text:
A Plan for Digital Cities
The "Festival of Love" on London's Southbank in 2014. Sometimes unattractive technologies – in this case concrete – can create great places.
As an IT Architect in the 1990s, I used Design Patterns as a tool to exchange knowledge with industry colleagues as we tried to solve what were then complex challenges – the execution of failure-proof transactions across distributed applications on the early World Wide Web, for example.
Much of the digital technology suffusing today's world is engineered to those patterns – when we use a function of an app or website, we invoke a piece of software fitting the "Command" pattern. The Design Pattern was, of course, invented by Christopher Alexander, a town planner.
Today, the influence of technology back into the professions of the built environment is increasing rapidly. We can use computer vision, the internet of things and machine learning to measure the physical world and the behaviour of people and organisations within it; to analyse them; and to design places and services for them to use.
But whilst "Smart Buildings" have been talked about and sometimes built since the 1980s, and "Smart Cities" since the late 1990s, they are largely one-off showcases and experiments rather than our mainstream approach to creating great places in a digital world.
Partly this is because the technology, built environment and investment professions lack a common, modern understanding of value creation. A leading economist recently described the mechanism by which urban economies grow due to the presence of public physical infrastructure that enables us to travel, meet and transact, generating incentives for further investment. That description completely overlooks the astonishing growth in physical transactions that are mediated online.
This "interface design" in Frederiksberg, Copenhagen, prioritises pedestrian and bicycle traffic along a main road over cars joining from sideroads. Open Data and Open APIs are two equivalent technology policies that seek to promote individual adaptability of digital systems.
From dog-walking to household tool-sharing to transportation, thousands of services now make online introductions between people and companies who would never previously have connected. According to CrunchBase, Venture Capital investments in the technology start-up companies that enable those services is between $150 billion to $200 billion annually. They are changing the way that we work, meet and live.
As a consequence, communities, property companies and local authorities are arguably not setting the digital agenda for the built environment – individual citizens and tenants are selecting their own technology from the market, for their own reasons. The controversies created by "gig economy" employment and the sub-letting of accommodation through peer-to-peer services illustrate that the results are not always consistent with our aspirations.
If we want to create great places which benefit from flourishing digital innovation and enterprise, our first challenge is to better articulate the potential benefits of digital services. For example, students living in University accommodation with good internal and external 4G coverage – enabled by sufficient broadband capacity – will find the streaming video and social media services they use to socialise, access content, and perform research more reliable. They will provide better feedback on their student experience, helping the University to attract more students and to increase fee income.
We next need a common process for applying our expertise. So in "Digital Masterplanning", we complement traditional masterplanning, planning and design processes by specifying digital infrastructures, policies and services for buildings and places.
For a property developer or owner, that might involve defining a common set of digital services across a portfolio, along with open standards for interoperability so that they are not overwhelmed by a multitude of different systems. A local authority digital masterplan might require new infrastructure and property developments to provide open data and public wi-fi, so that the public realm is both physically and digitally adaptable. A digital masterplan for a new town of 10,000 residents in Scandinavia included digitally-enabled de-centralised renewable energy and low carbon mobility schemes, playful and informative environments, distributed places of work and learning, and data privacy and security.
Finally, we need to measure the value of digital infrastructure and services, and convert that value – which is often personal, social, environmental or economic – into the creation of a financial return for investors.
Digbeth in Birmingham, UK, is an example of an urban place that has attracted a variety of successful creative digital businesses, and that – despite its heritage of industrial decay – flourishes as a place.
For example, for companies that operate sites undergoing re-development or construction, we have explored the value of "digital wayfinding" tools that adapt as physical space is altered on a daily basis. Successful digital wayfinding can reduce time that is literally "lost" and reduce stress and frustration, contributing to productivity both directly and indirectly. In neighbourhood-scale regenerations, we can mediate a balance between the interests of local authorities to secure investment in public digital infrastructure, and the level of competition from private property and infrastructure investors seeking a reasonable rate of return.
In his 1964 book, "Notes on the Synthesis of Form", Alexander explained that new multidisciplinary approaches were necessary because "new materials are developed all the time, social patterns alter quickly, they also change faster than before". The amount of digital information in the world overtook the amount of information stored in traditional forms in the early 2000s, and is now doubling every 3 years. The creation of new digital materials and social patterns is still speeding up, and challenges professionals all of our disciplines working in the built environment to cooperate to turn them to our advantage.
(This article was originally published in the Summer 2018 "TripWire" magazine by the Royal Town Planning Institute in the West Midlands, UK.
Filed under Smarter Cities Tagged with Birmingham, DesignPatterns, DigitalMasterplanning, DigitalUrbanism, Economics, Education, Energy, HyperLocal, SharingEconomy
(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:
Whilst the globalised technological economy has lifted a billion people worldwide out of extreme poverty since 1990, it would have lifted 200 million more people out of extreme poverty if it hadn't also created staggering levels of inequality in which the majority of the wealth created in the process has gone into the hands of a minority of people.
As economies struggle to recover following the 2008 crash – caused by rampant irresponsible financial speculation by and on behalf of the world's wealthy elite – unemployment among young people in many European countries is between 20% and 50%.
Since the arrival of the personal computer in the 1980s first ushered in the age of mass use of digital technology, whilst US GDP has nearly doubled, median household income hasn't risen at all (see graph and text in box above).
There has been no increase in average earnings in the UK so far this decade; and young people in particular have become worse off. The situation is unlikely to improve for many years.
25% of employees in the US are paid a wage lower than necessary to live above the poverty line; and 20% of US children live in poverty.
… 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)
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.
(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)
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. 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 "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."
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.
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).
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)
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)
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:
Tom Baker, who introduced me to the power of social enterprise as a business model for improving communities when he was CIO at Sunderland City Council
The architect and urban designer Kelvin Campbell, founder of the Smart Urbanism movement and "Massive / Small change"
Indy Johar, the inspirational founder of the global Impact Hub network
Jeremy Pitt of Imperial College, who first introduced me to Elinor Ostrom's work
David Sloan Wilson who shared with me the principles from evolutionary social biology that he discovered underpin Elinor Ostrom's work
Leo Johnson, who first introduced me to the work of E.F. Schumacher through a debate on responsible business
Andy McAfee and Erik Brynjolfsson, who I was lucky enough to meet at a conference dinner, and whose book "The Second Machine Age" first convinced me we should take these issues seriously; along with their colleague Marshall Van Alstyne, an expert on platform business models
Azeem Azhar, curator of the Exponential View, probably the best regular source of social and technology insight on the web
Filed under Economics Tagged with API, ArtificialIntelligence, Birmingham, Blockchain, Bradford, Brexit, Burnley, Cloud, Demographics, DigitalUrbanism, DonaldTrump, Dubuque, Economics, Education, Energy, EuropeanUnion, Globalisation, Inequality, LocalGovernment, Localism, London, Manufacturing, MassiveSmall, OpenData, OpenSource, Preston, Robotics, Science & Technology, SharingEconomy, Skills, SmarterCities, SmartUrbanism, SocialEnterprise, SocialMedia, Sunderland, Urbanism, Walkability
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? 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)
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 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.
(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 printing, open-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.
Filed under Urbanism Tagged with 3DPrinting, Atlanta, Copenhagen, DesignThinking, DigitalUrbanism, Economics, Energy, Environment, HyperLocal, Localism, London, Placemaking, Science & Technology, SharingEconomy, SmarterCities, SmarterTransport, UnitedNations, Urbanism
11 reasons computers can't understand or solve our problems without human judgement
(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"?
(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:
Three reasons why we can't measure data perfectly;
Three limits to our ability to analyse data and draw insights from it;
Five reasons why the human world is messy, unpredictable, and can't be perfectly described using data and logic.
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 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.
(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 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.
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.
Filed under Science & Technology Tagged with Analytics, Bari, BehaviouralEconomics, Chicago, ComputerScience, Demographics, Economics, Energy, Food, HyperLocal, Localism, Logic, London, Maths, Medway, Memphis, Philosophy, Physics, Science & Technology, SharingEconomy, SmarterCities, socialcomputing, SocialMedia, SocialScience, Statistics, Turing
What's the risk of investing in a Smarter City?
(The two towers of the Bosco Verticale in Milan will be home to more than 10,000 plants that create shade and improve air quality. But to what degree do such characteristics make buildings more attractive to potential tenants than traditional structures, creating the potential to create financial returns to reward more widespread investment in this approach? Photo by Marco Trovo)
(Or "how to buy a Smarter City that won't go bump in the night")
There are good reasons why the current condition and future outlook of the world's cities have been the subject of great debate in recent years. Their population will double from 3 billion to 6 billion by 2050; and while those in the developing world are growing at such a rate that they are challenging our ability to construct resilient, efficient infrastructure, those in developed countries often have significant levels of inequality and areas of persistent poverty and social immobility.
Many people involved in the debate are convinced that new approaches are needed to transport, food supply, economic development, water and energy management, social and healthcare, public safety and all of the other services and infrastructures that support cities.
As a consequence, analysts such as Frost & Sullivan have estimated that the market for "Smart City" solutions that exploit technology to address these issues will be $1.5trillion by 2020.
But anyone who has tried to secure investment in an initiative to apply "smart" technology in a city knows that it is not always easy to turn that theoretical market value into actual investment in projects, technology, infrastructure and expertise.
It's not difficult to see why this is the case. Most investments are made in order to generate a financial return, but profit is not the objective of "Smart Cities" initiatives: they are intended to create economic, environmental or social outcomes. So some mechanism – an investment vehicle, a government regulation or a business model – is needed to create an incentive to invest in achieving those outcomes.
Institutions, Business, Infrastructure and Investment
Citizens expect national and local governments to use their tax revenues to deliver these objectives, of course. But they are also very concerned that the taxes they pay are spent wisely on programmes with transparent, predictable, deliverable outcomes, as the current controversy over the UK's proposed "HS2" high speed train network and previous controversies over the effectiveness of public sector IT programmes show.
Nevertheless, the past year has seen a growing trend for cities in Europe and North America to invest in Smart Cities technologies from their own operational budgets, on the basis of their ability to deliver cost savings or improvements in outcomes.
For example, some cities are replacing traditional parking management and enforcement services with "smart parking" schemes that are reducing congestion and pollution whilst paying for themselves through increased enforcement revenues. Others are investing their allocation of central government infrastructure funds in Smart solutions – such as Cambridge, Ontario's use of the Canadian government's Gas Tax Fund to invest in a sensor network and analytics infrastructure to manage the city's physical assets intelligently.
The providers of Smart Cities solutions are investing too, by implementing their services on Cloud computing platforms so that cities can pay incrementally for their use of them, rather than investing up-front in their deployment. Minneapolis, Minnesota and Montpelier, France, recently announced that they are using IBM's Cloud-based solutions for smarter water, transport and emergency management in this way. And entrepreneurial businesses, backed by Venture Capital investment, are also investing in the development of new solutions.
However, we have not yet tapped the largest potential investment streams: property and large-scale infrastructure. The British Property Federation, for example, estimates that £14 billion is invested in the development of new property in the UK each year. For the main part, these investment streams are not currently investing in "Smart City" solutions.
To understand why that is the case – and how we might change it – we need to understand the difference in three types of risk involved in investing in smart infrastructures compared with traditional infrastructures: construction risk; the impact of operational failures; and confidence in outcomes.
(A cyclist's protest in 2012 about the disruption caused in Edinburgh by the overrunning construction of the city's new tram system. Photo by Andy A)
Construction Risk
At a discussion in March of the financing of future city initiatives held within the Lord Mayor of the City of London's "Tommorrow's Cities" programme, Daniel Wong, Head of Infrastructure and Real Estate for Macquarie Capital Europe, said that only a "tiny fraction" – a few percent – of the investable resources of the pension and sovereign wealth funds often referred to as the "wall of money" seeking profitable long-term investment opportunities in infrastructure were available to invest in infrastructure projects that carry "construction risk" – the risk of financial loss or cost overruns during construction.
For conventional infrastructure, construction risk is relatively well understood. At the Tomorrow's Cities event, Jason Robinson, Bechtel's General Manager for Urban Development, said that the construction sector was well able to manage that risk on behalf of investors. There are exceptions – such as the delays, cost increases and reduction in scale of Edinburgh's new tram system – but they are rare.
So are we similarly well placed to manage the additional "construction risk" created when we add new technology to infrastructure projects?
Unfortunately, research carried out in 2013 by the Standish Group on behalf of Computerworld suggests not. Standish Group used data describing 3,555 IT projects between 2003 and 2012 that had labour costs of at least $10 million, and found that only 6.4% were wholly successful. 52% were delivered, but cost more than expected, took longer than expected, or failed to deliver everything that was expected of them. The rest – 41.4% – either failed completely or had to be stopped and re-started from scratch. Anecdotally, we are familiar with the press coverage of high profile examples of IT projects that do not succeed.
We should not be surprised that it is so challenging to deliver IT projects. They are almost always driven by requirements that represent an aspiration to change the way that an organisation or system works: such requirements are inevitably uncertain and often change as projects proceed. In today's interconnected world, many IT projects involve the integration of several existing IT systems operated by different organisations: most of those systems will not have been designed to support integration. And because technology changes so quickly, many projects use technologies that are new to the teams delivering them. All of these things will usually be true for the technology solutions required for Smart City projects.
By analogy, then, an IT project often feels like an exercise in building an ambitiously new style of building, using new materials whose weight, strength and stiffness isn't wholly certain, and standing on a mixture of sand, gravel and wetland. It is not surprising that only 6.4% deliver everything they intend to, on time and on budget – though it is also disappointing that as many as 41.4% fail so completely.
However, the real insight is that the characteristics of uncertainty, risk, timescales and governance for IT projects are very different from construction and infrastructure projects. All of these issues can be managed; but they are managed in very different ways. Consequently, it will take time and experience for the cultures of IT and construction to reconcile their approaches to risk and project management, and consequently to present a confident joint approach to investors.
The implementation of Smart Cities IT solutions on Cloud Computing platforms by their providers mitigates this risk to an extent by "pre-fabricating" these components of smart infrastructure. But there is still risk associated with the integration of these solutions with physical infrastructure and engineering systems. As we gain further experience of carrying out that integration, IT vendors, investors, construction companies and their customers will collectively increase their confidence in managing this risk, unlocking investment at greater scale.
(The unfortunate consequence of a driver who put more trust in their satellite navigation and GPS technology than its designers expected. Photo by Salmon Assessors)
We are all familiar with IT systems failing.
Our laptops, notebooks and tablets crash, and we lose work as a consequence. Our television set-top boxes reboot themselves midway through recording programmes. Websites become unresponsive or lose data from our shopping carts.
But when failures occur in IT systems that monitor and control physical systems such as cars, trains and traffic lights, the consequences could be severe: damage to property, injury; and death. Organisations that invest in and operate infrastructure are conscious of these risks, and balance them against the potential benefits of new technologies when deciding whether to use them.
The real-world risks of technology failure are already becoming more severe as all of us adopt consumer technologies such as smartphones and social media into every aspect of our lives (as the driver who followed his satellite navigation system off the roads of Paris onto the pavement, and then all the way down the steps into the Paris Metro, discovered).
The noted urbanist Jane Jacobs defined cities by their ability to provide privacy and safety amongst citizens who are usually strangers to each other; and her thinking is still regarded today by many urbanists as the basis of our understanding of cities. As digital technology becomes more pervasive in city systems, it is vital that we evolve the policies that govern digital privacy to ensure that those systems continue to support our lives, communities and businesses successfully.
Google's careful exploration of self-driving cars in partnership with driver licensing organisations is an example of that process working well; the discovery of a suspected 3D-printing gun factory in Manchester last year is an example of it working poorly.
These issues are already affecting the technologies involved in Smart Cities solutions. An Argentinian researcher recently demonstrated that traffic sensors used around the world could be hacked into and caused to create misleading information. At the time of installation it was assumed that there would never be a motivation to hack into them and so they were configured with insufficient security. We will have to ensure that future deployments are much more secure.
Conversely, we routinely trust automated technology in many aspects of our lives – the automatic pilots that land the planes we fly in, and the anit-lock braking systems that slow and stop our cars far more effectively than we are able to ourselves.
If we are to build the same level of trust and confidence in Smart City solutions, we need to be open and honest about their risks as well as their benefits; and clear how we are addressing them.
(Cars from the car club "car2go" ready to hire in Vancouver. Despite succeeding in many cities around the world, the business recently withdrew from the UK after failing to attract sufficient customers to two pilot deployments in London and Birmingham. The UK's cultural attraction of private car ownership has proved too strong at present for a shared ownership business model to succeed. Photo by Stephen Rees).
Outcomes Risk
Smart infrastructures such as Stockholm's road-use charging scheme and London's congestion charge were constructed in the knowledge that they would be financially sustainable, and with the belief that they would create economic and environmental benefits. Subsequent studies have shown that they did achieve those benefits, but data to predict them confidently in advance did not exist because they were amongst the first of their kind in the world.
The benefits of "Smart" schemes such as road-use charging and smart metering cannot be calculated deterministically in advance because they depend on citizens changing their behaviour – deciding to ride a bus rather than to drive a car; or deciding to use dishwashers and washing machines overnight rather than during the day.
There are many examples of Smart Cities projects that have successfully used technology to encourage behaviour change. In a smart water meter project in Dubuque, for example, households were given information that told them whether their domestic appliances were being used efficiently, and alerted to any leaks in their supply of water. To a certain extent, households acted on this information to improve the efficiency of their water usage. But a control group who were also given a "green points" score telling them 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.
However, these techniques are notoriously difficult to apply successfully. A recycling scheme 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?"
The financial vehicles that enable investment in infrastructure and property are either government-backed instruments that reward economic and social outcomes such as reductions in carbon footprint or the creation of jobs ; or market-based instruments based on the creation of direct financial returns.
So are we able to predict those outcomes confidently enough to enable investment in Smart Cities solutions?
I put that question to the debating panel at the Tomorrow's Cities meeting. In particular, I asked whether investors would be willing to purchase bonds in smart metering infrastructures with a rate of return dependent on the success of those infrastructures in encouraging consumers to reduce their use of water and energy.
The response was a clear "no". The application of those technologies and their effectiveness in reducing the use of water and electricity by families and businesses is too uncertain for such investment vehicles to be used.
Smart Cities solutions are not straightforward engineering solutions such as electric vehicles whose cost, efficiency and environmental impacts can be calculated in a deterministic way. They are complex socio-technical systems whose outcomes are emergent and uncertain.
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. As that happens, investors will be more willing to fund them; or, with government support, to create new financial vehicles that reward investment in initiatives that use smart technology to create social, environmental and economic improvements – just as the World Bank's Green Bonds, launched in 2008, support environmental schemes today.
(Recycling bins in Curitiba, Brazil. As Mayor of Curitaba Jaime Lerner started one of the world's earliest and most effective city recycling programmes by harnessing the enthusiasm of children to influence the behaviour of their parents. Lerner's many initiatives to transform Curitaba have the characteristic of entrepreneurial leadership. Photo by Ana Elisa Ribeiro)
Evidence and Leadership
The evidence base need to support new investment vehicles is already being created. In Canada, for example, a collaboration between Canadian insurers and cities has developed a set of tools to create a common understanding of the financial risk created by the effects of climate change on the resilience of city infrastructures.
More internationally, the "Little Rock Accord" between the Madrid Club of former national Presidents and Prime Ministers and the P80 group of pension funds agreed to create a task force to increase the degree to which pension and sovereign wealth funds invest in the deployment of technology to address climate change issues, shortages in resources such as energy, water and food, and sustainable, resilient growth. My colleague the economist Mary Keeling has been working for IBM's Institute for Business Value to more clearly analyse and express the benefits of Smart approaches – in water management and transportation, for example. And Peter Head's Ecological Sequestration Trust and Robert Bishop's International Centre for Earth Simulation are both pooling international data and expertise to create models that explore how more sustainable cities and societies might work.
But the Smart City programmes which courageously drive the field forward will not always be those that demand a complete and detailed cost/benefit analysis in advance. Writing in "The Plundered Planet", the economist Paul Collier asserts that any proposed infrastructure of reasonable novelty and significant scale is effectively so unique – especially when considered in its geographic, political, social and economic context – that an accurate cost/benefit case simply cannot be constructed.
Instead, initiatives such as London's congestion charge and bicycle hire scheme, Sunderland's City Cloud and Bogota's bikeways and parks were created by courageous leaders with a passionate belief that they could make their cities better. As more of those leaders come to trust technology and the people who deliver it, their passion will be another force behind the adoption of technology in city systems and infrastructure.
What's the risk of not investing in a Smarter City?
For at least the last 50 years, we have been observing that life is speeding up and becoming more complicated. In his 1964 work "Notes on the Synthesis of Form", the town planner Christopher Alexander wrote:
"At the same time that the problems increase in quantity, complexity and difficulty, they also change faster than ever before. New materials are developed all the time, social patterns alter quickly, the culture itself is changing faster than it has ever changed before … To match the growing complexity of problems, there is a growing body of information and specialist experience … [but] not only is the quantity of information itself beyond the reach of single designers, but the various specialists who retail it are narrow and unfamiliar with the form-makers' peculiar problems."
(Alexander's 1977 work "A Pattern Language: Towns, Buildings, Construction" is one of the most widely read books on urban design; it was also an enormous influence on the development of the computer software industry).
The physicist Geoffrey West has shown that this process is alive and well in cities today. As the world's cities grow, life in them speeds up, and they create ideas and wealth more rapidly, leading to further growth. West has observed that, in a world with constrained resources, this process will lead to a catastrophic failure when demand for fresh water, food and energy outstrips supply – unless we change that process, and change the way that we consume resources in order to create rewarding lives for ourselves.
There are two sides to that challenge: changing what we value; and changing how we create what we value from the resources around us.
("Makers" at the Old Print Works in Balsall Heath, Birmingham, sharing the tools, skills, contacts and ideas that create successful small businesses in local communities)
The Transition movement, started by Rob Hopkins in Totnes in 2006, is tackling both parts of that challenge. "Transition Towns" are communities who have decided to act collectively to transition to a way of life which is less resource-intensive, and to value the characteristics of such lifestyles in their own right – where possible trading regionally, recycling and re-using materials and producing and consuming food locally.
The movement does not advocate isolation from the global industrial economy, but it does advocate that local, alternative products and services in some cases can be more sustainable than mass-produced commodities; that the process of producing them can be its own reward; and that acting at community level is for many people the most effective way to contribute to sustainability. From local currencies, to food-trading networks to community energy schemes, many "Smart" initiatives have emerged from the transition movement.
We will need the ideas and philosophy of Transition to create sustainable cities and communities – and without them we will fail. But those ideas alone will not create a sustainable world. With current technologies, for example, one hectare of highly fertile, intensively farmed land can feed 10 people. Birmingham, my home city, has an area of 60,000 hectares of relatively infertile land, most of which is not available for farming at all; and a population of around 1 million. Those numbers don't add up to food self-sufficiency. And Birmingham is a very low-density city – between one-half and one-tenth as dense as the growing megacities of Asia and South America.
Cities depend on vast infrastructures and supply-chains, and they create complex networks of transactions supported by transportation and communications. Community initiatives will adapt these infrastructures to create local value in more sustainable, resilient ways, and by doing so will reduce demand. But they will not affect the underlying efficiency of the systems themselves. And I do not personally believe that in a world of 7 billion people in which resources and opportunity are distributed extremely unevenly that community initiatives alone will reduce demand significantly enough to achieve sustainability.
We cannot simply scale these systems up as the world's population grows to 9 billion by 2050, we need to change the way they work. That means changing the technology they use, or changing the way they use technology. We need to make them smarter.
Filed under Economics Tagged with 3DPrinting, Birmingham, Bogota, Cambridge, Cloud, Curitaba, DesignPatterns, Dubuque, Economics, Energy, Environment, EuropeanUnion, Food, Google, HS2, JaneJacobs, LocalGovernment, London, Milan, Minneapolis, Minnesota, Montpelier, OpenData, Paris, Resilience, Schumacher, SmarterCities, SmarterTransport, socialcomputing, SocialMedia, Stockholm, TechnologyStrategyBoard, Totnes, Transition, Urbanism, Water
No-one wants top-down, technology-driven cities. They'd be dumb, not smart.
(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.)
But "bottom up" is not enough; in order to succeed at scale, grass-roots innovation and localism need support from a new environment of policy, finance, infrastructure and technology.
I took part in a panel discussion last week with Leo Johnson, co-author of "Turnaround Challenge: Business and the City of the Future" (and, coincidentally, the brother of London's Mayor, Boris Johnson). Leo argued in an impassioned speech that we should avoid overly deterministic "top-down" approaches to developing sustainable cities, and should instead encourage "bottom-up" innovation. His points echoed some of the criticisms levelled at parts of the Smart Cities movement by writers such as Adam Greenfield and Richard Sennett.
But these are arguments against a proposition that I simply don't think anyone is advocating today.
In all of my contacts across the world, in technology, government and urban design, I don't know anyone who thinks it would be "smart" for cities to be run wholly by technological systems; who believes that digital data can provide "perfect knowledge" about city systems; or who thinks that cities built and run entirely by deterministic plans driven from the top down would be healthy, vibrant places to live (or indeed are possible at all).
Smart cities are not about putting machines in control, and they are not about imposing an idealistic, corporate way of life. They are simply about harnessing the ever-advancing capabilities of technology in our efforts to create a more sustainable, equitable, resilient world in the cities in which more and more of us are living.
The ultimate purpose of cities is to enable the people who live and work in them to lead safe and rewarding lives with their families. The raw material from which the life of cities is built is therefore small-scale – it is the activity of individual people in their personal and family life or going about their work. Consequently, there is an enormous focus in smart cities and smart urbanism on "bottom-up" thinking : how can we enable private businesses, community innovators and citizen-led initiatives to be successful, and to create sustainable wealth and social value? If the opportunities to do that are widely available, then cities as a whole will be more successful, and, when economic or climate events affect their circumstances, they will be more adaptable and resilient.
But let's be frank: that's an awfully big "if".
There's nothing new about "bottom-up" creativity – that's simply what people do as they get on with life, using whatever resources are available to them to craft a living, support their families and build successful businesses. But the truth is that we are not very good at all at creating environments in which everybody has an equal chance of succeeding in those efforts.
For bottom-up creativity to be broadly successful, citizens, communities and businesses must be able to adapt the city infrastructures that provide food, water, energy, transport and resources to serve their specific needs and opportunities. Those infrastructures are vast – they support 3 billion urban lives worldwide today, and will need to scale to support 3 billion more by 2050. Communities and neighbourhoods with persistently low levels of economic activity and social mobility – those most in need of innovative answers to their challenges – are often those who have the least access to those infrastructures, and whose issues can include poor schools, disconnection from transport networks, exclusion from mainstream financial systems, fuel poverty and so on. Those problems will not solve themselves: we will only adapt city infrastructures and institutions to serve these communities better through significant effort from the businesses and governments that control and govern them.
(When planning policy and other regulations allow, urban farms can adapt the physical infrastructure of cities to create new sources of food. A similar combination of policy innovation and grass-roots creativity could enable similarly creative uses of digital infrastructure and information in cities. Photo by ToadLickr)
From the governance of cities, to the policies that affect investment, to the oversight, administration and operation of city infrastructures – these processes work top-down; and in order for us to rely on "bottom-up" creativity improving cities for all of their citizens, we must adapt and improve them to better support that creativity.
Technology plays three roles in this context. Firstly, smartphones, tablets, 3D printers and social media are examples of new consumer and citizen tools that we could barely imagine as recently as a decade ago. They make immense power available to bottom-up, small-scale activity and local innovations, and have resulted in the emergence of significant economic trends such as the "sharing economy" of business models based on peer-to-peer transactions.
Secondly, though, many of those technologies depend fundamentally on the availability of connectivity infrastructure; and that infrastructure is not available everywhere. Some 18% of adults in the UK have never been online; and children today without access to the internet at home and in school are at an enormous disadvantage. Most cities and countries have not yet addressed this challenge. Private sector network providers will not deploy connectivity in areas which are insufficiently economically active for them to make a profit, and Government funding is not yet sufficient to close the gap. This challenge has not and will not be addressed by bottom-up creativity; it requires top-down legislation and investment.
Thirdly, technology can help to open up the operations and infrastructures of big institutions and companies to local innovation – from the provision of "open data" and API interfaces that allow these systems to be adapted to new uses; to the use of technology to measure and trace the social and environmental impact of goods and services in order to inform consumer choice so that it can become a lever to improve the impact of the vast supply chains that supply cities. Unilever and Tesco are just two examples of businesses pursuing this business strategy.
These are the roles of technology that enable a meeting or balance between top-down and bottom-up forces in cities – a balance that Anthony Townsend, author of "Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia" has advocated in our online exchanges.
Smart cities is not a prescriptive, top-down, corporate movement. The perception that it was arose because a handful of early and highly visible examples such as Masdar and Songdo were new, large-scale developments financed by strong economic growth in emerging markets; or because some of the rapid urbanisation taking place today is in countries with strongly hierarchical governance. These examples also gave emphasis to the importance of efficiently and intelligently operating large-scale city infrastructures – without which we'll never sustainably and resiliently support the 6 billion city inhabitants predicted by the United Nations' World Urbanisation Prospects report by 2050.
(Delegates at Gov Camp 2013 at IBM's Southbank office, London. Gov Camp is an annual conference which brings together anyone interested in creating new uses of digital technology in public services. Photo by W N Bishop)
But we must give equal recognition to the vast amount of bottom-up creativity that took place throughout this period; that continues today; and which has exploited technology in strikingly innovative ways.
The "open data" movement has become a force for transparency in government and for addressing social and environmental issues. "Civic hacking" communities have sprung up around the world, using this data to create novel new public services. Many of my colleagues have contributed to that movement, either representing IBM, or simply as personal contributions to the cities in which they live – as have the employees of many other businesses. And community initiatives everywhere now routinely exploit technologies such as social media and crowdfunding; or co-create schemes to apply commercial technologies for their own purposes. For example, in the village of Chale on the Isle of Wight, a community with significant levels of fuel poverty worked together to use smart energy meters to reduce their energy bills by up to 50%.
There are two serious challenges in how we apply these ideas more broadly that demand debate:
How we are going to pay for more widespread investments in technology infrastructure for cities?
How do we create the institutional practises and policies that ensure that the benefits of those investments are genuinely widely distributed, and enable and support "bottom-up" innovations everywhere?
The Economist magazine reminded us of the importance of those questions in a recent article describing the enormous investments made in public institutions 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.
We have only partially been successful in those efforts. As one measure, it's common for life expectancy to vary by around 20 years between the poorest and richest parts of the same city in the UK. Scandinavian cities do not show that disparity – their culture and system of taxation, benefits and collective insurance create a more equal opportunity to live. In the UK, the US and other societies that emphasise greater retention of private wealth and the distribution of opportunity through flexible market economies, how can we better approach Scandinavia's level of equality?
These questions are much more important than perpetuating an adversarial debate between "top down" and "bottom up" thinking. No-one wants top-down, technology driven cities. They'd be dumb, not smart. And no-one believes that digital data can provide "perfect knowledge" – we all understand that perfect knowledge is neither possible nor desirable.
Digital data and technology do much more realistic and exciting things. They allow us to uncover the hidden opportunity to transact locally with people and businesses in our community. They reveal patterns in the messy complexity of social, economic, physical and environmental systems that help us to look ahead to likely outcomes, take proactive measures and do more with less. And they make it possible for us to connect to people around the world who we've never met but with whom we share an interest or can create a new opportunity.
A smart city creates an environment in which technology, infrastructure, policies and culture make people safe, and provide the resources and opportunities they need – including better access to technology and information – to create safer and more rewarding lives.
That's not top-down or bottom-up. It's common sense. Let's stop arguing and start applying it.
Filed under Smarter Cities Tagged with 3DPrinting, Economics, Energy, Environment, HyperLocal, Localism, Masdar, OpenData, Resilience, SmarterCities, SmartUrbanism, SocialMedia, Songdo, UnitedNations, Water