A three step manifesto for a smarter, fairer economy

(United States GDP plotted against median household income from 1953 to present. Until about 1980, growth in the economy correlated to increases in household wealth. But from 1980 onwards as digital technology has transformed the economy, household income has remained flat despite continuing economic growth)

(United States GDP plotted against median household income from 1953 to present. Until about 1980, growth in the economy correlated to increases in household wealth. But from 1980 onwards as digital technology has transformed the economy, household income has remained flat despite continuing economic growth. From “The Second Machine Age“, by MIT economists Andy McAfee and Erik Brynjolfsson, summarised in this article.)

(Or, why technology created the economy that helped Donald Trump and Brexit to win, and why we have to fix it.)

The world has not just been thrown into crisis because the UK voted in June to leave the European Union, and because the USA has just elected a President whose campaign rhetoric promised to tear up the rulebook of international behaviour (that’s putting it politely; many have accused him of much worse) – including pulling out of the global climate accord that many believe is the bare minimum to save us from a global catastrophe.

Those two choices (neither of which I support, as you might have guessed) were made by people who feel that a crisis has been building for years or even decades, and that the traditional leaders of our political, media and economic institutions have either been ignoring it or, worse, are refusing to address it due to vested interests in the status quo.

That crisis – which is one of worklessness, disenfranchisement and inequality for an increasingly significant proportion of the world’s population – is real; and is evident in figures everywhere:

… and so on.

Brexit and Donald Trump are the wrong solutions to the wrong problems

Of course, leaving the EU won’t solve this crisis for the UK.

Take the supposed need to limit immigration, for example, one of the main reasons people in the UK voted to leave the EU.

The truth is that the UK needs migrants. Firstly, with no immigration, the UK’s birth rate would be much lower than that needed to maintain our current level of population. That means less young people working and paying taxes and more older people relying on state pensions and services. We wouldn’t be able to afford the public services we rely on.

Secondly, the people most likely to start new businesses that grow rapidly and create new jobs aren’t rich people who are offered tax cuts, they’re immigrants and their children. And of course, what will any country in the world, let alone the EU, demand in return for an open trade deal with the UK? Freedom of immigration.

So Brexit won’t fix this crisis, and whilst Donald Trump is showing some signs of moderating the extreme statements he made in his election campaign (like both the “Leave” and “Remain” sides of the abysmal UK Referendum campaign, he knew he was using populist nonsense to win votes, but wasn’t at all bothered by the dishonesty of it), neither will he.

[Update 29/01/17: I take it back: President Trump isn’t moderating his behaviour at all. What a disgrace.]

Whatever his claims to the contrary, Donald Trump’s tax plan will benefit the richest the most. Like most Republican politicians, he promotes policies that are criticised as “trickle-down” economics, in which wealth for all comes from providing tax cuts to rich people and large corporations so they can invest to create jobs.

But this approach does not stand up to scrutiny: history shows that – particularly in times of economic change –  jobs and growth for all require leadership, action and investment from public institutions – in other words they depend on the sensible use of taxation to redistribute the benefits of growth.

(Areas of relative wealth and deprivation in Birmingham as measured by the Indices of Multiple Deprivation. Birmingham, like many of the UK's Core Cities, has a ring of persistently deprived areas immediately outside the city centre, co-located with the highest concentration of transport infrastructure allowing traffic to flow in and out of the centre.)

(Areas of relative wealth and deprivation in Birmingham as measured by the Indices of Multiple Deprivation. Birmingham, like many of the UK’s Core Cities, has a ring of persistently deprived areas immediately outside the city centre, co-located with the highest concentration of transport infrastructure allowing traffic to flow in and out of the centre)

Similarly, scrapping America’s role in the Trans-Pacific Partnership trade deal is unlikely to bring back manufacturing jobs to the US economy at anything like the scale that some of those who voted for Donald Trump hope, and that he’s given the impression it will.

In fact, manufacturing jobs are already rising in the US as the need for agility in production in response to local market conditions outweighs the narrowing difference in manufacturing cost as the salaries of China’s workers have grown along with its economy.

However, the real challenge is that the skills required to secure and perform those jobs have changed: factory workers need increasingly technical skills to manage the robotic machinery that now performs most of the work.

Likewise, jobs in the US coal industry won’t return by changing the way the US trades with foreign countries. The American coal mined in some areas of the country has become an uncompetitive fuel compared to the American shale gas that is made accessible in other areas by the new technology of “fracking”. (I’m not in favour of fracking; I’d prefer we concentrate our resources developing genuinely low-carbon, renewable energy sources. My point is that Donald Trump’s policies won’t address the job dislocation it has caused).

So, if the UK’s choice to leave the EU and the USA’s choice to elect Donald Trump represent the wrong solutions to the wrong problems, what are the underlying problems that are creating a crisis? And how do we fix them?

The crisis begins in places that don’t work

When veteran BBC journalist John Humphreys travelled the UK to meet communities which have experienced a high degree of immigration, he found that immigration itself isn’t a problem. Rather, the rise in population  caused by immigration becomes a problem when it’s not accompanied by investment in local infrastructure, services and business support. Immigrants are the same as people everywhere: they want to work; they start businesses (and in fact, they’re more likely to do that well than those of us who live and work in the country where we’re born); and they do all the other things that make communities thrive.

But the degree to which people – whether they’re immigrants or not – are successful doing so depends on the quality of their local environment, services and economy. And the reality is that there are stark, place-based differences in the opportunity people are given to live a good life.

In UK cities, life expectancy between the poorest and richest parts of the same city varies by up to 28 years. Areas of low life expectancy typically suffer from “multiple deprivation“: poor health, low levels of employment, low income, high dependency on benefits, poor education, poor access to services … and so on. These issues tend to affect the same areas for decade after decade, and they occur in part because of the effects of the physical urban infrastructure around them.

eu-uk-regional-funding

(The UK’s less wealthy regions benefit enormously from EU investment; whilst it’s richer regions, made wealthy by London’s economy, are net contributors. The EU acts to redistribute UK taxes to the regions that need them most, in a way that the national Government in Westminster does not)

The failure to invest in local services and infrastructure to accommodate influxes of migrants isn’t the EU’s fault; it is caused by the failure of the UK national government to devolve spending power to the local authorities that understand local needs – local authorities in the UK control only 17% of local spending, as opposed to 55% on average across OECD countries.

Ironically, one of the crucial things the EU does (or did) with the UK’s £350 million per week contribution to its budget, a large share of which is paid for by taxes from London’s dominant share of the UK economy, is to give it back to support local infrastructure and projects which create jobs and improve communities. If the Remain campaign had done a better job of explaining the extent of this support, rather than trumpeting overblown scare stories about the national, London-centric economy from which many people feel they don’t benefit anyway, some of the regions most dependent on EU investment might not have voted to Leave.

Technology is exacerbating inequality

We should certainly try to improve urban infrastructure and services; and the “Smart City” movement argues for using digital technology to do so.

But ultimately, infrastructure and services simply support activity that is generated by the economy and by social activity, and the fundamental shift taking place today is not a technological shift that makes existing business models, services or infrastructure more effective. It is the transformation of economic and social interactions by new “platform” business models that exploit online transaction networks that couldn’t exist at all without the technologies we’ve become familiar with over the last decade.

Well known examples include:

  • Apple iTunes, exchanging music between producers and consumers
  • YouTube, exchanging video content between producers and consumers
  • Facebook, an online environment for social activity that has also become a platform for content, games, news, business and community activity
  • AirBnB – an online marketplace for peer-to-peer arrangement of accomodation
  • Über – an online marketplace for peer-to-peer arrangement of transport

… and so on. MIT economist Marshall Van Alstyne’s work shows that platform businesses are increasingly the most valuable and fastest growing in the world, across many sectors.

The last two examples in that list – AirBnB and Über – are particularly good examples of online marketplaces that create transactions that take place face-to-face in the real world; these business models are not purely digital as YouTube, for example, arguably is.

But whilst these new, technology-enabled business models can be extraordinarily successful – Airbnb has been valued at $30 billion only 8 years after it was founded, and Über recently secured investments that, 7 years after it was founded, valued the company at over $60 billion – many economists and social scientists believe that the impact of these new technology-enabled business models is contributing to increasing inequality and social disruption.

As Andy McAfee and Erik Bryjolfsson have explained in theory, and as a recent JP Morgan survey has demonstrated in fact (see graph and text in box below), as traditional businesses that provide permanent employment are replaced by online marketplaces that enable the exchange of casual labour and self-employed work, the share of economic growth that is captured by the owners of capital platforms – the owners and shareholders in companies like Amazon, Facebook and Über – is rising, and the share of economic growth that is distributed to people who provide labour – people who are paid for the work they do; by far the majority of us – is falling.

The impact of technology on the financial services sector is having a similar effect. Technology enables the industry to profit from the construction of increasingly complex derivative products that speculate on sub-second fluctuations in the value of stocks and other tradeable commodities, rather than by making investments in business growth. The effect again is to concentrate the wealth the industry creates into profits for a small number of rich investors rather than distributing it in businesses that more widely provide jobs and pay salaries.

Finally, this is also ultimately the reason why the various shifting forces affecting employment in traditional manufacturing industries – off-shoring, automation, re-shoring etc. – have not resulted in a belief that manufacturing industries are providing widespread opportunities for high quality employment and careers to the people and communities who enjoyed them in the past. Even whilst manufacturing activity grows in many developed countries, jobs in those industries require increasingly technical skills, at the same time that, once again, the majority of the profits are captured by a minority of shareholders rather than distributed to the workforce.

(Analysis by JP Morgan of 260,000 current account customers earnings from 30 sharing economy websites over 3 years. Customers using websites to sell labour do not increase their income; earnings from sharing economy websites simply replace earnings from other sources. Customers using sharing economy websites to exploit the value of capital assets they own, however, are able to increase their income. This evidence supports just one of the mechanisms explored by Andy McAfee and Erik Brynjolfsson through which it appears that the digital economy is contributing to increasing income inequality)

(Analysis by JP Morgan of 260,000 current account customers’ earnings from 30 sharing economy websites over 3 years. Customers using websites to sell labour do not increase their income; earnings from sharing economy websites simply replace earnings from other sources. Customers using sharing economy websites to exploit the value of capital assets they own, however, are able to increase their income. This evidence supports just one of the mechanisms explored by Andy McAfee and Erik Brynjolfsson through which it appears that the digital economy is contributing to increasing income inequality)

That is why inequality is rising across the world; and that is the ultimate cause of the sense of unfairness that led to the choice of people in the UK to leave the EU, and people in the USA to elect Donald Trump as their President.

I do not blame the companies at the heart of these developments for causing inequality – I do not believe that is their aim, and many of their leaders believe passionately that they are a force for good.

But the evidence is clear that their cumulative impact is to create a world that is becoming damagingly unequal, and the reason is straightforward. Our market economies reward businesses that maximise profit and shareholder return; and there is simply no direct link from those basic corporate responsibilities to wider social, economic and environmental outcomes.

There are certainly indirect links – successful businesses need customers with money to spend, and there are more of those when more people have jobs that pay good wages, for example. But technology is increasingly enabling phenomenally successful new business models that depend much less on those indirect links to work.

We’re about to make things worse

Finally, as has been frequently highlighted in the media recently, new developments in technology are likely to further exacerbate the challenges of worklessness and inequality.

After a few decades in which scientific and technology progress in Artifical Intelligence (AI) made relatively little impact on the wider world, in the last few years the exponential growth of data and the computer processing power to manipulate it have led to some striking accomplishments by “machine learning”, a particular type of AI technology.

Whilst Machine Learning works in a very different way to our own intelligence, and whilst the Artificial Intelligence experts I’ve spoken to believe that any technological equivalent to human intelligence is between 20 and 100 years away (if it ever comes at all), one thing that is obvious is that Machine Learning technologies have already started to automate jobs that previously required human knowledge. Some studies predict that nearly half of all jobs – including those in highly-skilled, highly-paid occupations such as medicine, the law and journalism- could be replaced over the next few decades.

(Population changes in Blackburn, Burnley and Preston from 1901-2001. In the early part of the century, all three cities grew, supported by successful manufacturing economies. But in the latter half, only Preston continued to grow as it transitioned successfully to a service economy. From Cities Outlook 1901 by Centre for Cities)


(Population changes in Blackburn, Burnley and Preston from 1901-2001. In the early part of the century, all three cities grew, supported by successful manufacturing economies. But in the latter half, only Preston continued to grow as it transitioned successfully to a service economy. If cities do not adapt to changes in the economy driven by technology, history shows that they fail. From “Cities Outlook 1901” by Centre for Cities)

Über is perhaps the clearest embodiment of these trends combined. Whilst several cities and countries have compelled the company to treat their drivers as employees and offer improved terms and conditions, their strategy is unapologetically to replace their drivers with autonomous vehicles anyway.

I’m personally convinced that what we’re experiencing through these changes – and what we’ve possibly been experiencing for 50 years or more – is properly understood to be an Information Revolution that will reshape our world every bit as significantly as the Industrial Revolution.

And history shows us we should take the economic and social consequences of that very seriously indeed.

In the last Century as automated equipment replaced factory workers, many cities in the UK such as Sunderland, Birmingham and Bradford, saw severe job losses, economic depression and social challenges as they failed to adapt from a manufacturing economy to new industries based on knowledge-working.

In this Century many knowledge-worker jobs will be automated too, and unless we knowingly and successfully manage this huge transition into an economy based on jobs we can’t yet predict, the social and economic consequences – the crisis that has already begun – will be just as bad, or perhaps even worse.

So if the problem is the lack of opportunity, what’s the answer?

If trickle-down economics doesn’t work, top-down public sector schemes of improvement won’t work either – they’ve been tried again and again without much improvement to those persistently, multiply-deprived areas:

“For three generations governments the world over have tried to order and control the evolution of cities through rigid, top-down action. They have failed. Masterplans lie unfulfilled, housing standards have declined, the environment is under threat and the urban poor have become poorer. Our cities are straining under the pressure of rapid population growth, rising inequality, inadequate infrastructure, and failing systems of urban planning, design and development.”

– from “The Radical Incrementalist” by Kelvin Campbell, summarised here.

One of the most forward-looking UK local authority Chief Executives said to me recently that the problem isn’t that a culture of dependency on benefits exists in deprived communities; it’s that a culture of doing things for and to people, rather than finding ways to support them succeeding for themselves, permeates local government.

This subset of findings from Sir Bob Kerslake’s report on Birmingham City Council reflects similar concerns:

  • “The council, members and officers, have too often failed to tackle difficult issues. They need to be more open about what the most important issues are and focus on addressing them;
  • Partnership working needs fixing. While there are some good partnerships, particularly operationally, many external partners feel the culture is dominant and over-controlling and that the council is complex, impenetrable and too narrowly focused on its own agenda;
  • The council needs to engage across the whole city, including the outer areas, and all the communities within it;
  • Regeneration must take place beyond the physical transformation of the city centre. There is a particularly urgent challenge in central and east Birmingham.”

One solution that’s being proposed to the challenges of inequality and the displacement of jobs by automation is the “Universal Basic Income” – an unconditional payment made by government to every citizen, regardless of income and employment status. The idea is that such a payment ensures a good enough standard of living for everyone, even if many people lose employment or see their salaries fall; or chose to work in less financially rewarding occupations that have strong social value – caring for others, for example. Several countries, including Finland, Canada and the Netherlands have already begun pilots of this idea.

I think it’s a terrible mistake for two reasons.

Firstly, the proposed level of income – about $1500 per month – isn’t at all sufficient to address the vast levels of inequality that our economy has created. Whilst it might allow a majority of people to live a basically comfortable life, why should we accept that a small elite should exist at such a phenomenally different level of technology-enabled wealth as to be reminiscent of a science fiction dystopia?

Andy McAfee and Erik Brynjofflsson best expressed the second problem with a Universal Basic Income by quoting Voltaire in “The Second Machine Age“:

“Work keeps at bay three great evils: boredom, vice, and need.”

A Universal Basic Income might address “need”, to a degree, but it will do nothing to address boredom and vice. Most people want to work because they want to be useful, they want their lives to make a difference and they want to feel fulfilled – this is the “self-actualisation” at the apex of Maslow’s Hierarchy of Needs. Surely enabling everyone to reach that condition should be our aspiration for society, not a subsidy that addresses only basic needs?

Our answer to these challenges should be an economy that properly rewards the application of effort, talent and courage to achieving the objectives that matter to us most; not one that rewards the amoral maximisation of profits for the owners of capital assets accompanied by a gesture of redistribution that’s just enough to prevent civil unrest.

(Maslow's

(Maslow’s “Hierarchy of Needs”)

Three questions that reveal the solution

There are three questions that I think define the way to answer these challenges in a way that neither the public, private nor third sectors have yet done.

The first is the question at the heart of the idea of a Smart City.

There are a million different definitions of a “Smart City”, but most of them are variations on the theme of “using digital technology to make cities better”. The most challenging part of that idea is not to do with how digital technology works, nor how it can be used in city systems; it is to do with how we pay for investments in technology to achieve outcomes that are social, economic and environmental – i.e. that don’t directly generate a financial return, which is usually why money is invested.

Of course, there are investment vehicles that translate achievement against social, economic or environmental objectives into a financial return – Social Impact Bonds and Climate Bonds, for example.

Using such vehicles to support the most interesting Smart City ideas can be challenging, however, due to the level of uncertainty in the outcomes that will be achieved. Many Smart City ideas provide people with information or services that allow them to make choices about the energy they use; how and when they travel; and the products and services they buy. The theory is that when given the option to improve their social, economic and environmental impact, people will chose to do so. But that’s only the theory; the extent to which people actually change their behaviour is notoriously unpredictable. That makes it very difficult to create an investment vehicle with a predictable level of return.

So the first key question that should be answered by any solution to the current crisis is:

  • QUESTION 1: How can we manage the risk of investing in technology to achieve uncertain social, economic or environmental aims such as improving educational attainment or social mobility in our most deprived areas?

The international Smart City community (of which I am a part) has so far utterly failed to answer that question. In the 20 years that the idea has been around, it simply hasn’t made a noticeable difference to economic opportunity, social mobility or resilience – if it had, I wouldn’t be writing this article about a crisis. Earlier this year, I described the examples of Smart City initiatives around the world that are finally starting to make an impact, and below I’ll describe some actions we can take to replicate them and drive them forward at scale.

The second question is inspired by the work of the architect and town planner Kelvin Campbell, whose “Smart Urbanism” is challenging the decades of orthodox thinking that has failed to improve those most deprived areas of our cities:

The solution lies in mobilising peoples’ latent creativity by harnessing the collective power of many small ideas and actions. This happens whenever people take control over the places they live in, adapting them to their needs and creating environments that are capable of adapting to future change. When many people do this, it adds up to a fundamental shift. This is what we call making Massive Small change.”

from “The Radical Incrementalist” by Kelvin Campbell, summarised here.

Kelvin’s concept of “Massive Small change” forms the second key question that defines the solution to our crisis:

  • QUESTION 2: What are the characteristics of urban environments and policy that give rise to massive amounts of small-scale innovation?

That’s one of the most thought-provoking and insightful questions I can think of. “Small-scale” innovation is what everybody does, every day, as we try to get by in life: fixing a leaky tap, helping our daughter with her maths homework, closing that next deal at work, losing another kilogram towards our weight target, becoming a trustee of a local charity … and so on.

For some people, what begin as small-scale innovations eventually amount to tremendously successful lives and careers. Mark Zuckerberg learned how to code, developed an online platform for friends to stay in touch with each other, and became the 6th richest man on the planet, worth approximately $40 billion. On the other hand, 15 million people around the world, including a vast number of children, show their resourcefulness by searching refuse dumps for re-usable objects.

Recent research on the platform economy by the not-for-profit PEW Research Centre confirms these vast gaps in opportunity; and most concerningly identifies clear biases based on race, class, wealth and gender.

The problem with small-scale innovation doesn’t lie in making it happen – it happens all the time. The problem lies in enabling it to have a bigger impact for those in the most challenging circumstances. Kelvin’s work has found ways to do that in the built environment; how do we translate those ideas into the digital economy?

The final question is more subtle:

  • QUESTION 3: How do we ensure that massive amounts of small-scale innovation create collective societal benefits, rather than lots of individual successes?

One way to explain what I mean by the difference between widespread individual success and societal success is in terms of resilience. Over the next 35 years, about 2 billion more people worldwide will acquire the level of wealth associated with the middle classes of developed economies. As a consequence, they are likely to dramatically increase their consumption of resources – eating more meat and less vegetables; buying cars; using more energy. Given that we are already consuming our planet’s resources at an unsustainable rate, such an increase in consumption could great an enormous global problem. So our concept of “success” should be collective as well as individual – it should result in us moderating our personal consumption in favour of a sustainable society.

One of the central tenets of economics for nearly 200 years, the “Tragedy of the Commons“, asserts that individual motives will always overwhelm societal motives and lead to the exhaustion of shared resources, unless those resoures are controlled by a system of private ownership or by government regulation – unless some people or organisations are able to own and control the use of resources by others. We’ll return to this subject shortly, and to its study in the field of Evolutionary Social Biology.

Calling out the failure of the free market: a Three Step Manifesto for Smart Community Economies

If we could answer those three questions, we’d have defined a digital economy in which individual citizens, businesses and communities everywhere would have the skills, opportunities and resources to create their own success on terms that matter to them; and in a way that was beneficial to us all.

That’s the only answer to our current crisis that makes sense to me. It’s not an answer that either Brexit or Donald Trump will help us to find.

So how do we find it?

(The White Horse Tavern in Greenwich Village, New York, one of the city’s oldest taverns. The rich urban life of the Village was described by one of the Taverns’ many famous patrons, the urbanist Jane Jacobs. Photo by Steve Minor).

I think the answers are at our fingertips. In one sense, they’re no more than “nudges” that influence what’s happening already; and they’re supported by robust research in technology, economics, social science, biology and urban design. They lay out a three step manifesto for successful community economies, enabled by technology and rooted in place.

But in another sense, this is a call for fundamental change. These “nudges” will only work if they are enacted as policies, regulations and laws by national and local governments. “Regulation” is a dirty word to the proponents of free markets; but free markets are failing us, and it’s time we admitted that, and shaped them to our needs.

A global-local economy

Globalisation is inevitable – and in many ways beneficial; but ironically the same technologies that enable it can also enable localism, and the two trends do not need to be mutually exclusive.

Many urban designers and environmental experts believe that the best path to a healthy, successful, sustainable and equitable future economy and society lies in a combination of medium density cities with a significant proportion of economic activity (from food to manufacturing to energy to re-use and recycling) based on local transactions supported by walking and cycling.

The same “platform” business models employed by Über, Airbnb and so on could in theory provide the new transaction infrastructure to stimulate and enable such economies. In fact, I believe that they are unique in their ability to do so. Examples already exist – “Borroclub“, for instance, whose platform business connects people who need tools to do jobs with near neighbours who own tools but aren’t using them at the time. A community that adopts Borroclub spends less money on tools; exchanges the money it does spend locally rather than paying it to importers; accomplishes more work using fewer resources; and undertakes fewer car journeys to out-of-town DIY stores.

This can only be accomplished using social digital technology that allows us to easily and cheaply share information with hundreds or thousands of neighbours about what we have and what we need. It could never have happened using telephones or the postal system – the communication technologies of the pre-internet age.

This could be a tremendously powerful way to address the crisis we are facing. Businesses using this model could create jobs, reinforce local social value, reduce the transport and environmental impact of economic transactions and promote the sustainable use of resources; all whilst tapping into the private sector investment that supports growing businesses.

But private sector businesses will only drive social outcomes at scale if we shape the markets they operate in to make that the most profitable business agenda to pursue. The fact that we haven’t shaped the market yet is why platform businesses are currently driving inequality.

There are three measures we could take to shape the market; and the best news is that the first one is already being taken.

1. Legislate to encourage and support social innovation with Open Data and Open Technology

The Director of one of the UK’s first incubators for technology start-up businesses recently told me that “20 years ago, the only way we could help someone to start a business was to help them write a better business plan in order to have a better chance of getting a bank loan. Today there are any number of ways to start a business, and lots of them don’t need you to have much money.”

Technologies such as smartphones, social media, cloud computing and open source software have made it possible to launch global businesses and initiatives almost for free, in return for little more than an investment of time and a willingness to learn new skills. Small-scale innovation has never before had access to such free and powerful tools.

(The inspirational Kilimo Salama scheme that uses

(The inspirational Kilimo Salama scheme that uses “appropriate technology” to make crop insurance affordable to subsistence farmers. Photo by Burness Communications)

These are all examples of what was originally described as “Intermediate Technology” by the economist Ernst Friedrich “Fritz” Schumacher in his influential work, “Small is Beautiful: Economics as if People Mattered“, and is now known as Appropriate Technology.

Schumacher’s views on technology were informed by his belief that our approach to economics should be transformed “as if people mattered”. He asked:

“What happens if we create economics not on the basis of maximising the production of goods and the ability to acquire and consume them – which ends up valuing automation and profit – but on the Buddhist definition of the purpose of work: “to give a man a chance to utilise and develop his faculties; to enable him to overcome his ego-centredness by joining with other people in a common task; and to bring forth the goods and services needed for a becoming existence.”

Schumacher pointed out that the most advanced technologies, to which we often look to create value and growth, are in fact only effective in the hands of those with the resources and skills required to use them – i.e. those who are already wealthy. Further, by emphasising efficiency, output and profit those technologies tend to further concentrate economic value in the hands of the wealthy – often specifically by reducing the employment of people with less advanced skills and roles.

His writing seems prescient now.

A perfect current example is the UK Government’s strategy to drive economic growth by making the UK an international leader in autonomous vehicles, to counter the negative economic impacts of leaving the European Union. That strategy is based on further increasing the number of highly skilled technology and engineering jobs at companies and research insitutions already involved in the sector; and on the UK’s relative lack of regulations preventing the adoption of such technology on the country’s roads.

The strategy will benefit those people with the technological and engineering skills needed to create improvements in autonomous vehicle technology. But what will happen to the far greater number of people who earn their living simply by driving vehicles? They will first see their income fall, and second see their jobs disappear, as technology firstly replaces their permanent jobs with casual labour through platforms such as Über, and secondly completely removes their jobs from the economy by replacing them with self-driving technology. The UK economy might grow in the process; but vast numbers of ordinary people will see their jobs and incomes disappear or decline.

From the broad perspective of the UK workforce, that strategy would be great if we were making a massive investment in education to enable more people to earn a living as highly paid engineers rather than an average or low-paid living as drivers. But of course we’re not doing that at all; at best our educational spend per student is stagnant, and at worst it’s declining as class-sizes grow and we reduce the number of teaching assistants we employ.

In contrast, Schumacher felt that the most genuine “development ” of our society would occur when the most possible people were employed in a way that gave them the practical ability to earn a living; and that also offered a level of human reward – much as Maslow’s “Hierarchy of Needs” first identifies our most basic requirements for food, water, shelter and security; but next relates the importance of family, friends and “self-actualisation” (which can crudely be described as the process of achieving things that we care about).

This led him to ask:

“What is that we really require from the scientists and technologists? I should answer:

We need methods and equipment which are:

    • Cheap enough so that they are accessible to virtually everyone;
    • Suitable for small-scale application; and
    • Compatible with man’s need for creativity”

These are precisely the characteristics of the Cloud Computing, social media, Open Source and smartphone technologies that are now so widely available, and so astonishingly powerful. What we need to do next is to provide more support to help people everywhere put them to use for their own purposes.

Firstly, Open data, open algorithms and open APIs should be mandatory for any publicly funded service or infrastructure. They should be included in the procurement criteria for services and goods procured on behalf of the public sector. Our public infrastructure should be digitally open, accessible and accountable.

Secondly, some of the proceeds from corporate taxation – whether at national level or from local business rates – should be used to provide regional investment funds to support local businesses and social enterprises that contribute to local social, economic and environmental objectives; and to support the regional social innovation communities such as the network of Impact Hubs that help such initiatives start, succeed and grow.

But perhaps most importantly, those proceeds should also be used to fund improvements to state education everywhere. People can only use tools if they are given the opportunity to acquire skills; and as tools and technologies change, we need the opportunity to learn new skills. If our jobs – or more broadly our roles in society – are not ultimately to be replaced by machines, we need to develop the creativity to use those tools to create the human value that technology will never understand.

It is surely insane that we are pouring billions of pounds and dollars into the development of technologies that mean we need to develop new skills in order to remain employable, and that those investments are making our economy richer and richer; but that at the same time we are making a smaller and smaller proportion of that wealth available to educate our children.

Just as some of the profits of the Industrial Revolution were spent on infrastructure with a social purpose, so should some of the profits of the Information Revolution be.

2. Legislate to encourage and support business models with a positive social outcome

(Hancock Bank’s vault, damaged by Hurricane Katrina. Photo by Social Stratification)

The social quality of the behaviour of private sector businesses varies enormously.

The story of Hancock Bank’s actions to assist the citizens of New Orleans to recover from hurricane Katrina in 2005 – by lending cash to anyone who needed it and was prepared to sign an IoU – is told in this video, and is an extraordinary example of responsible business behaviour. In an unprecedented situation, the Bank’s leaders based their decisions on the company’s purpose, expressed in its charter, to support the communities of the city. This is in contrast to the behaviour of Bob Diamond, who resigned as CEO of Barclays Bank following the LIBOR rate-manipulation scandal, and who under questioning by parliamentary committee could not remember what the Bank’s founding principles, written by community-minded Quakers, stated.

Barclays’ employees’ behaviour under Bob Diamond was driven purely by the motivation to earn bigger bonuses by achieving the Bank’s primary objective, to increase shareholder value.

But the overriding focus on shareholders as the primary stakeholder in private sector business is relatively new. Historically, customers and employees have been treated as equally important. Some leading economists now believe we should return to such balanced models.

There are already models of business – such as “social enterprise” – which promote more balanced corporate governance, and that even offer accreditation schemes. We could incentivise such models to be more successful in our economy by creating a preferential market for them – lower rates of taxation; preferential scoring in public sector procurements; and so on.

An alternative is to use technology to enable entirely new, entirely open systems. “Blockchains” are the technology that enable the digital currency “Bitcoin“. The Bitcoin Blockchain is a single, distributed ledger that records every Bitcoin transaction so that anyone in the world can see it. So unlike the traditional system of money in which we depend on physical tokens, banks and payment services to define the ownership of money and to govern transactions, Bitcoin transactions work because everybody can see who owns which Bitcoins and when they’re being exchanged.

This principle of a “distributed, open ledger” – implemented by a blockchain – is thought by many technology industry observers to be the most important, powerfully disruptive invention since the internet. The Ethereum “smart contracts” platfom adds behaviour to the blockchain – open algorithms that cannot be tampered with and that dictate how transactions take place and what happens as a consequence of them. It is leading to some strikingly different new business models, including the “Distributed Autonomous Organisation” (or “DAO” for short), a multi-$million investment fund that is entirely, democratically run by smart contracts on behalf of its investors.

By promoting distributed, non-repudiatable transparency in this way, blockchain technologies offer unprecedented opportunities to ensure that all of the participants in an economic system have the opportunity to influence the distribution of the benefits of the system in a fair way. This idea is already at the heart of an array of initiatives to ensure that some of the least wealthy people in the world benefit more fairly from the information economy.

Finally, research in economics and in evolutionary social biology is yielding prescriptive insights into how we can design business models that are as wildly successful as those of Über and Airbnb, but with models of corporate governance that ensure that the wealth they create is more broadly and fairly distributed.

In conversation with a researcher at Imperial College London a few years ago, I said that I thought we needed to find criteria to distinguish “platform” businesses like Casserole Club that create social value from those like Über that concentrate the vast majority of the wealth they create in the hands of the platform owners. (Casserole Club uses social media to match people who are unable to provide meals for themselves with neighbours who are happy to cook and share an extra portion of their meal).

The researcher told me I should consult Elinor Ostrom’s work in Economics. Ostrom, who won the Nobel prize in 2009, spent her life working with communities around the world who successfully manage shared resources (land, forests, fresh water, fisheries etc.) sustainably, and writing down the common features of their organisational models. Her Nobel prize was awarded for using this evidence to disprove the “tragedy of the commons” doctrine which economists previously believed proved that sustainable commons management was impossible.

(Elinor Ostrom working with irrigation management in Nepal)

(Elinor Ostrom working with irrigation management in Nepal)

Most of Ostrom’s principles for organisational design and behaviour are strikingly similar to the models used by platform businesses such as Über and Airbnb. But the principles she discovered that are the most interesting are the ones that Über and Airbnb don’t follow – the price of exchange being agreed by all of the participants in a transaction, for example, rather than it being set by the platform owner. Ostrom’s work has been continued by David Sloan Wilson who has demonstrated that the principles she discovered follow from evolutionary social biology – the science that studies the evolution of human social behaviour.

Elinor Ostrom’s design principles for commons organisations offer us not only a toolkit for the design of successful, socially responsible platform businesses; they offer us a toolkit for their regulation, too, by specifying the characteristics of businesses that we should preferentially reward through market regulation and tax policy.

3. Legislate for individual ownership of personal data, and a right to share in the profits it creates. 

Platform business models may depend less and less on our labour – or at least, may have found ways to pay less for it as a proportion of their profits; but they depend absolutely on our data.

Of course, we – usually – get some value in return for our data – useful search results, guidance to the quickest route to our journey, recommendations of new songs, films or books we might like.

But is massive inequality really a price worth paying for convenience?

The ownership of private property and intellectual property underpin the capitalist economy, which until recently was primarily based on the value of physical assets and closed knowledge, made difficult to replicate through being stored primarily in physical, analogue media (including our brains).

Our economy is now being utterly transformed by easy to replicate, easy to transfer digital data – from news to music to video entertainment to financial services, business models that had operated for decades have been swept away and replaced by models that are constantly adapting, driven by advances in technology.

But data legislation has not kept pace. Despite several revisions of data protection and privacy legislation, the ownership of digital data is far from clearly defined in law, and in general its exchange is subject to individual agreements between parties.

It is time to legislate more strongly that the value of the data we create by our actions, our movement and our communication belongs to us as individuals, and that in turn we receive a greater share of the profits that are made from its use.

That is the more likely mechanism to result in the fair distribution of value in the economy as the value of labour falls than a Universal Basic Income that rewards nothing.

One last plea to our political leaders to admit that we face a crisis

Whilst the UK and the USA argue – and even riot – about the outcomes of the European Union referendum and the US Presidential election, the issues of inequality, loss of jobs and disenfranchisement from the political system are finally coming to light in the media.

But it’s a disgrace that they barely featured at all in either of those campaigns.

Emotionally right now I want to castigate our politicians for getting us into this mess through all sorts of venality, complacency, hubris and untruthfulness. But two things I know they are not – including Donald Trump – are stupid or ignorant. They surely must be aware of these issues – why will they not recognise and address them?

Robert Wright’s mathematical analysis of the evolution of human society, NonZero, describes the emergence of our current model of nation states through the European Middle Ages as a tension between the ruling and working classes. The working classes pay a tax to the ruling classes, who they accept will live a wealthier life, in return for a safe and peaceful environment in which to live. Whenever the price paid for safety and peace grew unreasonably high, the working classes revolted and overthrew the ruling classes, resulting eventually in a new, better-balanced model.

Is it scaremongering to suggest we are close to a similar era of instability?

(Anti-Donald Trump protesters in San Jose, California in June. Trump supporters leaving a nearby campaign rally were attacked)

(Anti-Donald Trump protesters in San Jose, California in June. Trump supporters leaving a nearby campaign rally were attacked)

I don’t think so. At the same time that the Industrial Revolution created widespread economic growth and improvements in prosperity, it similarly exacerbated inequality between the general population and the property- and business-owning elite. Just as I have argued in this article, that inequality was corrected not by “big government” and grand top-down redistributive schemes, but by measures that shaped markets and investments in education and enablement for the wider population.

We have not yet taken those corrective actions for the Information Revolution – nor even realised and acknowledged that we need to take them. Inequality is rising as a consequence, and it is widely appreciated that inequality creates social unrest.

Brexit and the election of Donald Trump following a campaign of such obvious lies, misogyny and – at best – narrow-minded nationalism are unprecedented in modern times. They have already resulted in social unrest in the form of riots and increased incidents of racism – as has the rise in the price of staple food caused by severe climate events as a vast number of people around the world struggle to feed themselves when hurricanes and droughts affect the production of basic crops. It’s no surprise that the World Economic Forum’s 2016 Global Risks Report identifies “unemployment and underemployment” and “profound social instability” as amongst the top 10 most likely and impactful global risks facing the world.

Brexit and Donald Trump are not crises in themselves; but they are symptoms of a real crisis that we face now; and until we – and our political leaders – face up to that and start dealing with it properly, we are putting ourselves, our future and our childrens’ future at unimaginable risk.

Thankyou to the following, whose opinions and expertise, expressed in articles and conversations, helped me to write this post:

Smart Digital Urbanism: creating the conditions for equitably distributed opportunity in the digital age

(The sound artists FA-TECH [http://fa-tech.tumblr.com/] improvising in Shoreditch, London. Shoreditch's combination of urban character, cheap rents and proximity to London's business, financial centres and culture led to the emergence of a thriving technology startup community - although that community's success is now driving rents up, challenging some of the characteristics that enabled it.)

(The sound artists FA-TECH improvising in Shoreditch, London. Shoreditch’s combination of urban character, cheap rents and proximity to London’s business, financial centres and culture led to the emergence of a thriving technology startup community – although that community’s success is now driving rents up, challenging some of the characteristics that enabled it.)

(I first learned of the architect Kelvin Campbell‘s concept of “massive/small” just over two years ago – the idea that certain characteristics of policy and the physical environment in cities could encourage “massive amounts of small-scale innovation” to occur. Kelvin recently launched a collaborative campaign to capture ideas, tools and tactics for massive/small “Smart Urbanism“. This is my first contribution to that campaign.)

Over the past 5 years, enormous interest has developed in the potential for digital technologies to contribute to the construction and development of cities, and to the operation of the services and infrastructures that support them. These ideas are often referred to as “Smart Cities” or “Future Cities”.

Indeed, as the price of digital technologies such as smartphones, sensors, analytics, open source software and cloud platforms reduces rapidly, market dynamics will drive their aggressive adoption to make construction, infrastructure and city services more efficient, and hence make their providers more competitive.

But those market dynamics do not guarantee that we will get everything we want for the future of our cities: efficiency and resilience are not the same as health, happiness and opportunity for every citizen.

Is it realistic to ask ourselves whether we can achieve those objectives? Yes, it has to be.

Many of us believe in that possibility, and spend a lot of our efforts finding ways to achieve it. And over the same timeframe that interest in “smart” and “future” cities has emerged, a belief has developed around the world that the governance institutions of cities – local authorities and elected mayors, rather than the governments of nations – are the most likely political entities to implement the policies that lead to a sustainable, resilient future with more equitably distributed economic growth.

Consequently many Mayors and City Councils are considering or implementing legislation and policy frameworks that change the economic and financial context in which construction, infrastructure and city services are deployed and operated. The British Standards Institute recently published guidance on this topic as part of its overall Smart Cities Standards programme.

But whilst in principle these trends and ideas are incredibly exciting in their potential to create better cities, communities, places and lives in the future, in practise many debates about applying them falter on a destructive and misleading argument between “top-down” and “bottom-up” approaches – the same chasm that Smart Urbanism seeks to bridge in the physical world.

Policies and programmes driven by central government organisations or implemented by technology and infrastructure corporations that drive digital technology into large-scale infrastructures and public services are often criticised as crude, “top-down” initiatives that prioritise resilience and efficiency at the expense of the concerns and values of ordinary people, businesses and communities. However, the organic, “bottom-up” innovation that critics of these initatives champion as the better, alternative approach is ineffective at creating equality.

("Lives on the Line" by James Cheshire at UCL's Centre for Advanced Spatial Analysis, showing the variation in life expectancy and correlation to child poverty in London. From Cheshire, J. 2012. Lives on the Line: Mapping Life Expectancy Along the London Tube Network. Environment and Planning A. 44 (7). Doi: 10.1068/a45341)

(“Lives on the Line” by James Cheshire at UCL’s Centre for Advanced Spatial Analysis, showing the variation in life expectancy and correlation to child poverty in London. From Cheshire, J. 2012. Lives on the Line: Mapping Life Expectancy Along the London Tube Network. Environment and Planning A. 44 (7). Doi: 10.1068/a45341)

“Bottom-up innovation” is what every person, community and business does every day: using our innate creativity to find ways to use the resources and opportunities available to us to make a better life.

But the degree to which we fail to distribute those resources and opportunities equally is illustrated by the stark variation in life expectancy between the richest and poorest areas of cities in the UK: often this variation is as much as 20 years within a single city.

Just as the “design pattern”, a tool invented by a town planner in the 1970s, Christopher Alexander, is probably the single most influential concept that drove the development of the digital technology we all use today, two recent movements in town planning and urban design – “human scale cities” and “smart urbanism” – offer the analogies that can connect “top-down” technology policies and infrastructure with the factors that affect the success of “bottom-up” creativity to create “massive / small” success: future, digital cities that create “massive amounts of small-scale innovation“.

The tools to achieve this are relatively cheap, and the right policy environment could make it fairly straightforward to augment the business case for efficient, resilient “smart city” infrastructures to ensure that they are deployed. They are the digital equivalents of the physical concepts of Smart Urbanism – the use of open grid structures for spatial layouts, and the provision of basic infrastructure components such as street layouts and party walls in areas expected to attract high growth in informal housing. Some will be delivered as a natural consequence of market forces driving technology adoption; but others will only become economically viable when local or national government policies shape the market by requiring them:

  • Broadband, wi-if and 3G / 4G connectivity should be broadly available so that everyone can participate in the digital economy.
  • The data from city services should be made available as Open Data and published through “Application Programming Interfaces” (APIs) so that everybody knows how they work; and can adapt them to their own individual needs.
  • The data and APIs should be made available in the form of Open Standards so that everybody can understand them; and so that the systems that we rely on can work together.
  • The data and APIs should be available to developers working on Cloud Computing platforms with Open Source software so that anyone with a great idea for a new service to offer to people or businesses can get started for free.
  • The technology systems that support the services and infrastructures we rely on should be based on Open Architectures, so that we have freedom to chose which technologies we use, and to change our minds.
  • Governments, institutions, businesses and communities should participate in an open dialogue about the places we live and work in, informed by open data, enabled by social media and smartphones, and enlightened by empathy.

(Casserole Club, a social enterprise developed by FutureGov uses social media to connect people who have difficulty cooking for themselves with others who are happy to cook an extra portion for a neighbour; a great example of a locally-focused “sharing economy” business model which creates financially sustainable social value.)

These principles would encourage good “digital placemaking“: they would help to align the investments that will be made in improving cities using technology with the needs and motivations of the public sector, the private sector, communities and businesses. They would create “Smart Digital Urbanism”: the conditions and environment in which vibrant, fair digital cities grow from the successful innovations of their citizens, communities and businesses in the information economy.

In my new role at Amey, a vast organisation in the UK that delivers public services and operates and supports public infrastructure, I’m leading a set of innovative projects with our customers and technology partners to explore these ideas and to understand how we can collaboratively create economic, social and environmental value for ourselves; for our customers; and for the people, communities and businesses who live in the areas our services support.

It’s a terrifically exciting role; and I’ll soon be hiring a small team of passionate, creative people to help me identify, shape and deliver those projects. I’ll post an update here with details of the skills, experience and characteristics I’m looking for. I hope some of you will find them attractive and get in touch.

11 reasons computers can’t understand or solve our problems without human judgement

(Photo by Matt Gidley)

(Photo by Matt Gidley)

Why data is uncertain, cities are not programmable, and the world is not “algorithmic”.

Many people are not convinced that the Smart Cities movement will result in the use of technology to make places, communities and businesses in cities better. Outside their consumer enjoyment of smartphones, social media and online entertainment – to the degree that they have access to them – they don’t believe that technology or the companies that sell it will improve their lives.

The technology industry itself contributes significantly to this lack of trust. Too often we overstate the benefits of technology, or play down its limitations and the challenges involved in using it well.

Most recently, the idea that traditional processes of government should be replaced by “algorithmic regulation” – the comparison of the outcomes of public systems to desired objectives through the measurement of data, and the automatic adjustment of those systems by algorithms in order to achieve them – has been proposed by Tim O’Reilly and other prominent technologists.

These approaches work in many mechanical and engineering systems – the autopilots that fly planes or the anti-lock braking systems that we rely on to stop our cars. But should we extend them into human realms – how we educate our children or how we rehabilitate convicted criminals?

It’s clearly important to ask whether it would be desirable for our society to adopt such approaches. That is a complex debate, but my personal view is that in most cases the incredible technologies available to us today – and which I write about frequently on this blog – should not be used to take automatic decisions about such issues. They are usually more valuable when they are used to improve the information and insight available to human decision-makers – whether they are politicians, public workers or individual citizens – who are then in a better position to exercise good judgement.

More fundamentally, though, I want to challenge whether “algorithmic regulation” or any other highly deterministic approach to human issues is even possible. Quite simply, it is not.

It is true that our ability to collect, analyse and interpret data about the world has advanced to an astonishing degree in recent years. However, that ability is far from perfect, and strongly established scientific and philosophical principles tell us that it is impossible to definitively measure human outcomes from underlying data in physical or computing systems; and that it is impossible to create algorithmic rules that exactly predict them.

Sometimes automated systems succeed despite these limitations – anti-lock braking technology has become nearly ubiquitous because it is more effective than most human drivers at slowing down cars in a controlled way. But in other cases they create such great uncertainties that we must build in safeguards to account for the very real possibility that insights drawn from data are wrong. I do this every time I leave my home with a small umbrella packed in my bag despite the fact that weather forecasts created using enormous amounts of computing power predict a sunny day.

(No matter how sophisticated computer models of cities become, there are fundamental reasons why they will always be simplifications of reality. It is only by understanding those constraints that we can understand which insights from computer models are valuable, and which may be misleading. Image of Sim City by haljackey)

We can only understand where an “algorithmic” approach can be trusted; where it needs safeguards; and where it is wholly inadequate by understanding these limitations. Some of them are practical, and limited only by the sensitivity of today’s sensors and the power of today’s computers. But others are fundamental laws of physics and limitations of logical systems.

When technology companies assert that Smart Cities can create “autonomous, intelligently functioning IT systems that will have perfect knowledge of users’ habits” (as London School of Economics Professor Adam Greenfield rightly criticised in his book “Against the Smart City”), they are ignoring these challenges.

A blog published by the highly influential magazine Wired recently made similar overstatements: “The Universe is Programmable” argues that we should extend the concept of an “Application Programming Interface (API)” – a facility usually offered by technology systems to allow external computer programmes to control or interact with them – to every aspect of the world, including our own biology.

To compare complex, unpredictable, emergent biological and social systems to the very logical, deterministic world of computer software is at best a dramatic oversimplification. The systems that comprise the human body range from the armies of symbiotic microbes that help us digest food in our stomachs to the consequences of using corn syrup to sweeten food to the cultural pressure associated with “size 0” celebrities. Many of those systems can’t be well modelled in their own right, let alone deterministically related to each other; let alone formally represented in an accurate, detailed way by technology systems (or even in mathematics).

We should regret and avoid the hubris that leads to the distrust of technology by overstating its capability and failing to recognise its challenges and limitations. That distrust is a barrier that prevents us from achieving the very real benefits that data and technology can bring, and that have been convincingly demonstrated in the past.

For example, an enormous contribution to our knowledge of how to treat and prevent disease was made by John Snow who used data to analyse outbreaks of cholera in London in the 19th century. Snow used a map to correlate cases of cholera to the location of communal water pipes, leading to the insight that water-borne germs were responsible for spreading the disease. We wash our hands to prevent diseases spreading through germs in part because of what we would now call the “geospatial data analysis” performed by John Snow.

Many of the insights that we seek from analytic and smart city systems are human in nature, not physical or mathematical – for example identifying when and where to apply social care interventions in order to reduce the occurrence of  emotional domestic abuse. Such questions are complex and uncertain: what is “emotional domestic abuse?” Is it abuse inflicted by a live-in boyfriend, or by an estranged husband who lives separately but makes threatening telephone calls? Does it consist of physical violence or bullying? And what is “bullying”?

IMG_0209-1

(John Snow’s map of cholera outbreaks in 19th century London)

We attempt to create structured, quantitative data about complex human and social issues by using approximations and categorisations; by tolerating ranges and uncertainties in numeric measurements; by making subjective judgements; and by looking for patterns and clusters across different categories of data. Whilst these techniques can be very powerful, just how difficult it is to be sure what these conventions and interpretations should be is illustrated by the controversies that regularly arise around “who knew what, when?” whenever there is a high profile failure in social care or any other public service.

These challenges are not limited to “high level” social, economic and biological systems. In fact, they extend throughout the worlds of physics and chemistry into the basic nature of matter and the universe. They fundamentally limit the degree to which we can measure the world, and our ability to draw insight from that information.

By being aware of these limitations we are able to design systems and practises to use data and technology effectively. We know more about the weather through modelling it using scientific and mathematical algorithms in computers than we would without those techniques; but we don’t expect those forecasts to be entirely accurate. Similarly, supermarkets can use data about past purchases to make sufficiently accurate predictions about future spending patterns to boost their profits, without needing to predict exactly what each individual customer will buy.

We underestimate the limitations and flaws of these approaches at our peril. Whilst Tim O’Reilly cites several automated financial systems as good examples of “algorithmic regulation”, the financial crash of 2008 showed the terrible consequences of the thoroughly inadequate risk management systems used by the world’s financial institutions compared to the complexity of the system that they sought to profit from. The few institutions that realised that market conditions had changed and that their models for risk management were no longer valid relied instead on the expertise of their staff, and avoided the worst affects. Others continued to rely on models that had started to produce increasingly misleading guidance, leading to the recession that we are only now emerging from six years later, and that has damaged countless lives around the world.

Every day in their work, scientists, engineers and statisticians draw conclusions from data and analytics, but they temper those conclusions with an awareness of their limitations and any uncertainties inherent in them. By taking and communicating such a balanced and informed approach to applying similar techniques in cities, we will create more trust in these technologies than by overstating their capabilities.

What follows is a description of some of the scientific, philosophical and practical issues that lead inevitability to uncertainty in data, and to limitations in our ability to draw conclusions from it:

But I’ll finish with an explanation of why we can still draw great value from data and analytics if we are aware of those issues and take them properly into account.

Three reasons why we can’t measure data perfectly

(How Heisenberg’s Uncertainty Principle results from the dual wave/particle nature of matter. Explanation by HyperPhysics at Georgia State University)

1. Heisenberg’s Uncertainty Principle and the fundamental impossibility of knowing everything about anything

Heisenberg’s Uncertainty Principle is a cornerstone of Quantum Mechanics, which, along with General Relativity, is one of the two most fundamental theories scientists use to understand our world. It defines a limit to the precision with which certain pairs of properties of the basic particles which make up the world – such as protons, neutrons and electrons – can be known at the same time. For instance, the more accurately we measure the position of such particles, the more uncertain their speed and direction of movement become.

The explanation of the Uncertainty Principle is subtle, and lies in the strange fact that very small “particles” such as electrons and neutrons also behave like “waves”; and that “waves” like beams of light also behave like very small “particles” called “photons“. But we can use an analogy to understand it.

In order to measure something, we have to interact with it. In everyday life, we do this by using our eyes to measure lightwaves that are created by lightbulbs or the sun and that then reflect off objects in the world around us.

But when we shine light on an object, what we are actually doing is showering it with billions of photons, and observing the way that they scatter. When the object is quite large – a car, a person, or a football – the photons are so small in comparison that they bounce off without affecting it. But when the object is very small – such as an atom – the photons colliding with it are large enough to knock it out of its original position. In other words, measuring the current position of an object involves a collision which causes it to move in a random way.

This analogy isn’t exact; but it conveys the general idea. (For a full explanation, see the figure and link above). Most of the time, we don’t notice the effects of Heisenberg’s Uncertainty Principle because it applies at extremely small scales. But it is perhaps the most fundamental law that asserts that “perfect knowledge” is simply impossible; and it illustrates a wider point that any form of measurement or observation in general affects what is measured or observed. Sometimes the effects are negligible,  but often they are not – if we observe workers in a time and motion study, for example, we need to be careful to understand the effect our presence and observations have on their behaviour.

2. Accuracy, precision, noise, uncertainty and error: why measurements are never fully reliable

Outside the world of Quantum Mechanics, there are more practical issues that limit the accuracy of all measurements and data.

(A measurement of the electrical properties of a superconducting device from my PhD thesis. Theoretically, the behaviour should appear as a smooth, wavy line; but the experimental measurement is affected by noise and interference that cause the signal to become "fuzzy". In this case, the effects of noise and interference - the degree to which the signal appears "fuzzy" - are relatively small relative to the strength of the signal, and the device is usable)

(A measurement of the electrical properties of a superconducting device from my PhD thesis. Theoretically, the behaviour should appear as a smooth, wavy line; but the experimental measurement is affected by noise and interference that cause the signal to become “fuzzy”. In this case, the effects of noise and interference – the degree to which the signal appears “fuzzy” – are relatively small compared to the strength of the signal, and the device is usable)

We live in a “warm” world – roughly 300 degrees Celsius above what scientists call “absolute zero“, the coldest temperature possible. What we experience as warmth is in fact movement: the atoms from which we and our world are made “jiggle about” – they move randomly. When we touch a hot object and feel pain it is because this movement is too violent to bear – it’s like being pricked by billions of tiny pins.

This random movement creates “noise” in every physical system, like the static we hear in analogue radio stations or on poor quality telephone connections.

We also live in a busy world, and this activity leads to other sources of noise. All electronic equipment creates electrical and magnetic fields that spread beyond the equipment itself, and in turn affect other equipment – we can hear this as a buzzing noise when we leave smartphones near radios.

Generally speaking, all measurements are affected by random noise created by heat, vibrations or electrical interference; are limited by the precision and accuracy of the measuring devices we use; and are affected by inconsistencies and errors that arise because it is always impossible to completely separate the measurement we want to make from all other environmental factors.

Scientists, engineers and statisticians are familiar with these challenges, and use techniques developed over the course of more than a century to determine and describe the degree to which they can trust and rely on the measurements they make. They do not claim “perfect knowledge” of anything; on the contrary, they are diligent in describing the unavoidable uncertainty that is inherent in their work.

3. The limitations of measuring the natural world using digital systems

One of the techniques we’ve adopted over the last half century to overcome the effects of noise and to make information easier to process is to convert “analogue” information about the real world (information that varies smoothly) into digital information – i.e. information that is expressed as sequences of zeros and ones in computer systems.

(When analogue signals are amplified, so is the noise that they contain. Digital signals are interpreted using thresholds: above an upper threshold, the signal means “1”, whilst below a lower threshold, the signal means “0”. A long string of “0”s and “1”s can be used to encode the same information as contained in analogue waves. By making the difference between the thresholds large compared to the level of signal noise, digital signals can be recreated to remove noise. Further explanation and image by Science Aid)

This process involves a trade-off between the accuracy with which analogue information is measured and described, and the length of the string of digits required to do so – and hence the amount of computer storage and processing power needed.

This trade-off can be clearly seen in the difference in quality between an internet video viewed on a smartphone over a 3G connection and one viewed on a high definition television using a cable network. Neither video will be affected by the static noise that affects weak analogue television signals, but the limited bandwidth of a 3G connection dramatically limits the clarity and resolution of the image transmitted.

The Nyquist–Shannon sampling theorem defines this trade-off and the limit to the quality that can be achieved in storing and processing digital information created from analogue sources. It determines the quality of digital data that we are able to create about any real-world system – from weather patterns to the location of moving objects to the fidelity of sound and video recordings. As computers and communications networks continue to grow more powerful, the quality of digital information will improve,  but it will never be a perfect representation of the real world.

Three limits to our ability to analyse data and draw insights from it

1. Gödel’s Incompleteness Theorem and the inconsistency of algorithms

Kurt Gödel’s Incompleteness Theorem sets a limit on what can be achieved by any “closed logical system”. Examples of “closed logical systems” include computer programming languages, any system for creating algorithms – and mathematics itself.

We use “closed logical systems” whenever we create insights and conclusions by combining and extrapolating from basic data and facts. This is how all reporting, calculating, business intelligence, “analytics” and “big data” technologies work.

Gödel’s Incompleteness Theorem proves that any closed logical system can be used to create conclusions that  it is not possible to show are true or false using the same system. In other words, whilst computer systems can produce extremely useful information, we cannot rely on them to prove that that information is completely accurate and valid. We have to do that ourselves.

Gödel’s theorem doesn’t stop computer algorithms that have been verified by humans using the scientific method from working; but it does mean that we can’t rely on computers to both generate algorithms and guarantee their validity.

2. The behaviour of many real-world systems can’t be reduced analytically to simple rules

Many systems in the real-world are complex: they cannot be described by simple rules that predict their behaviour based on measurements of their initial conditions.

A simple example is the “three body problem“. Imagine a sun, a planet and a moon all orbiting each other. The movement of these three objects is governed by the force of gravity, which can be described by relatively simple mathematical equations. However, even with just three objects involved, it is not possible to use these equations to directly predict their long-term behaviour – whether they will continue to orbit each other indefinitely, or will eventually collide with each other, or spin off into the distance.

(A computer simulation by Hawk Express of a Belousov–Zhabotinsky reaction,  in which reactions between liquid chemicals create oscillating patterns of colour. The simulation is carried out using “cellular automata” a technique based on a grid of squares which can take different colours. In each “turn” of the simulation, like a turn in a board game, the colour of each square is changed using simple rules based on the colours of adjacent squares. Such simulations have been used to reproduce a variety of real-world phenomena)

As Stephen Wolfram argued in his controversial book “A New Kind of Science” in 2002, we need to take a different approach to understanding such complex systems. Rather than using mathematics and logic to analyse them, we need to simulate them, often using computers to create models of the elements from which complex systems are composed, and the interactions between them. By running simulations based on a large number of starting points and comparing the results to real-world observations, insights into the behaviour of the real-world system can be derived. This is how weather forecasts are created, for example. 

But as we all know, weather forecasts are not always accurate. Simulations are approximations to real-world systems, and their accuracy is restricted by the degree to which digital data can be used to represent a non-digital world. For this reason, conclusions and predictions drawn from simulations are usually “average” or “probable” outcomes for the system as a whole, not precise predictions of the behaviour of the system or any individual element of it. This is why weather forecasts are often wrong; and why they predict likely levels of rain and windspeed rather than the shape and movement of individual clouds.

(Hello)

(A simple and famous example of a computer programme that never stops running because it calls itself. The output continually varies by printing out characters based on random number generation. Image by Prosthetic Knowledge)

3. Some problems can’t be solved by computing machines

If I consider a simple question such as “how many letters are in the word ‘calculation’?”, I can easily convince myself that a computer programme could be written to answer the question; and that it would find the answer within a relatively short amount of time. But some problems are much harder to solve, or can’t even be solved at all.

For example, a “Wang Tile” (see image below) is a square tile formed from four triangles of different colours. Imagine that you have bought a set of tiles of various colour combinations in order to tile a wall in a kitchen or bathroom. Given the set of tiles that you have bought, is it possible to tile your wall so that triangles of the same colour line up to each other, forming a pattern of “Wang Tile” squares?

In 1966 Robert Berger proved that no algorithm exists that can answer that question. There is no way to solve the problem – or to determine how long it will take to solve the problem – without actually solving it. You just have to try to tile the room and find out the hard way.

One of the most famous examples of this type of problem is the “halting problem” in computer science. Some computer programmes finish executing their commands relatively quickly. Others can run indefinitely if they contain a “loop” instruction that never ends. For others which contain complex sequences of loops and calls from one section of code to another, it may be very hard to tell whether the programme finishes quickly, or takes a long time to complete, or never finishes its execution at all.

Alan Turing, one of the most important figures in the development of computing, proved in 1936 that a general algorithm to determine whether or not any computer programme finishes its execution does not exist. In other words, whilst there are many useful computer programmes in the world, there are also problems that computer programmes simply cannot solve.

(A set of Wang Tiles, and a pattern created by tiling them so that tiles are placed next to other tiles so that their edges have the same colour. Given any particular set of tiles, it is impossible to determine whether such a pattern can be created by any means other than trial and error)

(A set of Wang Tiles, and a pattern of coloured squares created by tiling them. Given any random set of tiles of different colour combinations, there is no set of rules that can be relied on to determine whether a valid pattern of coloured squares can be created from them. Sometimes, you have to find out by trial and error. Images from Wikipedia)

Five reasons why the human world is messy, unpredictable, and can’t be perfectly described using data and logic

1. Our actions create disorder

The 2nd Law of Thermodynamics is a good candidate for the most fundamental law of science. It states that as time progresses, the universe becomes more disorganised. It guarantees that ultimately – in billions of years – the Universe will die as all of the energy and activity within it dissipates.

An everyday practical consequence of this law is that every time we act to create value – building a shed, using a car to get from one place to another, cooking a meal – our actions eventually cause a greater amount of disorder to be created as a consequence – as noise, pollution, waste heat or landfill refuse.

For example, if I spend a day building a shed, then to create that order and value from raw materials, I consume structured food and turn it into sewage. Or if I use an electric forklift to stack a pile of boxes, I use electricity that has been created by burning structured coal into smog and ash.

So it is literally impossible to create a “perfect world”. Whenever we act to make a part of the world more ordered, we create disorder elsewhere. And ultimately – thankfully, long after you and I are dead – disorder is all that will be left.

2. The failure of Logical Atomism: why the human world can’t be perfectly described using data and logic

In the 20th Century two of the most famous and accomplished philosophers in history, Bertrand Russell and Ludwig Wittgenstein, invented “Logical Atomism“, a theory that the entire world could be described by using “atomic facts” – independent and irreducible pieces of knowledge – combined with logic.

But despite 40 years of work, these two supremely intelligent people could not get their theory to work: “Logical Atomism” failed. It is not possible to describe our world in that way.

One cause of the failure was the insurmountable difficulty of identifying truly independent, irreducible atomic facts. “The box is red” and “the circle is blue”, for example, aren’t independent or irreducible facts for many reasons. “Red” and “blue” are two conventions of human language used to describe the perceptions created when electro-magnetic waves of different frequencies arrive at our retinas. In other words, they depend on and relate to each other through a number of sophisticated systems.

Despite centuries of scientific and philosophical effort, we do not have a complete understanding of how to describe our world at its most basic level. As physicists have explored the world at smaller and smaller scales, Quantum Mechanics has emerged as the most fundamental theory for describing it – it is the closest we have come to finding the “irreducible facts” that Russell and Wittgenstein were looking for. But whilst the mathematical equations of Quantum Mechanics predict the outcomes of experiments very well, after nearly a century, physicists still don’t really agree about what those equations mean. And as we have already seen, Heisenberg’s Uncertainty Principle prevents us from ever having perfect knowledge of the world at this level.

Perhaps the most important failure of logical atomism, though, was that it proved impossible to use logical rules to turn “facts” at one level of abstraction – for example, “blood cells carry oxygen”, “nerves conduct electricity”, “muscle fibres contract” – into facts at another level of abstraction – such as “physical assault is a crime”. The human world and the things that we care about can’t be described using logical combinations of “atomic facts”. For example, how would you define the set of all possible uses of a screwdriver, from prising the lids off paint tins to causing a short-circuit by jamming it into a switchboard?

Our world is messy, subjective and opportunistic. It defies universal categorisation and logical analysis.

(A Pescheria in Bari, Puglia, where a fish-market price information service makes it easier for local fisherman to identify the best buyers and prices for their daily catch. Photo by Vito Palmi)

3. The importance and inaccessibility of “local knowledge” 

Because the tool we use for calculating and agreeing value when we exchange goods and services is money, economics is the discipline that is often used to understand the large-scale behaviour of society. We often quantify the “growth” of society using economic measures, for example.

But this approach is notorious for overlooking social and environmental characteristics such as health, happiness and sustainability. Alternatives exist, such as the Social Progress Index, or the measurement framework adopted by the United Nations 2014 Human Development Report on world poverty; but they are still high level and abstract.

Such approaches struggle to explain localised variations, and in particular cannot predict the behaviours or outcomes of individual people with any accuracy. This “local knowledge problem” is caused by the fact that a great deal of the information that determines individual actions is personal and local, and not measurable at a distance – the experienced eye of the fruit buyer assessing not just the quality of the fruit but the quality of the farm and farmers that produce it, as a measure of the likely consistency of supply; the emotional attachments that cause us to favour one brand over another; or the degree of community ties between local businesses that influence their propensity to trade with each other.

Sharing economy” business models that use social media and reputation systems to enable suppliers and consumers of goods and services to find each other and transact online are opening up this local knowledge to some degree. Local food networks, freecycling networks, and land-sharing schemes all use this technology to the benefit of local communities whilst potentially making information about detailed transactions more widely available. And to some degree, the human knowledge that influences how transactions take place can be encoded in “expert systems” which allow computer systems to codify the quantitative and heuristic rules by which people take decisions.

But these technologies are only used in a subset of the interactions that take place between people and businesses across the world, and it is unlikely that they’ll become ubiquitous in the foreseeable future (or that we would want them to become so). Will we ever reach the point where prospective house-buyers delegate decisions about where to live to computer programmes operating in online marketplaces rather than by visiting places and imagining themselves living there? Will we somehow automate the process of testing the freshness of fish by observing the clarity of their eyes and the freshness of their smell before buying them to cook and eat?

In many cases, while technology may play a role introducing potential buyers and sellers of goods and services to each other, it will not replace – or predict – the human behaviours involved in the transaction itself.

(Medway Youth Trust use predictive and textual analytics to draw insight into their work helping vulnerable children. They use technology to inform expert case workers, not to take decisions on their behalf.)

4. “Wicked problems” cannot be described using data and logic

Despite all of the challenges associated with problems in mathematics and the physical sciences, it is nevertheless relatively straightforward to frame and then attempt to solve problems in those domains; and to determine whether the resulting solutions are valid.

As the failure of Logical Atomism showed, though, problems in the human domain are much more difficult to describe in any systematic, complete and precise way – a challenge known as the “frame problem” in artificial intelligence. This is particularly true of “wicked problems” – challenges such as social mobility or vulnerable families that are multi-faceted, and consist of a variety of interdependent issues.

Take job creation, for example. Is that best accomplished through creating employment in taxpayer-funded public sector organisations? Or by allowing private-sector wealth to grow, creating employment through “trickle-down” effects? Or by maximising overall consumer spending power as suggested by “middle-out” economics? All of these ideas are described not using the language of mathematics or other formal logical systems, but using natural human language which is subjective and inconsistent in use.

The failure of Logical Atomism to fully represent such concepts in formal logical systems through which truth and falsehood can be determined with certainty emphasises what we all understand intuitively: there is no single “right” answer to many human problems, and no single “right” action in many human situations.

(An electricity bill containing information provided by OPower comparing one household’s energy usage to their neighbours. Image from Grist)

5. Behavioural economics and the caprice of human behaviour

Behavioural economics” attempts to predict the way that humans behave when taking choices that have a measurable impact on them – for example, whether to put the washing machine on at 5pm when electricity is expensive, or at 11pm when it is cheap.

But predicting human behaviour is notoriously unreliable.

For example, in a smart water-meter project in Dubuque, Iowa, households that were told how their water conservation compared to that of their near neighbours were found to be twice as likely to take action to improve their efficiency as those who were only told the details of their own water use. In other words, people who were given quantified evidence that they were less responsible water user than their neighbours changed their behaviour. OPower have used similar techniques to help US households save 1.9 terawatt hours of power simply by including a report based on data from smart meters in a printed letter sent with customers’ electricity bills.

These are impressive achievements; but they are not always repeatable. A recycling scheme in the UK that adopted a similar approach found instead that it lowered recycling rates across the community: households who learned that they were putting more effort into recycling than their neighbours asked themselves “if my neighbours aren’t contributing to this initiative, then why should I?”

Low carbon engineering technologies like electric vehicles have clearly defined environmental benefits and clearly defined costs. But most Smart Cities solutions are less straightforward. They are complex socio-technical systems whose outcomes are emergent. Our ability to predict their performance and impact will certainly improve as more are deployed and analysed, and as University researchers, politicians, journalists and the public assess them. But we will never predict individual actions using these techniques, only the average statistical behaviour of groups of people. This can be seen from OPower’s own comparison of their predicted energy savings against those actually achieved – the predictions are good, but the actual behaviour of OPower’s customers shows a high degree of apparently random variation. Those variations are the result of the subjective, unpredictable and sometimes irrational behaviour of real people.

We can take insight from Behavioural Economics and other techniques for analysing human behaviour in order to create appropriate strategies, policies and environments that encourage the right outcomes in cities; but none of them can be relied on to give definitive solutions to any individual person or situation. They can inform decision-making, but are always associated with some degree of uncertainty. In some cases, the uncertainty will be so small as to be negligible, and the predictions can be treated as deterministic rules for achieving the desired outcome. But in many cases, the uncertainty will be so great that predictions can only be treated as general indications of what might happen; whilst individual actions and outcomes will vary greatly.

(Of course it is impossible to predict individual criminal actions as portrayed in the film “Minority Report”. But is is very possible to analyse past patterns of criminal activity, compare them to related data such as weather and social events, and predict the likelihood of crimes of certain types occurring in certain areas. Cities such as Memphis and Chicago have used these insights to achieve significant reductions in crime)

Learning to value insight without certainty

Mathematics and digital technology are incredibly powerful; but they will never perfectly and completely describe and predict our world in human terms. In many cases, our focus for using them should not be on automation: it should be on the enablement of human judgement through better availability and communication of information. And in particular, we should concentrate on communicating accurately the meaning of information in the context of its limitations and uncertainties.

There are exceptions where we automate systems because of a combination of a low-level of uncertainty in data and a large advantage in acting autonomously on it. For example, anti-lock braking systems save lives by using automated technology to take thousands of decisions more quickly than most humans would realise that even a single decision needed to be made; and do so based on data with an extremely low degree of uncertainty.

But the most exciting opportunity for us all is to learn to become sophisticated users of information that is uncertain. The results of textual analysis of sentiment towards products and brands expressed in social media are far from certain; but they are still of great value. Similar technology can extract insights from medical research papers, case notes in social care systems, maintenance logs of machinery and many other sources. Those insights will rarely be certain; but properly assessed by people with good judgement they can still be immensely valuable.

This is a much better way to understand the value of technology than ideas like “perfect knowledge” and “algorithmic regulation”. And it is much more likely that people will trust the benefits that we claim new technologies can bring if we are open about their limitations. People won’t use technologies that they don’t trust; and they won’t invest their money in them or vote for politicians who say they’ll spend their taxes on it.

Thankyou to Richard Brown and Adrian McEwen for discussions on Twitter that helped me to prepare this article. A more in-depth discussion of some of the scientific and philosophical issues I’ve described, and an exploration of the nature of human intelligence and its non-deterministic characteristics, can be found in the excellent paper “Answering Descartes: Beyond Turing” by Stuart Kauffman published by MIT press.

Six ways to design humanity and localism into Smart Cities

(Birmingham’s Social Media Cafe, where individuals from every part of the city share their experience using social media to promote their businesses and community initiatives. Photograph by Meshed Media)

The Smart Cities movement is sometimes criticised for appearing to focus mainly on the application of technology to large-scale city infrastructures such as smart energy grids and intelligent transportation.

It’s certainly vital that we manage and operate city services and infrastructure as intelligently as possible – there’s no other way to deal with the rapid urbanisation taking place in emerging economies; or the increasing demand for services such as health and social care in the developed world whilst city budgets are shrinking dramatically; and the need for improved resilience in the face of climate change everywhere.

But to focus too much on this aspect of Smart Cities and to overlook the social needs of cities and communities risks forgetting what the full purpose of cities is: to enable a huge number of individual citizens to live not just safe, but rewarding lives with their families.

Maslow’s Hierarchy of Needs identifies our most basic requirements to be food, water, shelter and security. The purpose of many city infrastructures is to answer those needs, either directly (buildings, utility infrastructures and food supply chains) or indirectly (the transport systems that support us and the businesses that we work for).

Important as those needs are, though – particularly to the billions of people in the world for whom they are not reliably met – life would be dull and unrewarding if they were all that we aspired to.

Maslow’s hierarchy next relates the importance of family, friends and “self-actualisation” (which can crudely be described as the process of achieving things that we care about). These are the more elusive qualities that it’s harder to design cities to provide. But unless cities provide them, they will not be successful. At best they will be dull, unrewarding places to live and work, and will see their populations fall as those can migrate elsewhere. At worst, they will create poverty, poor health and ultimately short, unrewarding lives.

A Smart City should not only be efficient, resilient and sustainable; it should improve all of these qualities of life for its citizens.

So how do we design and engineer them to do that?

(Maslow’s Hierarchy of Needs, image by Factoryjoe via Wikimedia Commons)

Tales of the Smart City

Stories about the people whose lives and businesses have been made better by technology tell us how we might answer that question.

In the Community Lover’s Guide to Birmingham, for example, Nick Booth describes the way his volunteer-led social media surgeries helped the Central Birmingham Neighbourhood Forum, Brandwood End Cemetery and Jubilee Debt Campaign to benefit from technology.

Another Birmingham initiative, the Northfield Ecocentre, crowdfunded £10,000 to support their “Urban Harvest” project. The funds helped the Ecocentre pick unwanted fruit from trees in domestic gardens in Birmingham and distribute it between volunteers, children’s centres, food bank customers and organisations promoting healthy eating; and to make some of it into jams, pickles and chutneys to raise money so that in future years the initiative can become self-sustaining.

In the village of Chale on the Isle of Wight, a community not served by the national gas power network and with significant levels of fuel poverty, my colleague Andy Stanford-Clark has helped an initiative not only to deploy smart meters to measure the energy use of each household; but to co-design with residents how they will use that technology, so that the whole community feels a sense of ownership and inclusion in the initiative. The project has resulted in a significant drop in rent arrears as residents use the technology to reduce their utility bills, in some cases by up to 50 percent. Less obviously, the sense of shared purpose has extended to the creation of a communal allotment area in the village and a successful compaign to halve bus fares in the area.

There are countless other examples. Play Fitness “gamify” exercise to persuade children to get fit, and work very hard to ensure that their products are accessible to children in communities of any level of wealth.  Casserole Club use social media to introduce people who can’t cook for themselves to people who are prepared to volunteer to cook for others. The West Midlands Collaborative Commerce Marketplace uses analytics technology to help it’s 10,000 member businesses win more than £4billion in new contracts each year. … and so on.

None of these initiatives are purely to do with technology. But they all use technologies that simply were not available and accessible as recently as a few years ago to achieve outcomes that are important to cities and communities. By understanding how the potential of technology was apparent to the stakeholders in such initiatives, why it was affordable and accessible to them, and how they acquired the skills to exploit it, we can learn how to design Smart Cities in a way that encourages widespread grass-roots, localised innovation.

(Top: Birmingham's Masshouse Circus roundabout, part of the inner-city ringroad that famously impeded the city's growth. Bottom: This pedestrian roundabout in Lujiazui, China, constructed over a busy road junction, is a large-scale city infrastructure that balances the need to support traffic flows through the city with the importance that Jane Jacobs first described of allowing people to walk freely about the areas where they live and work. Photo by ChrisUK)

(Top: Birmingham’s Masshouse Circus roundabout, part of the inner-city ringroad that famously impeded the city’s growth until it was demolished. Photo by Birmingham City Council. Bottom: Pedestrian roundabout in Lujiazui, China, constructed over a busy road junction, is a large-scale city infrastructure that balances the need to support traffic flows through the city with the importance that Jane Jacobs first described of allowing people to walk freely about the areas where they live and work. Photo by ChrisUK)

A tale of two roundabouts

History tells us that we should not assume that it will be straightforward to design Smart Cities to achieve that objective, however.

A measure of our success in building the cities we know today from the generations of technology that shaped them – concrete, cars and lifts – is the variation in life expectancy across them. In the UK, it’s common for life expectancy to vary by around 20 years between the poorest and richest parts of the same city.

That staggering difference is the outcome of a complex set of issues including the availability of education and opportunity, lifestyle factors such as diet and exercise, and the accessibility of city services. But a significant influence on many of those issues is the degree to which the large-scale infrastructures built to support our physiological needs and the demands of the economy also create a high-quality environment for daily life.

The photograph on the right shows two city transport infrastructures that are visually similar, but that couldn’t be more different in their influence on the success of the cities that they are part of.

The picture at the top shows Masshouse Circus in Birmingham in 2001 shortly before it was demolished. It was constructed in the 1960s as part of the city’s inner ring-road, intended to improve connectivity to the national economy through the road network. However, the impact of the physical barrier that it created to pedestrian traffic can be seen by the stark difference in land value inside and outside the “concrete collar” of the ring-road. Inside the collar, land is valuable enough for tall office blocks to be constructed on it; whilst outside it is of such low value that it is used as a ground-level carpark.

In contrast, the pedestrian roundabout in Lujiazui, China pictured at the bottom, constructed over a busy road junction, balances the need to support traffic flows through the city with the need for people to walk freely about the areas in which they live and work. As can be seen from the people walking all around it, it preserves the human vitality of an area that many busy roads flow through. 

We should take insight from these experiences when considering the design of Smart City infrastructures. Unless those infrastructures are designed to be accessible to and usable by citizens, communities and local businesses, they will be as damaging as poorly constructed buildings and poorly designed transport networks. If that sounds extreme, then consider the dangers of cyber-stalking, or the implications of the gun-parts confiscated from a suspected 3D printing gun factory in Manchester last year that had been created on general purpose machinery from digital designs shared through the internet. Digital technology has life and death implications in the real world.

For a start, we cannot take for granted that city residents have the basic ability to access the internet and digital technology. Some 18% of adults in the UK have never been online; and children today without access to the internet at home and in school are at an enormous disadvantage. As digital technology becomes even more pervasive and important, the impact of this digital divide – within and between people, cities and nations – will become more severe. This is why so many people care passionately about the principle of “Net Neutrality” – that the shared infrastructure of the internet provides the same service to all of its users; and does not offer preferential access to those individuals or corporations able to pay for it.

These issues are very relevant to cities and their digital strategies and governance. The operation of any form of network requires physical infrastructure such as broadband cables, wi-fi and 4G antennae and satellite dishes. That infrastructure is regulated by city planning policies. In turn, those planning policies are tools that cities can and should use to influence the way in which technology infrastructure is deployed by private sector service providers.

(Photograph of Aesop’s fable “The Lion and the Mouse” by Liz West)

Little and big

Cities are enormous places in which what matters most is that millions of individually small matters have good outcomes. They work well when their large scale systems support the fine detail of life for every one of their very many citizens: when “big things” and “little things” work well together.

A modest European or US city might have 200,000 to 500,000 inhabitants; a large one might have between one and ten million. The United Nations World Urbanisation Prospects 2011 revision recorded 23 cities with more than 10 million population in 2011 (only six of them in the developed world); and predicted that there would be nearly 40 by 2025 (only eight of them in the developed world – as we define it today). Overall, between now and 2050 the world’s urban population will double from 3 billion to 6 billion. 

A good example of the challenges that this enormous level of urbanisation is already creating is the supply of food. One hectare of highly fertile, intensively farmed land can feed 10 people. Birmingham, my home city, has an area of 60,000 hectares of relatively infertile land, most of which is not available for farming at all; and a population of around 1 million. Those numbers don’t add up to food self-sufficiency; and Birmingham is a very low-density city – between one-half and one-tenth as dense as the growing megacities of Asia and South America Feeding the 7 to 10 billion people who will inhabit the planet between now and 2050, and the 3 to 6 billion of them that will live in dense cities, is certainly a challenge on an industrial scale. 

In contrast, Casserole Club, the Northfield Eco-Centre, the Chale Project and many other initiatives around the world have demonstrated the social, health and environmental benefits of producing and distributing food locally. Understanding how to combine the need to supply food at city-scale with the benefits of producing it locally and socially could make a huge difference to the quality of urban lives.

The challenge of providing affordable broadband connectivity throughout cities demonstrates similar issues. Most cities and countries have not yet addressed that challenge: private sector network providers will not deploy connectivity in areas which are insufficiently economically active for them to make a profit, and Government funding is not yet sufficient to close the gap.

In his enjoyable and insightful book “Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia“, Anthony Townsend describes a grass-roots effort by civic activists to provide New York with free wi-fi connectivity. I have to admire the vision and motivation of those involved, but – rightly or wrongly; and as Anthony describes – wi-fi has ultimately evolved to be dominated by commercial organisations.  

As technology continues to improve and to reduce in price, the balance of power between large, commercial, resource-rich institutions and small, agile, resourceful  grassroots innovators will continue to changeTechnologies such as Cloud Computing, social media, 3D printing and small-scale power generation are reducing the scale at which many previously industrial technologies are now economically feasible; however, it will remain the case for the foreseeable future that many city infrastructures – physical and digital – will be large-scale, expensive affairs requiring the buying power and governance of city-scale authorities and the implementation resources of large companies.

But more importantly, neither small-scale nor large-scale solutions alone will meet all of our needs. Many areas in cities – usually those that are the least wealthy – haven’t yet been provided with wi-fi or broadband connectivity by either.  

(Cars in Frederiksberg, Copenhagen wishing to join a main road must give way to cyclists and pedestrians)

(A well designed urban interface between people and infrastructure. Cars in Frederiksberg, Copenhagen wishing to join a main road must give way to cyclists and pedestrians passing along it)

We need to find the middle ground between the motivations, abilities and cultures of large companies and formal institutions on one hand; and those of agile, local innovators and community initiatives on the other. The pilot project to provide broadband connectivity and help using the internet to Castle Vale in Birmingham is a good example of finding that balance.

And I am optimistic that we can find it more often. Whilst Anthony is rightly critical of approaches to designing and building city systems that are led by technology, or that overlook the down-to-earth and sometimes downright “messy” needs of people and communities for favour of unrealistic technocratic and corporate utopias; the reality of the people I know that are employed by large corporations on Smart City projects is that they are acutely aware of the limitations as well as the value of technology, and are passionately committed to the human value of their work. That passion is often reflected in their volunteered commitment to “civic hacking“, open data initiatives, the teaching of technology in schools and other activities that help the communities in which they live to benefit from technology.

But rather than relying on individual passion and integrity, how do we encourage and ensure that large-scale investments in city infrastructures and technology enable small-scale innovation, rather than stifle it?

Smart urbanism and massive/small innovation

I’ve taken enormous inspiration in recent years from the architect Kelvin Campbell whose “Massive / Small” concept and theory of “Smart Urbanism” are based on the belief that successful cities emerge from physical environments that encourage “massive” amounts of “small”-scale innovation – the “lively, diversified city, capable of continual, close- grained improvement and change” that Jane Jacobs described in “The Death and Life of Great American Cities“.

We’ll have to apply similar principles in order for large-scale city technology infrastructures to support localised innovation and value-creation. But what are the practical steps that we can take to put those principles into practise?

Step 1: Make institutions accessible

There’s a very basic behaviour that most of us are quite bad at – listening. In particular, if the institutions of Smart Cities are to successfully create the environment in which massive amounts of small-scale innovation can emerge, then they must listen to and understand what local activists, communities, social innovators and entrepreneurs want and need.

Many large organisations – whether they are local authorities or private sector companies – are poor at listening to smaller organisations. Their decision-makers are very busy; and communications, engagement and purchasing occur through formally defined processes with legal, financial and confidentiality clauses that can be difficult for small or informal organisations to comply with. The more that we address these barriers, the more that our cities will stimulate and support small-scale innovation. One way to do so is through innovations in procurement; another is through the creation of effective engagements programmes, such as the Birmingham Community Healthcare Trust’s “Healthy Villages” project which is listening to communities expressing their need for support for health and wellbeing. This is why IBM started our “Smarter Cities Challenge” which has engaged hundreds of IBM’s Executives and technology experts in addressing the opportunities and challenges of city communites; and in so doing immersed them in very varied urban cultures, economies, and issues.

But listening is also a personal and cultural attitude. For example, in contrast to the current enthusiasm for cities to make as much data as possible available as “open data”, the Knight Foundation counsel a process of engagement and understanding between institutions and communities, in order to identify the specific information and resources that can be most usefully made available by city institutions to individual citizens, businesses and social organisations.

(Delegates at Gov Camp 2013 at IBM’s Southbank office, London. Gov Camp is an annual conference which brings together anyone interested in the use of digital technology in public services. Photo by W N Bishop)

In IBM, we’ve realised that it’s important to us to engage with, listen to and support small-scale innovation in its many forms when helping our customers and partners pursue Smarter City initiatives; from working with social enterprises, to supporting technology start-ups through our Global Entrepreneur Programme, to engaging with the open data and civic hacking movements.

More widely, it is often talented, individual leaders who overcome the barriers to engagement and collaboration between city institutions and localised innovation. In “Resilience: why things bounce back“, Andrew Zolli describes many examples of initiatives that have successfully created meaningful change. A common feature is the presence of an individual who shows what Zolli calls”translational leadership“: the ability to engage with both small-scale, informal innovation in communities and large-scale, formal institutions with resources.

Step 2: Make infrastructure and technology accessible

Whilst we have a long way to go to address the digital divide, Governments around the world recognise the importance of access to digital technology and connectivity; and many are taking steps to address it, such as Australia’s national deployment of broadband internet connectivity and the UK’s Urban Broadband Fund. However, in most cases, those programmes are not sufficient to provide coverage everywhere.

Some businesses and social initiatives are seeking to address this shortfall. CommunityUK, for example, are developing sustainable business models for providing affordable, accessible connectivity, and assistance using it, and are behind the Castle Vale project in Birmingham. And some local authorities, such as Sunderland and Birmingham, have attempted to provide complete coverage for their citizens – although just how hard it is to achieve that whilst avoiding anti-competition issues is illustrated by Birmingham’s subsequent legal challenges.

We should also tap into the enormous sums spent on the physical regeneration of cities and development of property in them. As I first described in June last year, while cities everywhere are seeking funds for Smarter City initiatives, and often relying on central government or research grants to do so, billions of Pounds, Euros, and Dollars are being spent on relatively conventional property development and infrastructure projects that don’t contribute to cities’ technology infrastructures or “Smart” objectives.

Local authorities could use planning regulations to steer some of that investment into providing Smart infrastructure, basic connectivity, and access to information from city infrastructures to citizens, communities and businesses. Last year, I developed a set of “Smart City Design Principles” on behalf a city Council considering such an approach, including:

Principle 4: New or renovated buildings should be built to contain sufficient space for current and anticipated future needs for technology infrastructure such as broadband cables; and of materials and structures that do not impede wireless networks. Spaces for the support of fixed cabling and other infrastructures should be easily accessible in order to facilitate future changes in use.

Principle 6: Any development should ensure wired and wireless connectivity is available throughout it, to the highest standards of current bandwidth, and with the capacity to expand to any foreseeable growth in that standard.

(The Birmingham-based Droplet smartphone payment service, now also operating in London, is a Smart City start-up that has won backing from Finance Birmingham, a venture capital company owned by Birmingham City Council)

Step 3: Support collaborative innovation

Small-scale, local innovations will always take place, and many of them will be successful; but they are more likely to have significant, lasting, widespread impact when they are supported by city institutions with resources.

That support might vary from introducing local technology entrepreneurs to mentors and investors through the networks of contacts of city leaders and their business partners; through to practical assistance for social enterprises, helping them to put in place very basic but costly administration processes to support their operations.

City institutions can also help local innovations to thrive simply by becoming their customers. If Councils, Universities and major local employers buy services from innovative local providers – whether they be local food initiatives such as the Northfield Ecocentre or high-tech innovations such as Birmingham’s Droplet smartphone payment service – then they provide direct support to the success of those businesses.

In Birmingham,for example, Finance Birmingham (a Council-owned venture capital company) and the Entrepreneurs for the Future (e4F) scheme provide real, material support to the city’s innovative companies; whilst Bristol’s Mayor George Ferguson and Lambeth’s Council both support their local currencies by allowing salaries to be paid in them.

It becomes more obvious  why stakeholders in a city might become involved in collaborative innovation when they have the opportunity to co-create a clear set of shared priorities. Those priorities can be compared to the objectives of innovative proposals seeking support, whether from social initiatives or businesses; used as the basis of procurement criteria for goods, services and infrastructure; set as the objectives for civic hacking and other grass-roots creative events; or even used as the criteria for funding programmes for new city services, such as the “Future Streets Incubator” that will shortly be launched in London as a result of the Mayor of London’s Roads Task Force.

In this context, businesses are not just suppliers of products and services, but also local institutions with significant supply chains, carbon and economic footprints, purchasing power and a huge number of local employees. There are many ways such organisations can play a role in supporting the development of an open, Smarter, more sustainable city.

The following “Smart City Design Principles” promote collaborative innovation in cities by encouraging support from development and regeneration initiatives:

Principle 12: Consultations on plans for new developments should fully exploit the capabilities of social media, virtual worlds and other technologies to ensure that communities affected by them are given the widest, most immersive opportunity possible to contribute to their design.

Principle 13: Management companies, local authorities and developers should have a genuinely engaging presence in social media so that they are approachable informally.

Principle 14: Local authorities should support awareness and enablement programmes for social media and related technologies, particularly “grass roots” initiatives within local communities.

Step 4: Promote open systems

A common principle between the open data movement; civic hacking; localism; the open government movement; and those who support “bottom-up” innovations in Smart Cities is that public systems and infrastructure – in cities and elsewhere – should be “open”. That might mean open and transparent in their operation; accessible to all; or providing open data and API interfaces to their technology systems so that citizens, communities and businesses can adapt them to their own needs. Even better, it might mean all of those things.

The “Dublinked” information sharing partnership, in which Dublin City Council, three surrounding County Councils and  service providers to the city share information and make it available to their communities as “open data”, is a good example of the benefits that openness can bring. Dublinked now makes 3,000 datasets available to local authority analysts; to researchers from IBM Research and the National University of Ireland; and to businesses, entrepreneurs and citizens. The partnership is identifying new ways for the city’s public services and transport, energy and water systems to work; and enabling the formation of new, information-based businesses with the potential to export the solutions they develop in Dublin to cities internationally. It is putting the power of technology and of city information not only at the disposal of the city authority and its agencies, but also into the hands of communities and innovators.

(I was delighted this year to join Innovation Birmingham as a non-Executive Director in addition to my role with IBM. Technology incubators – particularly those, like Innovation Birmingham and Sunderland Software City, that are located in city centres – are playing an increasingly important role in making the support of city institutions and major technology corporations available to local communities of entrepreneurs and technology activists)

In a digital future, the more that city infrastructures and services provide open data interfaces and APIs, the more that citizens, communities and businesses will be able to adapt the city to their own needs. This is the modern equivalent of the grid system that Jane Jacobs promoted as the most adaptable urban form. A grid structure is the basis of Edinburgh’s “New Town”, often regarded as a masterpiece of urban planning that has proved adaptable and successful through the economic and social changes of the past 250 years, and is also the starting point for Kelvin Campbell’s work.

But open data interfaces and APIs will only be widely exploitable if they conform to common standards. In order to make it possible to do something as simple as changing a lightbulb, we rely on open standards for the levels of voltage and power from our electricity supply; the physical dimensions of the socket and bulb and the characteristics of their fastenings; specifications of the bulb’s light and heat output; and the tolerance of the bulb and the fitting for the levels of moisture found in bathrooms and kitchens. Cities are much more complicated than lightbulbs; and many more standards will be required on order for us to connect to and re-configure their systems easily and reliably.

Open standards are also an important tool in avoiding city systems becoming “locked-in” to any particular supplier. By specifying common characteristics that all systems are required to demonstrate, it becomes more straightforward to exchange one supplier’s implementation for another.

Some standards that Smarter City infrastructures can use are already in place – for example, Web services and REST that specify the general ways in which computer systems interact, and the Common Alerting Protocol which is more specific to interactions between systems that monitor and control the physical world. But many others will need to be invented and encouraged to spread. The City Protocol Society is one organisation seeking to develop those new standards; and the British Standards Institute recently published the first set of national standards for Smarter Cities in the UK, including a standard for the interoperability of data between Smart City systems.

Some open source technologies will also be pivotal; open source (software whose source code is freely available to anyone, and which is usually written by unpaid volunteers) is not the same as open standards (independently governed conventions that define the way that technology from any provider behaves). But some open source technologies are so widely used to operate the internet infrastructures that we have become accustomed to – the “LAMP” stack of operating system, web server, database and web progamming language, for example – that they are “de facto” standards that convey some of the benefits of wide usability and interoperability of open standards. For example, IBM recently donated MQTT, a protocol for connecting information between small devices such as sensors and actuators in Smart City systems to the open source community, and it is becoming increasingly widely adopted as a consequence.

Once again, local authorities can contribute to the adoption of open standards through planning frameworks and procurement practises:

Principle 7: Any new development should demonstrate that all reasonable steps have been taken to ensure that information from its technology systems can be made openly available without additional expenditure. Whether or not information is actually available will be dependent on commercial and legal agreement, but it should not be additionally subject to unreasonable expenditure. And where there is no compelling commercial or legal reason to keep data closed, it should actually be made open.

Principle 8: The information systems of any new development should conform to the best available current standards for interoperability between IT systems in general; and for interoperability in the built environment, physical infrastructures and Smarter Cities specifically.

(The town plan for Edinburgh’s New Town, clearly showing the grid structure that gives rise to the adaptability that it is famous for showing for the past 250 years. Image from the JR James archive)

Finally, design skills will be crucial both to creating interfaces to city infrastructures that are truly useful and that encourage innovation; and in creating innovations that exploit them that in turn are useful to citizens.

At the technical level, there is already a rich corpus of best practise in the design of interfaces to technology systems and in the architecture of technology infrastructures that provide them.

But the creativity that imagines new ways to use these capabilities in business and in community initiatives will also be crucial. The new academic discipline of “Service Science” describes how designers can use technology to create new value in local contexts; and treats services such as open data and APIs as “affordances” – capabilities of infrastructure that can be adapted to the needs of an individual. In the creative industries, “design thinkers” apply their imagination and skills to similar subjects.

Step 5: Provide common services

At the 3rd EU Summit on Future Internet, Juanjo Hierro, Chief Architect for the FI-WARE “future internet platform” project, identified the specific tools that local innovators need in order to exploit city information infrastructures. They include real-time access to information from physical city infrastructures; tools for analysing “big data“; and access to technologies to ensure privacy and trust.

The Dublinked information sharing partnership is already putting some of these ideas into practise. It provides assistance to innovators in using, analysing and visualising data; and now makes available realtime data showing the location and movements of buses in the city. The partnership is based on specific governance processes that protect data privacy and manage the risk associated with sharing data.

As we continue to engage with communities of innovators in cities, we will discover further requirements of this sort. Imperial College’s “Digital Cities Exchange” research programme is investigating the specific digital services that could be provided as enabling infrastructure to support innovation and economic growth in cities, for example. And the British Standards Institute’s Smart Cities programme includes work on standards that will enable small businesses to benefit from Smart City infrastructure.

Local authorities can adapt planning frameworks to encourage the provision of these services:

Principle 9: New developments should demonstrate that they have considered the commercial viability of providing the digital civic infrastructure services recommended by credible research sources.

Step 6: Establish governance of the information economy

From the exponential growth in digital information we’ve seen in recent years, to the emergence of digital currencies such as Bitcoin, to the disruption of traditional industries by digital technology; it’s clear that we are experiencing an “information revolution” just as significant as the “industrial revolution” of the 18th and 19th centuries. We often refer to the resulting changes to business and society as the development of an “information economy“.

But can we speak in confidence of an information economy when the basis of establishing the ownership and value of its fundamental resource – digital information – is not properly established?

(Our gestures when using smartphones may be directed towards the phones, or the people we are communicating with through them; but how are they interpreted by the people around us? “Oh, yeah? Well, if you point your smartphone at me, I’m gonna point my smartphone at you!” by Ed Yourdon)

A great deal of law and regulation already applies to information, of course – such as the European Union’s data privacy legislation. But practise in this area is far less established than the laws governing the ownership of physical and intellectual property and the behaviour of the financial system that underlie the rest of the economy. This is evident in the repeated controversies concerning the use of personal information by social media businesses, consumer loyalty schemes, healthcare providers and telecommunications companies.

The privacy, security and ownership of information, especially personal information, are perhaps the greatest challenges of the digital age. But that is also a reflection of their importance to all aspects of our lives. Jane Jacobs’ description of urban systems in terms of human and community behaviour was based on those concepts, and is still regarded as the basis of our understanding of cities. New technologies for creating and using information are developing so rapidly that it is not only laws specifically concerning them that are failing to keep up with progress; laws concerning the other aspects of city systems that technology is transforming are failing to adapt quickly enough too.

A start might be to adapt city planning regulations to reflect and enforce the importance of the personal information that will be increasingly accessed, created and manipulated by city systems:

Principle 21: Any information system in a city development should provide a clear policy for the use of personal information. Any use of that information should be with the consent of the individual.

The triumph of the commons

I wrote last week that Smarter Cities should be a “middle-out” economic investment – in other words, an investment in common interests – and compared them to the Economist’s report on the efforts involved in distributing the benefits of the industrial revolution to society at large rather than solely to business owners and the professional classes.

One of the major drivers for the current level of interest in Smarter Cities and technology is the need for us to adapt to a more sustainable way of living in the face of rising global populations and finite resources. At large scale, the resources of the world are common; and at local scale, the resources of cities are common too.

For four decades, it has been widely assumed that those with access to common resources will exploit them for short term gain at the expense of long term sustainability – this is the “tragedy of the commons” first described by the economist Garrett Hardin. But in 2009, Elinor Ostrum won the Nobel Prize for economics by demonstrating that the “tragedy” could be avoidedand that a community could manage and use shared resources in a way that was sustainable in the long-term.

Ostrum’s conceptual framework for managing common resources successfully is a set of criteria for designing “institutions” that consist of people, processes, resources and behaviours. These need not necessarily be formal political or commercial institutions, they can also be social structures. It is interesting to note that some of those criteria – for example, the need for mechanisms of conflict resolution that are local, public, and accessible to all the members of a community – are reflected in the development over the last decade of effective business models for carrying out peer-to-peer exchanges using social media, supported by technologies such as reputation systems.

Of course, there are many people and communities who have championed and practised the common ownership of resources regardless of the supposed “tragedy” – not least those involved in the Transition movement founded by Rob Hopkins, and which has developed a rich understanding of how to successfully change communities for the better using good ideas; or the translational leaders described by Andrew Zolli. But Elinor Ostrum’s ideas are particularly interesting because they could help us to link the design, engineering and governance of Smarter Cities to the achievement of sustainable economic and social objectives based on the behaviour of citizens, communities and businesses.

Combined with an understanding of the stories of people who have improved their lives and communities using technology, I hope that the work of Kelvin Campbell, Rob Hopkins, Andrew Zolli, Elinor Ostrum and many others can inspire technologists, urban designers, architects and city leaders to develop future cities that fully exploit modern technology to be efficient, resilient and sustainable; but that are also the best places to live and work that we can imagine, or that we would hope for for our children.

Cities created by people like that really would be Smart.

Can Smarter City technology measure and improve our quality of life?

(Photo of Golden Gate Bridge, San Francisco, at night by David Yu)

Can information and technology measure and improve the quality of life in cities?

That seems a pretty fundamental question for the Smarter Cities movement to address. There is little point in us expending time and money on the application of technology to city systems unless we can answer it positively. It’s a question that I had the opportunity to explore with technologists and urbanists from around the world last week at the Urban Systems Collaborative meeting in London, on whose blog this article will also appear.

Before thinking about how we might approach such a challenging and complex issue, I’d like to use two examples to support my belief that we will eventually conclude that “yes, information and technology can improve the quality of life in cities.”

The first example, which came to my attention through Colin Harrison, who heads up the Urban Systems Collaborative, concerns public defibrillator devices – equipment that can be used to give an electric shock to the victim of a heart attack to restart their heart. Defibrillators are positioned in many public buildings and spaces. But who knows where they are and how to use them in the event that someone nearby suffers a heart attack?

To answer those questions, many cities now publish open data lists of the locations of publically-accessible Defibrillators. Consequently, SmartPhone apps now exist that can tell you where the nearest one to you is located. As cities begin to integrate these technologies with databases of qualified first-aiders and formal emergency response systems, it becomes more feasible that when someone suffers a heart attack in a public place, a nearby first-aider might be notified of the incidence and of the location of a nearby defibrillator, and be able to respond valuable minutes before the arrival of emergency services. So in this case, information and technology can increase the chancees of heart attack victims recovering.

(Why Smarter Cities matter: "Lives on the Line" by James Cheshire at UCL's Centre for Advanced Spatial Analysis, showing the variation in life expectancy and correlation to child poverty in London. From Cheshire, J. 2012. Lives on the Line: Mapping Life Expectancy Along the London Tube Network. Environment and Planning A. 44 (7). Doi: 10.1068/a45341)

(Why Smarter Cities matter: “Lives on the Line” by James Cheshire at UCL’s Centre for Advanced Spatial Analysis, showing the variation in life expectancy across London. From Cheshire, J. 2012. Lives on the Line: Mapping Life Expectancy Along the London Tube Network. Environment and Planning A. 44 (7). Doi: 10.1068/a45341)

In a more strategic scenario, the Centre for Advanced Spatial Analysis (CASA) at University College London have mapped life expectancy at birth across London. Life expectancy across the city varies from 75 to 96 years, and CASA’s researchers were able to correlate it with a variety of other issues such as child poverty.

Life expectancy varies by 10 or 20 years in many cities in the developed world; analysing its relationship to other economic, demographic, social and spatial information can provide insight into where money should be spent on providing services that address the issues leading to it, and that determine quality of life. The UK Technology Strategy Board cited Glasgow’s focus on this challenge as one of their reasons for investing £24 million in Glasgow’s Future Cities Demonstrator project – life expectancy at birth for male babies in Glasgow varies by 26 years between the poorest and wealthiest areas of the city.

These examples clearly show that in principle urban data and technology can contribute to improving quality of life in cities; but they don’t explain how to do so systematically across the very many aspects of quality of life and city systems, and between the great variety of urban environments and cultures throughout the world. How could we begin to do that?

Deconstructing “quality of life”

We must first think more clearly about what we mean by “quality of life”. There are many needs, values and outcomes that contribute to quality of life and its perception. Maslow’s “Hierarchy of Needs” is a well-researched framework for considering them. We can use this as a tool for considering whether urban data can inform us about, and help us to change, the ability of a city to create quality of life for its inhabitants.

(Maslow’s Hierarchy of Needs, image by Factoryjoe via Wikimedia Commons)

But whilst Maslow’s hierarchy tells us about the various aspects that comprise the overall quality of life, it only tells us about our relationship with them in a very general sense. Our perception of quality of life, and what creates it for us, is highly variable and depends on (at least) some of the following factors:

  • Individual lifestyle preferences
  • Age
  • Culture and ethnicity
  • Social standing
  • Family status
  • Sexuality
  • Gender
  • … and so on.

Any analysis of the relationship between quality of life, urban data and technology must take this variability into account; either by allowing for it in the analytic approach; or by enabling individuals and communities to customise the use of data to their specific needs and context.

Stress and Adaptability

Two qualities of urban systems and life within them that can help us to understand how urban data of different forms might relate to Maslow’s hierarchy of needs and individual perspectives on it are stress and adaptability.

Jurij Paraszczak, IBM’s Director of Research for Smarter Cities, suggested that one way to improve quality of life is to reduce stress. A city with efficient, well integrated services – such as transport; availability of business permits etc. – will likely cause less stress, and offer a higher quality of life, than a city whose services are disjointed and inefficient.

One cause of stress is the need to change. The Physicist Geoffrey West is one of many scientists who has explored the roles of technology and population growth in speeding up city systems; as our world changes more and more quickly, our cities will need to become more agile and adaptable – technologists, town planners and economists all seem to agree on this point.

The architect Kelvin Campbell has explored how urban environments can support adaptability by enabling actors within them to innovate with the resources available to them (streets, buildings, spaces, technology) in response to changes in local and global context – changes in the economy of cultural trends, for example.

Service scientists” analyse the adaptability of systems (such as cities) by considering the “affordances” they offer to actors within them. An “affordance” is a capability within a system that is not exercised until an actor chooses to exercise it in order to create value that is specific to them, and specific to the time, place and context within which they act.

An “affordance” might be the ability to start a temporary business or “pop-up” shop within a disused building by exploiting a temporary exemption from planning controls. Or it might be the ability to access open city data and use it as the basis of new information-based business services. (I explored some ideas from science, technology, economics and urbanism for creating adaptability in cities in an article in March this year).

(Photo by lecercle of a girl in Mumbai doing her homework on whatever flat surface she could find. Her use of a stationary tool usually employed for physical mobility to enhance her own social mobility is an example of the very basic capacity we all have to use the resources available to us in innovative ways)

Stress and adaptability are linked. The more personal effort that city residents must exert in order to adapt to changing circumstances (i.e. the less that a city offers them useful affordances), then the more stress they will be subjected to.

Stress; rates of change; levels of effort and cost exerted on various activities: these are all things that can be measured.

Urban data and quality of life in the district high street

In order to explore these ideas in more depth, our discussion at the Urban Systems Collaborative meeting explored a specific scenario systematically. We considered a number of candidate scenarios – from a vast city such as New York, with a vibrant economy but affected by issues such as flood risk; through urban parks and property developments down to the scale of an individual building such as a school or hospital.

We chose to start with a scenario in the middle of that scale range that is the subject of particularly intense debate in economics, policy and urban design: a mixed-demographic city district with a retail centre at its heart spatially, socially and economically.

We imagined a district with a population of around 50,000 to 100,000 people within a larger urban area; with an economy including the retail, service and manufacturing sectors. The retail centre is surviving with some new businesses starting; but also with some vacant property; and with a mixture of national chains, independent specialist stores, pawnshops, cafes, payday lenders, pubs and betting shops. We imagined that local housing stock would support many levels of wealth from benefits-dependent individuals and families through to millionaire business owners. A district similar to Kings Heath in Birmingham, where I live, and whose retail economy was recently the subject of an article in the Economist magazine.

We asked ourselves what data might be available in such an environment; and how it might offer insight into the elements of Maslow’s hierarchy.

We began by considering the first level of Maslow’s hierarchy, our physiological needs; and in particular the availability of food. Clearly, food is a basic survival need; but the availability of food of different types – and our individual and cultural propensity to consume them – also contributes to wider issues of health and wellbeing.

(York Road, Kings Heath, in the 2009 Kings Heath Festival. Photo by Nick Lockey)

Information about food provision, consumption and processing can also give insights into economic and social issues. For example, the Economist reported in 2011 that since the 2008 financial crash, some jobs lost in professional service industries such as finance in the UK had been replaced by jobs created in independent artisan industries such as food. Evidence of growth in independent businesses in artisan and craft-related sectors in a city area may therefore indicate the early stages of its recovery from economic shock.

Similarly, when a significant wave of immigration from a new cultural or ethnic group takes place in an area, then it tends to result in the creation of new, independent food businesses catering to preferences that aren’t met by existing providers. So a measure of diversity in food supply can be an indicator of economic and social growth.

So by considering a need that Maslow’s hierarchy places at the most basic level, we were able to identify data that describes an urban area’s ability to support that need – for example, the “Enjoy Kings Heath” website provides information about local food businesses; and furthermore, we identified ways that the same data related to needs throughout the other levels of Maslow’s hierarchy.

We next considered how economic flows within and outside an area can indicate not just local levels of economic activity; but also the area’s trading surplus or deficit. Relevant information in principle exists in the form of the accounts and business reports of businesses. Initiatives such as local currencies and loyalty schemes attempt to maximise local synergies by minimising the flow of money out of local economies; and where they exploit technology platforms such as Droplet’s SmartPhone payments service, which operates in London and Birmingham, the money flows within local economies can be measured.

These money flows have effects that go beyond the simple value of assets and property within an area. Peckham high street in London has unusually high levels of money flow in and out of its economy due to a high degree of import / export businesses; and to local residents transferring money to relatives overseas. This flow of money makes business rents in the area disproportionally high  compared to the value of local assets.

Our debate also touched on environmental quality and transport. Data about environmental quality is increasingly available from sensors that measure water and air quality and the performance of sewage systems. These clearly contribute insights that are relevant to public health. Transport data provides perhaps more subtle insights. It can provide insight into economic activity; productivity (traffic jams waste time); environmental impact; and social mobility.

My colleagues in IBM Research have recently used anonymised data from GPS sensors in SmartPhones to analyse movement patterns in cities such as Abidjan and Istanbul on behalf of their governments and transport authorities; and to compare those movement patterns with public transport services such as bus routes. When such data is used to alter public transport services so that they better match the end-to-end journey requirements of citizens, an enormous range of individual, social, environmental and economic benefits are realised.

(The origins and destinations of end-to-end journeys made in Abidjan, identified from anonymised SmartPhone GPS data)

(The origins and destinations of end-to-end journeys made in Abidjan, identified from anonymised SmartPhone GPS data)

Finally, we considered data sources and aspects of quality of life relating to what Maslow called “self-actualisation”: the ability of people within the urban environment of our scenario to create lifestyles and careers that are individually fulfilling and that reward creative self-expression. Whilst not direct, measurements of the registration of patents, or of the formation and survival of businesses in sectors such as construction, technology, arts and artisan crafts, relate to those values in some way.

In summary, the exercise showed that a great variety of data is available that relates to the ability of an urban environment to provide Maslow’s hierarchy of needs to people within it. To gain a fuller picture, of course, we would need to repeat the exercise with many other urban contexts at every scale from a single building up to the national, international and geographic context within which the city exists. But this seems a positive start.

Recognising the challenge

Of course, it is far from straightforward to convert these basic ideas and observations into usable techniques for deriving insight and value concerning quality of life from urban data.

What about the things that are extremely hard to measure but which are often vital to quality of life – for example the cash economy? Physical cash is notoriously hard to trace and monitor; and arguably it is particularly important to the lives of many individuals and communities who have the most significant quality of life challenges; and to those who are responsible for some of the activities that detract from quality of life – burglary, mugging and the supply of narcotics, for example.

The Urban Systems Collaborative’s debate also touched briefly on the question of whether we can more directly measure the outcomes that people care about – happiness, prosperity, the ability to provide for our families, for example. Antti Poikola has written an article on his blog, “Vital signs for measuring the quality of life in cities“, based on the presentation on that topic by Samir Menon of Tata Consulting Services. Samir identified a number of “happiness indices” that have been proposed by the UK Prime Minister, David Cameron, the European Quality of Life Survey, the OECD’s Better Life Index, and the Social Progress Index created by economist Michael Porter. Those indices generally attempt to correlate a number of different quantitative indicators with qualitative information from surveys into an overall score. Their accuracy and usefulness is the subject of contentious debate.

As an alternative, Michael Mezey of the Royal Society for the Arts recently collected descriptions of attempts to measure happiness more directly by identifying the location of issues or events associated with positive or negative emotions – such as parks and pavements fouled by dog litter or displays of emotion in public. It’s fair to say that the results of these approaches are very subjective and selective so far, but it will be interesting to observe what progress is made.

There is also a need to balance our efforts between creating value from the data that is available to us – which is surely a resource that we should exploit – with making sure that we focus our efforts on addressing our most important challenges, whether or not data relevant to them is easily accessible.

And in practise, a great deal of the data that describes cities is still not very accessible or useful. Most of it exists within IT systems that were designed for a specific purpose – for example, to allow building owners to manage the maintenance of their property. Those systems may not be very good at providing data in a way that is useful for new purposes – for example, identifying whether a door is connected to a pavement by a ramp or by steps, and hence how easy it is for a wheelchair user to enter a building.

(Photo by Closed 24/7 of the Jaguar XF whose designers used “big data” analytics to optimise the emotional response of potential customers and drivers)

Generally speaking, transforming data that is useful for a specific purpose into data that is generally useful takes time, effort and expertise – and costs money. We may desire city data to be tidied up and made more readily accessible; just as we may desire a disused factory to be converted into useful premises for shops and small businesses. But securing the investment required to do so is often difficult – this is why open city data is a “brownfield regeneration” challenge for the information age.

We don’t yet have a general model for addressing that challenge, because the socio-economic model for urban data has not been defined. Who owns it? What does it cost to create? What uses of it are acceptable? When is it proper to profit from data?

Whilst in principle the data available to us, and our ability to derive insight and knowledge from it, will continue to grow, our ability to benefit from it in practise will be determined by these crucial ethical, legal and economic issues.

There are also more technical challenges. As any mathematician or scientist in a numerate discipline knows, data, information and analysis models have significant limitations.

Any measurement has an inherent uncertainty. Location information derived from Smartphones is usually accurate to within a few meters when GPS services are available, for example; but only to within a few hundred meters when derived by triangulation between mobile transmission masts. That level of inaccuracy is tolerable if you want to know which city you are in; but not if you need to know where the nearest defibrilator is.

These limitations arise both from the practical limitations of measurement technology; and from fundamental scientific principles that determine the performance of measurement techniques.

We live in a “warm” world – roughly 300 degrees Celsius above what scientists call “absolute zero“, the coldest temperature possible. Warmth is created by heat energy; that energy makes the atoms from which we and our world are made “jiggle about” – to move randomly. When we touch a hot object and feel pain it is because this movement is too violent to bear – it’s like being pricked by billions of tiny pins. This random movement creates “noise” in every physical system, like the static we hear in analogue radio stations or on poor quality telephone lines.

And if we attempt to measure the movements of the individual atoms that make up that noise, we enter the strange world of quantum mechanics in which Heisenberg’s Uncertainty Principle states that the act of measuring such small objects changes them in unpredictable ways. It’s hardly a precise analogy, but imagine trying to measure how hard the surface of a jelly is by hitting it with a hammer. You’d get an idea of the jelly’s hardness by doing so, but after the act of “measurement” you wouldn’t be left with the same jelly. And before the measurement you wouldn’t be able to predict the shape of the jelly afterwards.

(A graph from my PhD thesis showing experimental data plotted against the predictions of an analytic. Notice that whilst the theoretical prediction (the smooth line) is a good guide to the experimental data, that each actual data point lies above or below the line, not on it. In addition, each data point has a vertical bar expressing the level of uncertainty involved in its measurement. In most circumstances, data is uncertain and theory is only a rough guide to reality.)

Even if our measurements were perfect, our ability to understand what they are telling us is not. We draw insight into the behaviour of a real system by comparing measurements of it to a theoretical model of its behaviour. Weather forecasters predict the weather by comparing real data about temperature, air pressure, humidity and rainfall to sophisticated models of weather systems; but, as the famous British preoccupation with talking about the weather illustrates, their predictions are frequently inaccurate. Quite simply this is because the weather system of our world is more complicated than the models that weather forecasters are able to describe using mathematics; and process using today’s computers.

This may all seem very academic; and indeed it is – these are subjects that I studied for my PhD in Physics. But all scientists, mathematicians and engineers understand them; and whether our work involves city systems, motor cars, televisions, information technology, medicine or human behaviour, when we work with data, information and analysis technology we are very much aware and respectful of their limitations.

Most real systems are more complicated than the theoretical models that we are able to construct and analyse. That is especially true of any system that includes the behaviour of people – in other words, the vast majority of city systems. Despite the best efforts of psychology, social science and artificial intelligence we still do not have an analytic model of human behaviour.

For open data and Smarter Cities to succeed, we need to openly recognise these challenges. Data and technology can add immense value to city systems – for instance, IBM’s “Deep Thunder” technology creates impressively accurate short-term and short-range predictions of weather-related events such as flash-flooding that have the potential to save lives. But those predictions, and any other result of data-based analysis, have limitations; and are associated with caveats and constraints.

It is only by considering the capabilities and limitations of such techniques together that we can make good decisions about how to use them – for example, whether to trust our lives to the automated analytics and control systems involved in anti-lock braking systems, as the vast majority of us do every time we travel by road; or whether to use data and technology only to provide input into a human process of consideration and decision-making – as takes place in Rio when city agency staff consider Deep Thunder’s predictions alongside other data and use their own experience and that of their colleagues in determining how to respond.

In current discussions of the role of technology in the future of cities, we risk creating a divide between “soft” disciplines that deal with qualitative, subjective matters – social science and the arts for example; and “hard” disciplines that deal with data and technology – such as science, engineering, mathematics.

In the most polarised debates, opinion from “soft” disciplines is that “Smart cities” is a technology-driven approach that does not take human needs and nature into account, and does not recognise the variability and uncertainty inherent in city systems; and opinion from “hard” disciplines is that operational, design and policy decisions in cities are taken without due consideration of data that can be used to inform them and predict their outcomes. As Stephan Shakespeare wrote in the “Shakespeare Review of Public Sector Information“, “To paraphrase the great retailer Sir Terry Leahy, to run an enterprise without data is like driving by night with no headlights. And yet that is what government often does.”

There is no reason why these positions cannot be reconciled. In some domains “soft” and “hard” disciplines regularly collaborate. For example, the interior and auditory design of the Jaguar XF car, first manufactured in 2008, was designed by re-creating the driving experience in a simulator at the University of Warwick, and analysing the emotional response of test subjects using physiological sensors and data. Such techniques are now routinely used in product design. And many individuals have a breadth of knowledge that extends far beyond their core profession into a variety of areas of science and the arts.

But achieving reconciliation between all of the stakeholders involved in the vastly complex domain of cities – including the people who live in them, not just the academics, professionals and politicians who study, design, engineer and govern them – will not happen by default. It will only happen if we have an open and constructive debate about the capabilities and the limitations of data, information and technology; and if we are then able to communicate them in a way that expresses to everyone why Smarter City systems will improve their quality of life.

(“Which way to go?” by Peter Roome)

What’s next?
It’s astonishing and encouraging that we could use a model of individual consciousness to navigate the availability and value of data in the massively collective context of an urban scenario. To continue developing an understanding of the ability of information and technology to contribute to quality of life within cities, we need to expand that approach to explore the other dimensions we identified that affect perceptions of quality of life: culture, age and family status, for example; and within both larger and smaller scales of city context than the “district” scenario that we started with.

And we need to compare that approach to existing research work such as the Liveable Cities research collaboration between UK Universities that is establishing an evidence-based technique for assessing wellbeing; or the IBM Research initiative “SCRIBE” which seeks to define the meaning of and relationships between the many types of data that describe cities.

As a next step, the Urban Systems Collaborative attendees suggested that it would be useful to consider how people in different circumstances in cities use data, information and technology to take decisions:  for example, city leaders, businesspeople, parents, hostel residents, commuters, hospital patients and so forth across the incredible variety of roles that we play in cities. You can find out more about how the Collaborative is taking this agenda forward on their website.

But this is not a debate that belongs only within the academic community or with technologists and scientists. Information and technology are changing the cities, society and economy that we live in and depend on. But that information results from data that in large part is created by all of our actions and activities as individuals, as we carry out our lives in cities, interacting with systems that from a technology perspective are increasingly instrumented, interconnected and intelligent. We are the ultimate stakeholders in the information economy, and we should seek to establish an equitable consensus for how our data is used; and that consensus should include an understanding and acceptance between all parties of both the capabilities and limitations of information and technology.

I’ve written before about the importance of telling stories that illustrate ways in which technology and information can change lives and communities for the better. The Community Lovers’ Guide to Birmingham is a great example of doing this. As cities such as Birmingham, Dublin and Chicago demonstrate what can be achieved by following a Smarter City agenda, I’m hoping that those involved can tell stories that will help other cities across the world to pursue these ideas themselves.

(This article summarises a discussion I chaired this week to explore the relationship between urban data, technology and quality of life at the Urban Systems Collaborative’s London workshop, organised by my ex-colleague, Colin Harrison, previously an IBM Distinguished Engineer responsible for much of our Smarter Cities strategy; and my current colleague, Jurij Paraszczak, Director of Industry Solutions and Smarter Cities for IBM ResearchI’m grateful for the contributions of all of the attendees who took part. The article also appears on the Urban Systems Collaborative’s blog).

Seven steps to a Smarter City; and the imperative for taking them (updated 8th September 2013)

(Interior of the new Library of Birmingham, opened in September 2013. Photo by Andy Mabbett)

(Interior of the new Library of Birmingham, opened in September 2013. Photo by Andy Mabbett licensed under Creative Commons via Wikimedia Commons)

(This article originally appeared in September 2012 as “Five steps to a Smarter City: and the philosophical imperative for taking them“. Because it contains an overall framework for approaching Smart City transformations, I keep it updated to reflect the latest content on this blog; and ongoing developments in the industry. It can also be accessed through the page link “Seven steps to a Smarter City” in the navigation bar above).

As I’ve worked with cities over the past two years developing their “Smarter City” strategies and programmes  to deliver them, I’ve frequently written articles on this blog exploring the main challenges they’ve faced: establishing a cross-city consensus to act; securing funding; and finding the common ground between the institutional and organic natures of city ecosystems.

We’ve moved beyond exploration now. There are enough examples of cities making progress on the “Smart” agenda for us to identify  the common traits that lead to success. I first wrote “Five steps to a Smarter City: and the philosophical imperative for taking them” in September 2012 to capture what at the time seemed to be emerging practises with promising potential, and have updated it twice since then. A year later, it’s time for a third and more confident revision.

In the past few months it’s also become clear that an additional step is required to recognise the need for new policy frameworks to enable the emergence of Smarter City characteristics, to complement the direct actions and initiatives that can be taken by city institutions, businesses and communities.

The revised seven steps involved in creating and achieving a Smarter City vision are:

  1. Define what a “Smarter City” means to you (Updated)
  2. Convene a stakeholder group to co-create a specific Smarter City vision; and establish governance and a credible decision-making process (Updated)
  3. Structure your approach to a Smart City by drawing on the available resources and expertise (Updated)
  4. Establish the policy framework (New)
  5. Populate a roadmap that can deliver the vision (Updated)
  6. Put the financing in place (Updated)
  7. Enable communities and engage with informality: how to make “Smarter” a self-sustaining process (Updated)

I’ll close the article with a commentary on a new form of leadership that can be observed at the heart of many of the individual initiatives and city-wide programmes that are making the most progress. Described by Andrew Zolli in “Resilience: why things bounce back” as “translational leadership“, it is characterised by an ability to build unusually broad collaborative networks across the institutions and communities – both formal and informal – of a city.

But I’ll begin with what used to be the ending to this article: why Smarter Cities matter. Unless we’re agreed on the need for them, it’s unlikely we’ll take the steps required to achieve them.

The Smarter City imperative

(Why Smarter Cities matter: "Lives on the Line" by James Cheshire at UCL's Centre for Advanced Spatial Analysis, showing the variation in life expectancy and correlation to child poverty in London. From Cheshire, J. 2012. Lives on the Line: Mapping Life Expectancy Along the London Tube Network. Environment and Planning A. 44 (7). Doi: 10.1068/a45341)

(Why Smarter Cities matter: “Lives on the Line” by James Cheshire at UCL’s Centre for Advanced Spatial Analysis, showing the variation in life expectancy across London. From Cheshire, J. 2012. Lives on the Line: Mapping Life Expectancy Along the London Tube Network. Environment and Planning A. 44 (7). Doi: 10.1068/a45341)

I think it’s vitally important to take a pro-active approach to Smarter Cities.

According to the United Nations Department of Economic and Social Affairs’ 2011 revision to their “World Urbanisation Prospects” report, between now and 2050 the world’s population will rise by 2-3 billion. The greatest part of that rise will be accounted for by the growth of Asian, African and South American “megacities” with populations of between 1 and 35 million people.

As a crude generalisation, this unprecedented growth offers four challenges to cities in different circumstances:

  • For rapidly growing cities: we have never before engineered urban infrastructures to support such growth. Whenever we’ve tried to accommodate rapid urban growth before, we’ve failed to provide adequate infrastructure, resulting in slums. One theme within Smarter Cities is therefore the attempt to use technology to respond more successfully to this rapid urbanisation.
  • For cities in developed economies with slower growth: urbanisation in rapidly growing economies is creating an enormous rise in the size of the world’s middle-class, magnifying global growth in demand for resources such as energy, water, food and materials; and creating new competition for economic activity. So a second theme of Smarter Cities that applies in mature economies is to remain vibrant economically and socially in this context, and to improve the distribution of wealth and opportunity, against a background of modest economic growth, ageing populations with increasing service needs, legacy infrastructure and a complex model of governance and operation of city services.
  • For cities in countries that are still developing slowly: increasing levels of wealth and economic growth elsewhere  create an even tougher hurdle than before in creating opportunity and prosperity for the populations of those countries not yet on the path to growth. At the same time that economists and international development organisations attempt to ensure that these nations benefit from their natural resources as they are sought by growing economies elsewhere, a third strand of Smarter Cities is concerned with supporting wider growth in their economies despite a generally low level of infrastructure, including technology infrastructure.
(Photo of Masshouse Circus, Birmingham, a concrete urban expressway that strangled the citycentre before its redevelopment in 2003, by Birmingham City Council)

(Photo of Masshouse Circus, Birmingham, a concrete urban expressway that strangled the citycentre before its redevelopment in 2003, by Birmingham City Council)

We have only been partly successful in meeting these challenges in the past. As public and private sector institutions in Europe and the United States evolved through the previous period of urbanisation driven by the Industrial Revolution they achieved mixed results: standards of living rose dramatically; but so unequally that life expectancy between the richest and poorest areas of a single UK city often varies by 10 to 20 years.

In the sense that city services and businesses will always seek to exploit the technologies available to them, our cities will become smarter eventually as an inevitable consequence of the evolution of technology and growing competition for resources and economic activity.

But if those forces are allowed to drive the evolution of our cities, rather than supporting a direction of evolution that is proactively chosen by city stakeholders, then we will not solve many of the challenges that we care about most: improving the distribution of wealth and opportunity, and creating a better, sustainable quality of life for everyone. As I argued in “Smarter City myths and misconceptions“, “business as usual” will not deliver what we want and need – we need new approaches.

I do not pretend that it will be straightforward to apply our newest tool – digital technology – to achieve those objectives. In “Death, Life and Place in Great Digital Cities“, I explored the potential for unintended consequences when applying technology in cities, and compared them to the ongoing challenge of balancing the impacts and benefits of the previous generations of technology that shaped the cities we live in today – elevators, concrete and the internal combustion engine. Those technologies enabled the last century of growth; but in some cases have created brutal and inhumane urban environments which limit the quality of life that is possible within them.

But there are nevertheless many ways for cities in every circumstance imaginable to benefit from Smarter City ideas, as I described in my presentation earlier this year to the United Nations Commission on Science and Technology for Development, “Science, technology and innovation for sustainable cities and peri-urban communities“.

The first step in doing so is for each city and community to decide what “Smarter Cities “means to them.

Singapore Traffic Prediction

(A prediction of traffic speed and volume 30 minutes into the future in Singapore. In a city with a growing economy and a shortage of space, the use of technology to enable an efficient transportation system has long been a priority)

1. Define what a “Smarter City” means to you

Many urbanists and cities have grappled with how to define what a “Smart City”, a “Smarter City” or a “Future City” might be. It’s important for cities to agree to use an appropriate definition because it sets the scope and focus for what will be a complex collective journey of transformation.

In his article “The Top 10 Smart Cities On The Planet“, Boyd Cohen of Fast Company defined a Smart City as follows:

“Smart cities use information and communication technologies (ICT) to be more intelligent and efficient in the use of resources, resulting in cost and energy savings, improved service delivery and quality of life, and reduced environmental footprint–all supporting innovation and the low-carbon economy.”

IBM describes a Smarter City in similar terms, more specifically stating that the role of technology is to create systems that are “instrumented, interconnected and intelligent.”

Those definitions are useful; but they don’t reflect the different situations of cities everywhere, which are only very crudely described by the four contexts I identified above. We should not be critical of any of the general definitions of Smarter Cities; they are useful in identifying the nature and scope of powerful ideas that could have widespread benefits. But a broad definition will never provide a credible direction for any individual city given the complexities of its challenges, opportunities, context and capabilities.

Additionally, definitions of “Smarter Cities” that are based on relatively advanced technology concepts don’t reflect the origins of the term “Smart” as recognised by the social scientists I met with in July at a workshop at the University of Durham.  The “Smart” idea is more than a decade old, and emerged from the innovative use of relatively basic digital technologies to stimulate economic growth, community vitality and urban renewal.

As I unifying approach, I’ve therefore come recently to conceive of a Smarter City as follows:

A Smarter City systematically creates and encourages innovations in city systems that are enabled by technology; that change the relationships between the creation of economic and social value and the consumption of resources; and that contribute in a coordinated way to achieving a vision and clear objectives that are supported by a consensus amongst city stakeholders.

In co-creating a consensual approach to “Smarter Cities” in any particular place, it’s important to embrace the richness and variety of the field. Many people are very sceptical of the idea of Smarter Cities; often I find that their scepticism arises from the perception that proponents of Smarter Cities are intent on applying the same ideas everywhere, regardless of their suitability, as I described in Smarter City myths and misconceptions” in July.

For example, highly intelligent, multi-modal transport infrastructures are vital in cities such as Singapore, where a rapidly growing economy has created an increased demand for transport; but where there is no space to build new road capacity. But they are much less relevant – at least in the short term – for cities such as Sunderland where the priority is to provide better access to digital technology to encourage the formation and growth of new businesses in high-value sectors of the economy. Every city, individual or organisation that I know of that is successfully pursuing a Smarter City initiative or strategy recognises and engages with that diversity,

Creating a specific Smarter City vision is therefore a task for each city to undertake for itself, taking into account its unique character, strengths and priorities. This process usually entails a collaborative act of creativity by city stakeholders – I’ll explore how that takes place in the next section.

To conclude, it’s likely that the following generic objectives should be considered and adapted in that process:

  • A Smarter City is in a position to make a success of the present: for example, it is economically active in high-value industry sectors and able to provide the workforce and infrastructure that companies in those sectors need.
  • A Smarter City is on course for a successful future: with an education system that provides the skills that will be needed by future industries as technology evolves.
  • A Smarter City creates sustainable, equitably distributed growth: where education and employment opportunities are widely available to all citizens and communities, and with a focus on delivering social and environmental outcomes as well as economic growth.
  • A Smarter City operates as efficiently & intelligently as possible: so that resources such as energy, transportation systems and water are used optimally, providing a low-cost, low-carbon basis for economic and social growth, and an attractive, healthy environment in which to live and work.
  • A Smarter City enables citizens, communities, entrepreneurs & businesses to do their best; because making infrastructures Smarter is an engineering challenge; but making cities Smarter is a societal challenge; and those best placed to understand how societies can change are those who can innovate within them.
  • A Smarter City harnesses technology effectively and makes it accessible; because technology continues to define the new infrastructures that are required to achieve efficiencies in operation; and to enable economic and social growth.

2. Convene a stakeholder group to co-create a specific Smarter City vision

For a city to agree a shared “Smarter City” vision involves bringing an unusual set of stakeholders together in a single forum: political leaders, community leaders, major employers, transport and utility providers, entrepreneurs and SMEs, universities and faith groups, for example. The task for these stakeholders is to agree a vision that is compelling, inclusive; and specific enough to drive the creation of a roadmap of individual projects and initiatives to move the city forward.

It’s crucial that this vision is co-created by a group of stakeholders; as a city leader commented to me last year: “One party can’t bring the vision to the table and expect everyone else to buy into it”.

This is a process that I’m proud to be taking part in in Birmingham through the City’s Smart City Commission, whose vision for the city was published in December. I discussed how such processes can work, and some of the challenges and activities involved, in July 2012 in an article entitled “How Smarter Cities Get Started“.

To be sufficiently creative, empowered and inclusive, the group of stakeholders needs to encompass not only the leaders of key city institutions and representatives of its breadth of communities; it needs to contain original thinkers; social entrepreneurs and agents of change. As someone commented to me recently following a successful meeting of such a group: “this isn’t a ‘usual’ group of people”. In a similar meeting this week, a colleague likened the process of assembling such a group to that of building the Board of a new company.

To attract the various forms of investment that are required to support a programme of “Smart” initiatives, these stakeholder groups need to be decision-making entities, such as Manchester’s “New Economy” Commission, not discussion forums.  They need to take investment decisions together in the interest of shared objectives; and they need a mature understanding and agreement of how risk is shared and managed across those investments.

Whatever specific form a local partnership takes, it needs to demonstrate transparency and consistency in its decision-making and risk management, in order that its initiatives and proposals are attractive to investors. These characteristics are straightforward in themselves; but take time to establish amongst a new group of stakeholders taking a new, collaborative approach to the management of a programme of transformation.

Finally, to create and execute a vision that can succeed, the group needs to tell stories. A Smarter City encompasses all of a city’s systems, communities and businesses; the leaders in that ecosystem can only act with the support of their shareholders, voters, citizens, employees and neighbours. We will only appeal to such a broad constituency by telling simple stories that everyone can understand. I discussed some of the reasons that lead to this in “Better stories for Smarter Cities: three trends in urbanism that will reshape our world” in January and “Little/big; producer/consumer; and the story of the Smarter City” in March. Both articles cover similar ground; and were written as I prepared for my TEDxWarwick presentation, “Better Stories for Smarter Cities”, also in March.

The article “Smart ideas for everyday cities” from December 2012 discusses all of these challenges, and examples of groups that have addressed them, in more detail.

3. Structure your approach to a Smart City by drawing on the available resources and expertise

Any holistic approach to a Smarter City needs to recognise the immensely complex context that a city represents: a rich “system of systems” comprising the physical environment, economy, transport and utility systems, communities, education and many other services, systems and human activities.

(The components of a Smart City architecture I described in “The new architecture of Smart Cities“)

In “The new architecture of Smart Cities” in September 2012 I laid out a framework  for thinking about that context; in particular highlighting the need to focus on the “soft infrastructure” of conversations, trust, relationships and engagement between people, communities, enterprises and institutions that is fundamental to establishing a consensual view of the future of a city.

In that article  I also asserted that whilst in Smarter Cities we are often concerned with the application of technology to city systems, the context in which we do so – i.e. our understanding of the city as a whole – is the same context as that in which other urban professionals operate: architects, town planners and policy-makers, for example. An implication is that when looking for expertise to inform an approach to “Smarter Cities”, we should look broadly across the field of urbanism, and not restrict ourselves to that material which pertains specifically to the application of technology to cities.

Formal sources include:

  • UN-HABITAT, the United Nations agency for human settlements, which recently published its “State of the World’s Cities 2012/2013” report. UNHABITAT promote socially and environmentally sustainable towns and cities, and their reports and statistics on urbanisation are frequently cited as authoritative. Their 2012/2013 report includes extensive consultation with cities around the world, and proposes a number of new mechanisms intended to assist decision-makers.
  • The Academy of Urbanism, a UK-based not-for-profit association of several hundred urbanists including policy-makers, architects, planners and academics, publishes the “Friebrug Charter for Sustainable Urbanism” in collaboration with the city of Frieburg, Germany. Frieburg won the Academy’s European City of the Year award in 2010 but its history of recognition as a sustainable city goes back further. The charter contains a number of useful principles and ideas for achieving consensual sustainability that can be applied to Smarter Cities.
  • The UK Technology Strategy Board’s “Future Cities” programme (link requires registration) and the ongoing EU investments in Smart Cities are both investing in initiatives that transfer Smarter City ideas and technology from research into practise, and disseminating the knowledge created in doing so.

(Photo by lecercle of a girl in Mumbai doing her homework on whatever flat surface she could find. Her use of a stationary tool usually employed for physical mobility to enhance her own social mobility is an example of the very basic capacity we all have to use the resources available to us in innovative ways)

It is also important to consider how change is achieved in systems as complex as cities. In “Do we need a Pattern Language for Smarter Cities” I noted some of the challenges involve in driving top-down programmes of change; and contrasted them to what can happen when an environment is created that encourages innovation and attempts to influence it to achieve desired outcomes, rather than to adopt particular approaches to doing so. And in “Zen and the art of messy urbanism” I explored the importance of unplanned, informal and highly creative “grass-roots” activity in creating growth in cities, particularly where resources and finances are constrained.

Some very interesting such approaches have emerged from thinking in policy, economics, planning and architecture: the Collective Research Initiatives Trust‘s study of Mumbai, “Being Nicely Messy“; Colin Rowe and Fred Koetter’s “Collage City“; Manu Fernandez’s “Human Scale Cities” project; and the “Massive / Small” concept and associated “Urban Operating System” from Kelvin Campbell and Urban Initiatives, for example have all suggested an approach that involves a “toolkit” of ideas for individuals and organisations to apply in their local context.

The “tools” in such toolkits are similar to the “design patterns“ invented by the town planner Christopher Alexander in the 1970s as a tool for capturing re-usable experience in town planning, and later adopted by the Software industry. I believe they offer a useful way to organise our knowledge of successful approaches to “Smarter Cities”, and am slowly creating a catalogue of them, including the “City information partnership” and “City-centre enterprise incubation“.

A good balance between the top-down and bottom-up approaches can be found in the large number of “Smart Cities” and “Future Cities” communities on the web, such as UBM’s “Future Cities” site; Next City; the Sustainable Cities Collective; the World Cities Network; and Linked-In discussion Groups including “Smart Cities and City 2.0“, “Smarter Cities” and “Smart Urbanism“.

Finally, I published an extensive article on this blog in December 2012 which provided a framework for identifying the technology components required to support Smart City initiatives of different kinds – “Pens, paper and conversations. And the other technologies that will make cities smarter“.

4. Establish the policy framework

The influential urbanist Jane Jacobs wrote in her seminal 1961 work ”The Death and Life of Great American Cities“:

“Private investment shapes cities, but social ideas (and laws) shape private investment. First comes the image of what we want, then the machinery is adapted to turn out that image. The financial machinery has been adjusted to create anti-city images because, and only because, we as a society thought this would be good for us. If and when we think that lively, diversified city, capable of continual, close- grained improvement and change, is desirable, then we will adjust the financial machinery to get that.”

Jacobs’ was concerned with redressing the focus of urban design away from vehicle traffic and back to meeting the daily requirements of human lives; but today, it is similarly true that our planning and procurement practises do not recognise the value of the Smart City vision, and therefore are not shaping the financial instruments to deliver it. This is not because those practises are at fault; it is because technologists, urbanists, architects, procurement officers, policy-makers and planners need to work together to evolve those practises to take account of the new possibilities available to cities through technology.

It’s vitally important that we do this. As I described in November 2012 in “No-one is going to pay cities to become Smarter“, the sources of research and innovation funding that are supprting the first examples of Smarter City initiatives will not finance the widespread transformation of cities everywhere. But there’s no need for them to: the British Property Federation, for example, estimate that £14 billion is invested in the development of new space in the UK each year – that’s 500 times the annual value of the UK Government’s Urban Broadband Fund. If planning regulations and other policies can be adapted to promote investment in the technology infrastructures that support Smarter Cities, the effect could be enormous.

I ran a workshop titled “Can digital technology help us build better cities?” to explore these themes in May at the annual Congress of the Academy of Urbanism in Bradford; and have been exploring them with a number of city Councils and institutions such as the British Standards Institute throughout the year. In June I summarised the ideas that emerged from that work in the article “How to build a Smarter City: 23 design principles for digital urbanism“.

Two of the key issues to address are open data and digital privacy.

As I explored in “Open urbanism:  why the information economy will lead to sustainable cities” in December 2012, open data is a vital resource for creating successful, sustainable, equitable cities. But there are thousands of datasets relevant to any individual city; owned by a variety of public and private sector institutions; and held in an enormous number of fragmented IT systems of varying ages and designs. Creating high quality, consistent, reliable data in this context is a “Brownfield regeneration challenge for the information age”, as I described in October 2012. Planning and procurement regulations that require city information to be made openly available will be an important tool in creating the investment required to overcome that challenge.

(The image on the right was re-created from an MRI scan of the brain activity of a subject watching the film shown in the image on the left. By Shinji Nishimoto, Alex G. Huth, An Vu and Jack L. Gallant, UC Berkley, 2011)

(The image on the right was re-created from an MRI scan of the brain activity of a subject watching the film shown in the image on the left. By Shinji Nishimoto, Alex G. Huth, An Vu and Jack L. Gallant, UC Berkley, 2011)

Digital privacy matters to Smarter Cities in part because technology is becoming ever more fundamental to our lives as more and more of our business is transacted online through e-commerce and online banking. Additionally, the boundary between technology, information and the physical world is increasingly disappearing – as shown recently by the scientists who demonstrated that one person’s thoughts could control another’s actions, using technology, not magic or extrasensory phenomena. That means that our physical safety and digital privacy are increasingly linked – the emergence this year of working guns 3D-printed from digital designs is one of the most striking examples. 

Jane Jacobs defined cities by their ability to provide privacy and safety amongst their citizens; and her thinking is still regarded by many urbanists as the basis of our understanding of cities. As digital technology becomes more pervasive in city systems, it is vital that we evolve the policies that govern digital privacy to ensure that those systems continue to support our lives, communities and businesses successfully.

5. Populate a roadmap that can deliver the vision

In order to fulfill a vision for a Smarter City, a roadmap of specific projects and initiatives is needed, including both early “quick wins” and longer term strategic programmes.

Those projects and initiatives take many forms; and it can be worthwhile to concentrate initial effort on those that are simplest to execute because they are within the remit of a single organisation; or because they build on cross-organisational initiatives within cities that are already underway.

In my August 2012 article “Five roads to a Smarter City” I gave some ideas of what those initiatives might be, and the factors affecting their viability and timing, including:

  1. Top-down, strategic transformations across city systems;
  2. Optimisation of individual infrastructures such as energy, water and transportation;
  3. Applying “Smarter” approaches to “micro-city” environments such as industrial parks, transport hubs, university campuses or leisure complexes;
  4. Exploiting the technology platforms emerging from the cost-driven transformation to shared services in public sector;
  5. Supporting the “Open Data” movement.

In “Pens, paper and conversations. And the other technologies that will make cities smarter” in December 2012, I described a framework for identifying the technology components required to support Smart City initiatives of different kinds, such as:

  1. Re-engineering the physical components of city systems (to improve their efficiency)
  2. Using information  to optimise the operation of city systems
  3. Co-ordinating the behaviour of multiple systems to contribute to city-wide outcomes
  4. Creating new marketplaces to encourage sustainable choices, and attract investment

The Smarter City design patterns I described in the previous section also provide potential ideas, including City information partnerships and City-centre enterprise incubation; I’m hoping shortly to add new patterns such as Community Energy Initiatives, Social Enterprises, Local Currencies and Information-Enabled Resource Marketplaces.

It is also worthwhile to engage with service and technology providers in the Smart City space; they have knowledge of projects and initiatives with which they have been involved elsewhere. Many are also seeking suitable locations in which to invest in pilot schemes to develop or prove new offerings which, if successful, can generate follow-on sales elsewhere. The “First of a Kind” programme in IBM’s Research division is one example or a formal programme that is operated for this purpose.

A roadmap consisting of several such individual activities within the context of a set of cross-city goals, and co-ordinated by a forum of cross-city stakeholders, can form a powerful programme for making cities Smarter.

(Photo of the Brixton Pound by Charlie Waterhouse)

6. Put the financing in place

A crucial factor in assessing the viability of those activities, and then executing them, is putting in place the required financing. In many cases, that will involve cities approaching investors or funding agencies. In “Smart ideas for everyday cities” in December 2012 I described some of the organisations from whom funds could be secured; and some of the characteristics they are looking for when considering which cities and initiatives to invest in.

But for cities to seek direct funding for Smarter Cities is only one approach; I compared it to four other approaches in “Gain and responsibility: five business models for sustainable cities” in August:

  1. Cross-city Collaborations
  2. Scaling-up Social Enterprise
  3. Creativity in finance
  4. Making traditional business sustainable
  5. Encouraging entrepreneurs everywhere

The role of traditional business is of particular importance. Billions of us depend for our basic needs – not to mention our entertainment and leisure – on global supply chains operated on astounding scales by private sector businesses. Staples such as food, cosmetics and cleaning products consume a vast proportion of the world’s fresh water and agricultural capacity; and a surprisingly small number of organisations are responsible for a surprisingly large proportion of that consumption as they produce the products and services that many of us use. We will only achieve smarter, sustainable cities, and a smarter, sustainable world, in collaboration with them. The CEOs of  Unilever and Tesco have made statements of intent along these lines recently, and IBM and Hilton Hotels are two businesses that have described the progress they have already made.

There are very many individual ways in which funds can be secured for Smart City initiatives, of course; I described some more in “No-one is going to pay cities to become Smarter” in November 2012, and several others in two articles in September 2012:

In “Ten ways to pay for a Smarter City (part one)“:

And in “Ten ways to pay for a Smarter City (part two):

I’m a technologist, not a financier or economist; so those articles are not intended to be exhaustive or definitive. But they do suggest a number of practical options that can be explored.

(The discussion group at #SmartHack in Birmingham, described in “Tea, trust and hacking – how Birmingham is getting Smarter“, photographed by Sebastian Lenton)

 

7. Think beyond the future and engage with informality: how to make “Smarter” a self-sustaining process

Once a city has become “Smart”, is that the end of the story?

I don’t think so. The really Smart city is one that has put in place soft and hard infrastructures that can be used in a continuous process of reinvention and creativity.

In the same way that a well designed urban highway should connect rather than divide the city communities it passes through, the new technology platforms put in place to support Smarter City initiatives should be made open to communities and entrepreneurs to constantly innovate in their own local context. As I explored in “Smarter city myths and misconceptions” this idea should really be at the heart of our understanding of Smarter Cities.

I’ve explored those themes frequently in articles on this blog; including the two articles that led to my TEDxWarwick presentation, “Better stories for Smarter Cities: three trends in urbanism that will reshape our world” and “Little/big; producer/consumer; and the story of the Smarter City“. Both of them explored the importance of large city institutions engaging with and empowering the small-scale hyperlocal innovation that occurs in cities and communities everywhere; and that is often the most efficient way of creating social and economic value.

I described that process along with some examples of it in “The amazing heart of a Smarter City: the innovation boundary” in August 2012. In October 2012, I described some of the ways in which Birmingham’s communities are exploring that boundary in “Tea, trust and hacking: how Birmingham is getting smarter“; and in November I emphasised in “Zen and the art of messy urbanism” the importance of recognising the organic, informal nature of some of the innovation and activity within cities that creates value.

The Physicist Geoffrey West is one of many scientists who has explored the roles of technology and population growth in speeding up city systems; as our world changes more and more quickly, our cities will need to become more agile and adaptable – technologists, town planners and economists all seem to agree on this point. In “Refactoring, nucleation and incubation: three tools for digital urban adaptability” I explored how ideas from all of those professions can help them to do so.

Smarter, agile cities will enable the ongoing creation of new products, services or even marketplaces that enable city residents and visitors to make choices every day that reinforce local values and synergies. I described some of the ways in which technology could enable those markets to be designed to encourage transactions that support local outcomes in “Open urbanism: why the information economy will lead to sustainable cities” in October 2012 and “From Christmas lights to bio-energy: how technology will change our sense of place” in August 2012. The money-flows within those markets can be used as the basis of financing their infrastructure, as I discussed in “Digital Platforms for Smarter City Market-Making” in June 2012 and in several other articles described in “5. Put the financing in place” above.

Commentary: a new form of leadership

Andrew Zolli’s book “Resilience: why things bounce back” contains many examples of “smart” initiatives that have transformed systems such as emergency response, agriculture, fishing, finance and gang culture, most, but not all, of which are enabled by technology.

A common theme from all of them is productive co-operation and co-creation between large formal organisations (such as businesses and public sector institutions) and informal community groups or individuals (examples in Resilience include subsistence farmers, civic activitists and pacific island fishermen). Jared Diamond made similar observations about successful examples of socially and environmentally sustainable resource extraction businesses, such as Chevron’s sustainable operations in the Kutubu oilfield in Papua New Guinea, in his book “Collapse“.

Zolli identified a particular style of individual behaviour that was crucial in bringing about these collaborations that he called “translational leadership“: the ability to build new bridges; to bring together the resources of local communities and national and international institutions; to harness technology at appropriate cost for collective benefit; to step in and out of institutional and community behaviour and adapt to different cultures, conversations and approaches to business; and to create business models that balance financial health and sustainability with social and environmental outcomes.

That’s precisely the behaviour and leadership that I see in successful Smarter Cities initiatives. It’s sometimes shown by the leaders of public authorities, Universities or private businesses; but it’s equally often shown by community activists or entrepreneurs.

For me, this is one of the most exciting and optimistic insights about Smarter Cities: the leaders who catalyse their emergence can come from anywhere. And any one of us can choose to take a first step in the city where we live.

Gain and responsibility: five business models for sustainable cities

(Photo by Mark Vauxhall of public Peugeot Ions on Rue des Ponchettes, Nice, France)

It’s strange how you can find inspiration in the most surprising places; and the first time I came across the philosophy of sustainability at the heart of big business was certainly unexpected.

Five years ago I was creating a business model in a UK city for a car-sharing scheme using social media (which at the time was a new technology); the scheme was being put together by a collaboration of technology entrepreneurs, University researchers and local employers who wanted to offer the scheme to their employees as a benefit in kind. What we lacked was a business partner with expertise in offering transport services to consumers.

A colleague suggested we speak to an international car rental company for whom they’d recently run an innovation workshop. Initially, we were sceptical: why would a car rental company encourage people to share cars – in other words, to need to hire less of them?

Nevertheless, we called the global Vice President of Sales of the company concerned. This person was responsible for the sales performance of a company in an extremely competitive, commoditised market, so we were expecting the social and environmental philosophy behind our proposal to be given little consideration compared to its revenue-earning potential.

Instead, I remember feeling as if I was being blown away down the telephone line by  his enthusiasm for sustainable business. The reason he had spent his career making a car rental company as successful as possible was his belief that it was the most viable business model for sustainable transport of its time: hire cars are much more effective than public transport for some journeys; and because they are heavily used throughout their lives, the environmental cost of manufacturing and decommissioning them is much less per mile travelled than for privately owned vehicles.

The proposition that technology offers to the sustainability debate – whether in Smarter Cities, intelligent transport or supply-chain optimisation – is to enable business models that create better social and environmental outcomes. In some cases, those outcomes are the objectives of a business; but more often they are the side effects of business operations whose objectives are to create financial returns. So in order to justify investments in technologies or practises that promote sustainability, we need to do just what the car rental company’s Vice President had done early in his career: think creatively about how to balance social and environmental outcomes with the financial imperatives of our existing economic systems.

We’ll need to find that balance in order to develop realistic business models for Smarter Cities. It will not always be an easy balance to find; and finding it will sometimes be a controversial process. But five approaches can already be seen that show how it can be achieved in different ways.

1. Cross-city Collaborations

Many initiatives that contribute to city-wide outcomes require either co-ordinated action across city systems; or an investment in one system to achieve an outcome that is not a simple financial return within that system. For example, the ultimate objective of many changes to transportation systems is to improve economic growth and productivity, or to reduce environmental impact.

Such initiatives are often shaped and carried out by a group of collaborating stakeholders in a city – perhaps including the City Council, nearby Universities, local businesses and community groups, and private sector partners.

To attract the various forms of investment that are required to support a programme of “Smart” initiatives, these partnerships need to be decision-making entities, not discussion groups. Investors will look for a history of collective action to achieve clear, shared objectives; and for a mature approach to the mutual management of risk in delivering projects.

Such partnerships take time to form, and it is notable that in last year’s Technology Strategy Board Future Cities Demonstrator competition, most of the shortlisted entries had been prepared by collaborations in cities such as Glasgow and Peterborough that had existed for some time before the competition began. Other examples include the Dublinked information-sharing partnership in Dublin, Ireland, and the Sustainable Dubuque partnership in Dubuque, Iowa. I wrote about these examples and discussed how they form and operate successfully, in “Smart ideas for everyday cities” last December.

2. Scaling-up Social Enterprise

Social enterprise is a broad category of private businesses which in some way commit themselves to social and/or environmental objectives against which they audit themselves alongside their financial performance – a practise known as triple bottom-line accounting.

Given the similarities between triple-bottom-line accounting and the objectives of “Smarter” initiatives, it’s not surprising that social enterprises are carrying out a great deal of “Smart City” activity. They often use innovative, technology-enabled business models that combine elements of sectors such as food, energy and transport. A good example is “Casserole Club“, which uses social media as the basis of a peer-to-peer model which connects people who are unable to cook for themselves with people who are willing to cook for, and visit, others.

(Photo by Mermaid of the People’s Supermarket in Lamb’s Conduit Street, London, a social enterprise that aims to promote social cohesion by supporting local, independent food producers)

Social enterprises have a powerful potential to contribute to Smarter City objectives. They tend to create employment opportunities where they are most needed, for example – 39% of all social enterprises are working in the most deprived communities in the UK, in comparison to 13% of SMEs. And they are a significant contribution to the overall economy – in the UK,  a recent government report found that the sector employs more than 2 million people, is estimated to have total annual incomes of £163 billion and to contribute £55 billion Gross Value Added – about 14% of the national total. Social enterprise is 13% of Sweden’s GDP and 21% of Finland’s GDP; and 4 in 10 residents of the USA– the world’s flagship private enterprise economy – are members of a co-operative of some sort. Worldwide, social enterprises employ over 100 million people with a turnover of £1.1 trillion. That’s big business.

Many social enterprises are entirely independent ventures. There is great potential for cities to recognise the alignment between their philosophy and Smarter City objectives; and to support their role in achieving them. When the resources and assets of large, formal organisations are made available to local, social innovation, the results can be tremendously powerful.

In Resilience, Andrew Zolli gives the example of the Kilimo Salama scheme in Kenya which provides affordable insurance for subsistence farmers by using remote weather monitoring to trigger payouts via mobile phones, rather than undertaking expensive site visits to assess claims. This is a good example of large-scale infrastructures operated by formal institutions – mobile payments systems and remote weather monitoring technology – that have been adapated to the needs of a community which previously didn’t benefit from them – the farmers – by a creative, socially-minded organisation.

Awareness is growing of the importance of this sector; the alignment of its values with the objectives of Smarter Cities (as described by Knight Foundation Vice President Carol Coletta recently); and of the great potential of information economy technologies, especially social media, to empower it (see this article by ex-IBM Vice President Irving Wladawsky-Berger). It will be a major part of the economy and society of the sustainable cities of the future.

3. Creativity in finance

We don’t consider banks, insurers and other financial institutions enough in the world of Smarter Cities. Public sector and research grants will not finance the wholescale transformation of our cities; we will have to look to the broader financial markets for that support.

New forms of financial service are emerging from the online, collaborative economy such as crowdfunding and peer-to-peer lending. In the UK, the Trillion Fund, for example, offer a range of investment schemes in renewable energy to the retail investment market; and a variety of local and electronic currencies are emerging.

(Photo of a smart parking meter in San Francisco by Jun Seita)

More traditional financial institutions are also exploring the new products that they can create to support this market; and we are sure to need the depth of resources they can make available. Smarter city services create assets and offer services which people and businesses pay to use. With the appropriate banking, insurance and investment skills, those assets and services and the incomes they generate can be packaged as investable financial products. Citibank, IBM and Streetline partnered last year to offer a financing scheme for “Smart Parking” solutions, for example.

Citigroup were also amongst those who supported the recent “Innovation and the City” report by the Centre for an Urban Future and the Robert F. Wagner Graduate School of Public Service which recommended 15 policies for consideration by the next Mayor of New York, many of which are financial innovations intended to support Smarter City outcomes.

In recent years, the banking industry has not always been associated with social outcomes. But some financial institutions are very clearly social organisations – such as the credit unions to which 87 million US citizens belong; and many banks have social elements in their original charters – as Hancock Bank demonstrated when responding to Hurricane Katrina in 2005. They have the means, method and opportunity to contribute enormously to the development of Smarter, sustainable cities and we should encourage them to do so.

4. Making traditional business sustainable

A very many of our lives depend for our basic needs – not to mention our entertainment and leisure – on global supply chains operated on astounding scales by private sector businesses. Staples such as food, cosmetics and cleaning products consume a vast proportion of the world’s fresh water and agricultural capacity; and a surprisingly small number of organisations are responsible for a surprisingly large proportion of that consumption as they produce the products and services that many of us use.

The social and environmental impact of those supply chains is immense, and, of course, highly controversial. A notable recent development, though, is the number of statements made by the leaders of companies involved in them asserting the importance of evolving their businesses to adopt more sustainable practises. The CEOs of  Unilever and Tesco have made statements of intent along these lines recently, and IBM and Hilton Hotels have described the progress they have already made.

Any analysis of the motivations for such statements and the outlook for their impact also enters areas of great controversy, of course. But need there be any fundamental contradiction between profitable enterprise and sustainability?

Richard Powers’ 1998 novel “Gain” tells the story of “incorporation”, the creation of companies as entities with a legal and financial existence separate from that of the people who start, manage and work for them. It contrasts the story of three Irish brothers arriving in 19th Century New York who make a living manufacturing soap, and the subsequent growth of their business into a vast 20th Century multinational corporation; with that of a woman dying from a cancer likely to have been caused by exposure to the waste products of the industrial operations of that corporation. Its complex, nuanced story explores both the facility of private enterprise to create wealth for anybody; and its potential for ambivalence towards the fair distribution of that wealth, and towards its impact.

(An example from Indonesia of the deforestation that can be the result of palm oil production. Photo by the Rainforest Action Network)

Gain’s narrative makes clear that the model of private enterprise does not lead inevitably to any specific outcome. The success, sustainability and equitability of any enterprise, social or private, are ultimately the result of the actions and decisions of those involved in it – whether they run it; work for it; supply it or buy from it.

All of us can assert influence on the sustainability of business, through our buying decisions as consumers and by campaigning. Jared Diamond explored in depth how we can do so effectively in his book “Collapse“. But the role of the investment markets is also crucial.

In one sense, the markets are already playing a role: in a recent report, 53% of fund managers collectively responsible for $14 trillion of assets indicated that they had divested stocks, or chosen not to invest in stocks, due to concerns over the impact of climate change on the businesses concerned.

However, that is a negative, not a positive action. It is driven by the impact of climate change on business, not by the impact of business on climate change. To grossly generalise, whilst the CEOs of Tesco and Unilever, for example, are following Jared Diamond’s argument that sustainability is simply good, long-term business sense; by and large investors are largely ambivalent to this argument. They choose which companies to invest in based first and foremost on the prospect of their short-term financial returns.

So whatever motivations influence the CEOs of companies that manage the vast supply chains that play such a major role on our planet to adopt sustainability as a business objective, it is not to win short-term investment. It may be to appeal to consumer opinion; or it may be to attract investors who take a longer-view.

One thing is certain, though. Our world as a whole, and the cities in which life is concentrated, will not become socially and environmentally equitable and sustainable unless private businesses adopt sustainable strategies. So it is in all of our interests to encourage them to do so, whilst putting in place the governance to ensure that those strategies are carried out effectively.

5. Encouraging entrepreneurs everywhere

Smarter city services are innovations that change the relationships between the creation of social and financial value and the consumption of resources: they involve new ways of doing things; and they often depend on consumers choosing to buy different products or use different services than those that they are accustomed to.

Investing in a new product or service on the basis that consumers will change their behaviour in order to buy or use it is a risky business. Too risky, in many cases, for traditional institutions.

In the developed world, public sector finances are under extreme pressure. Economic growth is slow, so tax returns are stagnant. Populations are, on the whole, growing older, and requiring increased levels of healthcare. So public sector has little ability to make risky investments.

But the private sector is also under pressure. The same slow economic growth, coupled with competition from rapidly growing countries in emerging markets, means that money is short and the future is uncertain. Risky investments are unlikely here, too.

(The QR code that enabled Will Grant of Droplet to buy me a coffee at Innovation Birmingham using Droplet’s local smartphone payment solution, an example of a Smarter City service created by an entrepreneurial company.)

But some investors are seeking new investment opportunities, even risky ones – especially as the rate of return offered by many traditional forms of investment is so poor. One consequence is that many Smarter Cities services are delivered by entrepreneurial companies backed by venture capital. Examples include “Droplet“, a smartphone payment system operating in Birmingham and London; and Shutl, who provide a marketplace for home delivery services through a community of independent couriers in London.

However, many cities face a challenge in exploiting the ability of entrepreneurial businesses to deliver Smarter services.

Such businesses may be inherently risky; but those that succeed still do so by minimising risk wherever possible. One way to minimise the risk involved in any new business is to operate that business as closely as possible to its largest possible market. So entrepreneurial businesses that offer services to city ecosystems (as opposed to national or international customers) tend to start in and provide services to capital cities.

If cities that are not capitals wish to encourage this sort of entrepreneurial business, they will need to make themselves attractive in some other way: by offering tailored programmes of support (as IBM and Sunderland Software City are doing); by making available unique assets created by geography, culture or existing business clusters (such as the cluster of wireless technology companies in Cambridge); or by exploiting the strength of local teaching and research (as Birmingham are doing through institutions such as Birmingham Ormiston Academy and the Aston Engineering Academy; or as “Science Vale” has long done in Oxfordshire).

Entrepreneurial businesses can and will make a huge contribution to Smarter Cities; and those that succeed will eventually scale their businesses to cities across the world. But in order to benefit from their creativity early, cities that are not capitals will need to take action to attract and support them.

Evolution and revolution

As I remarked in my last article on this blog, “business as usual” will not deliver Smarter, sustainable cities. We would not be so collectively concerned with this subject otherwise. But while we will need new approaches, sometimes revolutionary ones; we are not entering wholly uncharted territory.

We will need new cross-city collaborations; but the idea of such collaborations is not new. The collaboration that submitted Peterborough’s short-listed proposal for the Technology Strategy Board’s Future Cities Demonstrator has its origins in the Greater Peterborough Partnership which was formed in 1994, for example.

Social enterprises and sustainable business models are hardly new, either – co-operative businesses have existing for centuries, and IBM, Sony and Cadbury are just three examples of private businesses started 50 to 100 years ago by Quakers with a strong sense of civic and community duty.

So whilst change is required, we are not entering the unknown. Our challenge is rather to realise that there is no single approach that can be adopted in all circumstances. All of the approaches I’ve described in this article – and doubtless others too – will be needed. But not all of them will be popular all of the time.

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