Abstract

The design-research illustrated in this research article focus on the emerging field of interactive architecture focusing on developing real-time information exchanging architectural bodies. These interactive bodies demonstrate a fusion between the material, the electronic and the digital domains. This fusion is explicitly attained through a synergistic merger between the fields of ambient sensing, control systems, ubiquitous computing, architectural design, pneumatic systems and computation. The resultant spatial bodies are thus visualised as complex adaptive systems, continually engaged in activities of data-exchange resulting in physical and ambient adaptations of their constituting components in response to contextual variations. Interdependent nodal networks, where every node/junction of a spatial prototype becomes a potential information hub by means of its ability to collect, process and communicate contextual data apart from working as an actuated detail owing to its ability to kinetically re-position itself in three-dimensional space is thus a critical outcome of this inter-disciplinary way of working. A strategy apt for binding material logistics with the digital to materialize dynamic spatial behaviours owing to real time data exchange between the prototypes and their context is thus embarked upon via three research and design projects, namely: Electronic Media Augmented Spatial Skins, The InteractiveWall and the Muscle Re-configured.

Abstract

We investigate the hypothesis that long-term memory in populations of agents can lead to counterproductive emergent properties at the system level. Our investigation is framed in the context of a discrete, one-dimensional road-traffic congestion model: we investigate the influence of simple cognition in a population of rational commuter agents that use memory to optimise their departure time, taking into account congestion delays on previous trips. Our results differ from the well-known minority game in that crowded slots do not carry any explicit penalty. We use Markov chain analysis to uncover fundamental properties of this model and then use the gained insight as a benchmark. Then, using Monte Carlo simulations, we study two scenarios: one in which "myopic” agents only remember the outcome (delay) of their latest commute, and one in which their memory is practically infinite. We show that there exists a trade-off, whereby myopic memory reduces congestion but increases uncertainty, while infinite memory does the opposite. We evaluate the performance against the optimal distribution of departure times (i.e. where both delay and uncertainty are minimised simultaneously). This optimal but unstable distribution is identified using a genetic algorithm. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.