Complex City Systems

Information and communications technology (ICT) is being exploited within cities to enable them to better compete in a global knowledge-based service-led economy. In the nineteenth and twentieth centuries, cities exploited large technical systems (LTSs) such as the telegraph, telephony, electrical networks, and other technologies to enhance their social and economic position. This paper examines how the LTS model applies to ICT deployments, including broadband network, municipal wireless, and related services, and how cities and city planners in the twenty-first century are using or planning to use these technologies. This paper also examines their motivations and expectations, the contribution to date, and the factors affecting outcomes. The findings extend the LTS model by proposing an increased role for organizations with respect to an individual agency. The findings show how organizations form themselves into networks that interact and influence the outcome of the system at the level of the city. The extension to LTS, in the context of city infrastructure, is referred to as the complex city system framework. This proposed framework integrates the role of these stakeholder networks, as well as that of the socioeconomic, technical, and spatial factors within a city, and shows how together they shape the technical system and its socioeconomic contribution. The CCS framework has been presented at Digital Cities Conferences in Eindhoven, Barcelona, Taiwan, London and at IBM’s Global Smart Cities Conference in Shanghai between 2010 and 2012. Its finding are timely in the context of major policy decisions on investments at regional, national and international level on ICT infrastructure and related service transformation, as well as the governance of such projects, their planning and their deployment

Complex Systems: A Survey

A complex system is a system composed of many interacting parts, often called agents, which displays collective behavior that does not follow trivially from the behaviors of the individual parts. Examples include condensed matter systems, ecosystems, stock markets and economies, biological evolution, and indeed the whole of human society. Substantial progress has been made in the quantitative understanding of complex systems, particularly since the 1980s, using a combination of basic theory, much of it derived from physics, and computer simulation. The subject is a broad one, drawing on techniques and ideas from a wide range of areas. Here I give a survey of the main themes and methods of complex systems science and an annotated bibliography of resources, ranging from classic papers to recent books and reviews.Comment: 10 page

Evolutionary Subnetworks in Complex Systems

Links in a practical network may have different functions, which makes the original network a combination of some functional subnetworks. Here, by a model of coupled oscillators, we investigate how such functional subnetworks are evolved and developed according to the network structure and dynamics. In particular, we study the case of evolutionary clustered networks in which the function of each link (either attractive or repulsive coupling) is updated by the local dynamics. It is found that, during the process of system evolution, the network is gradually stabilized into a particular form in which the attractive (repulsive) subnetwork consists only the intralinks (interlinks). Based on the properties of subnetwork evolution, we also propose a new algorithm for network partition which is distinguished by the convenient operation and fast computing speed.Comment: 4 pages, 4 figure

Design Environments for Complex Systems

The paper describes an approach for modeling complex systems by hiding as much formal details as possible from the user, still allowing verification and simulation of the model. The interface is based on UML to make the environment available to the largest audience. To carry out analysis, verification and simulation we automatically extract process algebras specifications from UML models. The results of the analysis is then reflected back in the UML model by annotating diagrams. The formal model includes stochastic information to handle quantitative parameters. We present here the stochastic -calculus and we discuss the implementation of its probabilistic support that allows simulation of processes. We exploit the benefits of our approach in two applicative domains: global computing and systems biology

Studying complex adaptive systems using molecular classifier systems

Complex Adaptive Systems (CAS) are dynamical networks of interacting agents occurring in a variety of natural and artificial systems (e.g. cells, societies, stock markets). These complex systems have the ability to adapt, evolve and learn from experience. To study CAS, Holland proposed to employ agent-based systems in which Learning Classifier Systems (LCS) are used to determine the agents behavior and adaptivity. We argue that LCS are limited for the study of CAS: the rule-discovery mechanism is pre-specified and may limit the evolvability of CAS. Secondly, LCS distinguish a demarcation between messages and rules, however operations are reflexive in CAS, e.g. in a cell, an agent (a molecule) may both act as a message (substrate) and as a catalyst (rule). To address these issues, we proposed the Molecular Classifier Systems (MCS.b), a string-based artificial chemistry based on Holland’s Broadcast Language. In the MCS.b, no explicit fitness function is specified, moreover no distinction is made between messages and rules. In the context of the ESIGNET project, we employ the MCS.b to study a subclass of CAS : Cell Signaling Networks (CSNs) which are complex biochemical networks responsible for coordinating cellular activities. As CSNs occur in cells, these networks must replicate themselves prior to cell division. In this poster we present a series of experiments focusing on the self-replication ability of these CAS