How did the slime mould cross the maze?
- Emergence: The Connected Lives of Ants, Brains, Cities and Software by Steven Johnson
Allen Lane, 288 pp, £14.99, October 2001, ISBN 0 7139 9400 2
- The Moment of Complexity: Emerging Network Culture by Mark Taylor
Chicago, 340 pp, £20.50, January 2002, ISBN 0 226 79117 3
In London Labour and the London Poor (1861), Henry Mayhew recorded seeing a watercress girl who, eight years old and ‘dressed only in a thin cotton gown and a threadbare shawl wrapped round her shoulders’, walked the streets crying ‘four bunches a penny’; and mudlarks, principally young boys, girls, old men and many old women, who spent the day with ‘their trousers tucked up, groping about picking out pieces of coal, iron, wood and copper nails’ from the mud on the banks of the Thames. Unlike the street-sellers, whose lives were solitary, the mudlarks formed organised communities. But they were loosely connected. Indeed, Mayhew noted their lack of cohesiveness, observing that they ‘peered anxiously about’ and held ‘but little converse with one another’. Chimney-sweeps, on the other hand, formed distinct and coherent communities, their filthy appearance and offensive smell forcing them into collective isolation. This resulted in the acquisition and nurturing of ‘unique habits and peculiarities’. In some communities, the tendrils of continuity reached far back into the past. The fish market at Billingsgate, for example, whose official records date back to the 11th century but whose origins most likely lie in antiquity, remained the geographical and economic centre of fish-selling. While operating within the context and constraints of higher-order organising influences such as the common law, town-planning and the dictates of city guilds, the detail of London’s physical and social structure originated according to a dialectic between such ‘top-down’ principles and less well understood ‘bottom-up’ self-organisation emerging from the life of the city itself.
That the workings of a system as complex as a city might be computable stretches the limits of credulity. There was nothing inevitable about the emergence of mudlarks by the river’s edge, or the watercress girl loitering near Farringdon market. But much like a living organism, a city is defined by an underlying dynamic that helps generate and perpetuate its core features. If we were able to obtain even a rudimentary insight into these processes we might be able broadly to predict the types of phenomenon likely to emerge within such complex systems, while having to acknowledge that the specifics will remain beyond our grasp.
Jane Jacobs’s The Death and Life of Great American Cities (1961) fundamentally changed the way in which cities are perceived, arguing that they were ‘learning machines’, with ‘marvellous innate abilities for understanding, communicating, contriving and inventing’. Jacobs made no attempt to express the idea in a formal notation, but is it possible that the broad workings of this complex and distributed ‘intelligence’ could be captured mathematically? And, if so, could the insights gained enable us to predict a city’s likely future? Might we be compelled to take such principles into consideration when planning new cities, if not to dictate their exact form, then to manage their behavioural repertoire and development? Could we in fact work out a tentative formal mathematical science of city structure and dynamics?
In its simplest sense, a city is an example of a ‘complex adaptive system’. The fledgling mathematical laws underpinning the behaviour of such systems are indifferent to the material nature of the networks of components that make them up. In this respect, the emergent aspect of a city’s existence, which appears to transcend the actions of any individual, warns against descriptions of the informational content of living organisms that begin and end with DNA. Although the information encoded in DNA can account for organisation at the level of protein molecules, it doesn’t explain how those molecules interconnect to form metabolisms, or how such networks can self-organise to form the structures of living things. Whether the system involved is biological or non-biological, what we need are mathematical laws of emergence, laws that would apply as well to a network of neurons in the brain of a porcupine as to the individuals within a specific community or organisation, or to the intricate dynamics of global stock exchanges.
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