Infrastructure modelling 2.0. (22nd January 2010)

• Record Type: Journal Article
• Authors: Davis, Chris; Nikolic, Igor; Dijkema, Gerard P.J.
• Is Part Of: International journal of critical infrastructures. Volume 6:Number 2(2010)
• Page Start: 168
• Page End: 186
• Publication Date: 2010-01-22
• Subjects: agent-based modelling -- ABM -- complexity -- bioelectricity infrastructures -- information technology -- infrastructure modelling -- critical infrastructures -- evolutionary modelling -- agent-based systems -- multi-agent systems -- MAS -- visualisation -- power generation options -- system dynamics -- Web 20 -- semantic web; Infrastructure (Economics) -- Security measures -- Periodicals; Risk assessment -- Periodicals; Emergency management -- Periodicals; 363.05
• Languages: English
• ISSNs: 1475-3219

Abstract

To support stakeholders involved in infrastructure development, we develop evolutionary models of these complex systems, which is a formidable task with respect to data requirements, information representation and knowledge management. Re-addressing a case on bio-electricity infrastructure evolution, we demonstrate first a series of visualisations of economic and ecologic system parameters as they change during infrastructure development over simulated decades. This setup allows us to demonstrate to stakeholders a means to anticipate the consequences of decisions on (dis)investment of power generation options available. In developing these tools, our approach needed to be expanded to better handle the complexity of infrastructure systems, due to the multiple relevant social and technical contexts from which these systems need to be considered. The second part of this paper describes our work on enabling collaborative mapping of our knowledge of infrastructure systems to help integrate diverse types of knowledge. Current internet-enabled developments such as Web 2.0 and the Semantic Web offer tremendous scope to lower the transaction cost of gathering and assembling data. Already, these are changing the ways scientific collaboration is conducted. Finally, we suggest to connect this to evolutionary models to elucidate the dynamics of these systems.