Cities, from information to interaction

This research approaches the city as a vital form of information that helps people know about ongoing activities, and find counterparts in their social world. Essentially, it aims to measure information in the urban environment, assess how much information people retrieve from it, and how they put it to use in order to enact their everyday lives.

It explores three related issues: first, a physical problem: how can we create and preserve information in physical form? Second, a semantic problem: how do we make spatial information meaningful? And third, a pragmatic problem: how do we enact spatial information in our lives? Aiming to advance spatial information measures, and a computational model connecting agents and the built environment, this research proposes a framework to understand how information bridges minds, actions and cities, and helps people create large-scale systems of interaction.

In that sense, we pose questions like how can unpredictable individual acts amount into coherent systems of action? Is there a role for cities in the way we coordinate seemingly chaotic decisions?

 

We attempt to answer those questions by exploring how people use informational differences in their urban environment. Our proposition is that cities ease peoples’ recognition of action choices and help them make selections of activities and actors to interact with. By guiding interaction, they help to reduce social entropy in the collective coordination of actions.

 

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team

Vinicius M. Netto [Urban studies, UFF]

João Meirelles [Complex systems, EPFL]

Fabiano Ribeiro [Statistical Physics, DFI UFL]

The reduction of entropy

Linearising a process that in fact occurs simultaneously, the cycle of entropy of actions would take the following form:

a) Think of the actions as lines moving in time, in an initial state free from space, when actors can do anything and we cannot foresee what they will do – a potential state of high entropy. The colours of the lines in the diagram on the right represent different orientations guiding actions.

entropy-loop_dyn(b) Then action lines converge into distinct positions of a spatial system – a system of differentiated informational and practical contents that is urban space (represented by the vertical strip in the diagram), arranged in distinct places and built forms. The colours of the action lines might have subtle differences in relation to the colours of the places they approach. Action lines converge in places by informational and spatial approximation.

(c) As more action lines approach places where interactions are supposed to happen, the initially unpredictable maze becomes a more coordinated system: an interaction system where agents cooperate and coordinate their doings.

(d) After each spatially held interaction, action lines move into a new stage where they may change again, according to new orientations or intentions. Accordingly, the colours of the lines may change. Entropy increases as our actions go once more into a state of unpredictability, as new possibilities are presented to actors and some must be selected if action is to be actualised.

(e) …And then action lines ‘refer’ again to positions in space, starting a new cycle of reduction of entropy via interactions and the connectivity of the system (see diagram).

These moments are theoretical, of course: in reality, these stages are likely to happen simultaneously, all the time. This cycle is a metaphor for the role of space in reducing the action of entropy: a complex tangle toward the cleanest streams, connected, where individual actions converge to coordinate interactions in urban places.

We examine this proposition through computational experiments able to assess how social entropy relates to semantic space, and how spatial patterns of spatial information changes possibilities of actions. Ultimately, we argue that the forms in which social entropy is dealt with is one of the deep connections between society and space – and between the social and the physical city.

 

In our model, a linear city in the shape of a ring is composed of a hundred activity places, offering ‘social information’ about possible actions available to agents. A thousand agents have their own action orientations and are able to move around the city, attempting to perform their actions in places akin to their orientations. Distance also plays a role: proximity is valued by agents.

The figure above shows initial results from archetypical situations. First we simulated an urban scenario where agents are able to continuously align their orientations with activity places in time, as they attempt to perform their actions moving around the city. Second, agents are still able to align orientations to activity places, but they can also change their orientations in time. A pattern of fluctuations in entropy emerges more strongly in the situation where agents change orientations and decisions. And the city seems to play a part in the reduction of entropy engendered by such recursive changes – as agents move and interact, producing action systems.

 

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More on Social Interaction and the City here.

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