it is almost non-existent. Yet Covid-19 has forced faster action to create such assemblies than anything in my lifetime, and in time we will need comparable arrangements for other much slower burn crises like climate change or ageing – linking observation, models, memory and creativity in coherent ways that help the system as a whole to think.
I became interested in government’s thought processes through a career that included spells in local government and the European Commission, and then in No 10. A first attempt to apply these ideas came with setting up the Social Exclusion Unit, drawing on work done at Demos on how government could operate more holistically. To develop better solutions for poverty and social exclusion we created mixed teams of insiders and outsiders, working on very clearly defined problems, and using cross-cutting policy, budgets, data and implementation teams to achieve targets like cutting rough sleeping by two thirds, halving teenage pregnancy, and reducing the gap between poorest neighbourhoods and the average (perhaps surprisingly, all of these targets were met).
When I took over the Performance and Innovation Unit and evolved it into the Strategy Unit we provided a similar capacity for problem solving and longer term thinking across all areas of policy, again linking the aspects of intelligence mentioned earlier. There was, to be explicit use of evidence (published at the beginning of projects); gathering of data; open processes; mixed teams of civil servants and outsiders, experts and generalists; and active use of methods like red teams and scenarios and formal modelling. One of our best innovations was anonymised exercises to get the true beliefs of the top ministers and civil servants to surface what they thought but couldn’t say. Another project helped put in place a comprehensive system for managing risk – including the framework that identified pandemics as the UK’s top risk – which, again, involved linking observations of possible threats, analysis, prediction, memory and judgement.
The unit grew to around 150, encouraged departments to build up their own strategy teams, and became part of a network of similar teams around the world, from France and Singapore to China, many of which I have worked with subsequently, and all of which exist to help government escape the tyranny of the urgent rather than the important.
This experience convinced me of the practical and theoretical task of better understanding government as a system of cognition, and one that is constantly in a struggle not to be deceived, diverted and deluded.
Apart from everything else that requires better metaphors. Government is often imagined as like the COBRA room – a command centre with a single brain and the Prime Minister at its heart – and there are a few moments when this is true. But a much more accurate picture of government accords with the contemporary view of neuroscience which sees the brain more as a network of sometimes cooperating and often competing modules, constantly jostling for primacy, rather than as a neat pyramid. Within governments multiple different ways of thinking combine and collide. They include the three types of thought that Aristotle described: techne – the practical knowledge on how to build a hospital in a week or distribute emergency loans, which is closest to engineering; episteme – the more analytic knowledge of macroeconomics, or evidence on what works, or the modelling of pandemics, closer to what we call science; and phronesis – the practical wisdom that comes from experience, and hopefully includes an ethical sense and an understanding of contexts.
They also include the kinds of knowledge owned by different professions with government:
- Statistical knowledge (for example of unemployment rises in the crisis)
- Policy knowledge (for example, on what works in stimulus packages)
- Scientific knowledge (for example, antibody testing)
- Professional knowledge (for example, on treatment options)
- Public opinion (for example quantitative poll data and qualitative data)
- Practitioner views and insights (for example, police experience in handling breaches of the new rules)
- Political knowledge (for example, on when parliament might revolt)
- Economic knowledge (for example, on which sectors are likely to contract most)
- ‘Classic’ intelligence (for example on how global organised crime might be exploiting the crisis)
The crucial point is that there is no metatheory to tell which you should pay most attention to at which time. Faced by an epidemic it’s wise to lean on your scientists – but they can’t tell you whether it will turn out to be socially acceptable to ban human contact, close the schools or arrest people for leaving exclusion zones, and in most cases the different types of knowledge will point in conflicting directions.
So any government badly needs the integrative intelligence of phronesis, or wisdom. That means being fluent in many frameworks and models and having the experience and judgement to apply the right ones, or combine them, to fit the context.
Yet this kind of wisdom is scarce at the best of times. Leaders with backgrounds in law, journalism or economics, may have little sense of neighbouring disciplines and the same may be true of civil servants. A century ago the need for some integrative skills justified creating a degree in PPE at Oxford – Politics, Philosophy and Economics – which was certainly a step forward.
But its elements are poorly suited to today’s problems like pandemics or climate change or regulation of global financial markets, which instead require familiarity with systems thinking and complexity, science and psychology.
So, for government to work well as a brain, we need not just the infrastructures and systems described above, but also people prepared with a new curriculum that’s better suited to the tasks they’ll face, a curriculum that helps them use, question and synthesise multiple kinds of insight, models and knowledge, and that’s given them a feel for how complex and dynamic systems behave in practice.
A few conclusions
This should be a golden age for government as a brain, given the data and technologies governments have at their disposal. Around the world are many impressive attempts to mobilise collective intelligence of all kinds – like vTaiwan involving millions in decision making, the widespread use of experiments in countries like Canada and Finland, the open data movement, the evidence movement, and the creation of large scale societal platforms like India’s Aadhaar project.
Each in their different ways reinforces my message here – that good government depends on the quantity and quality of feedback of all kinds.
But these more systematic approaches to cognition remain the exceptions. While business has dramatically shifted so that the best capitalised are the ones founded on data and knowledge – there has been no comparable shift in the public sector. More common is either misplaced faith in the intuitions of a single leader; or variants of the British tradition of the ‘clever chaps’ theory of government, that if only you could put a few smart people in No 10, any problem could be solved. Meanwhile the digital teams in governments are much more focused on the admittedly useful work of applying the lessons of online services in business rather than addressing how government could think more intelligently.
Yet it’s not too hard to describe a more ideal kind of government: one that attends to the various elements of intelligence it needs, from observation to empathy to prediction; one that links them together in intelligence assemblies for all the tasks that matter most; and one that is led by officials and politicians with sufficient integrative skills that they can make sense of complex systems and the messages that come from very different ways of seeing and knowing.
Covid-19 has shown once again just how utterly dependent we are on the quality of government. Improving its ability to think, act and learn is probably the greatest meta-task of our times, not just vital for the pandemic but also for the big tasks ahead. Covid-19 has been a horrible shock and a horrible test for government. But one of its legacies may be to remind us that government is, and should be, quite like a brain, and that on balance it would be better to have one that isn’t trapped in delusions but is able to face-up to the world as it is, and then change it for the better.
Biography: Professor Geoff Mulgan
Geoff Mulgan CBE is Professor of Collective Intelligence, Public Policy and Social Innovation at University College London (UCL). Prior to that he was Chief Executive of Nesta, the UK’s innovation foundation (2011-2019). From 1997 to 2004 Geoff had roles in the UK government including director of the Government’s Strategy Unit and head of policy in the Prime Minister’s office. From 2004 to 2011 he was the first Chief Executive of The Young Foundation. He was the first director of the think-tank Demos; and has been a reporter on BBC TV and radio. He has a PhD in telecommunications and has been a visiting professor at London School of Economics (LSE) and Melbourne University, a senior visiting scholar at Harvard University. He has advised many governments around the world and is a World Economic Forum Schwab Fellow from 2019-22. Past books include ‘The Art of Public Strategy’ (Oxford University Press), ‘Good and Bad Power’ (Penguin), ‘The Locust and the Bee’ (Princeton University Press) and ‘Big Mind: how collective intelligence can change our world’ (Princeton University Press). His latest book – on social innovation – was published in late 2019 by Policy Press.
People need nature and nature needs people
By Dame Fiona Reynolds
If we ever questioned the dependence of the human spirit on nature, fresh air and beauty, the coronavirus crisis will surely have laid an end to it. The sight of people flooding to their local parks, to the Peak and Lake Districts, and to beaches in the first sunny weather of 2020 despite Government orders to stay at home, sent a clear message. We need fresh air, we need to get outdoors and close to nature, and we’ll do almost anything to achieve it.
I’ve seen this before, though on a lesser scale, in the Foot and Mouth epidemic of 2001. Then I was the very new Director-General of the National Trust, and six weeks after the start of my new job we were ordered by the Government to close all our houses, gardens, coast and countryside properties. It was a shock: economically, certainly (no income from visitors for several months) but also culturally, as the countryside was closed and people were confined to towns and cities. Like now, though, nature asserted itself and by mid-summer there were reports of rare orchids flowering where they hadn’t been seen for decades, and birds nesting in places usually too crowded for them to dare.
Lifting restrictions at the end of foot and mouth, though it felt challenging at the time, was much more straightforward than it will be at the end of the coronavirus pandemic, if such an end exists. But I remember it well. The joy with which people streamed out into the countryside as soon as it was finally open again could not have illustrated more clearly how much they needed nature and beauty. It caused us in the National Trust to think radically and deeply about the way we engaged with both nature and people, and to renew our focus on the founding purposes of the organisation. It also made us reappraise the centrality of natural and cultural capital to society today.
Natural and cultural capital are twenty-first century words for the resources – tangible and intangible – which underpin our very existence and enrich our lives. Natural capital embraces the soil, nature, water and air on which life on earth depends. Cultural capital is that which humans have created: tangible things like beautiful buildings, artwork and craft, and intangible attributes such as education, skills and knowledge. Many of these resources are both renewable and replenishable, and perhaps because of that we have taken them for granted. In particular, for centuries we have exploited nature, but around the 1950s we reached a tipping point when a combination of population pressure, increased consumption and the intensification of the use of natural resources overcame the ability of nature to replenish itself. Since then it has been in a state of serious decline.
The figures, produced annually in the UK in the State of Nature Reports produced by a consortium of NGOs, are alarming: 2019’s report states that of 8,418 species assessed, 15% are at risk of extinction. The decline in abundance of common species of butterflies (16% since 1976) and moths (25% since 1970) continues; and the total number of breeding birds in the UK fell by 44 million between 1967 and 2009. The report also charts improvements in the state of some rare birds and insect species, showing that when we focus on conservation we can turn things around. But the overall position remains one of stubborn decline.
This is not for the want of ambition. A UK Government White Paper of 2010 ‘The Natural Choice’ enshrined in public policy the ambition to leave nature in a better state than we inherited it. This ambition was rolled forward, with many others, into the Government’s ‘25 Year Plan to improve the Environment’ published in 2018. To read it is inspiring, but too little is happening. The focus of public policy remains on economic progress, with GDP its constant reference point. Yet GDP is a terrible way to judge success when it comes to natural and cultural capital, because it only takes into account flows of income and expenditure. GDP has no balance sheet, and yet it’s the balance sheet of nature we diminish with every percentage increase in GDP.
And yet. And yet. Perhaps now things will change. Perhaps now they have to change. We have – rightly and bravely – thrown the rule book away when it comes to dealing with the coronavirus pandemic. Through the crisis the Government is supporting people whose jobs have been lost, companies that would have gone under without its help, and small businesses that have been painstakingly built-up over a lifetime. It is prioritising our health above everything else.
As we begin to rebuild and to restore after the crisis, perhaps we can prioritise the health of our planet alongside that of our people. Without healthy ecosystems and diverse, productive natural resources we can’t survive in the long term. But the long term always seems so far away. Perhaps this is the moment to reprioritise, to establish new norms that ensure we can live within our environmental means, and to safeguard nature and stabilise the climate alongside measures to restore social and economic wellbeing to people whose lives have been transformed out of all recognition.
People’s hearts may well be with us in such a move. As Octavia Hill, one of the National Trust’s nineteenth century founders said: ‘the need of quiet, the need of air; the sight of sky and of things growing seem human needs, common to all’. This we have seen, time and time again in recent weeks. Our need for nature is visceral, often unarticulated. But it is real, profound, and ever more urgent.
Now is the time to recognise that we simply have to build natural and cultural capital into our plans for the future. There’s never been a better time to reframe our priorities and ensure that the Government’s ambitious plans for nature are finally realised. Because not only do we, as people, need nature; but nature needs us: to value and restore it, for its own and our collective benefit.
Biography: Dame Fiona Reynolds
Dame Fiona Reynolds DBE became Master of Emmanuel College, Cambridge in 2012. She came to the college after a long career in the voluntary sector, latterly as Director-General of the National Trust from 2001-2012. During her time as DG she made the Trust warmer and more welcoming, bringing the houses to life and raising the profile of the Trust’s work in the countryside.
Before the Trust, she was Director of the Women’s Unit in the Cabinet Office (1998-2000), Director of the Council for the Protection of Rural England (now Campaign to Protect Rural England) from 1987-98 and Secretary to the Council for National Parks (now Campaign to Protect National Parks) from 1980-87.
Fiona also holds a number of non-Executive roles. She is a Trustee of the Grosvenor Estate, a Non-Executive Director of Wessex Water, Chair of the Green Alliance, the International National Trusts Organisation, the Cathedrals Fabric Commission for England and Cambridge University’s Botanic Garden. A Member of the Advisory Panel for the Dasgupta Review of the Economics of Biodiversity. She was a Panel Member for the Glover Review of Protected Landscapes and Adviser to the Building Better Building Beautiful Commission.
Shaping the State in a Digital Age
By Dr Tanya Filer
When discussing the Bennett Institute conference with our panellists earlier this year, I suggested that we consider ‘the people, infrastructures, networks and technologies required to make the future state a success, for both citizens and administrators.’ I hoped that we might consider how these various organisations, individuals, and devices should relate to one another, and how much control democratic states should retain over those relationships.
Recent days have crystallised why these questions matter so much. Palantir, a data-mining company, is working with officials in at least twelve countries to determine where to deploy medical staff and supplies based on computerised predictions of the locations of Covid-19 outbreaks. In the UK, media sources have delicately described government and Palantir, alongside other tech companies, as ‘teaming up’ (The Economist here, and BBC here), as if unsure how, accurately, to label the relationship. The division of roles and responsibilities between state and corporation seems to be blurring.
Palantir holds major Covid-19 response contracts, but it is providing some services without charge, sidestepping the slow proceduralism —and accountability structures —of government technology procurement and contracting. Much has been made of the temporary powers accrued to the state in the current emergency in return for providing us with greater security. But state capacity for crisis management may at least partly now depend upon technology-sector volunteerism.
Governments need a multi-faceted relationship with the entrepreneurial sector. Yet, instinctively, this particular dynamic concerns me. How have we arrived at this kind of model, and as democratic governments face increasingly complex technology-related decisions, how can they retain relevant forms of control and oversight for digitalisation projects realised in their name?
In his now infamous wish-list of ‘assorted weirdos’ to staff Downing Street, Dominic Cummings pinpointed as desirable those who have ‘written software for a YC startup’, and explained that government needed the same skills as the ‘tech world or investing.’ The senior advisor made no explicit mention of government as its own organisational context, with distinctive constraints, needs, and standards of accountability.
Cummings caused a splash, but he was not the first to suggest a shake-up of the government machinery based on transferring not only knowledge but also cultural and organisational habits drawn from the technology sector. When the UK Government Digital Service (GDS), responsible for digitalising public service provision, was established in 2010, its very existence was pioneering. Yet its founders built it, in their own words, on a desire for cultural transference. As Bennett Institute research affiliate Antonio Weiss writes, ‘the ‘global influence and standing of Silicon Valley, which embodied a new, disruptive “start-up” culture of agile working methods, was foremost in thinking as the culture of GDS was set.’ The UK was not alone. When Barack Obama invited 50 CEOs to the US Forum on Government Modernization in 2010, he received praise for his ‘effort toward imitation’ of successful digital businesses.
Underpinning these early approaches was the idea that nimble young technology companies understood how to deliver public services better than governments themselves did. Bringing some of their ‘magic’ in house would reduce reliance on costly and inefficient outsourcing contracts with legacy providers. In the UK, the technologies that GDS introduced certainly brought about notable benefits.
Recent analysis argues that innovative technology companies may also more clearly understand the societal implications of computerising service provision. As I have documented previously, incumbent technology providers have long guided government technology decision-making, to mixed effect. Yet scholars have recently argued that vendors of artificial intelligence (AI) systems, in certain instances, know so much more than the public sector organisations to which they are selling that they should be considered as de facto ‘state actors.’ Writing in the Columbia Law Review, Kate Crawford and Jason Schultz cite examples of US state and local governments disclaiming ‘any knowledge or ability to understand, explain, or remedy problems created by AI systems that they have procured from third parties.’
Outsourcing algorithmic systems to third-party vendors, they find, may leave public officials unaware of the range of risks that those systems might pose. To bridge this accountability gap, Crawford and Schultz maintain that developers whose AI systems directly influence government decisions should be considered ‘state actors for purposes of constitutional liability.’
Yet it is not outsourcing, per se, that is the problem. A wider complex web of social practices, technologies, and undergirding logics can affect the outcomes of state technology projects or partnerships. In this case, the underlying issue of a lack of technical understanding within public sector organisations also matters.
These issues are not unique to the US. In the UK, data from the civil service fast-stream suggests the ‘Data, Digital and Technology’ track attracts fewer graduates per role than others, and fewer applicants are ultimately appointed. A 2019 inquiry into digital government by the Science and Technology Select Committee also noted high attrition rates of digital talent. A technology skills gap could leave governments lacking the critical capacity to set policy, ask apposite questions during procurement, or to provide oversight for technical projects realised in their name.
Treating vendors as de facto state actors, as Crawford and Schultz suggest, could plug the accountability gap in the short term. But playing hot potato with algorithmic knowledge and responsibility also carries risks. It may plaster over the gap in state capabilities, and disincentivise technology companies from prioritising public sector-oriented innovation. It could also leave them to make values-led decisions that will profoundly impact the lives of citizens.
Deciding on a digital identity
Over the coming years, democratic governments will have to produce or procure an increasingly complex set of technologies. They will also have to take the lead in deciding upon values to underpin them, and make sure that these are respected.
The language available to describe government technology practices today makes clear that many governments have not yet made these choices. ‘Digital government’ and ‘data-driven governance’ name types of technologies that governments engage, but they reveal little about the shape and character of that usage. Open data and digital engagement are becoming increasingly normalised state practices. Yet, as Amanda Clarke writes, the ‘unchecked assumption’ that ‘unequivocally ties digital government to strengthened democracy, is both empirically inaccurate and dangerous.’ Technology choices shape our democratic practices, but across democracies at least some of these choices continue to be made outside the state, or, inside it, are modelled on organisations whose primary responsibility is to shareholders.
There are stand-out exceptions. Under its previous administration, Barcelona adopted a co-created digital identity focused on technological sovereignty and collective decision-making, linking innovation to the values of social and economic justice, solidarity, and gender equality. Singapore, markedly different in approach and political organisation, encourages innovation within pellucid, state-set parameters, premised on the belief that ‘otherwise either the country moves backwards or the private sector takes over the role of the government.’ In both cases, technology providers are not invited to disrupt governance models of their own accord but to implement clearly articulated visions of collective, digital futures that enjoy at least some support from citizens.
In the UK, new models and experiments are materialising. A way forward may be emerging in the current crisis from an unlikely source: the push for a virtual parliament. The idea is to engage technology to maintain friction in the system. A lot will ride on implementation, but this digitalisation is clearly intended to refresh, not upend, the commitment to oversight built into Westminster-style democracies. Disruption suggests tipping up even those aspects of bureaucratic practice that we value. In moving beyond the narrative and practice of digital state reform as primarily the replication or outsourcing of a version of ‘disruption’ in which commitment to efficiency has sometimes squeezed out other values, a virtual parliament could offer a quietly assertive recalibration of our understanding of digital governance.
Biography: Dr Tanya Filer
Dr. Tanya Filer leads the Digital State project at the Bennett Institute for Public Policy, University of Cambridge. Her work focuses on government innovation and digital government more broadly. Tanya teaches across several postgraduate programmes at Cambridge, and has taught at Yale and Oxford. She sits on the Steering Committee of the Cambridge Trust and Technology Initiative and Advisory Board of the Information Law and Policy Centre, University of London. Tanya is Founder and Director of StateUp. She was a UK-Israel British Council fellow in Cyber research in 2018 and has held fellowships at Harvard, Yale, and the Library of Congress.
Beware of ugly compromises: well-being science in the pursuit of policy influence
By Dr Anna Alexandrova
Could there be a more attractive goal for policy than the wellbeing of the people it serves? And could there be better grounds for judging whether a policy meets this goal than evidence, collected and analysed in accordance with the best available science? These undeniably worthwhile ideals motivate the recent rise of well-being science and evidence-based policy, and the union between the two. They are the reason the Office of National Statistics established a whole new system for collecting and representing data about the wellbeing of the UK; the reason why social scientists increasingly dedicate their efforts to understanding what drives these and other relevant data; and the reason institutes such as the What Works Centre for Wellbeing translate this research into usable guidelines for charities, local councils, and organisations. Outside the UK there are similar initiatives – subnational, national, and even international (supported by the UN, the WHO, and OECD to name a few) – all aiming to reorient academic research and public policy towards well-being. What lends credence to these pursuits is that they frame themselves in opposition to traditional economic methods of valuation and policy analysis focused on money, growth, and consumption. Well-being policy is supposed to be sensitive to a wider set of values and to peoples’ experiences rather than solely to their economic behaviour. This humanistic aspiration is exactly what keeps this project going, even as its fortunes in power fluctuate. Wellbeing enthusiasts may no longer be in the White House or at 10 Downing Street as they were ten years ago, but many civil servants, thinktank workers, and researchers are still keeping the flame burning.
I have watched wellbeing science and policy for the past twenty years as a supportive and generally optimistic outsider. As a philosopher I have the skills to evaluate these ideals on their own terms and I believe firmly that they are defensible. Both claims – that public policy should be sensitive to wellbeing and that a science of wellbeing is possible – are defensible on most plausible stances in political theory and in theory of knowledge. Some sceptics will disagree, but in my view there is nothing contradictory, incoherent, or deeply objectionable in the ideals themselves. As a philosopher of science, however, I tend to keep a close eye on how these ideals are implemented. I can follow exactly which research programs they motivate and how the findings of these programs get translated into practice. It is at this point that my faith and commitment are sometimes tested. Like many idealistic schemas, this one too can look much uglier in practice than in theory.
Enthusiasts of wellbeing science often quote a remarkable 1968 speech by Bobby Kennedy, in which he laments that GNP ‘measures everything except that which makes life worthwhile’. In quoting this passage as a motivation for their work, they make an implicit promise to do better, presumably to measure that which makes life worthwhile. But this is a tall order and a big promise to make. Today when social scientists claim to measure wellbeing, many of them in fact measure life satisfaction. Life satisfaction is measured by answers to this broad question: ‘How satisfied are you with your life as a whole?’. On a Likert scale this report generates a single number which then can be plugged into normal econometrics, thus monitoring the responsiveness of life satisfaction to changes in various other variables such as unemployment, housing, income, etc. The science of wellbeing then becomes an exercise of estimating an equation: on the left-hand side is the quantity represented by life satisfaction and on the right-hand side is the vector of demographic and socioeconomic variables plucked from available statistics. This equation can focus on nations or on more fine-grained groups; it can represent a snapshot in time or attempt to capture stable relations that hold over time.
Such a vision for wellbeing science characterises the work of many economists and some psychologists, most notably Andrew Clark, Richard Layard and their collaborators at the LSE Centre for Economic Performance. In their 2018 book The Origins of Happiness and the more recent publications with Paul Frijters, they take it further and argue a) that panel data allows them to estimate stable effects of housing, income, mental health and other variables on life satisfaction over time and b) that policy should use these coefficients to support only those programmes that maximise the sum total of life satisfaction (or its sum total among the most miserable).
There is a lot wrong with their proposal on simple ethical grounds. Following the tradition of classical utilitarianism, it completely disregards rights, entitlements, justice, and the democratic process. But I am more interested in the way that they have transformed the idealistic impulse to understand wellbeing into a methodology that is remarkably simplistic and shallow. In their approach, wellbeing is just one simple quantity based on one kind of self-report, its causes are represented by whatever variables official statistics contain, and the relations between these causes are presumed to be stable and universal, insensitive to the vagaries of culture and history. (They might disagree with the latter characterisation, but we develop it further in Singh and Alexandrova 2020). They adopt this methodology, I believe, consciously and with full awareness of its problems because its quantitative products can be easily sold to policy-makers in the Treasury and beyond. Wellbeing research on their approach looks recognisable to traditional economists and straightforwardly slots into existing forms of cost-benefit analysis.
This particular research programme is an extreme example of the trade-off between ideals and reality. I know of few wellbeing scientists who would subscribe to such a crude approach. Most recognise that wellbeing cannot be represented by a single quantum of life satisfaction, that its causes are more complex and multi-level, that the science of wellbeing needs to go beyond the estimation of econometric equations. Most users of this science would also agree that wellbeing policy should be participatory, deliberative, and sensitive to many values in addition to life satisfaction. But the example is still instructive. You may have admirable intentions to measure and understand ‘that which makes life worthwhile’, but you may end up making ugly compromises. Practicality may trump validity. Operationalisation may be driven by available statistics rather than by the best theory. Single indicators will stand in for complex states and processes. Epistemic and ethical corners will be cut for the sake of publications and the attention of policy-makers.
To some extent this is inevitable. We should expect compromises in science if these compromises arise out of the incompleteness of data, ethical constraints, and demands for coordination and communication among researchers. Impossibly high standards would mean that no work ever gets done. However, some compromises are driven by the desire to fit with existing structures of authority in academia and in governance. These prize simple numerical indicators and strong effect sizes above all. They give license to ignore cultural and historical complexity, they avoid mixed methods in favour of the familiar disciplinary matrices, and most importantly they replace hard democratic decision-making with technocratic ‘solutions’.
A responsible science of wellbeing may fail to know everything which makes life worthwhile. But we should watch carefully the price we pay for influence over policy-makers. All idealistic pursuits must meet reality, but they do not have to turn technocratic.
Clark, A. E., Flèche, S., Layard, R., Powdthavee, N., & Ward, G. (2019). The origins of happiness: the science of well-being over the life course. Princeton University Press.
Frijters, P., Clark, A., Krekel, C., & Layard, R. (n.d.). A happy choice: Wellbeing as the goal of government. Behavioural Public Policy, 1-40. doi:10.1017/bpp.2019.39
Singh, R., & Alexandrova, A. (2019). Happiness economics as technocracy. Behavioural Public Policy, 1-9.
Biography: Dr Anna Alexandrova
Anna Alexandrova is a Reader in Philosophy of Science at the University of Cambridge, and a Fellow of King’s College, Cambridge. She is the PI of the project Expertise Under Pressure, funded by the Humanities and Social Change International Foundation, and was previously one of the Directors of the project Limits of the Numerical, funded by the Independent Social Research Foundation. Before coming to Cambridge, Dr Alexandrova taught at University of Missouri St Louis and studied at University of California, San Diego. She has written on methodology of model-based science, role of rational choice theory in social science, authority of economics and more recently on measurement of well-being, happiness and quality of life. Her book A Philosophy for the Science of Well-being has appeared in 2017 with Oxford University Press.