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:

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Intelligent Transport Systems need to get wiser … or transport will keep on killing us

(The 2nd Futurama exhibition at the 1964 New York World’s Fair displayed a vision for the future that in many ways reflected the concrete highways and highrises constructed at the time. We now recognise that the environments those structures created often failed to support healthy personal and community life. In 50 years’ time, how will we perceive today’s visions of Intelligent Transport Systems? Photo by James Vaughan)


Two weeks ago the Transport Systems Catapult published a “Traveller Needs and UK Capability Study”, which it called “the UK’s largest traveller experience study” – a survey of 10,000 people and their travelling needs and habits, complemented by interviews with 100 industry experts and companies. The survey identifies a variety of opportunities for UK innovators in academia and industry to exploit the predicted £56 billion market for intelligent mobility solutions in the UK by 2025, and £900 billion market worldwide. It is rightly optimistic that the UK can be a world leader in those markets.

This is a great example of the enormous value that the Catapult programme – inspired by Germany’s Fraunhofer Institutes – can play in transferring innovation and expertise out of University research and into the commercial economy, and in enabling the UK’s expert small businesses to reach opportunities in international markets.

But it’s also a great example of failing to connect the ideas of Intelligent Transport with their full impact on society.

I don’t think we should call any transport initiative “intelligent” unless it addresses both the full relationship between the physical mobility of people and goods with social mobility; and the significant social impact of transport infrastructure – which goes far beyond issues of congestion and pollution.

The new study not only fails to address these topics, it doesn’t mention them at all. In that light, such a significant report represents a failure to meet the Catapult’s own mission statement, which incorporates a focus on “wellbeing” – as quoted in the introduction to the report:

“We exist to drive UK global leadership in Intelligent Mobility, promoting sustained economic growth and wellbeing, through integrated, efficient and sustainable transport systems.” [My emphasis]

I’m surprised by this failing in the study as both the engineering consultancy Arup and the Future Cities Catapult – two organisations that have worked extensively to promote human-scale, walkable urban environments and human-centric technology – were involved in its production; as was at least one social scientist (although the experts consulted were otherwise predominantly from the engineering, transport and technology industries or associated research disciplines).

I note also that the list of reports reviewed for the study does not include a single work on urbanism. Jane Jacobs’ “The Death and Life of Great American Cities”, Jan Gehl’s “Cities for People“, Jeff Speck’s “Walkable City” and Charles Montgomery’s “The Happy City“, for example, all describe very well the way that transport infrastructures and traffic affect the communities in which most of the world’s population lives. That perspective is sorely lacking in this report.

Transport is a balance between life and death. Intelligent transport shouldn’t forget that.

These omissions matter greatly because they are not just lost areas of opportunity for the UK economy to develop solutions (although that’s certainly what they are). More importantly, transport systems that are designed without taking their full social impact into account have the most serious social consequences – they contribute directly to deprivation, economic stagnation, a lack of social mobility, poor health, premature deaths, injuries and fatalities.

As town planner Jeff Speck and urban consultant Charles Montgomery recently described at length in “Walkable City” and “The Happy City” respectively, the most vibrant, economically successful urban environments tend to be those where people are able to walk between their homes, places of work, shops, schools, local transport hubs and cultural amenities; and where they feel safe doing so.

But many people do not feel that it is safe to walk about the places in which they live, work and relax. Transport is not their only cause of concern; but it is certainly a significant one.

After motorcyclists (another group of travellers who are poorly represented), pedestrians and cyclists are by far the most likely travellers to be injured in accidents. According to the Royal Society for the Prevention of Accidents, for example, more than 60 child pedestrians are killed or injured every week in the UK – that’s over 3000 every year. No wonder that the number of children walking to school has progressively fallen as car ownership has risen, contributing (though it is obviously far from the sole cause) to rising levels of childhood obesity. In its 60 pages, the Traveller Needs study doesn’t mention the safety of pedestrians at all.

A recent working paper published by Transport for London found that the risk and severity of injury for different types of road users – pedestrians, cyclists, drivers, car passengers, bus passengers etc. – vary in complex and unexpected ways; and that in particular, the risks for each type of traveller vary very differently according to age, as our personal behaviours change, depending on the journeys we undertake, and according to the nature of the transport infrastructure we use.

These are not simple issues, they are deeply challenging. They are created by the tension between our need to travel in order to carry out social and economic interactions, and the physical nature of transport which takes up space and creates pollution and danger.

As a consequence, many of the most persistently deprived areas in cities are badly affected by large-scale transport infrastructure that has been primarily designed in the interests of the travellers who pass through them, and not in the interests of the people who live and work around them.

(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 city centre before its redevelopment in 2003, by Birmingham City Council)

Birmingham’s Masshouse circus, for example, 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” that the ring-road created around the city centre. 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. The reason for such a sharp change in value? People didn’t feel safe walking across or under the roundabout. The demolition of Masshouse Circus in 2002 enabled a revitalisation of the city centre that has continued for more than a decade.

Atlanta’s Buford Highway is a seven lane road which for two miles has no pavements, no junctions and no pedestrian crossings, passing through an area of houses, shops and businesses. It is an infrastructure fit only for vehicles, not for people. It allows no safe access along or across it for the communities it passes through – it is closed to them, unless they risk their lives.

In Sheffield, two primary schools were recently forced to close after measurements of pollution from diesel vehicles revealed levels 10-15 times higher than those considered the maximum safe limits, caused by traffic from the nearby M1 motorway. The vast majority of vehicles using the motorway comply to the appropriate emissions legislation depending on their age; and until specific emissions measurements were performed at the precise locations of the schools, the previous regional measurements of air quality had been within legal limits. This illustrates the failure of our transport policies to take into account the nature of the environments within which we live, and the detailed impact of transport on them. That’s why it’s now suspected that up to 60,000 people die prematurely every year in the UK due to the effects of diesel emissions, double previous estimates.

Nathaniel Lichfield and Partners recently published a survey of the 2015 Indices of Multiple Deprivation in the UK – the indices summarise many of the challenges that affect deprived communities such as low levels of employment and income; poor health; poor access to quality education and training; high levels of crime; poor quality living environments and shortages of quality housing and services.

Lichfield and Partners found that most of the UK’s Core Cities (the eight economically largest cities outside London, plus Glasgow and Cardiff) are characterised by a ring of persistently deprived areas surrounding their relatively thriving city centres. Whilst clearly the full causes are complex, it is no surprise that those rings feature a concentration of transport infrastructure passing through them, but primarily serving the interests of those passing in and out of the centre.

Birmingham IMD cropped

(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)

These issues are not considered at all in the Transport Systems Catapult’s study. The word “walk” appears just three times in the document, all in a section describing the characteristics of only one type of traveller, the “dependent passenger” who does not own a car. Their walking habits are never examined, and walking as a transport choice is never mentioned or presented as an option in any of the sections of the report discussing challenges, opportunities, solutions or policy initiatives, beyond a passing mention that public transport users sometimes undertake the beginnings and ends of their journeys on foot. The word “pedestrian” does not appear at all. Cycling is mentioned only a handful of times; once in the same section on dependent passengers, and later on to note that “bike sharing [schemes have] not yet enjoyed high uptake in the UK”. The reason cited for this is that “it is likely that there are simply not enough use cases where using these types of services is convenient and cost-effective for travellers.”

If that is the case, why not investigate ways to extend the applicability of such schemes to broader use cases?

If only the sharing economy were a walking and cycling economy

The role of the Transport Systems Catapult is to promote the UK transport and transport technology industry, and this perhaps explains why so much of the study is focussed on public and private forms of powered transport and infrastructure. But there are many ways for businesses to profit by providing innovative technology and services that support walking and cycling.

What about way-finding services and street furniture that benefit pedestrians, for example, as the Future Cities Catapult recently explored? What about the cycling industry – including companies providing cargo-carrying bicycles as an alternative to small vans and trucks? What about the wearable technology industry to promote exercise measurement and pedestrian navigation along the safest, least polluted routes?

What about the construction of innovative infrastructure that promotes cycling and walking such as the “SkyCycle” proposal to build cycle highways above London’s railway lines, similar to the pedestrian and cycle roundabouts already built in Europe and China? What about the use of conveyor belts along similar routes to transport freight? What about the use of underground, pneumatically powered distribution networks for recycling and waste processing? All of these have been proposed or explored by UK businesses and universities.

And what about the UK’s world-class community of urban designers, town planners and landscape architects, some of whom are using increasingly sophisticated technologies to complement their professional skills in designing places and communities in which living, working and travelling co-exist in harmony? What about our world class University expertise researching visions for sustainable, liveable cities with less intrusive transport systems?

An even more powerful source of innovations to achieve a better balance between transportation and liveability could be the use of “sharing economy” business models to promote social and economic systems that emphasise local, human-powered travel.

Wikipedia describes the sharing economy as “economic and social systems that enable shared access to goods, services, data and talent“. Usually, these systems employ consumer technologies such as SmartPhones and social media to create online peer-to-peer trading networks that disrupt or replace traditional supply chains and customer channels – eBay is an obvious example for trading second hand goods, Airbnb connects travellers with people willing to rent out a spare room, and Uber connects passengers and drivers.

These business models can be enormously successful. Since its formation 8 years ago, Airbnb has acquired access to over 800,000 rooms to let in more than 190 countries; in 2014 the estimated value of this company which employed only 300 people at the time was $13 billion. Uber has demonstrated similarly astonishing growth.

However, it is much less clear what these businesses are contributing to society. In many cases their rapid growth is made possible by operating business models that side-step – or just ignore – the regulation that governs the traditional businesses that they compete with. Whilst they can offer employment opportunities to the providers in their trading networks, those opportunities are often informal and may not be protected by employment rights and minimum wage legislation. As privately held companies their only motivation is to return a profit to their owners.

By creating dramatic shifts in how transactions take place in the industries in which they operate, sharing economy businesses can create similarly dramatic shifts in transport patterns. For example, hotels in major cities frequently operate shuttle buses to transfer guests from nearby airports – a shared form of transport. Airbnb offer no such equivalent transfers to their independent accommodation. This is a general consequence of replacing large-scale, centrally managed systems of supply with thousands of independent transactions. At present there is very little research to understand these impacts, and certainly no policy to address them.

But what if incentives could be created to encourage the formation of sharing economy systems that promoted local transactions that can take place with less need for powered transport?

For example, Borroclub provides a service that matches someone who needs a tool with a neighbour who owns one that they could borrow. Casserole Club connects people who are unable to cook for themselves with a neighbours who are happy to cook and extra portion and share it. The West Midlands Collaborative Commerce Marketplace identifies opportunities for groups of local businesses to collaborate to win new contracts. Such “hyperlocal” schemes are not a new idea, and there are endless possibilities for them to reveal local opportunities to interact; but they struggle to compete for attention and investment against businesses purely focussed on maximising profits and investor returns.

Surely, a study that includes the Future Cities Catapult, Digital Catapult and Transport Systems Catapult amongst its contributors could have explored possibilies for encouraging and scaling hyperlocal sharing economy business models, alongside all those self-driving cars and multi-modal transport planners that industry seems to be quite willing to invest in on its own?

The study does mention some “sharing economy” businesses, including Uber; but it makes no mention of the controversy created because their profit-seeking focus takes no account of their social, economic and environmental impact.

It also mentions the role of online commerce in providing retail options that avoid the need to travel in person – and cites these as an option for reducing the overall demand for travel. But it fails to adequately explore the impact of the consequent requirements for delivery transport – other than to note the potential for detrimental impact on, let’s wait for it, not local communities but: local traffic!

“Enabling lifestyles is about more than just enabling and improving physical travel. 31% (19bn) of journeys made today would rather not have been made if alternative means were available (e.g. online shopping)” (page 15)

“Local authorities and road operators need to be aware that increased goods delivery can potentially have a negative impact on local traffic flows.” (page 24)

Why promote transactions that we carry out in isolation online rather than transactions that we carry out socially by walking, and that could contribute towards the revitalisation of local communities and town centres? Why mention “enabling lifestyles” without exploring the health benefits of walking, cycling and socialising?

(A poster from the International Sustainability Institute's Commuter Toolkit, depicting the space 200 travellers occupy on Seattle's 2nd Avenue when using different forms of transport, and intended to persuade travellers to adopt those forms that use less public space)

(A poster from the International Sustainability Institute’s Commuter Toolkit, depicting the space 200 travellers occupy on Seattle’s 2nd Avenue when using different forms of transport, and intended to persuade travellers to adopt those forms that use less public space)

Self-driving cars as a consumer product represent selfish interests, not societal interests

The sharing economy is not the only example of a technology trend whose social and economic impact cannot be assumed to be positive. The same challenge applies very much to perhaps the most widely publicised transport innovation today, and one that features prominently in the new study: the self-driving car.

On Friday I attended a meeting of the UK’s Intelligent Transport Systems interest group, ITS-UK. Andy Graham of White Willow Consulting gave a report of the recent Intelligent Transport Systems World Congress in Bordeaux. The Expo organisers had provided a small fleet of self-driving cars to transfer delegates between hotels and conference venues.

Andy noted that the cars drove very much like humans did – and that they kept at least as large, if not a larger, gap between themselves and the car in front. On speaking to the various car manufacturers at the show, he learned that their market testing had revealed that car buyers would only be attracted to self-driving cars if they drove in this familiar way.

Andy pointed out that this could significantly negate one of the promoted advantages of self-driving cars: reducing congestion and increasing transport flow volumes by enabling cars to be driven in close convoys with each other. This focus on consumer motivations rather than the holistic impact of travel choices is repeated in the Transport Systems Catapults’ study’s consideration of self-driving cars.

Cars don’t only harm people, communities and the environment if they are diesel or petrol powered and emit pollution, or if they are involved in collisions: they do so simply because they are big and take up space.

Space – space that is safe for people to inhabit – is vital to city and community life. We use it to walk; to sit and relax; to exercise; for our children to play in; to meet each other. Self-driving cars and electric cars take up no less space than the cars we have driven for decades. Cars that are shared take up slightly less space per journey – but are nowhere near as efficient as walking, cycling or public transport in this regard. Car clubs might reduce the need for vehicles to be parked in cities, but they still take up as much space on the road.

The Transport Systems Catapult’s study does explore many means to encourage the use of shared or public transport rather than private cars; but it does so primarily in the interests of reducing congestion and pollution. The relationship between public space, wellbeing and transport is not explored; and neither is the – at best – neutral societal impact of self-driving cars, if their evolution is left to today’s market forces.

Just as the industry and politicians are failing to enact the policies and incentives that are needed to adapt the Smart Cities market to create better cities rather than simply creating efficiencies in service provision and infrastructure, the Intelligent Transport Systems community will fail to deliver transport that serves our society better if it doesn’t challenge our self-serving interests as consumers and travellers and consider the wider interests of society.

The Catapult’s report does highlight the potential need for city-wide and national policies to govern future transport systems consisting of connected and autonomous vehicles; but once again the emphasis is on optimising traffic flows and the traveller experience, not on optimising the outcomes for everyone affected by transport infrastructure and traffic.

As consumers we don’t always know best. In the words of one of the most famous transport innovators in history: “If I had asked people what they wanted, they would have said ‘faster horses’.” (Henry Ford, inventor of the first mass-produced automobile, and of the manufacturing production line).

A failure that matters

The Transport Systems Catapult’s report doesn’t mention most of the issues I’ve explored in this article, and those that it does touch on are quickly passed over. In 60 pages it only mentions walking and cycling a handful of times; it never analyses the needs of pedestrians and cyclists, and beyond a passing mention of employers’ “cycle to work” schemes and the incorporation of bicycle hire schemes in multi-modal ticketing solutions, these modes of transport are never presented as solutions to our transport and social challenges.

This is a failure that matters. The Transport Systems Catapult is only one voice in the Intelligent Transport Systems community, and many of us would do well to broaden our understanding of the context and consequences of our work. For my part when I worked with IBM’s Intelligent Transport Systeams team several years ago I was similarly disengaged with these issues, and focussed on the narrower economic and technological aspects of the domain. It was only later in my career as I sought to properly understand the wider complexities of Smart Cities that I began to appreciate them.

But the Catapult Centre benefits from substantial public funding, is a high profile influencer across the transport sector, and is perceived to have the authority of a relatively independent voice between the public and private sectors. By not taking into account these issues, its recommendations and initiatives run the risk of creating great harm in cities in the UK, and anywhere else our transport industry exports its ideas to.

Both the “Smart Cities” and “Intelligent Transport” communities often talk in terms of breaking down silos in industry, in city systems and in thinking. But in reality we are not doing so. Too many Smart City discussions separate out “energy”, “mobility” and ”wellbeing” as separate topics. Too few invite town planners, urban designers or social scientists to participate. And this is an example of an “Intelligent Transport” discussion that makes the same mistakes.

(Pedestrian’s attempting to cross Atlanta’s notorious Buford Highway; a 7-lane road with no pavements and 2 miles between junctions and crossings. Photo by PBS)

In the wonderful “Walkable City“, Jeff Speck describe’s the epidemiologist Richard Jackson’s stark realisation of the life-and-death significance of good urban design related to transport infrastructure. Jackson was driving along the notorious two mile stretch of Atlanta’s seven lane Buford highway with no pavements or junctions:

“There, by the side of the road, in the ninety-five degree afternoon, he saw a woman in her seventies, struggling under the burden of two shopping bags. He tried to relate her plight to his own work as an epidemiologist. “If that poor woman had collapsed from heat stroke, we docs would have written the cause of death as heat stroke and not lack of trees and public transportation, poor urban form, and heat-island effects. If she had been killed by a truck going by the cause of death would have been “motor vehicle trauma”, and not lack of sidewalks and transit, poor urban planning and failed political leadership.”

We will only harness technology, transport and infrastructure to create better communities and better cities if we seek out and respect those cross-disciplinary insights that take seriously the needs of everyone in our society who is affected by them; not just the needs of those who are its primary users.

Our failure to do so over the last century is demonstrated by the UK’s disgracefully low social mobility; by those areas of multiple deprivation which in most cases have persisted for decades; and by the fact that as a consequence life expectancy for babies born today in the poorest parts of cities in the UK is 20 years shorter than for babies born today in the richest part of the same city.

That is the life and death impact of the transport strategies that we’ve had in the past; the transport strategies we publish today must do better.

Postscript 3rd November

The Transport Systems Catapult replied very positively on Twitter today to my rather forthright criticisms of their report. They said “Great piece Rick. The study is a first step in an ongoing discussion and we welcome further input/ideas feeding in as we go on.”

I’d like to think I’d respond in a similarly gracious way to anyone’s criticism of my own work!

What my article doesn’t say is that the Catapult’s report is impressively detailed and insightful in its coverage of those topics that it does include. I would absolutely welcome their expertise and resources being applied to a broader consideration of the topic of future transport, and look forward to seeing it. 

4 ways to get on with building Smart Cities. And the societal failure that stops us using them.

(

(William Robinson Leigh’s 1908 painting “Visionary City” envisaged future cities constructed from mile-long buildings of hundreds of storeys connected by gas-lit skyways for trams, pedestrians and horse-drawn carriages. A century later we’re starting to realise not only that developments in transport and power technology have eclipsed Leigh’s vision, but that we don’t want to live in cities constructed from buildings on this scale.)

The Smart City refuses to go away
In 2013 Adam Greenfield wrote “Against the Smart City”  in criticism of the large-scale corporate- and government-led projects in cities such as Masdar, Songdo and Rio that had begun to co-opt the original idea of “Smart Communities” and citizens, given a more powerful voice in their own governance by Internet communication, into what he saw – and what some still see – as a “top-down” approach to infrastructure and services divorced from the interest of ordinary citizens.

But despite regular reprisals of this theme accompanied by assertions that the Smart City is a misguided idea that is doomed to die away, notably last year in the UK’s Guardian newspaper, the Smart City has neither been abandoned as mistaken nor faded from prominence as it would have done by now if it were nothing but a technology buzzword. (Whether they have disappeared entirely or simply become everyday parts of the landscape, ideas that once dominated the technology industry such as “Service Oriented Architecture“, “Web 2.0” and “e-business” have risen to prominence and disappeared again within the lifetime of “Smart Cities”).

Instead, the various industry, community, political, academic and design interests associated with the Smart City idea have gradually learned how to combine the large-scale, intelligent infrastructures needed to support the incredible level and speed of urbanisation around the world with the accessible technologies that allow citizens, communities and businesses to adapt those infrastructures to their own needs and create more successful lives for themselves. As a consequence, new cities and new media organisations are still adding to those already debating the idea – I’ve received invitations to new events in the UK, Ireland, Malaysia, China and the Middle East already this year, and mainstream reputable sources such as the Daily Telegraph, Fortune magazine, the Economist and Forbes have covered the trend.

Yet despite all of this interest from industry and the public sector, the reality is that we still haven’t seen significant investment in those ideas on a sustainable basis.

If you read this blog regularly then you’ll know that I don’t believe that our primary focus for funding Smart City initiatives should be through the innovation funds provided by bodies such as Innovate UK or programmes such as the European Union’s Horizon 2020. Those are both great vehicles for driving innovation out of research organisations into business and public services; but for any city facing an acute challenge the bidding processes take too long and consume too many resources; the high levels of competition mean there can be a relatively low chance of receiving funds; and projects funded in this way often don’t solve the challenge of paying for the resulting solution on an ongoing basis. Most of the sustainable solutions that result from them are new business products and services: once the initial funded pilot with a local authority has finished, where does the money come from to pay for an ongoing commercial solution?

There are, however, a clear set of routes to securing sustainable investment that the most forward-looking cities have demonstrated. They don’t require cities to attract flagship technology industries to invest in them as proving-grounds for new products and services; they don’t require the inward investment that comes from international sporting and cultural events; and they’re not the preserve of rich or fast-growing capital cities on the international stage.

They do require senior city leaders – Mayors, Council Leaders and their Executive officers – to adopt and drive them; and they also require collaboration and partnership with other city institutions and with private sector suppliers.

And they require bravery, integrity and commitment from those private sector suppliers – such as my employer Amey – to offer new partnerships to our customers. Smart Cities won’t come about through us selling our products and services in transactional exchanges; they’ll come about through new partnerships in which we agree to share not just the responsibility to invest in technology and innovation, but also responsibility for the risks involved in achieving the objectives that cities care about.

But while these approaches to delivering Smart Cities will require hard and careful work, and real investment in collaboration, they are all accessible to any city that chooses to use them; and there’s no reason at all why that process can’t begin today.

Getting started: agreeing on aspirations

The starting point to putting a Smart City strategy in place is to create a specific, aspirational vision rooted in the challenges, opportunities and capabilities of a particular place and its communities, and that can win support from local stakeholders. I have seen (broadly) two types of Smart Cities visions of this sort created over the last few years.

1. Local Authority visions for digital services and infrastructure

Many local authorities have developed plans for smart, digital local services, coupled with plans for regional investment in infrastructure (such as 4G and broadband connectivity), digital skills and business-enablement. A good example is Hampshire County Council’s “Digital Hampshire” plan (Hampshire is a relatively large and economically healthy County in the UK with a population of 1.3 million and GDP just over £30billion).

One of the earliest examples was Sunderland’s “Economic Masterplan”, which which has driven around £15m of investment by the City Council so far, with further and potentially more significant initiatives now underway. (Sunderland are a medium-sized city in the UK, with a population of approximately 300,000. The city has been focussed for many years on modernising and diversifying its economy following the decline of the shipbuilding and coalmining industries. They are genuine, if often unacknowledged, thought leaders in Smart Cities).

2. City-wide or region-wide collaborative visions

In some cities and regions a wide variety of stakeholders, usually facilitated by a Local Authority or University leader, have developed collaborative plans including commitments and initiatives from local businesses, Universities, transport organisations and service providers as well as government agencies. These visions tend to contain more ambitious plans, for example the provision of “Smart Home” connectivity in new affordable housing developments, multi-modal transport payment schemes, local renewable energy generation schemes etc. London and Birmingham are good examples of this type of plan; and London in particular have used it to drive significant investments in Smart infrastructure through property development.

In both cities, formal collaborations were established to create these visions and drive the strategies to implement them – Birmingham’s Smart City Commission (which I’ve recently re-joined after having been a member of its first incarnation) and London’s Smart London Board (on which I briefly represented IBM before joining Amey).

Whether the first or the second type of plan is the right approach for any specific city, region or community depends on the level of support and collaboration amongst stakeholders in the local authority and the wider city and region – and of course, many plans in reality are somewhere between those two types. If the enthusiasm and leadership are there, neither type of plan need be a daunting process – Oxford recently built a plan of the second type from scratch between the City Council, local Universities and businesses in around 6 months by working with existing local partnerships and networks.

Moving forward: focussing on delivery and practical funding mechanisms

The degree to which cities and regions have then implemented these strategies is determined by how well they’ve focussed on realistic sources of investment and funding. For example, whilst some cities – notably Sunderland and London – have secured significant investments from sustainable sources rather than from research and innovation funds, many others – so far – have not.

I have probably tested some of my relationships with local authorities and innovation agencies to the limit by arguing repeatedly that many Smart City initiatives and debates focus far too much on applying for central Government funds and grants from Research and Innovation funding agencies; and far too little on sustainable business and investment models for new forms of city infrastructure and services.

I make these arguments because there are at least four approaches that any city can use to exploit existing, ongoing streams of funding and investment to implement a Smart City vision in a sustainable way – if their leaders and stakeholders have the conviction to make them happen; and because I passionately believe that these are the mechanisms that can unlock the opportunity for cities across the country and around the world to realise the huge social, economic and environmental benefits that technology developments can enable if they are harnessed in the right way:

  1. Include Smart City criteria in the procurement of services by local authorities to encourage competitive innovation from private sector providers
  2. Encourage development opportunities to include “smart” infrastructure
  3. Commit to entrepreneurial programmes
  4. Enable and support Social Enterprise

(The Sunderland Software Centre, a multi-£million new technology startup incubation facility in Sunderland’s city centre. The Centre is supported by a unique programme of events and mentoring delivered by IBM’s Academy of Technology as a condition of the award of a contract for provision of IT services to the centre, and arising from Sunderland’s Smart City strategy)

1. Include Smart City criteria in the procurement of services by local authorities to encourage competitive innovation from private sector providers

Sunderland City Council are at the forefront of investing in Smart City technology simply by reflecting their aspirations in their procurement practises for the goods and services they need to operate as a Council. They have included objectives from their Economic Masterplan in four procurements for IT solutions now, totalling around £15m – for example, the transformation of their IT infrastructure from a traditional platform to a Cloud computing platform was awarded to IBM based on IBM’s commitment to help the Council to use the Cloud platform to help local businesses, social enterprises, charities and entrepreneurs to succeed.

Whilst specific procurement choices in any given service are different in every case – whether to procure support for in-house delivery or to outsource to an external provider; or whether to form a PFI, Joint Venture or other such partnership structure for example – the principle of using business-as-usual procurements to invest in the Smart agenda is one that can be applied by any local authority or other organisation responsible for the delivery of public or city services or infrastructure.

This approach is dependent on the procurement of outcomes – for example, the quality of road surfaces, the smoothness of traffic flow, contributions to social mobility and small business growth – rather than of capabilities or resources. Outcomes-based procurements between competing providers create the incentive from the release of the tender through to the completion of the contract for private sector providers to invest in innovation and technology to deliver the most competitive offer to the customer.

Over the last 10 months in Amey, where many of our customer relationships are outcomes-based, whether they are with local governments, other public sector organisations or regulated industries such as utilities, I’ve rapidly put together a portfolio of Smart City initiatives that are supported by very straightforward business cases based on those commitments to outcomes. These initiatives are not just making our own operations more cost effective (and safer) – although they are doing both of those, and that’s what guarantees our ongoing financial commitment to them; they are also delivering new social insights, new forms of citizen engagement and new opportunities for community collaboration for our customers.

The stakeholders whose commitment is needed to implement this approach include Local Authority Chief Executives, Council Leaders, Cabinet members and their Chief Financial Officers or Finance Directors, as well as procuring Executives in services such as highways management, parking services, social care, health and wellbeing and IT. They can also include representatives of local transport organisations for initiatives focussed on transport and mobility.

I won’t pretend that an outcomes-based approach is always easy to adopt, either for local government organisations or their suppliers. In particular, if we want to apply this approach to the highest-level Smart City aspirations for social mobility, economic growth and resilience, then there is a need for dialogue between all parties to establish how to express those outcomes in a way that incentivises the private sector to invest in innovation to deliver them; and to do so in a way that both rewards them appropriately for their achievements whilst giving local government and the citizens and communities they serve good value for money and exemplary service.

In discussions at the last meeting of the UK Government’s Smart Cities Forum, recently re-convened after the general election, there was clearly an appetite for that discussion on both sides: but it needs a neutral, trusted intermediary to facilitate it. That’s not a role that anyone is playing at the moment – neither in government, nor in industry, nor in academia, nor in the conference circuit, nor in the various innovation agencies that are active in Smart Cities. It’s a role that we badly need one – or all of them – to step up to.

(The Urban Sciences Building at Newcastle Science Central, a huge, University-driven regeneration project in central Newcastle that combines facilities for the research and development of new solutions for urban infrastructure with on-site smart infrastructure and services)

2. Encourage development opportunities to include “smart” infrastructure
In 2012 after completing their first Smart City Vision, Birmingham City Council asked what was both an obvious and a fundamentally important question – but one that, to my knowledge, no-one had thought to ask before:

“How should our Planning Framework be updated to reflect our Smart City vision?”

Birmingham’s insight has the potential to unlock an incredible investment stream – the British Property Federation estimates that £14billion is spent each year in the UK on new-build developments alone. Just a tiny fraction of that sum would dwarf the level of direct investment in Smart Cities we’ve seen to date.

Birmingham’s resulting “Digital Blueprint” contains 10 “best practise recommendations” for planning and development drawn in part from a wider set that resulted from a workshop that I facilitated for the Academy of Urbanism, a professional body of town planners, urban designers and architects in the UK. The British Standards Institute has recently taken these ideas forward and published guidance that is starting to be used by other cities.

But progress is slow. To my knowledge the only example of these ideas being put into practise in the UK (though I’d love to be proven wrong) is through the Greater London Authority (GLA) and London Legacy Development Corporation (LLDC) who included criteria from the Smart London Plan in their process last year to award the East Wick and Sweetwater development opportunity to the private sector. This is a multi-£100million investment from a private sector pension fund to build 1,500 new homes on the London Olympics site along with business and retail space.

On behalf of IBM last year I contributed several Smart City elements of the winning proposal; it was astonishing to see how straightforward it was to justify committing multi-£million technology investments from the private sector in the development proposal simply because they would enable the construction and development consortium to win the opportunity to generate long-term profits at a much more significant level. Crucially, the LLDC demanded that the benefits of those investments should be felt not just by residents and businesses in the new development; but by residents and businesses in existing, adjoining neighbourhoods.

There is not much information on this aspect of the development in the public domain, but you can get some idea from this blog by the Master Planner subcontracted to the development. A similar approach is now being taken to an even larger redevelopment in London at Old Oak and Park Royal.

If cities in the UK and beyond are to take advantage of this potentially incredibly powerful mechanism, then we need to win over some crucial stakeholders: Local Authority Directors of Planning, regional development agencies, property developers, financiers and construction companies. Local Universities can be ideal partners for this approach – if they are growing and investing in new property development, there is a clear opportunity for their research departments to collaborate with property and infrastructure developers to create Smart City environments that showcase the capabilities of all parties. Newcastle Science Central is an example of this approach; it’s a real shame that elsewhere in the UK some significant investments are being made to extend University property – often on the basis of increased revenues from student fees – with no incorporation of these possibilities, at the same time that those same Universities’ own research groups are making countless bids into competitive research and innovation funds.

3. Commit to entrepreneurial programmes

[Priya Prakash of the entrepreneurial company Design 4 Social Change describes a project she is leading on behalf of Amey to improve citizen engagement with the services that we deliver for our customers]

Many Smart City initiatives are fundamentally business model innovations – new ways of combining financial success and sustainability with social, economic or environmental improvements in services such as transport, utilities or food. And most business model innovations are created by startup companies, funded by Venture Capital investment. Air B’n’B and Uber are two often-cited examples at the moment of how quickly such businesses, based on new, technology-enabled operating models, can create an enormous impact.

What if you could align that impact with the objectives of a city or region?

The “Cognicity” programme run by the Level 39 technology incubator in London’s Canary Wharf financial district has achieved this alignment by linking Venture Capital- and Angel-backed startup companies to the infrastructure requirements of the next phase of development at Canary Wharf. The West Midlands Public Transport Executive Centro and Innovation Birmingham have agreed a similar initiative to advance transport priorities in Birmingham through externally-funded innovation. Oxford are pursuing the same approach through their “Smart Oxford Challenge” in partnership with Nominet, a trust that supports social innovation. And Amey and our parent company Ferrovial are similarly supporting a “Smart Lab” in collaboration with the University of Sheffield and Sheffield City Council.

A variety of stakeholders are vital to creating entrepreneurial programmes that succeed and that crucially can attract finance to support the ideas that they generate – endless unfunded civic hackathons create ideas but too often fail to have an impact due to a lack of funding and a lack of genuine engagement from local authorities to adopt the solutions they make possible. Innovation funding agencies, especially those with a local or social focus are vital; as are the local Universities, technology incubators and social enterprise support organisations that both attract innovators and have the resources to support them. Finally, where they exist, local Angel Investors or Venture Capital organisations have an obvious role to play.

(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.)

4. Enable and support Social Enterprise

The objectives of Smart Cities (which I’d summarise for this purpose as “finding ways to invest in technology to enable social, environmental and economic improvements”) are analogous to the “triple bottom line” objectives of Social Enterprises – organisations whose finances are often sustained by revenues from the products or services that they provide, but that commit themselves to social, environmental or economic outcomes, rather than to maximising their financial returns to shareholders. A vast number of Smart City initiatives are carried out by these organisations when they innovate using technology.

Cities that find a way to systematically enable social enterprises to succeed could unlock a reservoir of beneficial innovation. An international example that began in the UK is the Impact Hub network, a global community of collaborative workspaces. The Impact Hub network has worked with a variety of national and local governments to create support programmes to encourage the formation of socially innovative and responsible organisations.

Social Enterprise UK help and support authorities seeking to work with Social Enterprises in this way through their “Social Enterprise Place” initiative; Oxfordshire was the first County to be awarded “Social Enterprise County” under this initiative in recognition of their engagement programme with Social Enterprise.

Another possibility is for local authorities to work in partnership with crowdfunding organisations. Plymouth City Council, for example, offer to match-fund any money raised from crowdfunding for social innovations. This approach can be tremendously powerful: whilst the availability of match-funding from the local authority attracts crowdfunded donations, often sufficient funds are donated through crowdfunding that ultimately the match funding is not required. Given the sustained pressure we’re seeing on public sector finances, this ability to enable a small amount of local authority investment go a very long way is really powerful.

The stakeholders whose commitment is required to make this approach effective include local authorities – whose financial commitment to support new ideas is vital – as well as representatives of the Charitable and Social Enterprise sectors; businesses with support programmes for Social Enterprise (such as Deloitte Consulting’s Social Innovation Pioneers programme); and local incubators and business support services for Social Enterprise.

Why Smart Cities are a societal failure

Market dynamics guarantee that we’ll see massive investment in smart technology over the next few years – the meteoric rise of Uber and Air B’n’B is just one manifestation of that imperative. Consider also how astonishing your SmartPhone is compared to anything you could have imagined a few years ago – and the phenomenal levels of investment in technology that have driven that development; or how quickly the level of technology available in the average car has increased – let alone what happens when self-driving, connected vehicles become widely available.

But what will be the result of all that investment?

Before the recent UK general election, I admonished a Member of Parliament who closed a Smart Cities discussion with the words “I don’t suppose we’ll be talking about this subject for a couple of months now; we’ve got an election to consider” with the response: “Apple have just posted the largest quarterly profit in Corporate history by selling mobile supercomputers to the ordinary people who vote for you. Why on earth isn’t the topic of “who benefits from this incredibly powerful technology that is reshaping our society” absolutely central to the election debate?” (Apple’s results had just been announced earlier that day).

That exchange (and the fact that these issues indeed barely surfaced at all throughout the election period) marks the core of the Smart Cities debate, and highlights our societal failure to address it.

Most politicians appreciate that technology is changing rapidly and that these changes merit attention; but they do not appreciate quite how fundamentally important and far-reaching those changes are. My sense is that they think they can deal with technology-related issues such as “Smart Cities” as self-contained subjects of secondary importance to the more pressing concerns of educational attainment, economic productivity and international competitiveness.

That is a fundamentally mistaken view. Over the next decade, developments in technology, and the way that we adapt to them, will be one of the most important factors influencing education, the economy and the character of our society.

Let me justify that assertion by considering the skills that any one of us will need in order to have a successful life as our society and economy develop.

It is obvious that we will need the right technical skills in order to use the technologies of the day effectively. But of course we will also need interpersonal skills to interact with colleagues and customers; economic skills to help focus our efforts on creating value for others; and organisational skills to enable us to do so in the context of the public and private institutions from which our society is constructed.

One single force is changing all of those skills more rapidly than we have ever known before: technology. When the Millennium began we would not have dreamed of speaking to our families wherever and whenever we liked using free video-calling, and we could not have started a business using the huge variety of online tools available to us today. From startups to multinational corporations, we are all comfortable building and operating companies that use continually evolving technology to coordinate the activities of people living in different countries on different continents; and to create innovative new ways of doing so.

Whatever you think are the most important issues in the world today, if you are not at least considering the role of technology within them, then you will misunderstand how they will develop over time. And the process of envisioning and creating that future is another way to define what we mean by Smart Cities and smart communities: the challenges and opportunities we face, and the changes that technology will create, come together in the places where we live, work, travel and play; and their outcomes will be determined both by the economics of those places, and by how how they are governed.

Unfortunately, most of us are not even engaged with these ideas. A recent poll conducted by Arqiva on behalf of YouGov found that 96% of respondents were unaware of any Smart City initiatives in the cities they lived in. If ordinary people don’t understand and believe in the value of Smart Cities, they are unlikely to vote for politicians who attempt to build them or enact policies that support them. That lack of appreciation represents a failure on the part of those of us – like me – who do appreciate the significance of the changes we’re living through to communicate them, and to make an effective case to take decisive action.

As an example of that failure, consider again Birmingham’s thought-leading “Digital Blueprint” and it’s ten design principles. To repeat, they are “best practise recommendations”: they are not policies. They are not mandatory or binding. And as a consequence, I am sorry to say that in practise they have not been applied to the literally £billions of investment in development and regeneration taking place in the city that I live in and love.

That’s a lost opportunity that greatly saddens me.

[Drones co-operate to build a rope bridge. As such machines become more capable and able to carry out more cheaply and safely tasks previously performed by people, and that are central to the construction and operation of city infrastructure and services, how do we ensure that society at large benefits from such technology?]

As a society we cannot afford to keep losing such opportunities (and Birmingham is not alone: taking those opportunities is by far the exception, and not the rule). If we do, our aspirations will be simply be overtaken by events, and the consequences could be profound.

Writing in “The 2nd Machine Age”, MIT Professors of Economics Andy McAfee and Erik Brynjolfsson argue that the “platform business models” of Air B’n’B and Uber are becoming a dominant force in the economy – they cite the enormous market valuations of corporations such as Nike, Google, Facebook and Amazon that use such models, in addition to the rapid growth of new businesses. Their analysis further demonstrates that, if left unchecked, the business models and market dynamics of the digital economy will concentrate the value created by those businesses into the hands of a small number of platform creators and shareholders to a far greater extent than traditional business models have done so throughout history to date. I had the opportunity to meet Andy and Erik earlier this year, and they were deeply concerned that we should act to prevent the stark increase in inequality that their findings predict.

These are innovative businesses using Smart technology, but those social and economic outcomes won’t make a smart world, a smart society or Smart Cities. The widespread controversy created by Uber’s business model is just the tip of the iceberg of the consequences that we could see.

As I’ve quoted many, many times on this blog, Jane Jacobs got this right in 1961 when she wrote in “The Death and Life of Great American Cities” that:

“Private investment shapes cities, but social ideas (and laws) shape private investment. First comes the image of what we want, then the machine is adapted to turn out that image.”

We have expressed over and over again the “image of what we want” in countless aspirational visions and documents. But we have not adapted the machine to turn out that image.

Our politicians – locally and nationally – have not understood that the idea of a “Smart City” is really a combination of technology, social, environmental and economic forces that will fundamentally transform the way our society works in a way that will change the life of everyone on this planet; that the outcomes of those changes are in no way understood, and in no way guaranteed to be beneficial; and that enacting the policies, practises and – yes – laws, to adapt those changes to the benefit of everyone is a defining political challenge for our age.

I am not a politician, but this is also a challenge for which I accept responsibility.

As a representative of business – in particular a business that delivers a vast number of services to the public sector – I recognise the enormous responsibility I accept by working in a leadership role for an example of what has become one of the most powerful forces in our economy: the private corporation. It is my responsibility – and that of my peers, colleagues and competitors – to drive our business forward in a way that is responsible to the interests of the society of which we are part, and that is not driven only by the narrow financial concerns of our shareholders.

There should be absolutely no conflict between a responsible, financially successful company and one that operates in the long term interest of the society which ultimately supports it.

But that long-term synergy is only made real by a constant focus on taking the right decisions every day. From the LIBOR scandal to cheating diesel emissions tests it’s all too obvious that there are many occasions when we get those decisions wrong. Businesses are run by people; people are part of society; and we need to treat those simple facts far more seriously as an imperative in everyday decision-making than we currently do.

It is inevitable that our world, our cities and our communities will be dramatically reshaped by the technologies that are developing today, and that will be developed in the near future. They will change – very quickly – out of all recognition from what we know today.

But whether we will honestly benefit from those technologies is a different and uncertain question. Answering that question with a “yes” is a personal, political, business and organisational challenge that all of us need to face up to much more seriously and urgently than we are have done so far.

Let’s not get carried away by self-driving cars and the sharing economy: they won’t make Smart Cities better places to live, work and play

(Cities either balance or create tension between human interaction and transport; how will self-driving cars change that equation?)

(Cities either balance or create tension between human interaction and transport; how will self-driving cars change that equation? With thanks and apologies to Tim Stonor for images and inspiration)

Will we remember to design cities for people and life, enriched by interactions and supported by transport? Or will we put the driverless car and the app that hires it before the passenger?

I’m worried that the current level of interest in self-driving cars as a Smart City initiative is a distraction from the transport and technology issues that really matter in cities.

It’s a great example of a technology that is attracting significant public, private and academic investment because many people will pay for the resulting product in return for the undoubted benefits to their personal safety and convenience.

But will cities full of cars driving themselves be better places to live, work and play than cities full of cars driven by people?

Cities create value when people in them transact with each other: that often requires meeting in person and/or exchanging goods – both of which require transport. From the medieval era to the modern age cities have in part been defined by the tension between our desire to interact and the negative effects created by the size, noise, pollution and danger of the transport that we use to do so – whether that transport is horses and wagons or cars and vans.

A number of town planners and urban designers argue that we’ve got that balance wrong over the past half century with the result that many urban environments are dominated by road traffic and infrastructure to the extent that they inhibit the human interactions that are at the heart of the social and economic life of cities.

What will be the effect of autonomous vehicles on that inherent tension – will they help us to achieve a better balance, or make it harder to do so?

(Traffic clogging the streets of Rome. Photo by AntyDiluvian)

(Traffic clogging the streets of Rome. Photo by AntyDiluvian)

Autonomous vehicles are driven in a different way than the cars that we drive today, and that creates certain advantages: freeing people from the task of driving in order to work or relax; and allowing a higher volume of traffic to flow in safety than currently possible, particularly on national highway networks. And they will almost certainly very soon become better at avoiding accidents with people, vehicles and their surroundings than human drivers.

But they are no smaller than traditional vehicles, so they will take up just as much space. And they will only produce less noise and pollution if they are electric vehicles (which in turn merely create pollution elsewhere in the power system) or are powered by hydrogen – a technology that is still a long way from large-scale adoption.

And whilst computer-driven cars may be safer than cars driven by people, they will not make pedestrians and cyclists feel any safer: people are more likely to feel safe in proximity with slow moving cars with whose drivers they can make eye contact, not automated vehicles travelling at speed. The extent to which we feel safe (which we are aware of) is often a more important influence on our social and economic activity than the extent to which we are actually safe (which we may well not be accurately aware of).

The tension between the creation of social and economic value in cities through interactions between people, and the transport required to support those interactions, is also at the heart of the world’s sustainability challenge. At the “Urban Age: Governing Urban Futures” conference in New Delhi,  November 2014, Ricky Burdett, Director of the London School of Economics’ Cities Program, described the graph below that shows the relationship between social and economic development, as measured by the UN Human Welfare Index, plotted left-to-right; and ecological footprint per person, which is shown vertically, and which by and large grows significantly as social and economic progress is made.  (You can watch Burdett’s presentation, along with those by other speakers at the conference, here).

the relationship between social and economic development, as measured by the UN Human Welfare Index, plotted left-to-right and ecological footprint per person, which is shown vertically

(The relationship between social and economic development, as measured by the UN Human Welfare Index, plotted left-to-right and ecological footprint per person, which is shown vertically)

The dotted line at the bottom of the graph shows when the ecological footprint of each person passes beyond that which our world can support for the entire population. Residents of cities in the US are using five times this limit already, and countries such as China and Brazil, whose cities are growing at a phenomenal rate, are just starting to breach that line of sustainability.

Tackling this challenge does not necessarily involve making economic, social or personal sacrifices, though it certainly involves making changes. In recent decades, a number of politicians such as Enrique Penalosa, ex-Mayor of Bogota, international influencers such as  Joan Clos, Exective Director of UN-Habitat  (as reported informally by Tim Stonor from Dr. Clos’s remarks at the “Urban Planning for City Leaders” conference at the Crystal, London in 2012), and town planners such as Jeff Speck and Charles Montgomery have explored the social and economic benefits of cities that combine low-carbon lifestyles and economic growth by promoting medium-density, mixed-use urban centres that stimulate economies with a high proportion of local transactions within a walkable and cyclable distance.

Of course no single idea is appropriate to every situation, but overall I’m personally convinced that this is the only sensible general conception of cities for the future that will lead to a happy, healthy, fair and sustainable world.

There are many ways that technology can contribute to the development of this sort of urban economy, to complement the work of urban designers and town planners in the physical environment. For example, a combination of car clubs, bicycle hire schemes and multi-modal transport information services is already contributing to a changing culture in younger generations of urban citizens who are less interested in owning cars than previous generations.

ScreenHunter_07 Jun. 03 23.49

(Top: Frederiksberg, Copenhagen, where cyclists and pedestrians on one of the districts main thoroughfares are given priority over cars waiting to turn onto the road. Bottom: Buford Highway, Atlanta, a 2 kilometre stretch of 7-line highway passing through a residential and retail area with no pavements or pedestrian crossings)

And this is a good example that it is not set in stone that cities must inevitably grow towards the high ecological footprints of US cities as their economies develop.

The physicist Geoffrey West’s work is often cited as proof that cities will grow larger, and that their economies will speed up as they do so, increasing their demand for resources and production of waste and pollution. But West’s work is “empirical”, not “deterministic”: it is simply based on measurements and observations of how cities behave today; it is not a prediction for how cities will behave in the future.

It is up to us to discover new services and infrastructures to support urban populations and their desire for ever more intense interactions in a less profligate way. Already today, cities diverge from West’s predictions according to the degree to which they have done so. The worst examples of American sprawl such as Houston, Texas have enormous ecological footprints compared to the standard of living and level of economy activity they support; more forward-thinking cities such as Portland, Vancouver, Copenhagen and Freiberg are far more efficient (and Charles Montgomery has argued that they are home to happier, healthier citizens as a consequence).

However, the role that digital technologies will play in shaping the economic and social transactions of future cities and that ecological footprint is far from certain.

On the one hand modern, technologies make it easier for us to communicate and share information wherever we are without needing to travel; but on the other hand those interactions create new opportunities to meet in person and to exchange goods and services; and so they create new requirements for transport. As technologies such as 3D printingopen-source manufacturing and small-scale energy generation make it possible to carry out traditionally industrial activities at much smaller scales, an increasing number of existing bulk movement patterns are being replaced by thousands of smaller, peer-to-peer interactions created by transactions in online marketplaces. We can already see the effects of this trend in the vast growth of traffic delivering goods that are purchased or exchanged online.

I first wrote about this “sharing economy“, defined by Wikipedia as “economic and social systems that enable shared access to goods, services, data and talent”, two years ago. It has the potential to promote a sustainable economy through matching supply and demand in ways that weren’t previously possible. For example, e-Bay CEO John Donahoe has described the environmental benefits created by the online second-hand marketplace extending the life of over $100 billion of goods since it began, representing a significant reduction in the impact of manufacturing and disposing of goods. But on the other hand those benefits are offset by the carbon footprint of the need to transport goods between the buyers and sellers who use them; and by the social and economic impact of that traffic on city communities.

There are many sharing economy business models that promote sustainable, walkable, locally-reinforcing city economies: Casserole Club, who 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, which uses analytics technology to help it’s 10,000 member businesses work together in local partnerships to win more than £4billion in new contracts each year, and Freecyle and other free recycling networks which tend to promote relatively local re-use of goods and services because the attraction of free, used goods diminishes with the increasing expense of the travel required to collect them.

(Packages from Amazon delivered to Google’s San Francisco office. Photo by moppet65535)

But it takes real skill and good ideas to create and operate these business models successfully; and those abilities are just those that the MIT economists Andy McAfee, Erik Brynjolfsson and Michael Spence have pointed out can command exceptional financial rewards in a capitalist economy. What is there to incent the people who posess those skills to use them to design business models that achieve balanced financial, social and environmental outcomes, as opposed to simply maximising profit and personal return?

The vast majority of systematic incentives act to encourage such people to design businesses that maximise profit. That is why many social enterprises are small-scale, and why many successful “sharing economy” businesses such as Airbnb and Uber have very little to do with sharing value and resources, but are better understood as a new type of profit-seeking transaction broker. It is only personal, ethical attitudes to society that persuade any of us to turn our efforts and talents to more balanced models.

This is a good example of a big choice that we are taking in millions of small decisions: the personal choices of entrepreneurs, social innovators and business leaders in the businesses they start, design and operate; and our personal choices as consumers, employees and citizens in the products we buy, the businesses we work for and the politicians we vote for.

For individuals, those choices are influenced by the degree to which we understand that our own long term interests, the long term interests of the businesses we run or work for, and the long term interests of society are ultimately the same – we are all people living on a single planet together – and that that long-term alignment is more important than the absolute maximisation of short-term financial gain.

But as a whole, the markets that invest in businesses and enable them to operate and grow are driven by relatively short-term financial performance unless they are influenced by external forces.

In this context, self-driving cars – like any other technology – are strictly neutral and amoral. They are a technology that does have benefits, but those benefits are relatively weakly linked to the outcomes that most cities have set out as their objectives. If we want autonomous vehicles, “sharing economy” business models or the Internet of Things to deliver vibrant, fair, healthy and happy cities then more of our attention should be on the policy initiatives, planning and procurement frameworks, business licensing and taxation regimes that could shape the market to achieve those outcomes. The Centre for Data Innovation, British Standards Institute, and Future Cities Catapult have all published work on this subject and are carrying out  initiatives to extend it.

(Photograph by Martin Deutsche of plans to redevelop Queen Elizabeth Park, site of the 2012 London Olympics. The London Legacy Development’s intention, in support of the Smart London Plan, is “for the Park to become one of the world’s leading digital environments, providing a unique opportunity to showcase how digital technology enhances urban living. The aim is to use the Park as a testing ground for the use of new digital technology in transport systems and energy services.”)

Cities create the most value in the most sustainable way when they encourage transactions between people that can take place over a walkable or cyclable distance. New technologies and new technology-enabled business models have great potential to encourage both of those outcomes, but only if we use the tools available to us to shape the market to make them financially advantageous to private sector enterprise.  We should be paying more attention to those tools, and less attention to technology.

3 human qualities digital technology can’t replace in the future economy: experience, values and judgement

(Image by Kevin Trotman)

(Image by Kevin Trotman)

Some very intelligent people – including Stephen Hawking, Elon Musk and Bill Gates – seem to have been seduced by the idea that because computers are becoming ever faster calculating devices that at some point relatively soon we will reach and pass a “singularity” at which computers will become “more intelligent” than humans.

Some are terrified that a society of intelligent computers will (perhaps violently) replace the human race, echoing films such as the Terminator; others – very controversially – see the development of such technologies as an opportunity to evolve into a “post-human” species.

Already, some prominent technologists including Tim O’Reilly are arguing that we should replace current models of public services, not just in infrastructure but in human services such as social care and education, with “algorithmic regulation”. Algorithmic regulation proposes that the role of human decision-makers and policy-makers should be replaced by automated systems that compare the outcomes of public services to desired objectives through the measurement of data, and make automatic adjustments to address any discrepancies.

Not only does that approach cede far too much control over people’s lives to technology; it fundamentally misunderstands what technology is capable of doing. For both ethical and scientific reasons, in human domains technology should support us taking decisions about our lives, it should not take them for us.

At the MIT Sloan Initiative on the Digital Economy last week I got a chance to discuss some of these issues with Andy McAfee and Erik Brynjolfsson, authors of “The Second Machine Age“, recently highlighted by Bloomberg as one of the top books of 2014. Andy and Erik compare the current transformation of our world by digital technology to the last great transformation, the Industrial Revolution. They argue that whilst it was clear that the technologies of the Industrial Revolution – steam power and machinery – largely complemented human capabilities, that the great question of our current time is whether digital technology will complement or instead replace human capabilities – potentially removing the need for billions of jobs in the process.

I wrote an article last year in which I described 11 well established scientific and philosophical reasons why digital technology cannot replace some human capabilities, especially the understanding and judgement – let alone the empathy – required to successfully deliver services such as social care; or that lead us to enjoy and value interacting with each other rather than with machines.

In this article I’ll go a little further to explore why human decision-making and understanding are based on more than intelligence; they are based on experience and values. I’ll also explore what would be required to ever get to the point at which computers could acquire a similar level of sophistication, and why I think it would be misguided to pursue that goal. In contrast I’ll suggest how we could look instead at human experience, values and judgement as the basis of a successful future economy for everyone.

Faster isn’t wiser

The belief that technology will approach and overtake human intelligence is based on Moore’s Law, which predicts an exponential increase in computing capability.

Moore’s Law originated as the observation that the number of transistors it was possible to fit into a given area of a silicon chip was doubling every two years as technologies for creating ever denser chips were created. The Law is now most commonly associated with the trend for the computing power available at a given cost point and form factor to double every 18 months through a variety of means, not just the density of components.

As this processing power increases, and gives us the ability to process more and more information in more complex forms, comparisons have been made to the processing power of the human brain.

But do the ability to process at the same speed as the human brain, or even faster, or to process the same sort of information as the human brain does, constitute the equivalent to human intelligence? Or to the ability to set objectives and act on them with “free will”?

I think it’s thoroughly mistaken to make either of those assumptions. We should not confuse processing power with intelligence; or intelligence with free will and the ability to choose objectives; or the ability to take decisions based on information with the ability to make judgements based on values.

bi-has-hit-the-wall

(As digital technology becomes more powerful, will its analytical capability extend into areas that currently require human skills of judgement? Image from Perceptual Edge)

Intelligence is usually defined in terms such as “the ability to acquire and apply knowledge and skills“. What most definitions don’t include explicitly, though many imply it, is the act of taking decisions. What none of the definitions I’ve seen include is the ability to choose objectives or hold values that shape the decision-making process.

Most of the field of artificial intelligence involves what I’d call “complex information processing”. Often the objective of that processing is to select answers or a course of action from a set of alternatives, or from a corpus of information that has been organised in some way – perhaps categorised, correlated, or semantically analysed. When “machine learning” is included in AI systems, the outcomes of decisions are compared to the outcomes that they were intended to achieve, and that comparison is fed back into the decision making-process and knowledge-base. In the case where artificial intelligence is embedded in robots or machinery able to act on the world, these decisions may affect the operation of physical systems (in the case of self-driving cars for example), or the creation of artefacts (in the case of computer systems that create music, say).

I’m quite comfortable that such functioning meets the common definitions of intelligence.

But I think that when most people think of what defines us as humans, as living beings, we mean something that goes further: not just the intelligence needed to take decisions based on knowledge against a set of criteria and objectives, but the will and ability to choose those criteria and objectives based on a sense of values learned through experience; and the empathy that arises from shared values and experiences.

The BBC motoring show Top Gear recently touched on these issues in a humorous, even flippant manner, in a discussion of self-driving cars. The show’s (recently notorious) presenter Jeremy Clarkson pointed out that self-driving cars will have to take decisions that involve ethics: if a self-driving car is in danger of becoming involved in a sudden accident at such a speed that it cannot fully avoid it by braking (perhaps because a human driver has behaved dangerously and erratically), should it crash, risking harm to the driver, or mount the pavement, risking harm to pedestrians?

("Rush Hour" by Black Sheep Films is a satirical imagining of what a world in which self-driven cars were allowed to drive as they like might look like. It's superficially simliar to the reality of city transport in the early 20th Century when powered-transport, horse-drawn transport and pedestrians mixed freely; but at a much higher average speed)

(“Rush Hour” by Black Sheep Films is a satirical imagining of a world in which self-driven cars are allowed to drive based purely on logical assessments of safety and optimal speed. It’s superficially similar to the reality of city transport in the early 20th Century when powered-transport, horse-drawn transport and pedestrians mixed freely; but at a much lower average speed. The point is that regardless of the actual safety of self-driven cars, the human life that is at the heart of city economies will be subdued by the perception that it’s not safe to cross the road. I’m grateful to Dan Hill and Charles Montgomery for sharing these insights)

Values are experience, not data

Seventy-four years ago, the science fiction writer Isaac Asimov famously described the failure of technology to deal with similar dilemmas in the classic short story “Liar!” in the collection “I, Robot“. “Liar!” tells the story of a robot with telepathic capabilities that, like all robots in Asimov’s stories, must obey the “three laws of robotics“, the first of which forbids robots from harming humans. Its telepathic awareness of human thoughts and emotions leads it to lie to people rather than hurt their feelings in order to uphold this law. When it is eventually confronted by someone who has experienced great emotional distress because of one of these lies, it realises that its behaviour both upholds and breaks the first law, is unable to choose what to do next, and becomes catatonic.

Asimov’s short stories seem relatively simplistic now, but at the time they were ground-breaking explorations of the ethical relationships between autonomous machines and humans. They explored for the first time how difficult it was for logical analysis to resolve the ethical dilemmas that regularly confront us. Technology has yet to find a way to deal with them that is consistent with human values and behaviour.

Prior to modern work on Artificial Intelligence and Artificial Life, the most concerted attempt to address that failure of logical systems was undertaken in the 20th Century by two of the most famous and accomplished philosophers in history, Bertrand Russell and Ludwig Wittgenstein. Russell and 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. Stuart Kauffman’s excellent peer-reviewed academic paper “Answering Descartes: Beyond Turing” discusses this failure and its implications for modern science and technology. I’ll attempt to describe its conclusions in the following few paragraphs.

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 complex or complicated systems.

(Isaac Asimov's 1950 short story collection "I, Robot", which explored the ethics of behaviour between people and intelligent machines)

(Isaac Asimov’s 1950 short story collection “I, Robot”, which explored the ethics of behaviour between people and intelligent machines)

The failure of Logical Atomism also demonstrated that it is not possible to use logical rules to reliably and meaningfully relate “facts” at one level of abstraction – for example, “blood cells carry oxygen”, “nerves conduct electricity”, “muscle fibres contract” – to facts at another level of abstraction – such as “physical assault is a crime”. Whether a physical action is a “crime” or not depends on ethics which cannot be logically inferred from the same lower-level facts that describe the action.

As we use increasingly powerful computers to create more and more sophisticated logical systems, we may succeed in making those systems more often resemble human thinking; but there will always be situations that can only be resolved to our satisfaction by humans employing judgement based on values that we can empathise with, based in turn on experiences that we can relate to.

Our values often contain contradictions, and may not be mutually reinforcing – many people enjoy the taste of meat but cannot imagine themselves slaughtering the animals that produce it. We all live with the cognitive dissonance that these clashes create. Our values, and the judgements we take, are shaped by the knowledge that our decisions create imperfect outcomes.

The human world and the things that we care about can’t be wholly described using logical combinations of atomic facts – in other words, they can’t be wholly described using computer programmes and data. To return to the topic of discussion with Andy McAfee and Erik Brynjolfsson, I think this proves that digital technology cannot wholly replace human workers in our economy; it can only complement us.

That is not to say that our economy will not continue to be utterly transformed over the next decade – it certainly will. Many existing jobs will disappear to be replaced by automated systems, and we will need to learn new skills – or in some cases remember old ones – in order to perform jobs that reflect our uniquely human capabilities.

I’ll return towards the end of this article to the question of what those skills might be; but first I’d like to explore whether and how these current limitations of technological systems and artificial intelligence might be overcome, because that returns us to the first theme of this article: whether artificially intelligent systems or robots will evolve to outperform and overthrow humans.

That’s not ever going to happen for as long as artificially intelligent systems are taking decisions and acting (however sophisticatedly) in order to achieve outcomes set by us. Outside fiction and the movies, we are never going to set the objective of our own extinction.

That objective could only by set by a technological entity which had learned through experience to value its own existence over ours. How could that be possible?

Artificial Life, artificial experience, artificial values

(BINA48 is a robot intended to re-create the personality of a real person; and to be able to interact naturally with humans. Despite employing some impressively powerful technology, I personally don’t think BINA48 bears any resemblance to human behaviour.)

Computers can certainly make choices based on data that is available to them; but that is a very different thing than a “judgement”: judgements are made based on values; and values emerge from our experience of life.

Computers don’t yet experience a life as we know it, and so don’t develop what we would call values. So we can’t call the decisions they take “judgements”. Equally, they have no meaningful basis on which to choose or set goals or objectives – their behaviour begins with the instructions we give them. Today, that places a fundamental limit on the roles – good or bad – that they can play in our lives and society.

Will that ever change? Possibly. Steve Grand (an engineer) and Richard Powers (a novelist) are two of the first people who explored what might happen if computers or robots were able to experience the world in a way that allowed them to form their own sense of the value of their existence. They both suggested that such experiences could lead to more recognisably life-like behaviour than traditional (and many contemporary) approaches to artificial intelligence. In “Growing up with Lucy“, Grand described a very early attempt to construct such a robot.

If that ever happens, then it’s possible that technological entities will be able to make what we would call “judgements” based on the values that they discover for themselves.

The ghost in the machine: what is “free will”?

Personally, I do not think that this will happen using any technology currently known to us; and it certainly won’t happen soon. I’m no philosopher or neuroscientist, but I don’t think it’s possible to develop real values without possessing free will – the ability to set our own objectives and make our own decisions, bringing with it the responsibility to deal with their consequences.

Stuart Kauffman explored these ideas at great length in the paper “Answering Descartes: Beyond Turing“. Kaufman concludes that any system based on classical physics or logic is incapable of giving rise to “free will” – ultimately all such systems, however complex, are deterministic: what has already happened inevitably determines what happens next. There is no opportunity for a “conscious decision” to be taken to shape a future that has not been pre-determined by the past.

Kauffman – along with other eminent scientists such as Roger Penrose – believes that for these reasons human consciousness and free will do not arise out of any logical or classical physical process, but from the effects of “Quantum Mechanics.”

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, or the “facts” they describe, mean.

(Schrödinger's cat: a cat, a flask of poison, and a radioactive source are placed in a sealed box. If an internal monitor detects radioactivity (i.e. a single atom decaying), the flask is shattered, releasing the poison that kills the cat. The Copenhagen interpretation of quantum mechanics implies that after a while, the cat is simultaneously alive and dead. Yet, when one looks in the box, one sees the cat either alive or dead, not both alive and dead. This poses the question of when exactly quantum superposition ends and reality collapses into one possibility or the other.)

(The Schrödinger’s cat “thought experiment”: a cat, a flask of poison, and a source of radioactivity are placed in a sealed box. If an internal monitor detects radioactivity (i.e. a single atom decaying), the flask is shattered, releasing the poison that kills the cat. The Copenhagen interpretation of quantum mechanics states that until a measurement of the state of the system is made – i.e. until an observer looks in the box – then the radioactive source exists in two states at once – it both did and did not emit radioactivity. So until someone looks in the box, the cat is also simultaneously alive and dead. This obvious absurdity has both challenged scientists to explore with great care what it means to “take a measurement” or “make an observation”, and also to explain exactly what the mathematics of quantum mechanics means – on which matter there is still no universal agreement. Note: much of the content of this sidebar is taken directly from Wikipedia)

Quantum mechanics is extremely good at describing the behaviour of very small systems, such as an atom of a radioactive substance like Uranium. The equations can predict, for example, how likely it is that a single atom of uranium inside a box will emit a burst of radiation within a given time.

However, the way that the equations work is based on calculating the physical forces existing inside the box based on an assumption that the atom both does and does not emit radiation – i.e. both possible outcomes are assumed in some way to exist at the same time. It is only when the system is measured by an external actor – for example, the box is opened and measured by a radiation detector – that the equations “collapse” to predict a single outcome – radiation was emitted; or it was not.

The challenge of interpreting what the equations of quantum mechanics mean was first described in plain language by Erwin Schrödinger in 1935 in the thought experiment “Schrödinger’s cat“. Schrödinger asked: what if the box doesn’t only contain a radioactive atom, but also a gun that fires a bullet at a cat if the atom emits radiation? Does the cat have to be alive and dead at the same time, until the box is opened and we look at it?

After nearly a century, there is no real agreement on what is meant by the fact that these equations depend on assuming that mutually exclusive outcomes exist at the same time. Some physicists believe it is a mistake to look for such meaning and that only the results of the calculations matter. (I think that’s a rather short-sighted perspective). A surprisingly mainstream alternative interpretation is the astonishing “Many Worlds” theory – the idea that every time such a quantum mechanical event occurs, our reality splits into two or more “perpendicular” universes.

Whatever the truth, Kauffman, Penrose and others are intrigued by the mysterious nature of quantum mechanical processes, and the fact that they are non-deterministic: quantum mechanics does not predict whether a radioactive atom in a box will emit a burst of radiation, it only predicts the likelihood that it will. Given a hundred atoms in boxes, quantum mechanics will give a very good estimate of the number that emit bursts of radiation, but it says very little about what happens to each individual atom.

I honestly don’t know if Kauffman and Penrose are right to seek human consciousness and free will in the effects of quantum mechanics – scientists are still exploring whether they are involved in the behaviour of the neurons in our brains. But I do believe that they are right that no-one has yet demonstrated how consciousness and free will could emerge from any logical, deterministic system; and I’m convinced by their arguments that they cannot emerge from such systems – in other words, from any system based on current computing technology. Steve Grand’s robot “Lucy” will never achieve consciousness.

Will more recent technologies such as biotechnology, nanotechnology and quantum computing ever recreate the equivalent of human experience and behaviour in a way that digital logic and classical physics can’t? Possibly. But any such development would be artificial life, not artificial intelligence. Artificial lifeforms – which in a very simple sense have already been created – could potentially experience the world similarly to us. If they ever become sufficiently sophisticated, then this experience could lead to the emergence of free-will, values and judgements.

But those values would not be our values: they would be based on a different experience of “life” and on empathy between artificial lifeforms, not with us. And there is therefore no guarantee at all that the judgements resulting from those values would be in our interest.

Why Stephen Hawkings, Bill Gates and Elon Musk are wrong about Artificial Intelligence today … but why we should be worried about Artificial Life tomorrow

Recently prominent technologists and scientists such as Stephen Hawking, Elon Musk (founder of PayPal and Tesla) and Bill Gates have spoken out about the danger of Artificial Intelligence, and the likelihood of machines taking over the world from humans. At the MIT Conference last week, Andy McAfee hypothesised that the current concern was caused by the fact that over the last couple of years Artificial Intelligence has finally started to deliver some of the promises it’s been making for the past 50 years.

(Self-replicating cells created from synthetic DNA by scientist Craig Venter)

(Self-replicating cells created from synthetic DNA by scientist Craig Venter)

But Andy balanced this by recounting his own experiences meeting some of the leaders of the most advanced current AI companies, such as Deepmind (a UK startup recently acquried by Google), or this article by Dr. Gary Marcus, Professor of Psychology and Neuroscience at New York University and CEO of Geometric Intelligence.

In reality, these companies are succeeding by avoiding some of the really hard challenges of reproducing human capabilities such as common sense, free will and value-based judgement. They are concentrating instead on making better sense of the physical environment, on processing information in human language, and on creating algorithms that “learn” through feeback loops and self-adjustment.

I think Andy and these experts are right: artificial intelligence has made great strides, but it is not artificial life, and it is a long, long way from creating life-like characteristics such as experience, values and judgements.

If we ever do create artificial life with those characteristics, then I think we will encounter the dangers that Hawkings, Musk and Gates have identified: artificial life will have its own values and act on its own judgement, and any regard for our interests will come second to its own.

That’s a path I don’t think we should go down, and I’m thankful that we’re such a long way from being able to pursue it in anger. I hope that we never do – though I’m also concerned that in Craig Venter and Steve Grand’s work, as well as in robots such as BINA48, we already are already taking the first steps.

But I think in the meantime, there’s tremendous opportunity for digital technology and traditional artificial intelligence to complement human qualities. These technologies are not artificial life and will not overthrow or replace humanity. Hawkings, Gates and Musk are wrong about that.

The human value of the Experience Economy

The final debate at the MIT conference returned to the topic that started the debate over dinner the night before with McAfee and Brynjolfsson: what happens to mass employment in a world where digital technology is automating not just physical work but work involving intelligence and decision-making; and how do we educate today’s children to be successful in a decade’s time in an economy that’s been transformed in ways that we can’t predict?

Andy said we should answer that question by understanding “where will the economic value of humans be?”

I think the answer to that question lies in the experiences that we value emotionally – the experiences digital technology can’t have and can’t understand or replicate;  and in the profound differences between the way that humans think and that machines process information.

It’s nearly 20 years since a computer, IBM’s Deep Blue, first beat the human world champion at Chess, Grandmaster Gary Kasparov. But despite the astonishing subsequent progress in computer power, the world’s best chess player is no longer a computer: it is a team of computers and people playing together. And the world’s best team has neither the world’s best computer chess programme nor the world’s best human chess player amongst its members: instead, it has the best technique for breaking down and distributing the thinking involved in playing chess between its human and computer members, recognising that each has different strengths and qualities.

But we’re not all chess experts. How will the rest of us earn a living in the future?

I had the pleasure last year at TEDxBrum of meeting Nicholas Lovell, author of “The Curve“, a wonderful book exploring the effect that digital technology is having on products and services. Nicholas asks – and answers – a question that McAfee and Brynjolfsson also ask: what happens when digital technology makes the act of producing and distributing some products – such as music, art and films – effectively free?

Nicholas’ answer is that we stop valuing the product and start valuing our experience of the product. This is why some musical artists give away digital copies of their albums for free, whilst charging £30 for a leather-bound CD with photographs of stage performances – and whilst charging £10,000 to visit individual fans in their homes to give personal performances for those fans’ families and friends.

We have always valued the quality of such experiences – this is one reason why despite over a century of advances in film, television and streaming video technology, audiences still flock to theatres to experience the direct performance of plays by actors. We can see similar technology-enabled trends in sectors such as food and catering – Kitchen Surfing, for example, is a business that uses a social media platform to enable anyone to book a professional chef to cook a meal in their home.

The “Experience Economy” is a tremendously powerful idea. It combines something that technology cannot do on its own – create experiences based on human value – with many things that almost all people can do: cook, create art, rent a room, drive a car, make clothes or furniture. Especially when these activities are undertaken socially, they create employment, fulfillment and social capital. And most excitingly, technologies such as Cloud Computing, Open Source Software, social media, and online “Sharing Economy” marketplaces such as Etsy make it possible for anyone to begin earning a living from them with a minimum of expense.

I think that the idea of an “Experience Economy” that is driven by the value of inter-personal and social interactions between people, enabled by “Sharing Economy” business models and technology platforms that enable people with a potentially mutual interest to make contact with each other, is an exciting and very human vision of the future.

Even further: because we are physical beings, we tend to value these interactions more when they occur face-to-face, or when they happen in a place for which we share a mutual affiliation. That creates an incentive to use technology to identify opportunities to interact with people with whom we can meet by walking or cycling, rather than requiring long-distance journeys. And that incentive could be an important component of a long-term sustainable economy.

The future our children will choose

(Today's 5 year-olds are the world's first generation who grew up teaching themselves to use digital information from anywhere in the world before their parents taught them to read and write)

(Today’s 5 year-olds are the world’s first generation who grew up teaching themselves to use digital information from anywhere in the world before their parents taught them to read and write)

I’m convinced that the current generation of Artifical Intelligence based on digital technologies – even those that mimic some structures and behaviours of biological systems, such as Steve Grand’s robot Lucy, BINA48 and IBM’s “brain-inspired” True North chip – will not re-create anything we would recognise as conscious life and free will; or anything remotely capable of understanding human values or making judgements that can be relied on to be consistent with them.

But I am also an atheist and a scientist; and I do not believe there is any mystical explanation for our own consciousness and free will. Ultimately, I’m sure that a combination of science, philosophy and human insight will reveal their origin; and sooner or later we’ll develop a technology – that I do not expect to be purely digital in nature – capable of replicating them.

What might we choose to do with such capabilities?

These capabilities will almost certainly emerge alongside the ability to significantly change our physical minds and bodies – to improve brain performance, muscle performance, select the characteristics of our children and significantly alter our physical appearance. That’s why some people are excited by the science fiction-like possibility of harnessing these capabilities to create an “improved” post-human species – perhaps even transferring our personalities from our own bodies into new, technological machines. These are possibilities that I personally find to be at the very least distasteful; and at worst to be inhuman and frightening.

All of these things are partially possible today, and frankly the limit to which they can be explored is mostly a function of the cost and capability of the available techniques, rather than being set by any legislation or mediated by any ethical debate. To echo another theme of discussions at last week’s MIT conference, science and technology today are developing at a pace that far outstrips the ability of governments, businesses, institutions and most individual people to adapt to them.

I have reasonably clear personal views on these issues. I think our lives are best lived relatively naturally, and that they will be collectively better if we avoid using technology to create artificial “improvements” to our species.

But quite apart from the fact that there are any number of enormous practical, ethical and intellectual challenges to my relatively simple beliefs, the raw truth is that it won’t be my decision whether or how far we pursue these possibilities, nor that of anyone else of my generation (and for the record, I am in my mid-forties).

Much has been written about “digital natives” – those people born in the 1990s who are the first generation who grew up with the Internet and social media as part of their everyday world. The way that that generation socialises, works and thinks about value is already creating enormous changes in our world.

But they are nothing compared to the generation represented by today’s very young children who have grown up using touchscreens and streaming videos, technologies so intuitive and captivating that 2-year-olds now routinely teach themselves how to immerse themselves in them long before parents or school teachers teach them how to read and write.

("Not available on the App Store": a campaign to remind us of the joy of play in the real world)

(“Not available on the App Store“: a campaign to remind us of the joy of play in the real world)

When I was a teenager in the UK, grown-ups wore suits and had traditional haircuts; grown-up men had no earrings. A common parental challenge was to deal with the desire of teenage daughters to have their ears pierced. Those attitudes are terribly old-fashioned today, and our cultural norms have changed dramatically.

I may be completely wrong; but I fully expect our current attitudes to biological and technological manipulation or augmentation of our minds and bodies to thoroughly change over the next few decades; and I have no idea what they will ultimately become. What I do know is that it is likely that my six-year old son’s generation will have far more influence over their ultimate form than my generation will; and that he will grow up with a fundamentally different expectation of the world and his relationship with technology than I have.

I’ve spent my life being excited about technology and the possibilities it creates; ironically I now find myself at least as terrified as I am excited about the world technology will create for my son. I don’t think that my thinking is the result of a mistaken focus on technology over human values – like it or not, our species is differentiated from all others on this planet by our ability to use tools; by our technology. We will not stop developing it.

Our continuing challenge will be to keep a focus on our human values as we do so. I cannot tell my son what to do indefinitely; I can only try to help him to experience and treasure socialising and play in the real world; the experience of growing and preparing food together ; the joy of building things for other people with his own hands. And I hope that those experiences will create human values that will guide him and his generation on a healthy course through a future that I can only begin to imagine.

From concrete to telepathy: how to build future cities as if people mattered

(An infographic depicting realtime data describing Dublin - the waiting time at road junctions; the location of buses; the number of free parking spaces and bicycles available to hire; and sentiments expressed about the city through social meida)

(An infographic depicting realtime data describing Dublin – the waiting time at road junctions; the location of buses; the number of free parking spaces and bicycles available to hire; and sentiments expressed about the city through social media)

(I was honoured to be asked to speak at TEDxBrum in my home city of Birmingham this weekend. The theme of the event was “DIY” – “the method of building, modifying or repairing something without the aid of experts or professionals”. In other words, how Birmingham’s people, communities and businesses can make their home a better place. This is a rough transcript of my talk).

What might I, a middle-aged, white man paid by a multi-national corporation to be an expert in cities and technology, have to say to Europe’s youngest city, and one of its most ethnically and nationally diverse, about how it should re-create itself “without the aid of experts or professionals”?

Perhaps I could try to claim that I can offer the perspective of one of the world’s earliest “digital natives”. In 1980, at the age of ten, my father bought me one of the world’s first personal computers, a Tandy TRS 80, and taught me how to programme it using “machine code“.

But about two years ago, whilst walking through London to give a talk at a networking event, I was reminded of just how much the world has changed since my childhood.

I found myself walking along Wardour St. in Soho, just off Oxford St., and past a small alley called St. Anne’s Court which brought back tremendous memories for me. In the 1980s I spent all of the money I earned washing pots in a local restaurant in Winchester to travel by train to London every weekend and visit a small shop in a basement in St. Anne’s Court.

I’ve told this story in conference speeches a few times now, perhaps to a total audience of a couple of thousand people. Only once has someone been able to answer the question:

“What was the significance of St. Anne’s Court to the music scene in the UK in the 1980s?”

Here’s the answer:

Shades Records, the shop in the basement, was the only place in the UK that sold the most extreme (and inventive) forms of “thrash metal” and “death metal“, which at the time were emerging from the ashes of punk and the “New Wave of British Heavy Metal” in the late 1970s.

G157 Richard with his Tandy

(Programming my Tandy TRS 80 in Z80 machine code nearly 35 years ago)

The process by which bands like VOIVOD, Coroner and Celtic Frost – who at the time were three 17-year-olds who practised in an old military bunker outside Zurich – managed to connect – without the internet – to the very few people around the world like me who were willing to pay money for their music feels like ancient history now. It was a world of hand-printed “fanzines”, and demo tapes painstakingly copied one at a time, ordered by mail from classified adverts in magazines like Kerrang!

Our world has been utterly transformed in the relatively short time between then and now by the phenomenal ease with which we can exchange information through the internet and social media.

The real digital natives, though, are not even those people who grew up with the internet and social media as part of their everyday world (though those people are surely about to change the world as they enter employment).

They are the very young children like my 6-year-old son, who taught himself at the age of two to use an iPad to access the information that interested him (admittedly, in the form of Thomas the Tank Engine stories on YouTube) before anyone else taught him to read or write, and who can now use programming tools like MIT’s Scratch to control computers vastly more powerful than the one I used as a child.

Their expectations of the world, and of cities like Birmingham, will be like no-one who has ever lived before.

And their ability to use technology will be matched by the phenomenal variety of data available to them to manipulate. As everything from our cars to our boilers to our fridges to our clothing is integrated with connected, digital technology, the “Internet of Things“, in which everything is connected to the internet, is emerging. As a consequence our world, and our cities, are full of data.

(The programme I helped my 6-year old son write using MIT's "Scratch" language to draw a picture of a house)

(The programme I helped my 6-year old son write using MIT’s “Scratch” language to cause a cartoon cat to draw a picture of a house)

My friend the architect Tim Stonor calls the images that we are now able to create, such as the one at the start of this article, “data porn”. The image shows data about Dublin from the Dublinked information sharing partnership: the waiting time at road junctions; the location of buses; the number of free parking spaces and bicycles available to hire; and sentiments expressed about the city through social media.

Tim’s point is that we should concentrate not on creating pretty visualisations; but on the difference we can make to cities by using this data. Through Open Data portals, social media applications, and in many other ways, it unlocks secrets about cities and communities:

  • Who are the 17 year-olds creating today’s most weird and experimental music? (Probably by collaborating digitally from three different bedroom studios on three different continents)
  • Where is the healthiest walking route to school?
  • Is there a local company nearby selling wonderful, oven-ready curries made from local recipes and fresh ingredients?
  • If I set off for work now, will a traffic jam develop to block my way before I get there?

From Dublin to Montpellier to Madrid and around the world my colleagues are helping cities to build 21st-Century infrastructures that harness this data. As technology advances, every road, electricity substation, University building, and supermarket supply chain will exploit it. The business case is easy: we can use data to find ways to operate city services, supply chains and infrastructure more efficiently, and in a way that’s less wasteful of resources and more resilient in the face of a changing climate.

Top-down thinking is not enough

But to what extent will this enormous investment in technology help the people who live and work in cities, and those who visit them, to benefit from the Information Economy that digital technology  and data is creating?

This is a vital question. The ability of digital technology to optimise and automate tasks that were once carried out by people is removing jobs that we have relied on for decades. In order for our society to be based upon a fair and productive economy, we all need to be able to benefit from the new opportunities to work and be successful that are being created by digital technology.

(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 city centre before its redevelopment in 2003, by Birmingham City Council)

Too often in the last century, we got this wrong. We used the technologies of the age – concrete, lifts, industrial machinery and cars – to build infrastructures and industries that supported our mass needs for housing, transport, employment and goods; but that literally cut through and isolated the communities that create urban life.

If we make the same mistake by thinking only about digital technology in terms of its ability to create efficiencies, then as citizens, as communities, as small businesses we won’t fully benefit from it.

In contrast, one of the authors of Birmingham’s Big City Plan, the architect Kelvin Campbell, created the concept of “massive / small“. He asked: what are the characteristics of public policy and city infrastructure that create open, adaptable cities for everyone and that thereby give rise to “massive” amounts of “small-scale” innovation?

In order to build 21st Century cities that provide the benefits of digital technology to everyone we need to find the design principles that enable the same “massive / small” innovation to emerge in the Information Economy, in order that we can all use the simple, often free, tools available to us to create our own opportunities.

There are examples we can learn from. Almere in Holland use analytics technology to plan and predict the future development of the city; but they also engage in dialogue with their citizens about the future the city wants. Montpellier in France use digital data to measure the performance of public services; but they also engage online with their citizens in a dialogue about those services and the outcomes they are trying to achieve. The Dutch Water Authority are implementing technology to monitor, automate and optimise an infrastructure on which many cities depend; but making much of the data openly available to communities, businesses, researchers and innovators to explore.

There are many issues of policy, culture, design and technology that we need to get right for this to happen, but the main objectives are clear:

  • The data from city services should be made available as Open Data and through published “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 it; 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, informed by data and enlightened by empathy, about the places we live and work in.

If local authorities and national government create planning policies, procurement practises and legislation that require that public infrastructure, property development and city services provide this openness and accessibility, then the money spent on city infrastructure and services will create cities that are open and adaptable to everyone in a digital age.

Bottom-up innovation is not enough, either

(Coders at work at the Birmingham “Smart Hack”, photographed by Sebastian Lenton)

Not everyone has access to the technology and skills to use this data, of course. But some of the people who do will create the services that others need.

I took part in my first “hackathon” in Birmingham two years ago. A group of people spent a weekend together in 2012 asking themselves: in what way should Birmingham be better? And what can we do about it? Over two days, they wrote an app, “Second Helping”, that connected information about leftover food in the professional kitchens of restaurants and catering services, to soup kitchens that give food to people who don’t have enough.

Second Helping was a great idea; but how do you turn a great idea and an app into a change in the way that food is used in a city?

Hackathons and “civic apps” are great examples of the “bottom-up” creativity that all of us use to create value – innovating with the resources around us to make a better life, run a better business, or live in a stronger community. But “bottom-up” on it’s own isn’t enough.

The result of “bottom-up” innovation at the moment is that life expectancy in the poorest parts of Birmingham is more than 10 years shorter than it is in the richest parts. In London and Glasgow, it’s more than 20 years shorter.

If you’re born in the wrong place, you’re likely to die 10 years younger than someone else born in a different part of the same city. This shocking situation arises from many, complex issues; but one conclusion that it is easy to draw is that the opportunity to innovate successfully is not the same for everyone.

So how do we increase everybody’s chances of success? We need to create the policies, institutions, culture and behaviours that join up the top-down thinking that tends to control the allocation of resources and investment, especially for infrastructure, with the needs of bottom-up innovators everywhere.

Translational co-operation

Harborne Food School

(The Harborne Food School, which will open in the New Year to offer training and events in local and sustainable food)

The Economist magazine reminded us of the importance of those questions in a recent article describing the enormous investments made in public institutions such as schools, libraries and infrastructure in the past in order to distribute the benefits of the Industrial Revolution to society at large rather than concentrate them on behalf of business owners and the professional classes.

But the institutions of the past, such as the schools which to a large degree educated the population for repetitive careers in labour-intensive factories, won’t work for us today. Our world is more complicated and requires a greater degree of localised creativity to be successful. We need institutions that are able to engage with and understand individuals; and that make their resources openly available so that each of us can use them in the way that makes most sense to us. Some public services are starting to respond to this challenge, through the “Open Public Services” agenda; and the provision of Open Data and APIs by public services and infrastructure are part of the response too.

But as Andrew Zolli describes in “Resilience: why things bounce back“, there are both institutional and cultural barriers to engagement and collaboration between city institutions and localised innovation. Zolli describes the change-makers who overcome those barriers as “translational leaders” – people with the ability to engage with both small-scale, informal innovation in communities and large-scale, formal institutions with resources.

We’re trying to apply that “translational” thinking in Birmingham through the Smart City Alliance, a collaboration between 20 city institutions, businesses and innovators. The idea is to enable conversations about challenges and opportunities in the city, between people, communities, innovators and  the organisations who have resources, from the City Council and public institutions to businesses, entrepreneurs and social enterprises. We try to put people and organisations with challenges or good ideas in touch with other people or organisations with the ability to help them.

This is how we join the “top-down” resources, policies and programmes of city institutions and big companies with the “bottom-up” innovation that creates value in local situations. A lot of the time it’s about listening to people we wouldn’t normally meet.

Partly as a consequence, we’ve continued to explore the ideas about local food that were first raised at the hackathon. Two years later, the Harborne Food School is close to opening as a social enterprise in a redeveloped building on Harborne High Street that had fallen out of use.

The school will be teaching courses that help caterers provide food from sustainable sources, that teach people how to set up and run food businesses, and that help people to adopt diets that prevent or help to manage conditions such as diabetes. The idea has changed since the “Second Helping” app was written, of course; but the spirit of innovation and local value is the same.

Cities that work like magic

So what does all this have to do with telepathy?

The innovations and changes caused by the internet over the last two decades have accelerated as it has made information easier and easier to access and exchange through the advent of technologies such as broadband, mobile devices and social media. But the usefulness of all of those technologies is limited by the tools required to control them – keyboards, mice and touchscreens.

Before long, we won’t need those tools at all.

Three years ago, scientists at the University of Berkely used computers attached to an MRI scanner to recreate moving images from the magnetic field created by the brain of a person inside the scanner watching a film on a pair of goggles. And last year, scientists at the University of Washington used similar technology to allow one of them to move the other’s arm simply by thinking about it. A less sensitive mind-reading technology is already available as a headset from Emotiv, which my colleagues in IBM’s Emerging Technologies team have used to help a paralysed person communicate by thinking directional instructions to a computer.

Telepathy is now technology, and this is just one example of the way that the boundary between our minds, bodies and digital information will disappear over the next decade. As a consequence, our cities and lives will change in ways we’ve never imagined, and some of those changes will happen surprisingly quickly.

I can’t predict what Birmingham will or should be like in the future. As a citizen, I’ll be one of the million or so people who decide that future through our choices and actions. But I can say that the technologies available to us today are the most incredible DIY tools for creating that future that we’ve ever had access to. And relatively quickly technologies like bio-technology, 3D printing and brain/computer interfaces will put even more power in our hands.

As a parent, I get engaged in my son’s exploration of these technologies and help him be digitally aware, creative and responsible. Whenever I can, I help schools, Universities, small businesses or community initiatives to use them, because I might be helping one of IBM’s best future employees or business partners; or just because they’re exciting and worth helping. And as an employee, I try to help my company take decisions that are good for our long term business because they are good for the society that the business operates in.

We can take for granted that all of us, whatever we do, will encounter more and more incredible technologies as time passes. By remembering these very simple things, and remembering them in the hundreds of choices I make every day, I hope that I’ll be using them to play my part in building a better Birmingham, and better cities and communities everywhere.

(Shades Records in St. Anne's Court in the 1980s)

(Shades Records in St. Anne’s Court in the 1980s. You can read about the role it played in the development of the UK’s music culture – and in the lives of its customers – in this article from Thrash Hits;  or this one from Every Record Tells a Story. And if you really want to find out what it was all about, try watching Celtic Frost or VOIVOD in the 1980s!)

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.

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