Urbanisation and Complex Systems
The city is humanity’s greatest invention. An artificial ecosystem that enables millions of people to live in close proximity and to collaborate in the creation of new forms of value. While cities were invented many millennia ago, their economic importance has increased dramatically since the Industrial Revolution until they now account for the major fraction of the global economy. All human life is there and so the study of cities crosses boundaries among economics, finance, engineering, ecology, sociology, anthropology, and, well, almost all forms of knowledge. Yet, while we have great knowledge in each of these domains individually, we have little scientific knowledge of how they come together in the overall system of systems that is a city. In brief: How does a city work?
Such knowledge would be helpful in the coming decades. In the last sixty to seventy years, globalisation has spread the Industrial Revolution ever more widely, creating in cities new opportunities that attract hundreds of millions of internal and international migrants. This process is lifting many of these migrants out of deep poverty, while causing cities from London to Nairobi to struggle in differing ways with the unending influx.
Further, cities are responsible for large fractions of greenhouse gas emissions, for the consumption of natural resources such as water and air, and the resulting discharges of pollution into the environment. If the battle against climate change is to be won, it will be won in cities. Cities are also the principal centres for innovation and economic development, both of which are needed to continue lifting migrants out of poverty.
While the roots of urban planning can be traced back more than three thousand years in terms of the master plans of cities, it was the tremendous growth of cities in the late 19th century that transformed that field into considering the many services and affordances that are required for urban dwellers. But urban planning emerged mainly from the humanities and works primarily through extensive case studies, although it has adopted many digital tools. The notion of the city as an object of scientific study is more recent and still in its infancy, triggered in part by developments in complexity theory such as network theory, scaling laws, and systems science, and the growing availability of urban data.
Urban scaling laws have been explored at least since the early 20th century, when cities were found seen to be an example of Zipf’s law. In this case Zipf’s Law states that “for most countries, the number of cities with population greater than S is proportional to 1/S”. The understanding of scaling was greatly expanded in recent years by the works of West and Bettencourt and Batty. Their work showed that many properties of cities such as the number or lengths of roadways, the numbers of amenities such as restaurants, and so forth follow scaling laws over population ranges from ten thousand to tens of millions. Moreover these scaling laws have exponents in the ranges 0.85 to 1.15 that show large cities to be more productive, innovative, efficient in energy consumption, expensive, but also better paying than small cities. Likewise negative attributes such as crime, disease, and pollution also scale superlinearly, that is they don’t rise in strict proportion to the increase in city size. For example, GDP is proportional to the Size (S) of a city raised to a power that is slightly greater than 1, thus S1.15, while other attributes like energy consumption per capita scale sublinearly, at S0.85. Network laws also describe well the evolution over long time scales of roadways and railways in cities.
While scaling laws and network laws have great descriptive power, opinions vary on whether they apply across different countries or have predictive power. That is, the scaling of attributes is a snapshot of frequency versus size at a given time. If a city grows and “moves up the scale”, it may not achieve, in the short term, all of the positive benefits and negative impacts described. Nor do the laws provide explanations for the observed behaviours. Nonetheless, this is an important area for planners and developers seeing their cities growing or shrinking.
As urban data has become more pervasive, it is now possible to study cities as complex systems of interactions. We may view the city as a myriad of interactions among its inhabitants, its infrastructures and affordances, its natural environment, and its public, private, and civic organisations. Some of these interactions involve the exchange of goods or services for money, but many of them involve the exchange or transmission of information, enabling inhabitants and organisations to make choices. Public transportation is often studied in this way, revealing for example that small and medium sized cities evolve networks enabling commuting between small numbers of residential and business districts, while very large cities, such as London, have much richer networks that permit greater flexibility in where people live and work.
The operation of cities is also modelled using synthetic populations of software agents that represent the distribution of behaviours or preferences of much larger, real populations. Such agent-based models, with agents representing patterns of origin, destination, travel times, and modality preferences, are used to examine the overall impact of new services such as London’s Crossrail.
As the Internet of Things provides greater visibility into how inhabitants choose to exploit the opportunities offered by a given city, we may hope to discover abstract principles about how cities work. We may envision being able to construct agent-based models representing the complete spectrum of choices a city’s inhabitants make at timescales from minutes to years and spatial scales from meters to kilometers. Equally, given the increasing availability of real-time information, we might hope one day to understand the effective use of a city’s services in terms of a Nash Equilibrium, a game theory concept (often used to describe poker games), where no player can gain anything by changing their chosen strategy if other players don’t change theirs – all the players’ strategies are optimal. These are far in the future, but the EC’s Global Systems Science programme is the beginning of that journey.
The OECD is organising a Workshop on Complexity and Policy, 29-30 September, OECD HQ, Paris, along with the European Commission and INET. Watch the webcast: 29/09 morning; 29/09 afternoon; 30/09 morning