Technological change and innovation for sustainable cities: A multiagent-based ontological approach

Abstract

Debates on sustainable technologies for urban management-be they related to energy, water, transport, or any other major sectors-tend to be restricted to issues about how to support a transition to new and more efficient technologies, as part of a linear path of progress proceeding from old, backward technologies to modern ones. Technologies themselves tend to be treated like black-boxes, somehow exogenously defined by engineering R&D efforts, while societies are mainly conceived of as passive agents, which can, at most, accept or reject them on the basis of their preferences. In our paper we adopt a different perspective, i.e., we start from the acknowledgement of the importance of the work of many distributed agents and micro-learning processes in technological evolutions and innovations as well as from the idea that sustainable solutions are by no means restricted to linear progression from past to future, as they can also encompass returns and recombinations of traditional components and practices together with new ones. After grounding our approach in some of the main contributions from innovation studies as well as in the application of cognitive science to evolutionary studies of technologies, we specifically discuss the possibility to use a multi-agent and multi-indexing ontological system as a device to share and learn technological knowledge across a wide community, thus supporting those micro-learning processes of distributed agents, which-in our perspective-can boost innovation and change in practice. In so doing, we will refer to a case study developed within the EU-funded project ANTINOMOS "A knowledge network for solving real-life water problems in developing countries: bridging contrasts", which has largely dealt with enhancement and management of local/community-based knowledge for water sector management. The major aim is to create an appropriate learning environment for sharing and for actively generating knowledge through multi-actor synergies. In this paper, the above subject is discussed and carried out with a cross-disciplinary, cross-scale, multi-agent approach, considering the different forms of local knowledge and language involved.